add ABI stable method for updating constant buffer

.ci/docker/almalinux/build.sh

@@ -37,9 +37,9 @@ case ${DOCKER_TAG_PREFIX} in
   rocm*)
     BASE_TARGET=rocm
     PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-    # add gfx950, gfx115x conditionally starting in ROCm 7.0
+    # add gfx950 conditionally starting in ROCm 7.0
     if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
-        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
     fi
     EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;

.ci/docker/build.sh

@@ -344,7 +344,7 @@ docker build \
        --build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
        --build-arg "KATEX=${KATEX:-}" \
        --build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
-       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx1100}" \
+       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a;gfx942}" \
        --build-arg "IMAGE_NAME=${IMAGE_NAME}" \
        --build-arg "UCX_COMMIT=${UCX_COMMIT}" \
        --build-arg "UCC_COMMIT=${UCC_COMMIT}" \

.ci/docker/ci_commit_pins/executorch.txt

@@ -1 +1 @@
-deb42f2a8e48f5032b4a98ee781a15fa87a157cf
+e0dda9059d082537cee36be6c5e4fe3b18c880c0

.ci/docker/ci_commit_pins/nccl-cu13.txt

@@ -1 +1 @@
-v2.27.7-1
+v2.27.5-1

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +1 @@
-7416ffcb92cdbe98d9f97e4e6f95247e46dfc9fd
+27664085f804afc83df26f740bb46c365854f2c4

.ci/docker/common/install_onnx.sh

@@ -19,8 +19,8 @@ pip_install \
   transformers==4.36.2
 
 pip_install coloredlogs packaging
-pip_install onnxruntime==1.23.0
-pip_install onnxscript==0.5.3
+pip_install onnxruntime==1.22.1
+pip_install onnxscript==0.4.0
 
 # Cache the transformers model to be used later by ONNX tests. We need to run the transformers
 # package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/

.ci/docker/common/patch_libstdc.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-set -xe
-# Script used in Linux x86 and aarch64 CD pipeline
-
-# Workaround for exposing statically linked libstdc++ CXX11 ABI symbols.
-# see: https://github.com/pytorch/pytorch/issues/133437
-LIBNONSHARED=$(gcc -print-file-name=libstdc++_nonshared.a)
-nm -g $LIBNONSHARED | grep " T " | grep recursive_directory_iterator | cut -c 20-  > weaken-symbols.txt
-objcopy --weaken-symbols weaken-symbols.txt $LIBNONSHARED $LIBNONSHARED

.ci/docker/libtorch/build.sh

@@ -46,9 +46,9 @@ case ${DOCKER_TAG_PREFIX} in
         BASE_TARGET=rocm
         GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
         PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950, gfx115x conditionally starting in ROCm 7.0
+        # add gfx950 conditionally starting in ROCm 7.0
         if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
         fi
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;

.ci/docker/manywheel/Dockerfile_2_28

@@ -130,8 +130,7 @@ ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/op
 RUN for cpython_version in "cp312-cp312" "cp313-cp313" "cp313-cp313t"; do \
     /opt/python/${cpython_version}/bin/python -m pip install setuptools wheel; \
     done;
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
+
 
 # cmake-3.18.4 from pip; force in case cmake3 already exists
 RUN yum install -y python3-pip && \

.ci/docker/manywheel/Dockerfile_2_28_aarch64

@@ -78,6 +78,4 @@ RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
 RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
 COPY --from=openblas     /opt/OpenBLAS/  /opt/OpenBLAS/
 COPY --from=arm_compute /acl /acl
-ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:/acl/build/:$LD_LIBRARY_PATH
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
+ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:/acl/build/:$LD_LIBRARY_PATH

.ci/docker/manywheel/Dockerfile_cuda_aarch64

@@ -106,5 +106,3 @@ COPY --from=arm_compute /acl /acl
 RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
 ENV PATH=/usr/local/cuda/bin:$PATH
 ENV LD_LIBRARY_PATH=/acl/build/:$LD_LIBRARY_PATH
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh

.ci/docker/manywheel/Dockerfile_s390x

@@ -115,9 +115,6 @@ RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio
 # cmake-3.28.0 from pip for onnxruntime
 RUN python3 -mpip install cmake==3.28.0
 
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
-
 # build onnxruntime 1.21.0 from sources.
 # it is not possible to build it from sources using pip,
 # so just build it from upstream repository.

.ci/docker/manywheel/build.sh

@@ -84,9 +84,9 @@ case ${image} in
         DEVTOOLSET_VERSION="11"
         GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
         PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950, gfx115x conditionally starting in ROCm 7.0
+        # add gfx950 conditionally starting in ROCm 7.0
         if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
         fi
         DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
         ;;

.ci/docker/requirements-ci.txt

@@ -120,8 +120,9 @@ ninja==1.11.1.4
 numba==0.55.2 ; python_version == "3.10" and platform_machine != "s390x"
 numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
 #Description: Just-In-Time Compiler for Numerical Functions
-#Pinned versions: 0.55.2, 0.60.0
+#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
 #test that import: test_numba_integration.py
+#For numba issue see https://github.com/pytorch/pytorch/issues/51511
 #Need release > 0.61.2 for s390x due to https://github.com/numba/numba/pull/10073
 
 #numpy
@@ -241,9 +242,10 @@ pygments==2.15.0
 #Pinned versions: 14.1.0
 #test that import:
 
-scikit-image==0.22.0
+scikit-image==0.19.3 ; python_version < "3.10"
+scikit-image==0.22.0 ; python_version >= "3.10"
 #Description: image processing routines
-#Pinned versions: 0.22.0
+#Pinned versions:
 #test that import: test_nn.py
 
 #scikit-learn
@@ -339,7 +341,7 @@ onnx==1.18.0
 #Pinned versions:
 #test that import:
 
-onnxscript==0.5.3
+onnxscript==0.4.0
 #Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:

.ci/magma-rocm/Makefile

@@ -5,7 +5,7 @@ DESIRED_ROCM ?= 7.0
 DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
 PACKAGE_NAME = magma-rocm
 # inherit this from underlying docker image, do not pass this env var to docker
-#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201
+#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201
 
 DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	-v $(shell git rev-parse --show-toplevel)/.ci:/builder \
@@ -18,6 +18,7 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 .PHONY: all
 all: magma-rocm70
 all: magma-rocm64
+all: magma-rocm63
 
 .PHONY:
 clean:
@@ -33,3 +34,8 @@ magma-rocm70:
 magma-rocm64: DESIRED_ROCM := 6.4
 magma-rocm64:
 	$(DOCKER_RUN)
+
+.PHONY: magma-rocm63
+magma-rocm63: DESIRED_ROCM := 6.3
+magma-rocm63:
+	$(DOCKER_RUN)

.ci/pytorch/build.sh

@@ -233,9 +233,7 @@ if [[ "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
   export BUILD_STATIC_RUNTIME_BENCHMARK=ON
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *-full-debug* ]]; then
-  export CMAKE_BUILD_TYPE=Debug
-elif [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
+if [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
   export CMAKE_BUILD_TYPE=RelWithAssert
 fi
 
@@ -301,11 +299,6 @@ else
       python -m build --wheel --no-isolation
     fi
     pip_install_whl "$(echo dist/*.whl)"
-    if [[ "$BUILD_ENVIRONMENT" == *full-debug* ]]; then
-      # Regression test for https://github.com/pytorch/pytorch/issues/164297
-      # Torch should be importable and that's about it
-      pushd /; python -c "import torch;print(torch.__config__.show(), torch.randn(5) + 1.7)"; popd
-    fi
 
     if [[ "${BUILD_ADDITIONAL_PACKAGES:-}" == *vision* ]]; then
       install_torchvision

.ci/pytorch/macos-test.sh

@@ -256,7 +256,7 @@ test_torchbench_smoketest() {
   local device=mps
   local dtypes=(undefined float16 bfloat16 notset)
   local dtype=${dtypes[$1]}
-  local models=(llama BERT_pytorch dcgan yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor vgg16)
+  local models=(hf_T5 llama BERT_pytorch dcgan hf_GPT2 yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor timm_resnet timm_vovnet vgg16)
 
   for backend in eager inductor; do
 
@@ -319,7 +319,7 @@ test_aoti_torchbench_smoketest() {
   local device=mps
   local dtypes=(undefined float16 bfloat16 notset)
   local dtype=${dtypes[$1]}
-  local models=(llama BERT_pytorch dcgan yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor vgg16)
+  local models=(hf_T5 llama BERT_pytorch dcgan hf_GPT2 yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor timm_resnet timm_vovnet vgg16)
 
   echo "Launching torchbench inference performance run for AOT Inductor and dtype ${dtype}"
   local dtype_arg="--${dtype}"

