Update torch pin to 2.11 release candidate

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-659af3c353e49b35c191cdd2dba3b3c79d0e6822
+release/2.11

.ci/docker/common/install_pytorch.sh

@@ -32,9 +32,9 @@ install_pytorch_and_domains() {
   pip_install "$(echo dist/*.whl)"
 
   # Grab the pinned audio and vision commits from PyTorch
-  TORCHAUDIO_VERSION=$(cat .github/ci_commit_pins/audio.txt)
+  TORCHAUDIO_VERSION=release/2.11
   export TORCHAUDIO_VERSION
-  TORCHVISION_VERSION=$(cat .github/ci_commit_pins/vision.txt)
+  TORCHVISION_VERSION=release/0.26
   export TORCHVISION_VERSION
 
   install_domains

.ci/scripts/test_model_e2e.sh

@@ -248,7 +248,7 @@ if [ "$AUDIO_URL" != "" ]; then
 elif [[ "$MODEL_NAME" == *whisper* ]] || [ "$MODEL_NAME" = "voxtral_realtime" ]; then
   conda install -y -c conda-forge "ffmpeg<8"
   pip install datasets soundfile
-  pip install torchcodec==0.11.0.dev20260217 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+  pip install torchcodec==0.11.0 --extra-index-url https://download.pytorch.org/whl/test/cpu
   python -c "from datasets import load_dataset;import soundfile as sf;sample = load_dataset('distil-whisper/librispeech_long', 'clean', split='validation')[0]['audio'];sf.write('${MODEL_DIR}/$AUDIO_FILE', sample['array'][:sample['sampling_rate']*30], sample['sampling_rate'])"
 fi
 

.ci/scripts/test_wheel_package_qnn.sh

@@ -158,17 +158,17 @@ print(module_vars["TORCH_VERSION"])
 PY
 )
 
-  NIGHTLY_VERSION=$(
-  "$PYBIN" - <<'PY'
-import runpy
-module_vars = runpy.run_path("torch_pin.py")
-print(module_vars["NIGHTLY_VERSION"])
-PY
-)
-  echo "=== [$LABEL] Install torch==${TORCH_VERSION}.${NIGHTLY_VERSION} ==="
-
-  # Install torchao based on the pinned PyTorch version
-  "$PIPBIN" install torch=="${TORCH_VERSION}.${NIGHTLY_VERSION}" --index-url "https://download.pytorch.org/whl/nightly/cpu"
+#   NIGHTLY_VERSION=$(
+#   "$PYBIN" - <<'PY'
+# import runpy
+# module_vars = runpy.run_path("torch_pin.py")
+# print(module_vars["NIGHTLY_VERSION"])
+# PY
+# )
+  echo "=== [$LABEL] Install torch==${TORCH_VERSION} ==="
+
+  # Install torch based on the pinned PyTorch version, preferring the PyTorch test index
+  "$PIPBIN" install torch=="${TORCH_VERSION}" --extra-index-url "https://download.pytorch.org/whl/test"
   "$PIPBIN" install wheel
 
   # Install torchao based on the pinned commit from third-party/ao submodule

.ci/scripts/utils.sh

@@ -53,7 +53,7 @@ dedupe_macos_loader_path_rpaths() {
   pushd ..
   torch_lib_dir=$(python -c "import importlib.util; print(importlib.util.find_spec('torch').submodule_search_locations[0])")/lib
   popd
-  
+
   if [[ -z "${torch_lib_dir}" || ! -d "${torch_lib_dir}" ]]; then
     return
   fi
@@ -141,9 +141,9 @@ install_pytorch_and_domains() {
 
   dedupe_macos_loader_path_rpaths
   # Grab the pinned audio and vision commits from PyTorch
-  TORCHAUDIO_VERSION=$(cat .github/ci_commit_pins/audio.txt)
+  TORCHAUDIO_VERSION=release/2.11
   export TORCHAUDIO_VERSION
-  TORCHVISION_VERSION=$(cat .github/ci_commit_pins/vision.txt)
+  TORCHVISION_VERSION=release/0.26
   export TORCHVISION_VERSION
 
