feat(compression): update tooling to use DECODE operators

python/tflite_micro/BUILD

@@ -125,7 +125,7 @@ py_test(
         ":runtime",
         requirement("numpy"),
         requirement("tensorflow"),
-        "//tensorflow/lite/micro/compression",
+        "//tensorflow/lite/micro/compression:model_editor",
     ],
 )
 

python/tflite_micro/_runtime.cc

@@ -33,10 +33,11 @@ PYBIND11_MODULE(_runtime, m) {
       .def(py::init([](const py::bytes& data,
                        const std::vector<std::string>& registerers_by_name,
                        size_t arena_size, int num_resource_variables,
-                       tflite::InterpreterConfig config) {
-        return std::unique_ptr<InterpreterWrapper>(
-            new InterpreterWrapper(data.ptr(), registerers_by_name, arena_size,
-                                   num_resource_variables, config));
+                       tflite::InterpreterConfig config,
+                       size_t alt_decompression_memory_size) {
+        return std::unique_ptr<InterpreterWrapper>(new InterpreterWrapper(
+            data.ptr(), registerers_by_name, arena_size, num_resource_variables,
+            config, alt_decompression_memory_size));
       }))
       .def("PrintAllocations", &InterpreterWrapper::PrintAllocations)
       .def("Invoke", &InterpreterWrapper::Invoke)

python/tflite_micro/interpreter_wrapper.cc

@@ -238,7 +238,14 @@ InterpreterWrapper::~InterpreterWrapper() {
 
 InterpreterWrapper::InterpreterWrapper(
     PyObject* model_data, const std::vector<std::string>& registerers_by_name,
-    size_t arena_size, int num_resource_variables, InterpreterConfig config) {
+    size_t arena_size, int num_resource_variables, InterpreterConfig config,
+    size_t alt_decompression_memory_size)
+    : memory_arena_(new uint8_t[arena_size]),
+      alt_decompression_memory_(alt_decompression_memory_size > 0
+                                    ? new uint8_t[alt_decompression_memory_size]
+                                    : nullptr),
+      alt_decompression_region_{alt_decompression_memory_.get(),
+                                alt_decompression_memory_size} {
   interpreter_ = nullptr;
 
   // `model_data` is used as a raw pointer beyond the scope of this
@@ -266,7 +273,6 @@ InterpreterWrapper::InterpreterWrapper(
         "--//:with_compression=true to enable compression support.");
   }
 
-  memory_arena_ = std::unique_ptr<uint8_t[]>(new uint8_t[arena_size]);
   for (const std::string& registerer : registerers_by_name) {
     if (!AddCustomOpRegistererByName(registerer.c_str(),
                                      &python_ops_resolver_)) {
@@ -296,6 +302,14 @@ InterpreterWrapper::InterpreterWrapper(
   interpreter_ = new MicroInterpreter(model, python_ops_resolver_, allocator_,
                                       resource_variables_);
 
+  if (alt_decompression_memory_size > 0) {
+    TfLiteStatus status =
+        interpreter_->SetDecompressionMemory(&alt_decompression_region_, 1);
+    if (status != kTfLiteOk) {
+      ThrowRuntimeError("TFLM failed to set decompression memory");
+    }
+  }
+
   TfLiteStatus status = interpreter_->AllocateTensors();
   if (status != kTfLiteOk) {
     ThrowRuntimeError("TFLM failed to allocate tensors");

python/tflite_micro/interpreter_wrapper.h

@@ -19,6 +19,7 @@ limitations under the License.
 
 #include "python/tflite_micro/python_ops_resolver.h"
 #include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_context.h"
 #include "tensorflow/lite/micro/micro_interpreter.h"
 #include "tensorflow/lite/micro/recording_micro_allocator.h"
 