.ci/pytorch/smoke_test/check_binary_symbols.py

@@ -32,9 +32,6 @@ LIBTORCH_NAMESPACE_LIST = (
     "torch::",
 )
 
-# Patterns for detecting statically linked libstdc++ symbols
-STATICALLY_LINKED_CXX11_ABI = [re.compile(r".*recursive_directory_iterator.*")]
-
 
 def _apply_libtorch_symbols(symbols):
     return [
@@ -56,17 +53,12 @@ def get_symbols(lib: str) -> list[tuple[str, str, str]]:
     return [x.split(" ", 2) for x in lines.decode("latin1").split("\n")[:-1]]
 
 
-def grep_symbols(
-    lib: str, patterns: list[Any], symbol_type: str | None = None
-) -> list[str]:
+def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
     def _grep_symbols(
         symbols: list[tuple[str, str, str]], patterns: list[Any]
     ) -> list[str]:
         rc = []
         for _s_addr, _s_type, s_name in symbols:
-            # Filter by symbol type if specified
-            if symbol_type and _s_type != symbol_type:
-                continue
             for pattern in patterns:
                 if pattern.match(s_name):
                     rc.append(s_name)
@@ -88,18 +80,6 @@ def grep_symbols(
         return functools.reduce(list.__add__, (x.result() for x in tasks), [])
 
 
-def check_lib_statically_linked_libstdc_cxx_abi_symbols(lib: str) -> None:
-    cxx11_statically_linked_symbols = grep_symbols(
-        lib, STATICALLY_LINKED_CXX11_ABI, symbol_type="T"
-    )
-    num_statically_linked_symbols = len(cxx11_statically_linked_symbols)
-    print(f"num_statically_linked_symbols (T): {num_statically_linked_symbols}")
-    if num_statically_linked_symbols > 0:
-        raise RuntimeError(
-            f"Found statically linked libstdc++ symbols (recursive_directory_iterator): {cxx11_statically_linked_symbols[:100]}"
-        )
-
-
 def check_lib_symbols_for_abi_correctness(lib: str) -> None:
     print(f"lib: {lib}")
     cxx11_symbols = grep_symbols(lib, LIBTORCH_CXX11_PATTERNS)
@@ -127,7 +107,6 @@ def main() -> None:
 
     libtorch_cpu_path = str(install_root / "lib" / "libtorch_cpu.so")
     check_lib_symbols_for_abi_correctness(libtorch_cpu_path)
-    check_lib_statically_linked_libstdc_cxx_abi_symbols(libtorch_cpu_path)
 
 
 if __name__ == "__main__":

.ci/pytorch/test.sh

@@ -34,14 +34,12 @@ fi
 
 
 # Patch numba to avoid CUDA-13 crash, see https://github.com/pytorch/pytorch/issues/162878
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  NUMBA_CUDA_DIR=$(python -c "import os;import numba.cuda; print(os.path.dirname(numba.cuda.__file__))" 2>/dev/null || true)
-  if [ -n "$NUMBA_CUDA_DIR" ]; then
-    NUMBA_PATCH="$(dirname "$(realpath "${BASH_SOURCE[0]}")")/numba-cuda-13.patch"
-    pushd "$NUMBA_CUDA_DIR"
-    patch -p4 <"$NUMBA_PATCH"
-    popd
-  fi
+NUMBA_CUDA_DIR=$(python -c "import os;import numba.cuda; print(os.path.dirname(numba.cuda.__file__))" 2>/dev/null || true)
+if [ -n "$NUMBA_CUDA_DIR" ]; then
+  NUMBA_PATCH="$(dirname "$(realpath "${BASH_SOURCE[0]}")")/numba-cuda-13.patch"
+  pushd "$NUMBA_CUDA_DIR"
+  patch -p4 <"$NUMBA_PATCH"
+  popd
 fi
 
 echo "Environment variables:"
@@ -838,7 +836,7 @@ test_dynamo_benchmark() {
       elif [[ "${suite}" == "timm_models" ]]; then
         export TORCHBENCH_ONLY_MODELS="inception_v3"
       elif [[ "${suite}" == "torchbench" ]]; then
-        export TORCHBENCH_ONLY_MODELS="BERT_pytorch"
+        export TORCHBENCH_ONLY_MODELS="hf_Bert"
       fi
     fi
     test_single_dynamo_benchmark "dashboard" "$suite" "$shard_id" "$@"
@@ -869,13 +867,13 @@ test_inductor_torchbench_smoketest_perf() {
   mkdir -p "$TEST_REPORTS_DIR"
 
   python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --float16 --training \
-    --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only BERT_pytorch \
+    --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only hf_Bert \
     --output "$TEST_REPORTS_DIR/inductor_training_smoketest.csv"
   # The threshold value needs to be actively maintained to make this check useful
   python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_training_smoketest.csv" -t 1.4
 
   # Check memory compression ratio for a few models
-  for test in BERT_pytorch yolov3; do
+  for test in hf_Albert timm_vision_transformer; do
     python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --amp --training \
       --disable-cudagraphs --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" \
       --only $test --output "$TEST_REPORTS_DIR/inductor_training_smoketest_$test.csv"
@@ -886,7 +884,7 @@ test_inductor_torchbench_smoketest_perf() {
   done
 
   # Perform some "warm-start" runs for a few huggingface models.
-  for test in AllenaiLongformerBase DistilBertForMaskedLM DistillGPT2 GoogleFnet YituTechConvBert; do
+  for test in AlbertForQuestionAnswering AllenaiLongformerBase DistilBertForMaskedLM DistillGPT2 GoogleFnet YituTechConvBert; do
     python benchmarks/dynamo/huggingface.py --accuracy --training --amp --inductor --device cuda --warm-start-latency \
       --only $test --output "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv"
     python benchmarks/dynamo/check_accuracy.py \

.ci/pytorch/win-test-helpers/test_libtorch.bat

@@ -15,35 +15,37 @@ if errorlevel 1 exit /b 1
 if not errorlevel 0 exit /b 1
 
 cd %TMP_DIR_WIN%\build\torch\test
-
-:: Enable delayed variable expansion to make the list
-setlocal enabledelayedexpansion
-set EXE_LIST=
 for /r "." %%a in (*.exe) do (
-  if "%%~na" == "c10_intrusive_ptr_benchmark" (
-    @REM NB: This is not a gtest executable file, thus couldn't be handled by
-    @REM pytest-cpp and is excluded from test discovery by run_test
-    call "%%~fa"
+    call :libtorch_check "%%~na" "%%~fa"
     if errorlevel 1 goto fail
-    if not errorlevel 0 goto fail
-  ) else (
-    if "%%~na" == "verify_api_visibility" (
-      @REM Skip verify_api_visibility as it is a compile-level test
-    ) else (
-      set EXE_LIST=!EXE_LIST! cpp/%%~na
-    )
-  )
 )
 
+goto :eof
+
+:libtorch_check
+
 cd %CWD%
 set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\test
 
-:: Run python test\run_test.py on the list
-set NO_TD=True && python test\run_test.py --cpp --verbose -i !EXE_LIST!
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+:: Skip verify_api_visibility as it a compile level test
+if "%~1" == "verify_api_visibility" goto :eof
 
-goto :eof
+echo Running "%~2"
+if "%~1" == "c10_intrusive_ptr_benchmark" (
+  :: NB: This is not a gtest executable file, thus couldn't be handled by pytest-cpp
+  call "%~2"
+  goto :eof
+)
+
+python test\run_test.py --cpp --verbose -i "cpp/%~1"
+if errorlevel 1 (
+  echo %1 failed with exit code %errorlevel%
+  goto fail
+)
+if not errorlevel 0 (
+  echo %1 failed with exit code %errorlevel%
+  goto fail
+)
 
 :eof
 exit /b 0

.ci/pytorch/win-test.sh

@@ -38,7 +38,7 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
 fi
 
 # TODO: Move this to .ci/docker/requirements-ci.txt
-python -m pip install "psutil==5.9.1" nvidia-ml-py "pytest-shard==0.1.2"
+python -m pip install "psutil==5.9.1" "pynvml==11.4.1" "pytest-shard==0.1.2"
 
 run_tests() {
     # Run nvidia-smi if available

.ci/pytorch/windows/cuda128.bat

@@ -37,10 +37,10 @@ IF "%CUDA_PATH_V128%"=="" (
 )
 