   install_domains

examples/models/moshi/mimi/install_requirements.sh

@@ -8,7 +8,7 @@
 set -x
 
 sudo apt install ffmpeg -y
-pip install torchcodec==0.11.0.dev20260217 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+pip install torchcodec==0.11.0 --extra-index-url https://download.pytorch.org/whl/test/cpu
 pip install moshi==0.2.11
 pip install bitsandbytes soundfile einops
 # Run llama2/install requirements for torchao deps

examples/models/parakeet/export_parakeet_tdt.py

@@ -508,13 +508,11 @@ def _create_metal_partitioners(programs):
 
     # Run decompositions for non-preprocessor programs
     updated_programs = {}
+    decomp_table = torch.export.default_decompositions()
+    decomp_table[torch.ops.aten.linear.default] = _linear_bias_decomposition
     for key, ep in programs.items():
-        # print(f"Running decompositions for {key}")
-        # print(ep.graph_module)
         if key != "preprocessor":
-            updated_programs[key] = ep.run_decompositions(
-                {torch.ops.aten.linear.default: _linear_bias_decomposition}
-            )
+            updated_programs[key] = ep.run_decompositions(decomp_table)
         else:
             updated_programs[key] = ep
 

examples/models/voxtral_realtime/export_voxtral_rt.py

@@ -394,10 +394,10 @@ def lower_to_executorch(programs, metadata, backend="xnnpack"):
 
         # Run decompositions for Metal backend
         updated_programs = {}
+        decomp_table = torch.export.default_decompositions()
+        decomp_table[torch.ops.aten.linear.default] = _linear_bias_decomposition
         for key, ep in programs.items():
-            updated_programs[key] = ep.run_decompositions(
-                {torch.ops.aten.linear.default: _linear_bias_decomposition}
-            )
+            updated_programs[key] = ep.run_decompositions(decomp_table)
         programs = updated_programs
 
         partitioner = {}

exir/sym_util.py

@@ -25,7 +25,11 @@ def eval_expr(symint: Union[int, torch.SymInt]) -> Optional[int]:
     shape_env = node.shape_env
     expr = node.expr
     try:
-        output = shape_env.size_hint(expr)
+        if hasattr(shape_env, "guarding_hint_or_throw"):
+            output = shape_env.guarding_hint_or_throw(expr)
+        else:
+            # size_hint is deprecated, delete this code path.
+            output = shape_env.size_hint(expr)
     except torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode:
         return None
     return int(output)

install_requirements.py

@@ -12,11 +12,9 @@
 
 from install_utils import determine_torch_url, is_intel_mac_os, python_is_compatible
 
-from torch_pin import NIGHTLY_VERSION, TORCH_VERSION
-
 # The pip repository that hosts nightly torch packages.
 # This will be dynamically set based on CUDA availability and CUDA backend enabled/disabled.
-TORCH_NIGHTLY_URL_BASE = "https://download.pytorch.org/whl/nightly"
+TORCH_URL_BASE = "https://download.pytorch.org/whl/test"
 
 # Since ExecuTorch often uses main-branch features of pytorch, only the nightly
 # pip versions will have the required features.
@@ -44,18 +42,14 @@ def install_requirements(use_pytorch_nightly):
         sys.exit(1)
 
     # Determine the appropriate PyTorch URL based on CUDA delegate status
-    torch_url = determine_torch_url(TORCH_NIGHTLY_URL_BASE)
+    torch_url = determine_torch_url(TORCH_URL_BASE)
 
     # pip packages needed by exir.
     TORCH_PACKAGE = [
         # Setting use_pytorch_nightly to false to test the pinned PyTorch commit. Note
         # that we don't need to set any version number there because they have already
         # been installed on CI before this step, so pip won't reinstall them
-        (
-            f"torch=={TORCH_VERSION}.{NIGHTLY_VERSION}"
-            if use_pytorch_nightly
-            else "torch"
-        ),
+        ("torch==2.11.0" if use_pytorch_nightly else "torch"),
     ]
 