@@ -40,7 +41,8 @@ class InterpreterWrapper {
   InterpreterWrapper(
       PyObject* model_data, const std::vector<std::string>& registerers_by_name,
       size_t arena_size, int num_resource_variables,
-      InterpreterConfig config = InterpreterConfig::kAllocationRecording);
+      InterpreterConfig config = InterpreterConfig::kAllocationRecording,
+      size_t alt_decompression_memory_size = 0);
   ~InterpreterWrapper();
 
   void PrintAllocations();
@@ -57,6 +59,8 @@ class InterpreterWrapper {
   tflite::RecordingMicroAllocator* recording_allocator_ = nullptr;
   const PyObject* model_;
   std::unique_ptr<uint8_t[]> memory_arena_;
+  std::unique_ptr<uint8_t[]> alt_decompression_memory_;
+  tflite::MicroContext::AlternateMemoryRegion alt_decompression_region_;
   tflite::PythonOpsResolver python_ops_resolver_;
   tflite::MicroInterpreter* interpreter_;
 };

python/tflite_micro/runtime.py

@@ -100,6 +100,7 @@ def __init__(
       custom_op_registerers,
       arena_size,
       intrepreter_config=InterpreterConfig.kAllocationRecording,
+      alt_decompression_memory_size=0,
   ):
     if model_data is None:
       raise ValueError("Model must not be None")
@@ -122,6 +123,7 @@ def __init__(
         arena_size,
         num_resource_variables,
         _ENUM_TRANSLATOR[intrepreter_config],
+        alt_decompression_memory_size,
     )
 
   @classmethod
@@ -131,6 +133,7 @@ def from_file(
       custom_op_registerers=[],
       arena_size=None,
       intrepreter_config=InterpreterConfig.kAllocationRecording,
+      alt_decompression_memory_size=0,
   ):
     """Instantiates a TFLM interpreter from a model .tflite filepath.
 
@@ -140,6 +143,9 @@ def from_file(
         custom OP registerer
       arena_size: Tensor arena size in bytes. If unused, tensor arena size will
         default to 10 times the model size.
+      alt_decompression_memory_size: Size in bytes of alternate decompression
+        memory. If non-zero, DECODE operators will use this memory instead of
+        the main arena for decompressed tensor outputs.
 
     Returns:
       An Interpreter instance
@@ -155,6 +161,7 @@ def from_file(
         custom_op_registerers,
         arena_size,
         intrepreter_config,
+        alt_decompression_memory_size,
     )
 
   @classmethod
@@ -164,6 +171,7 @@ def from_bytes(
       custom_op_registerers=[],
       arena_size=None,
       intrepreter_config=InterpreterConfig.kAllocationRecording,
+      alt_decompression_memory_size=0,
   ):
     """Instantiates a TFLM interpreter from a model in byte array.
 
@@ -173,6 +181,9 @@ def from_bytes(
         custom OP registerer
       arena_size: Tensor arena size in bytes. If unused, tensor arena size will
         default to 10 times the model size.
+      alt_decompression_memory_size: Size in bytes of alternate decompression
+        memory. If non-zero, DECODE operators will use this memory instead of
+        the main arena for decompressed tensor outputs.
 
     Returns:
       An Interpreter instance
@@ -183,6 +194,7 @@ def from_bytes(
         custom_op_registerers,
         arena_size,
         intrepreter_config,
+        alt_decompression_memory_size,
     )
 
   def print_allocations(self):

python/tflite_micro/test_compression_unsupported.py

@@ -12,84 +12,84 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Test compression metadata detection when compression is disabled."""
+"""Test legacy compression metadata detection when compression is disabled."""
 
 import os
 import numpy as np
 import tensorflow as tf
 from tflite_micro.python.tflite_micro import runtime
-from tflite_micro.tensorflow.lite.micro import compression
+from tflite_micro.tensorflow.lite.micro.compression import model_editor
 
 
-class CompressionDetectionTest(tf.test.TestCase):
-  """Test compression metadata detection when compression is disabled."""
+def _create_test_model():
+  """Create a simple quantized model for testing."""
+  model = tf.keras.Sequential([
+      tf.keras.layers.Dense(10, input_shape=(5, ), activation='relu'),
+      tf.keras.layers.Dense(5, activation='softmax')
+  ])
+  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
 
-  def _create_test_model(self):
-    """Create a simple quantized model for testing."""
-    model = tf.keras.Sequential([
-        tf.keras.layers.Dense(10, input_shape=(5, ), activation='relu'),
-        tf.keras.layers.Dense(5, activation='softmax')
-    ])
-    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
+  converter = tf.lite.TFLiteConverter.from_keras_model(model)
+  converter.optimizations = [tf.lite.Optimize.DEFAULT]
 