 IF "%BUILD_VISION%" == "" (
-    set TORCH_CUDA_ARCH_LIST=7.0;7.5;8.0;8.6;9.0;10.0;12.0
+    set TORCH_CUDA_ARCH_LIST=6.1;7.0;7.5;8.0;8.6;9.0;10.0;12.0
     set TORCH_NVCC_FLAGS=-Xfatbin -compress-all
 ) ELSE (
-    set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
+    set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
 )
 
 set "CUDA_PATH=%CUDA_PATH_V128%"

.circleci/scripts/binary_populate_env.sh

@@ -71,7 +71,14 @@ export PYTORCH_BUILD_NUMBER=1
 
 # Set triton version as part of PYTORCH_EXTRA_INSTALL_REQUIREMENTS
 TRITON_VERSION=$(cat $PYTORCH_ROOT/.ci/docker/triton_version.txt)
-TRITON_CONSTRAINT="platform_system == 'Linux'"
+
+# Here PYTORCH_EXTRA_INSTALL_REQUIREMENTS is already set for the all the wheel builds hence append TRITON_CONSTRAINT
+TRITON_CONSTRAINT="platform_system == 'Linux' and platform_machine == 'x86_64'"
+
+# CUDA 12.9/13.0 builds have triton for Linux and Linux aarch64 binaries.
+if [[ "$DESIRED_CUDA" == "cu129" ]] || [[ "$DESIRED_CUDA" == "cu130" ]]; then
+  TRITON_CONSTRAINT="platform_system == 'Linux'"
+fi
 
 if [[ "$PACKAGE_TYPE" =~ .*wheel.* &&  -n "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" && ! "$PYTORCH_BUILD_VERSION" =~ .*xpu.* ]]; then
   TRITON_REQUIREMENT="triton==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"

.clang-tidy

@@ -59,14 +59,13 @@ performance-*,
 -performance-enum-size,
 readability-container-size-empty,
 readability-delete-null-pointer,
-readability-duplicate-include,
+readability-duplicate-include
 readability-misplaced-array-index,
-readability-redundant*,
+readability-redundant*
 readability-simplify-subscript-expr,
 readability-string-compare,
 -readability-redundant-access-specifiers,
 -readability-redundant-control-flow,
--readability-redundant-inline-specifier,
 '
 HeaderFilterRegex: '^(aten/|c10/|torch/).*$'
 WarningsAsErrors: '*'

.github/actions/setup-linux/action.yml

@@ -28,10 +28,6 @@ runs:
         echo "instance-type: $(get_ec2_metadata instance-type)"
         echo "system info $(uname -a)"
 
-    - name: Print GPU info (if present)
-      shell: bash
-      run: if [ -f /usr/bin/nvidia-smi ]; then nvidia-smi; fi
-
     - name: Check if in a container runner
       shell: bash
       id: check_container_runner
@@ -86,6 +82,37 @@ runs:
         # Prune all of the docker images
         docker system prune -af
 
+    - name: Manually resolve download.pytorch.org
+      shell: bash
+      continue-on-error: true
+      run: |
+        set +e
+        set -x
+
+        PT_DOMAIN=download.pytorch.org
+        # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
+        # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
+        # one is returned at random
+        RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
+
+        if [ -z "${RESOLVED_IP}" ]; then
+          echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
+          RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
+
+          if [ -z "${RESOLVED_IP}" ]; then
+            echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
+            exit 1
+          fi
+        fi
+
+        if grep -r "${PT_DOMAIN}" /etc/hosts; then
+          # Clean up any old records first
+          sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
+        fi
+
+        echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
+        cat /etc/hosts
+
     - name: Check that the docker daemon is running
       shell: bash
       continue-on-error: true

.github/ci_commit_pins/vllm.txt

@@ -1 +1 @@
-0ad9951c416d33c5da4f7a504fb162cbe62386f5
+78a47f87ce259a48f0391fa9ae15add05ea7432b

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-2a9138a26ee257fef05310ad3fecf7c55fe80d73
+0fc62aa26a30ed7ca419d285f285cb5ba02c4394

.github/ci_configs/vllm/Dockerfile.tmp_vllm

@@ -202,7 +202,7 @@ ARG max_jobs=16
 ENV MAX_JOBS=${max_jobs}
 ARG nvcc_threads=4
 ENV NVCC_THREADS=$nvcc_threads
-ARG torch_cuda_arch_list='8.0 8.6 8.9 9.0'
+ARG torch_cuda_arch_list='8.0;8.6;8.9;9.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 
 ARG USE_SCCACHE
@@ -297,28 +297,16 @@ RUN echo "[INFO] Listing current directory before torch install step:" && \
     echo "[INFO] Showing torch_build_versions.txt content:" && \
     cat torch_build_versions.txt
 
-# Install build and runtime dependencies, this is needed for flashinfer install
-COPY requirements/build.txt requirements/build.txt
-COPY use_existing_torch.py use_existing_torch.py
-RUN python3 use_existing_torch.py
-RUN cat requirements/build.txt
-
 # Install uv for faster pip installs if not existed
 RUN --mount=type=cache,target=/root/.cache/uv \
     if ! python3 -m uv --version > /dev/null 2>&1; then \
         python3 -m pip install uv==0.8.4; \
     fi
-
 ENV UV_HTTP_TIMEOUT=500
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 # Use copy mode to avoid hardlink failures with Docker cache mounts
 ENV UV_LINK_MODE=copy
 
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements/build.txt
-
-
 # Default mount file as placeholder, this just avoid the mount error
 ARG TORCH_WHEELS_PATH="./requirements"
 # Install torch, torchaudio and torchvision
@@ -344,11 +332,13 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Install xformers wheel from previous stage
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system /wheels/xformers/*.whl --verbose
+
 # Build flashinfer from source.
 ARG torch_cuda_arch_list='8.0;8.9;9.0a;10.0a;12.0'
 # install package for build flashinfer
 # see issue: https://github.com/flashinfer-ai/flashinfer/issues/738
 
+RUN pip install build==1.3.0
 RUN pip freeze | grep -E 'setuptools|packaging|build'
 
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}

.github/ci_configs/vllm/use_existing_torch.py

@@ -1,14 +1,9 @@
 import glob
-import os
 
 
 requires_files = glob.glob("requirements/*.txt")
 requires_files += ["pyproject.toml"]
-
 for file in requires_files:
-    if not os.path.exists(file):
-        print(f"!!! skipping missing {file}")
-        continue
     print(f">>> cleaning {file}")
     with open(file) as f:
         lines = f.readlines()

.github/pytorch-probot.yml

@@ -30,7 +30,6 @@ ciflow_push_tags:
 - ciflow/riscv64
 - ciflow/rocm
 - ciflow/rocm-mi300
-- ciflow/rocm-mi355
 - ciflow/s390
 - ciflow/slow
 - ciflow/torchbench

.github/scripts/generate_binary_build_matrix.py

@@ -87,7 +87,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | "
         "nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | "
         "nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | "
-        "nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | "
+        "nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | "
         "nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | "
         "nvidia-nvtx==13.0.39; platform_system == 'Linux' | "
         "nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | "

.github/scripts/github_utils.py

@@ -18,7 +18,6 @@ class GitHubComment:
     body_text: str
     created_at: str
     author_login: str
-    author_url: Optional[str]
     author_association: str
     editor_login: Optional[str]
     database_id: int

.github/scripts/test_check_labels.py

@@ -38,7 +38,6 @@ def mock_get_comments() -> list[GitHubComment]:
             body_text="mock_body_text",
             created_at="",
             author_login="",
-            author_url=None,
             author_association="",
             editor_login=None,
             database_id=1,
@@ -49,7 +48,6 @@ def mock_get_comments() -> list[GitHubComment]:
             body_text=" #" + LABEL_ERR_MSG_TITLE.replace("`", ""),
             created_at="",
             author_login=BOT_AUTHORS[1],
-            author_url=None,
             author_association="",
             editor_login=None,
             database_id=2,

.github/scripts/test_trymerge.py

@@ -32,7 +32,6 @@ from trymerge import (
     main as trymerge_main,
     MandatoryChecksMissingError,
     MergeRule,
-    PostCommentError,
     RE_GHSTACK_DESC,
     read_merge_rules,
     remove_job_name_suffix,
@@ -589,23 +588,6 @@ class TestTryMerge(TestCase):
             self.assertEqual(mock_merge_base, pr.get_merge_base())
             mocked_gh_fetch_merge_base.assert_called_once()
 