     # Install the requirements for core ExecuTorch package.
@@ -114,20 +108,12 @@ def install_requirements(use_pytorch_nightly):
 
 def install_optional_example_requirements(use_pytorch_nightly):
     # Determine the appropriate PyTorch URL based on CUDA delegate status
-    torch_url = determine_torch_url(TORCH_NIGHTLY_URL_BASE)
+    torch_url = determine_torch_url(TORCH_URL_BASE)
 
     print("Installing torch domain libraries")
     DOMAIN_LIBRARIES = [
-        (
-            f"torchvision==0.26.0.{NIGHTLY_VERSION}"
-            if use_pytorch_nightly
-            else "torchvision"
-        ),
-        (
-            f"torchaudio==2.11.0.{NIGHTLY_VERSION}"
-            if use_pytorch_nightly
-            else "torchaudio"
-        ),
+        ("torchvision==0.26.0" if use_pytorch_nightly else "torchvision"),
+        ("torchaudio==2.11.0" if use_pytorch_nightly else "torchaudio"),
     ]
     # Then install domain libraries
     subprocess.run(

runtime/core/portable_type/c10/c10/util/complex_math.h

@@ -86,6 +86,41 @@ C10_HOST_DEVICE inline c10::complex<T> pow(
 #endif
 }
 
+// Regression in ROCm 7.2. See https://github.com/ROCm/rocm-libraries/pull/3836.
+// Specialized version for complex<float> on AMD GPUs to use FMA-based
+// multiplication
+#if defined(__HIPCC__)
+namespace detail {
+// FMA-aware complex multiplication for float precision on AMD GPUs.
+// This prevents SLP vectorizer from breaking FMA formation, which causes
+// numerical precision loss in complex arithmetic.
+// The issue occurs when vectorizer packs scalar multiplies before backend
+// can form FMA instructions, resulting in double rounding instead of single.
+C10_HOST_DEVICE inline thrust::complex<float> complex_mul_fma(
+    thrust::complex<float> a,
+    thrust::complex<float> b) {
+  // Complex multiplication: (a.r + a.i*i) * (b.r + b.i*i)
+  // = (a.r*b.r - a.i*b.i) + (a.r*b.i + a.i*b.r)*i
+  // Using __builtin_fmaf ensures FMA at source level:
+  // real: a.r*b.r + (-(a.i*b.i)) = FMA(a.r, b.r, -(a.i*b.i))
+  // imag: a.i*b.r + a.r*b.i = FMA(a.r, b.i, a.i*b.r)
+  float real_part = __builtin_fmaf(a.real(), b.real(), -(a.imag() * b.imag()));
+  float imag_part = __builtin_fmaf(a.real(), b.imag(), a.imag() * b.real());
+  return thrust::complex<float>(real_part, imag_part);
+}
+} // namespace detail
+
+template <>
+C10_HOST_DEVICE inline c10::complex<float> pow(
+    const c10::complex<float>& x,
+    const c10::complex<float>& y) {
+  auto log_x = thrust::log(static_cast<thrust::complex<float>>(x));
+  auto y_log_x =
+      detail::complex_mul_fma(static_cast<thrust::complex<float>>(y), log_x);
+  return static_cast<c10::complex<float>>(thrust::exp(y_log_x));
+}
+#endif
+
 template <typename T>
 C10_HOST_DEVICE inline c10::complex<T> pow(
     const c10::complex<T>& x,

torch_pin.py

@@ -1,2 +1,2 @@
 TORCH_VERSION = "2.11.0"
-NIGHTLY_VERSION = "dev20260215"
+# NIGHTLY_VERSION = "dev20260318" Temporarily pinning to stable release candidate. Revert https://github.com/pytorch/executorch/pull/18287