-    # Convert to quantized TFLite
-    converter = tf.lite.TFLiteConverter.from_keras_model(model)
-    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+  def representative_dataset():
+    for _ in range(10):
+      yield [np.random.randn(1, 5).astype(np.float32)]
 
-    def representative_dataset():
-      for _ in range(10):
-        yield [np.random.randn(1, 5).astype(np.float32)]
+  converter.representative_dataset = representative_dataset
+  converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+  converter.inference_input_type = tf.uint8
+  converter.inference_output_type = tf.uint8
 
-    converter.representative_dataset = representative_dataset
-    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-    converter.inference_input_type = tf.uint8
-    converter.inference_output_type = tf.uint8
+  tflite_model = converter.convert()
+  return bytes(tflite_model) if isinstance(tflite_model,
+                                           bytearray) else tflite_model
 
-    tflite_model = converter.convert()
-    return bytes(tflite_model) if isinstance(tflite_model,
-                                             bytearray) else tflite_model
+
+def _inject_compression_metadata(model_data):
+  """Inject raw COMPRESSION_METADATA into a model's flatbuffer metadata.
+
+  This simulates a legacy-compressed model (one that uses the
+  COMPRESSION_METADATA metadata entry and kernel-level decompression) without
+  going through compress(), which now produces DECODE-based output.
+  """
+  model = model_editor.read(model_data)
+  model.metadata["COMPRESSION_METADATA"] = b"\x00"
+  return bytes(model.build())
+
+
+class LegacyCompressionDetectionTest(tf.test.TestCase):
+  """Test that legacy COMPRESSION_METADATA is rejected without the flag."""
 
   def test_regular_model_loads_successfully(self):
     """Non-compressed models should load without issues."""
-    model_data = self._create_test_model()
+    model_data = _create_test_model()
     interpreter = runtime.Interpreter.from_bytes(model_data)
     self.assertIsNotNone(interpreter)
 
-  def test_compressed_model_raises_runtime_error(self):
-    """Compressed models should raise RuntimeError when compression is disabled."""
-    # Create and compress a model
-    model_data = self._create_test_model()
+  def test_legacy_compressed_model_raises_runtime_error(self):
+    """Models with COMPRESSION_METADATA should raise RuntimeError."""
+    model_data = _create_test_model()
+    legacy_model = _inject_compression_metadata(model_data)
 
-    spec = (compression.SpecBuilder().add_tensor(
-        subgraph=0, tensor=1).with_lut(index_bitwidth=4).build())
-
-    compressed_model = compression.compress(model_data, spec)
-    if isinstance(compressed_model, bytearray):
-      compressed_model = bytes(compressed_model)
-
-    # Should raise RuntimeError
     with self.assertRaises(RuntimeError):
-      runtime.Interpreter.from_bytes(compressed_model)
-
-  def test_can_load_regular_after_compressed_failure(self):
-    """Verify we can still load regular models after compressed model fails."""
-    model_data = self._create_test_model()
+      runtime.Interpreter.from_bytes(legacy_model)
 
-    # First try compressed model (should fail)
-    spec = (compression.SpecBuilder().add_tensor(
-        subgraph=0, tensor=1).with_lut(index_bitwidth=4).build())
-    compressed_model = compression.compress(model_data, spec)
+  def test_can_load_regular_after_legacy_failure(self):
+    """Verify regular models still load after a legacy-compressed failure."""
+    model_data = _create_test_model()
+    legacy_model = _inject_compression_metadata(model_data)
 
     with self.assertRaises(RuntimeError):
-      runtime.Interpreter.from_bytes(bytes(compressed_model))
+      runtime.Interpreter.from_bytes(legacy_model)
 
-    # Then load regular model (should succeed)
     interpreter = runtime.Interpreter.from_bytes(model_data)
     self.assertIsNotNone(interpreter)
 
 
 if __name__ == '__main__':
-  # Set TF environment variables to suppress warnings
+  # Suppress TF C++ info/debug logs (0=DEBUG, 1=INFO, 2=WARNING, 3=ERROR)
   os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+  # Disable oneDNN to avoid non-deterministic floating point results
   os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
   tf.test.main()