-    def test_app_can_revert(self, *args: Any) -> None:
-        pr = GitHubPR("pytorch", "pytorch", 164660)
-        repo = DummyGitRepo()
-        app_comment_id, impostor_comment_id = 3375785595, 3377647892
-        # Check that app can revert
-        self.assertIsNotNone(validate_revert(repo, pr, comment_id=app_comment_id))
-        # But impostor can not
-        self.assertRaises(
-            PostCommentError,
-            lambda: validate_revert(repo, pr, comment_id=impostor_comment_id),
-        )
-        # Despite it's name being the name of the bot
-        self.assertEqual(
-            pr.get_comment_by_id(impostor_comment_id).author_login,
-            "pytorch-auto-revert",
-        )
-
 
 @mock.patch("trymerge.gh_graphql", side_effect=mocked_gh_graphql)
 @mock.patch("trymerge.gh_fetch_merge_base", return_value="")

.github/scripts/trymerge.py

@@ -234,7 +234,6 @@ query ($owner: String!, $name: String!, $number: Int!) {
           createdAt
           author {
             login
-            url
           }
           authorAssociation
           editor {
@@ -1094,7 +1093,6 @@ class GitHubPR:
             body_text=node["bodyText"],
             created_at=node["createdAt"] if "createdAt" in node else "",
             author_login=node["author"]["login"],
-            author_url=node["author"].get("url", None),
             author_association=node["authorAssociation"],
             editor_login=editor["login"] if editor else None,
             database_id=node["databaseId"],
@@ -2031,11 +2029,6 @@ def validate_revert(
     # For some reason, one can not be a member of private repo, only CONTRIBUTOR
     if pr.is_base_repo_private():
         allowed_reverters.append("CONTRIBUTOR")
-    # Special case the pytorch-auto-revert app, whose does not have association
-    # But should be able to issue revert command
-    if comment.author_url == "https://github.com/apps/pytorch-auto-revert":
-        allowed_reverters.append("NONE")
-
     if author_association not in allowed_reverters:
         raise PostCommentError(
             f"Will not revert as @{author_login} is not one of "

.github/workflows/_get-changed-files.yml

@@ -40,15 +40,6 @@ jobs:
               # Use gh CLI to get changed files in the PR with explicit repo
               CHANGED_FILES=$(gh api repos/${{ github.repository }}/pulls/$PR_NUMBER/files --paginate --jq '.[] | select(.status != "removed") | .filename' | tr '\n' ' ' | sed 's/ $//')
 
-              # See https://github.com/pytorch/pytorch/pull/134215#issuecomment-2332128790
-              PYI_FILES_TO_ADD=""
-              for file in ${CHANGED_FILES}; do
-                if [[ "${file}" == *".pyi.in" ]]; then
-                  PYI_FILES_TO_ADD="${PYI_FILES_TO_ADD} ${file//.in/}"
-                fi
-              done
-              CHANGED_FILES="${CHANGED_FILES}${PYI_FILES_TO_ADD}"
-
               if [ -z "$CHANGED_FILES" ]; then
                 echo "No changed files found, setting to '*'"
                 CHANGED_FILES="*"

.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml

@@ -224,7 +224,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -427,7 +427,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -630,7 +630,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -833,7 +833,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1036,7 +1036,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1239,7 +1239,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1442,7 +1442,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/generated-linux-binary-manywheel-nightly.yml

@@ -259,7 +259,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_10-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_10-cuda13_0-test:  # Testing
@@ -853,7 +853,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_11-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_11-cuda13_0-test:  # Testing
@@ -1447,7 +1447,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_12-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_12-cuda13_0-test:  # Testing
@@ -2041,7 +2041,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13-cuda13_0-test:  # Testing
@@ -2635,7 +2635,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13t-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13t-cuda13_0-test:  # Testing
@@ -3229,7 +3229,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14-cuda13_0-test:  # Testing
@@ -3823,7 +3823,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14t-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14t-cuda13_0-test:  # Testing

.github/workflows/h100-distributed.yml

@@ -37,7 +37,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: "linux.c7i.12xlarge"
+      runner: "linux.12xlarge"
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm90-dist
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

.github/workflows/inductor-perf-test-nightly-h100.yml

@@ -2,7 +2,7 @@ name: inductor-perf-nightly-h100
 
 on:
   schedule:
-    - cron: 15 0 * * 1-6
+    - cron: 15 0,12 * * 1-6
     - cron: 0 7 * * 0
   # NB: GitHub has an upper limit of 10 inputs here, so before we can sort it
   # out, let try to run torchao cudagraphs_low_precision as part of cudagraphs

.github/workflows/inductor-perf-test-nightly-macos.yml

@@ -63,7 +63,6 @@ jobs:
       # Same as the build job
       python-version: 3.12.7
       test-matrix: ${{ needs.macos-perf-py3-arm64-build.outputs.test-matrix }}
-      timeout-minutes: 300
       disable-monitor: false
       monitor-log-interval: 15
       monitor-data-collect-interval: 4

.github/workflows/inductor-periodic.yml

@@ -106,16 +106,6 @@ jobs:
           { config: "dynamic_aot_eager_huggingface", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.1" },
           { config: "dynamic_aot_eager_timm", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
           { config: "dynamic_aot_eager_timm", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "dynamic_inductor_huggingface", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "dynamic_inductor_timm", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "dynamic_inductor_timm", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "dynamic_inductor_torchbench", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "dynamic_inductor_torchbench", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "aot_inductor_huggingface", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "aot_inductor_timm", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "aot_inductor_timm", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "aot_inductor_torchbench", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "aot_inductor_torchbench", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
         ]}
     secrets: inherit
 

.github/workflows/operator_microbenchmark.yml

@@ -18,7 +18,6 @@ permissions:
   contents: read
 
 jobs:
-  # H100 A100 runners
   opmicrobenchmark-build:
     if: github.repository_owner == 'pytorch'
     name: opmicrobenchmark-build
@@ -45,56 +44,3 @@ jobs:
       docker-image: ${{ needs.opmicrobenchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.opmicrobenchmark-build.outputs.test-matrix }}
     secrets: inherit
-
-  # B200 runner
-  opmicrobenchmark-build-b200:
-    if: github.repository_owner == 'pytorch'
-    name: opmicrobenchmark-build-b200
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm100
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "operator_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.dgx.b200" },
-        ]}
-    secrets: inherit
-
-  opmicrobenchmark-test-b200:
-    name: opmicrobenchmark-test-b200
-    uses: ./.github/workflows/_linux-test.yml
-    needs: opmicrobenchmark-build-b200
-    with:
-      timeout-minutes: 500
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm100
-      docker-image: ${{ needs.opmicrobenchmark-build-b200.outputs.docker-image }}
-      test-matrix: ${{ needs.opmicrobenchmark-build-b200.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit
-
-  # ROCM MI300 runner
-  opmicrobenchmark-build-rocm:
-    if: github.repository_owner == 'pytorch'
-    name: opmicrobenchmark-build-rocm
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      build-environment: linux-jammy-rocm-py3_10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
-      test-matrix: |
-        { include: [
-          { config: "operator_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.1" },
-        ]}
-    secrets: inherit
-
-  opmicrobenchmark-test-rocm:
-    name: opmicrobenchmark-test-rocm
-    uses: ./.github/workflows/_rocm-test.yml
-    needs: opmicrobenchmark-build-rocm
-    with:
-      timeout-minutes: 500
-      build-environment: linux-jammy-rocm-py3_10
-      docker-image: ${{ needs.opmicrobenchmark-build-rocm.outputs.docker-image }}
-      test-matrix: ${{ needs.opmicrobenchmark-build-rocm.outputs.test-matrix }}
-    secrets: inherit

.github/workflows/periodic.yml

@@ -213,9 +213,9 @@ jobs:
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
       test-matrix: |
         { include: [
-          { config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
-          { config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
-          { config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
+          { config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
+          { config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
+          { config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
         ]}
     secrets: inherit
 

.github/workflows/pull.yml

@@ -127,6 +127,8 @@ jobs:
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
+      # More memory is needed to build with asan
+      runner: linux.2xlarge.memory
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-py3.10-clang18-asan
       docker-image-name: ci-image:pytorch-linux-jammy-py3-clang18-asan

.github/workflows/rocm-mi355.yml

@@ -1,9 +1,6 @@
 name: rocm-mi355
 
 on:
-  push:
-    tags:
-      - ciflow/rocm-mi355/*
   workflow_dispatch:
   schedule:
     - cron: 30 11,1 * * *  # about 4:30am PDT and 6:30pm PDT
@@ -67,7 +64,5 @@ jobs:
       build-environment: linux-noble-rocm-py3.12-mi355
       docker-image: ${{ needs.linux-noble-rocm-py3_12-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-noble-rocm-py3_12-build.outputs.test-matrix }}
-      tests-to-include: >-
-                        ${{ github.event_name == 'schedule' && 'test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor test_matmul_cuda test_scaled_matmul_cuda'
-                           || '' }}
+      tests-to-include: "test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor"
     secrets: inherit

.github/workflows/rocm.yml

@@ -59,29 +59,3 @@ jobs:
       docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
     secrets: inherit
-
-  linux-jammy-rocm-py3_10-gfx1100-test:
-    if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3_10-gfx1100
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: |
-        { include: [
-          { config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
-          { config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
-        ]}
-      tests-to-include: >
-         test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs
-         test_autograd inductor/test_torchinductor inductor/test_kernel_benchmark
-         inductor/test_pad_mm inductor/test_benchmark_fusion inductor/test_aot_inductor
-         inductor/test_torchinductor inductor/test_decompose_mem_bound_mm
-         inductor/test_flex_attention inductor/test_max_autotune
-    secrets: inherit

.github/workflows/slow.yml

@@ -140,6 +140,8 @@ jobs:
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
+      # More memory is needed to build with asan
+      runner: linux.2xlarge.memory
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-py3.10-clang18-asan
       docker-image-name: ci-image:pytorch-linux-jammy-py3-clang18-asan

.github/workflows/trunk.yml

@@ -160,10 +160,9 @@ jobs:
       runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
       test-matrix: |
         { include: [
-          { config: "default", shard: 1, num_shards: 4, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
-          { config: "default", shard: 2, num_shards: 4, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
-          { config: "default", shard: 3, num_shards: 4, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
-          { config: "default", shard: 4, num_shards: 4, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
+          { config: "default", shard: 1, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
+          { config: "default", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
+          { config: "default", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral" },
         ]}
     secrets: inherit
 
@@ -190,6 +189,41 @@ jobs:
       runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
     secrets: inherit
 
+  linux-jammy-rocm-py3_10-build:
+    if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/trunk') }}
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
+      sync-tag: rocm-build
+      test-matrix: |
+        { include: [
+          { config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "distributed", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.4" },
+        ]}
+    secrets: inherit
+
+  linux-jammy-rocm-py3_10-test:
+    if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/trunk') }}
+    permissions:
+      id-token: write
+      contents: read
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_rocm-test.yml
+    needs:
+      - linux-jammy-rocm-py3_10-build
+      - target-determination
+    with:
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
+      tests-to-include: "test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor distributed/test_c10d_common distributed/test_c10d_nccl"
+    secrets: inherit
+
   inductor-build:
     name: inductor-build
     uses: ./.github/workflows/_linux-build.yml
@@ -249,14 +283,3 @@ jobs:
       docker-image: ${{ needs.linux-jammy-py3-clang12-executorch-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-jammy-py3-clang12-executorch-build.outputs.test-matrix }}
     secrets: inherit
-
-  linux-jammy-py3_10-gcc11-full-debug-build-only:
-    name: linux-jammy-py3.10-gcc11-full-debug-build-only
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.2xlarge.memory
-      build-environment: linux-jammy-py3.10-gcc11-full-debug-build-only
-      docker-image-name: ci-image:pytorch-linux-jammy-py3.10-gcc11
-    secrets: inherit

.github/workflows/update-viablestrict.yml

@@ -23,7 +23,7 @@ jobs:
         with:
           repository: pytorch/pytorch
           stable-branch: viable/strict
-          requires: '[\"pull\", \"trunk\", \"lint\",  \"linux-aarch64\"]'
+          requires: '[\"pull\", \"trunk\", \"lint\", \"^linux-binary-manywheel$\", \"^linux-binary-libtorch-release$\", \"linux-aarch64\"]'
           secret-bot-token: ${{ secrets.MERGEBOT_TOKEN }}
           clickhouse-url: ${{ secrets.CLICKHOUSE_URL }}
           clickhouse-username: ${{ secrets.CLICKHOUSE_VIABLESTRICT_USERNAME }}

.github/workflows/vllm.yml

@@ -42,7 +42,7 @@ jobs:
       build-external-packages: "vllm"
       build-environment: linux-jammy-cuda12.8-py3.12-gcc11
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-vllm
-      cuda-arch-list: '8.0 8.9 9.0'
+      cuda-arch-list: '8.0;8.9;9.0'
       runner: linux.24xlarge.memory
       test-matrix: |
         { include: [

.github/workflows/xpu.yml

@@ -35,7 +35,7 @@ jobs:
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
       build-environment: linux-jammy-xpu-n-1-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-1-py3
-      runner: linux.c7i.12xlarge
+      runner: linux.12xlarge
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
@@ -56,7 +56,7 @@ jobs:
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
       build-environment: linux-jammy-xpu-n-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
-      runner: linux.c7i.12xlarge
+      runner: linux.12xlarge
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 8, runner: "linux.idc.xpu" },

.gitignore

@@ -88,7 +88,7 @@ torch_compile_debug/
 # Listed manually because some files in this directory are not generated
 torch/testing/_internal/generated/annotated_fn_args.py
 torch/testing/_internal/data/*.pt
-torch/headeronly/version.h
+torch/csrc/api/include/torch/version.h
 torch/csrc/cudnn/cuDNN.cpp
 torch/csrc/generated
 torch/csrc/generic/TensorMethods.cpp

.lintrunner.toml

@@ -18,7 +18,6 @@ exclude_patterns = [
     'torch/_inductor/autoheuristic/artifacts/**',
     'scripts/**',
     'test/generated_type_hints_smoketest.py',
-    'test/test_torchfuzz_repros.py',
     # CPython tests
     'test/dynamo/cpython/**',
     # Tests from the NumPy test suite
@@ -28,7 +27,6 @@ exclude_patterns = [
     'torch/lib/**',
     'venv/**',
     '**/*.pyi',
-    "tools/experimental/torchfuzz/**",
     'tools/test/test_selective_build.py',
 ]
 command = [
@@ -198,7 +196,7 @@ exclude_patterns = [
     'tools/test/gen_operators_yaml_test.py',
     'tools/test/gen_oplist_test.py',
     'tools/test/test_selective_build.py',
-    'tools/experimental/torchfuzz/**',
+    'tools/experimental/dynamic_shapes/torchfuzz/**',
 ]
 command = [
     'python3',
@@ -1573,7 +1571,6 @@ exclude_patterns = [
     'torch/_inductor/fx_passes/serialized_patterns/**',
     'torch/_inductor/autoheuristic/artifacts/**',
     'test/dynamo/cpython/**',
-    'test/test_torchfuzz_repros.py',
     'scripts/**',
     'third_party/**',
     'fb/**',

BUILD.bazel

@@ -13,9 +13,6 @@ load(":build_variables.bzl", "jit_core_sources", "lazy_tensor_ts_sources", "libt
 load(":ufunc_defs.bzl", "aten_ufunc_generated_cpu_kernel_sources", "aten_ufunc_generated_cpu_sources", "aten_ufunc_generated_cuda_sources")
 load("//:tools/bazel.bzl", "rules")
 
-# Export files for use by torch/headeronly (where version.h generation now lives)
-exports_files(["version.txt"])
-
 define_targets(rules = rules)
 
 COMMON_COPTS = [
@@ -693,9 +690,7 @@ cc_library(
             "torch/csrc/*/generated/*.h",
             "torch/csrc/jit/serialization/mobile_bytecode_generated.h",
         ] + torch_cuda_headers,
-    ) + GENERATED_AUTOGRAD_CPP + [
-        "//torch/headeronly:version_h",
-    ],
+    ) + GENERATED_AUTOGRAD_CPP + [":version_h"],
     includes = [
         "third_party/kineto/libkineto/include",
         "torch/csrc",

CMakeLists.txt

@@ -388,9 +388,9 @@ cmake_dependent_option(USE_PRIORITIZED_TEXT_FOR_LD "Use prioritized text linker
 
 option(USE_MIMALLOC "Use mimalloc" OFF)
 # Enable third party mimalloc library to improve memory allocation performance
-# on Windows and AArch64.
+# on Windows.
 option(USE_MIMALLOC_ON_MKL "Use mimalloc on MKL" OFF)
-if(WIN32 OR (CPU_AARCH64 AND NOT APPLE))
+if(WIN32)
   set(USE_MIMALLOC ON)
 
   # Not enable USE_MIMALLOC_ON_MKL due to it caused issue:

CODEOWNERS

@@ -181,15 +181,15 @@ caffe2/utils/hip @jeffdaily @jithunnair-amd
 /torch/csrc/jit/python/init.cpp @mikaylagawarecki
 
 # CUDA and CUDA math libraries
-aten/src/ATen/cuda/ @eqy @syed-ahmed @Aidyn-A
-aten/src/ATen/cudnn/ @eqy @syed-ahmed @Aidyn-A
-aten/src/ATen/native/cuda/ @eqy @syed-ahmed @Aidyn-A
-aten/src/ATen/native/cudnn/ @eqy @syed-ahmed @Aidyn-A
-c10/cuda @eqy @syed-ahmed @Aidyn-A
-torch/cuda/ @eqy @syed-ahmed @Aidyn-A
-torch/csrc/cuda/ @eqy @syed-ahmed @Aidyn-A
-torch/backends/cuda/ @eqy @syed-ahmed @Aidyn-A
-torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
+aten/src/ATen/cuda/ @eqy @syed-ahmed
+aten/src/ATen/cudnn/ @eqy @syed-ahmed
+aten/src/ATen/native/cuda/ @eqy @syed-ahmed
+aten/src/ATen/native/cudnn/ @eqy @syed-ahmed
+c10/cuda @eqy @syed-ahmed
+torch/cuda/ @eqy @syed-ahmed
+torch/csrc/cuda/ @eqy @syed-ahmed
+torch/backends/cuda/ @eqy @syed-ahmed
+torch/backends/cudnn/ @eqy @syed-ahmed
 
 # PyTree utilities
 /torch/utils/_pytree.py @XuehaiPan

Dockerfile

@@ -50,10 +50,11 @@ RUN git submodule update --init --recursive
 FROM conda as conda-installs
 ARG PYTHON_VERSION=3.11
 ARG CUDA_PATH=cu121
+ARG CUDA_CHANNEL=nvidia
 ARG INSTALL_CHANNEL=whl/nightly
 # Automatically set by buildx
-# pinning version of conda here see: https://github.com/pytorch/pytorch/issues/164574
-RUN /opt/conda/bin/conda install -y python=${PYTHON_VERSION} conda=25.7.0
+RUN /opt/conda/bin/conda update -y -n base -c defaults conda
+RUN /opt/conda/bin/conda install -y python=${PYTHON_VERSION}
 
 ARG TARGETPLATFORM
 

RELEASE.md

@@ -3,7 +3,6 @@
 <!-- toc -->
 
   - [Release Compatibility Matrix](#release-compatibility-matrix)
-    - [PyTorch CUDA Support Matrix](#pytorch-cuda-support-matrix)
   - [Release Cadence](#release-cadence)
   - [General Overview](#general-overview)
     - [Frequently Asked Questions](#frequently-asked-questions)
@@ -64,22 +63,6 @@ Following is the Release Compatibility Matrix for PyTorch releases:
 | 1.13 | >=3.7, <=3.10 | C++14 | CUDA 11.6, CUDNN 8.3.2.44 | CUDA 11.7, CUDNN 8.5.0.96 | ROCm 5.2 |
 | 1.12 | >=3.7, <=3.10 | C++14 | CUDA 11.3, CUDNN 8.3.2.44 | CUDA 11.6, CUDNN 8.3.2.44 | ROCm 5.0 |
 
-### PyTorch CUDA Support Matrix
-
-For Release 2.9 PyTorch Supports following CUDA Architectures:
-
-| CUDA | architectures supported for Linux x86 and Windows builds | notes |
-| --- | --- | --- |
-| 12.6.3 | Maxwell(5.0), Pascal(6.0), Volta(7.0), Turing(7.5), Ampere(8.0, 8.6), Hopper(9.0) | |
-| 12.8.1 | Volta(7.0), Turing(7.5), Ampere(8.0, 8.6), Hopper(9.0), Blackwell(10.0, 12.0)  | |
-| 13.0.0 | Turing(7.5), Ampere(8.0, 8.6), Hopper(9.0), Blackwell(10.0, 12.0+PTX) | +PTX available on linux builds only |
-
-| CUDA | architectures supported for Linux aarch64 builds |
-| --- | --- |
-| 12.6.3 | Ampere(8.0), Hopper(9.0) |
-| 12.8.1 | Ampere(8.0), Hopper(9.0), Blackwell(10.0, 12.0)  |
-| 13.0.0 | Ampere(8.0), Hopper(9.0), Blackwell(10.0, 11.0, 12.0+PTX) |
-
 ## Release Cadence
 
 Following is the release cadence. All future dates below are tentative. For latest updates on the release schedule, please follow [dev discuss](https://dev-discuss.pytorch.org/c/release-announcements/27). Please note: Patch Releases are optional.

aten/src/ATen/BlasBackend.h

@@ -28,19 +28,4 @@ inline std::ostream& operator<<(std::ostream& stream, at::BlasBackend backend) {
   return stream << BlasBackendToString(backend);
 }
 
-namespace blas {
-
-enum class ScalingType : std::uint8_t {
-  TensorWise, // fp32 scales
-  RowWise, // fp32 scales
-  BlockWise1x16, // fp8_e4m3fn scales
-  BlockWise1x32, // fp8_e8m0fnu scales
-  BlockWise1x128, // fp32 scales
-  BlockWise128x128, // fp32 scales
-};
-
-enum class SwizzleType : std::uint8_t { NO_SWIZZLE = 0, SWIZZLE_32_4_4 = 1 };
-
-} // namespace blas
-
 } // namespace at

aten/src/ATen/CMakeLists.txt

@@ -605,11 +605,6 @@ if(UNIX)
   if(HAVE_MALLOC_USABLE_SIZE)
     add_definitions(-DHAVE_MALLOC_USABLE_SIZE=1)
   endif(HAVE_MALLOC_USABLE_SIZE)
-  set(CMAKE_EXTRA_INCLUDE_FILES "fcntl.h")
-  CHECK_FUNCTION_EXISTS(posix_fallocate HAVE_POSIX_FALLOCATE)
-  if(HAVE_POSIX_FALLOCATE)
-    add_definitions(-DHAVE_POSIX_FALLOCATE=1)
-  endif(HAVE_POSIX_FALLOCATE)
 endif(UNIX)
 
 ADD_DEFINITIONS(-DUSE_EXTERNAL_MZCRC)

aten/src/ATen/CPUApplyUtils.h

@@ -144,7 +144,8 @@ inline std::string _all_equal_numel_error(at::ArrayRef<Tensor> tensors) {
 inline bool _apply_preamble(ArrayRef<Tensor> tensors) {
   checkDeviceType("CPU_tensor_apply", tensors, kCPU);
   checkLayout("CPU_tensor_apply", tensors, kStrided);
-  TORCH_CHECK(_all_equal_numel(tensors), _all_equal_numel_error(tensors));
+  if (!_all_equal_numel(tensors))
+    TORCH_CHECK(false, _all_equal_numel_error(tensors));
   // An empty tensor has no elements
   for (auto& t : tensors)
     if (t.numel() == 0)

aten/src/ATen/Context.cpp

@@ -40,6 +40,41 @@ namespace {
                 ->conv
                 ->rnn
 */
+const std::map<std::string, std::vector<std::string>> _fp32_precisions = {
+    {"generic", {{"ieee", "tf32", "bf16", "none"}}},
+    {"mkldnn", {{"ieee", "tf32", "bf16", "none"}}},
+    {"cuda", {{"ieee", "tf32", "none"}}}};
+
+// Check whether the backend and op are legal
+void check_fp32_prec_backend_and_op(
+    const std::string& backend,
+    const std::string& op) {
+  static std::vector<std::string> backends = {"generic", "mkldnn", "cuda"};
+  static std::vector<std::string> operators = {"conv", "matmul", "rnn", "all"};
+  TORCH_CHECK(
+      std::find(backends.begin(), backends.end(), backend) != backends.end(),
+      "Invalid backend: ",
+      backend);
+  TORCH_CHECK(
+      std::find(operators.begin(), operators.end(), op) != operators.end(),
+      "Invalid operator: ",
+      op);
+  if (backend == "generic") {
+    TORCH_CHECK(op == "all", "Invalid operation for generic backend: ", op);
+  }
+  }
+
+  // Return whether the precision is supported by backends
+  bool validate_fp32_prec(
+      const std::string& backend,
+      const std::string& precision) {
+    auto iterp = _fp32_precisions.find(backend);
+    TORCH_CHECK(iterp != _fp32_precisions.end());
+    auto precisions = iterp->second;
+    bool valid = std::find(precisions.begin(), precisions.end(), precision) !=
+        precisions.end();
+    return valid;
+  }
 
   C10_ALWAYS_INLINE void warn_deprecated_fp32_precision_api(){
     TORCH_WARN_ONCE(
@@ -51,54 +86,6 @@ namespace {
   }
 } // namespace
 
-Float32Backend str2backend(const std::string& name) {
-  if (name == "generic")
-    return Float32Backend::GENERIC;
-  else if (name == "cuda")
-    return Float32Backend::CUDA;
-  else if (name == "mkldnn")
-    return Float32Backend::MKLDNN;
-  TORCH_CHECK(false, "Unknown backend: ", name);
-}
-
-Float32Op str2op(const std::string& name) {
-  if (name == "all")
-    return Float32Op::ALL;
-  else if (name == "conv")
-    return Float32Op::CONV;
-  else if (name == "rnn")
-    return Float32Op::RNN;
-  else if (name == "matmul")
-    return Float32Op::MATMUL;
-  TORCH_CHECK(false, "Unknown op: ", name);
-}
-
-Float32Precision str2precision(const std::string& name) {
-  if (name == "none")
-    return Float32Precision::NONE;
-  else if (name == "ieee")
-    return Float32Precision::IEEE;
-  else if (name == "tf32")
-    return Float32Precision::TF32;
-  else if (name == "bf16")
-    return Float32Precision::BF16;
-  TORCH_CHECK(false, "Unknown precision: ", name);
-}
-
-std::string precision2str(Float32Precision prec) {
-  switch (prec) {
-    case Float32Precision::NONE:
-      return "none";
-    case Float32Precision::IEEE:
-      return "ieee";
-    case Float32Precision::TF32:
-      return "tf32";
-    case Float32Precision::BF16:
-      return "bf16";
-  }
-  TORCH_CHECK(false, "Invalid enum Float32Precision(", static_cast<int>(prec), ")");
-}
-
 Context::Context() = default;
 
 // TODO: This could be bad juju if someone calls globalContext() in the
@@ -192,10 +179,10 @@ void Context::setUserEnabledNNPACK(bool e) {
   enabled_nnpack = e;
 }
 
-bool Context::allowTF32CuDNN(std::optional<Float32Op> op) const {
-  if (!op.has_value()) {
-    bool allow_tf32_rnn = float32Precision(Float32Backend::CUDA, Float32Op::RNN) == Float32Precision::TF32;
-    bool allow_tf32_conv = float32Precision(Float32Backend::CUDA, Float32Op::CONV) == Float32Precision::TF32;
+bool Context::allowTF32CuDNN(const std::string& op) const {
+  if (op.empty()){
+    bool allow_tf32_rnn = float32Precision("cuda", "rnn") == "tf32";
+    bool allow_tf32_conv = float32Precision("cuda", "conv") == "tf32";
     TORCH_CHECK(
         allow_tf32_rnn == allow_tf32_conv && allow_tf32_rnn == allow_tf32_cudnn,
         "PyTorch is checking whether allow_tf32 is enabled for cuDNN without a specific operator name,",
@@ -204,15 +191,15 @@ bool Context::allowTF32CuDNN(std::optional<Float32Op> op) const {
         "We suggest only using the new API to set the TF32 flag(s). See also: ",
         "https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices");
   } else {
-    return float32Precision(Float32Backend::CUDA, op.value()) == Float32Precision::TF32;
+    return float32Precision("cuda", op) == "tf32";
   }
   warn_deprecated_fp32_precision_api();
   return allow_tf32_cudnn;
 }
 
 void Context::setAllowTF32CuDNN(bool b) {
-  setFloat32Precision(Float32Backend::CUDA, Float32Op::RNN, b ? Float32Precision::TF32 : Float32Precision::NONE);
-  setFloat32Precision(Float32Backend::CUDA, Float32Op::CONV, b ? Float32Precision::TF32 : Float32Precision::NONE);
+  setFloat32Precision("cuda", "rnn", b ? "tf32" : "none");
+  setFloat32Precision("cuda", "conv", b ? "tf32" : "none");
   allow_tf32_cudnn = b;
   warn_deprecated_fp32_precision_api();
 }
@@ -292,6 +279,42 @@ bool Context::userEnabledOverrideableSDP() const {
   return enabled_overrideable;
 }
 
+static constexpr const auto cublas_config_var_name = "CUBLAS_WORKSPACE_CONFIG";
+static constexpr const std::array<const char*, 2> cublas_deterministic_configs = {":4096:8", ":16:8"};
+
+bool Context::checkCuBLASConfigDeterministic() {
+  // If using CUDA 10.2 or greater, need to make sure CuBLAS workspace config
+  // is set to deterministic setting
+  if (hasCUDART()) {
+    const auto workspace_config = c10::utils::get_env(cublas_config_var_name);
+    return (workspace_config == cublas_deterministic_configs[0] || workspace_config == cublas_deterministic_configs[1]);
+  }
+  return true;
+}
+
+void Context::alertCuBLASConfigNotDeterministic() const {
+  static const bool cublas_config_deterministic = checkCuBLASConfigDeterministic();
+  if (C10_LIKELY(!deterministicAlgorithms() || cublas_config_deterministic)) {
+    return;
+  }
+
+  auto msg = c10::str(
+    "Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or ",
+    "`at::Context::setDeterministicAlgorithms(true)`, but this operation is not deterministic because ",
+    "it uses CuBLAS and you have CUDA >= 10.2. To enable deterministic behavior in this ",
+    "case, you must set an environment variable before running your PyTorch application: ",
+    cublas_config_var_name, "=", cublas_deterministic_configs[0], " or ",
+    cublas_config_var_name, "=", cublas_deterministic_configs[1], ". For more information, go to ",
+    "https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility"
+  );
+
+  if (deterministicAlgorithmsWarnOnly()) {
+    TORCH_WARN(msg);
+  } else {
+    TORCH_CHECK(false, msg);
+  }
+}
+
 bool Context::benchmarkCuDNN() const {
   return benchmark_cudnn;
 }
@@ -318,7 +341,7 @@ void Context::setImmediateMiopen(bool b) {
 
 bool Context::allowTF32CuBLAS() const {
   bool legacy_allow_tf32 = float32_matmul_precision != at::Float32MatmulPrecision::HIGHEST;
-  bool allow_tf32_new = float32Precision(Float32Backend::CUDA, Float32Op::MATMUL) == Float32Precision::TF32;
+  bool allow_tf32_new = float32Precision("cuda", "matmul") == "tf32";
   TORCH_CHECK(
       legacy_allow_tf32 == allow_tf32_new,
       "PyTorch is checking whether allow_tf32_new is enabled for cuBlas matmul,",
@@ -331,17 +354,17 @@ bool Context::allowTF32CuBLAS() const {
 
 void Context::setAllowTF32CuBLAS(bool b) {
   float32_matmul_precision = b ? at::Float32MatmulPrecision::HIGH : at::Float32MatmulPrecision::HIGHEST;
-  setFloat32Precision(Float32Backend::CUDA, Float32Op::MATMUL, b ? Float32Precision::TF32 : Float32Precision::IEEE);
+  setFloat32Precision("cuda", "matmul", b ? "tf32" : "ieee");
 }
 
 Float32MatmulPrecision Context::float32MatmulPrecision() const {
-  bool invalid = float32Precision(Float32Backend::CUDA, Float32Op::MATMUL) == Float32Precision::TF32 &&
+  bool invalid = float32Precision("cuda", "matmul") == "tf32" &&
       float32_matmul_precision == at::Float32MatmulPrecision::HIGHEST;
   invalid = invalid ||
-      (float32Precision(Float32Backend::MKLDNN, Float32Op::MATMUL) == Float32Precision::BF16 &&
+      (float32Precision("mkldnn", "matmul") == "bf16" &&
        float32_matmul_precision != at::Float32MatmulPrecision::MEDIUM);
   invalid = invalid ||
-      (float32Precision(Float32Backend::MKLDNN, Float32Op::MATMUL) == Float32Precision::TF32 &&
+      (float32Precision("mkldnn", "matmul") == "tf32" &&
        float32_matmul_precision != at::Float32MatmulPrecision::HIGH);
   TORCH_CHECK(
       !invalid,
@@ -353,26 +376,15 @@ Float32MatmulPrecision Context::float32MatmulPrecision() const {
   return float32_matmul_precision;
 }
 
-Float32Precision Context::float32Precision(Float32Backend backend, Float32Op op) const {
-  std::pair<Float32Backend, Float32Op> key{backend, op};
-  auto it = fp32_precision.find(key);
-  TORCH_CHECK(it != fp32_precision.end(), "Invalid (backend, op) pair: (", backend, ", ", op, ")");
-
-  Float32Precision precision = it->second;
-  if (precision == Float32Precision::NONE) {
-    key.second = Float32Op::ALL;
-    precision = fp32_precision.find(key)->second;
-  }
-  if (precision == Float32Precision::NONE) {
-    key.first = Float32Backend::GENERIC;
-    precision = fp32_precision.find(key)->second;
-  }
-
-  // "cuda" does not support "bf16"
-  if (backend == Float32Backend::CUDA && precision == Float32Precision::BF16) {
-    return Float32Precision::NONE;
-  }
-  return precision;
+std::string Context::float32Precision(const std::string& backend, const std::string& op) const {
+  check_fp32_prec_backend_and_op(backend, op);
+  auto precision = fp32_precision.find(backend)->second.find(op)->second;
+  if (precision == "none")
+    precision = fp32_precision.find(backend)->second.find("all")->second;
+  if (precision == "none")
+    precision = fp32_precision.find("generic")->second.find("all")->second;
+  bool valid_prec = validate_fp32_prec(backend, precision);
+  return valid_prec ? precision : "none";
 }
 
 void Context::setFloat32MatmulPrecision(const std::string &s) {
@@ -381,18 +393,18 @@ void Context::setFloat32MatmulPrecision(const std::string &s) {
     // TODO: consider if CuDNN field needs to also be set for potential future CuDNN ops like multi-headed attention
     if (s_ == "highest") {
       float32_matmul_precision = at::Float32MatmulPrecision::HIGHEST;
-      setFloat32Precision(Float32Backend::CUDA, Float32Op::MATMUL, Float32Precision::IEEE);
-      setFloat32Precision(Float32Backend::MKLDNN, Float32Op::MATMUL, Float32Precision::IEEE);
+      setFloat32Precision("cuda", "matmul", "ieee");
+      setFloat32Precision("mkldnn", "matmul", "ieee");
       return true;
     } else if (s_ == "high") {
       float32_matmul_precision = at::Float32MatmulPrecision::HIGH;
-      setFloat32Precision(Float32Backend::CUDA, Float32Op::MATMUL, Float32Precision::TF32);
-      setFloat32Precision(Float32Backend::MKLDNN, Float32Op::MATMUL, Float32Precision::TF32);
+      setFloat32Precision("cuda", "matmul", "tf32");
+      setFloat32Precision("mkldnn", "matmul", "tf32");
       return true;
     } else if (s_ == "medium") {
       float32_matmul_precision = at::Float32MatmulPrecision::MEDIUM;
-      setFloat32Precision(Float32Backend::CUDA, Float32Op::MATMUL, Float32Precision::TF32);
-      setFloat32Precision(Float32Backend::MKLDNN, Float32Op::MATMUL, Float32Precision::BF16);
+      setFloat32Precision("cuda", "matmul", "tf32");
+      setFloat32Precision("mkldnn", "matmul", "bf16");
       return true;
     }
     return false;
@@ -406,16 +418,25 @@ void Context::setFloat32MatmulPrecision(const std::string &s) {
     "setFloat32MatmulPrecision call has no effect.");
 }
 
-void Context::setFloat32Precision(Float32Backend backend, Float32Op op, Float32Precision p) {
-  auto it = fp32_precision.find(std::make_pair(backend, op));
-  TORCH_CHECK(
-      it != fp32_precision.end(),
-      "Invalid (backend, op) pair: (", backend, ", ", op, ")");
-  TORCH_CHECK(
-      !(backend == Float32Backend::CUDA && p == Float32Precision::BF16),
-      "backend 'cuda' does not support precision 'bf16'");
-
-  it->second = p;
+void Context::setFloat32Precision(const std::string& backend, const std::string& op, const std::string& p) {
+  check_fp32_prec_backend_and_op(backend, op);
+  if (validate_fp32_prec(backend, p)) {
+    fp32_precision[backend][op] = p;
+  } else {
+    std::string msg;
+    auto iterp = _fp32_precisions.find(backend);
+    TORCH_CHECK(iterp != _fp32_precisions.end());
+    for (const auto& p : iterp->second) {
+      msg += p;
+      msg += " ";
+    }
+    TORCH_WARN(
+        "you have set wrong precision for backend:",
+        backend,
+        " setFloat32Precision call has no effect.",
+        "Please choose precision from: ",
+        msg);
+  }
 }
 
 at::LinalgBackend Context::linalgPreferredBackend() const {
@@ -483,8 +504,8 @@ at::BlasBackend Context::blasPreferredBackend() {
 #if ROCM_VERSION >= 60300
           "gfx1100", "gfx1101", "gfx1200", "gfx1201", "gfx908",
 #endif
-#if ROCM_VERSION >= 70000
-          "gfx950", "gfx1150", "gfx1151"
+#if ROCM_VERSION >= 60500
+          "gfx950"
 #endif
       };
       for (auto index: c10::irange(detail::getCUDAHooks().deviceCount())) {
@@ -587,33 +608,20 @@ void Context::setROCmFAPreferredBackend(at::ROCmFABackend b) {
   rocm_fa_preferred_backend = b;
 }
 
-CuBLASReductionOption Context::allowFP16ReductionCuBLAS() const {
+bool Context::allowFP16ReductionCuBLAS() const {
   return allow_fp16_reduction_cublas;
 }
 
-CuBLASReductionOption inline get_reduction_option(bool allow_reduced_precision, bool allow_splitk) {
-  TORCH_CHECK(
-      !(allow_reduced_precision && !allow_splitk),
-      "allow_splitk=False is not supported when reduced precision reductions are enabled");
-  if (allow_reduced_precision) {
-    return CuBLASReductionOption::AllowReducedPrecisionWithSplitK;
-  } else if (allow_splitk) {
-    return CuBLASReductionOption::DisallowReducedPrecisionAllowSplitK;
-  } else {
-    return CuBLASReductionOption::DisallowReducedPrecisionDisallowSplitK;
-  }
+void Context::setAllowFP16ReductionCuBLAS(bool b) {
+  allow_fp16_reduction_cublas = b;
 }
 
-void Context::setAllowFP16ReductionCuBLAS(bool allow_reduced_precision, bool allow_splitk) {
-  allow_fp16_reduction_cublas = get_reduction_option(allow_reduced_precision, allow_splitk);
-}
-
-CuBLASReductionOption Context::allowBF16ReductionCuBLAS() const {
+bool Context::allowBF16ReductionCuBLAS() const {
   return allow_bf16_reduction_cublas;
 }
 
-void Context::setAllowBF16ReductionCuBLAS(bool allow_reduced_precision, bool allow_splitk) {
-  allow_bf16_reduction_cublas = get_reduction_option(allow_reduced_precision, allow_splitk);
+void Context::setAllowBF16ReductionCuBLAS(bool b) {
+  allow_bf16_reduction_cublas = b;
 }
 
 bool Context::allowFP16AccumulationCuBLAS() const {

aten/src/ATen/Context.h

@@ -25,13 +25,11 @@
 #include <c10/util/CallOnce.h>
 #include <c10/util/Exception.h>
 #include <c10/util/env.h>
-#include <c10/util/hash.h>
 #include <c10/util/irange.h>
 
 #include <cstdint>
 #include <map>
 #include <mutex>
-#include <unordered_map>
 
 namespace at {
 
@@ -39,20 +37,6 @@ class Tensor;
 
 enum class TORCH_API Float32MatmulPrecision { HIGHEST, HIGH, MEDIUM };
 
-enum class CuBLASReductionOption : uint8_t {
-  AllowReducedPrecisionWithSplitK = 0,
-  DisallowReducedPrecisionAllowSplitK = 1,
-  DisallowReducedPrecisionDisallowSplitK = 2,
-};
-enum class TORCH_API Float32Backend { GENERIC, CUDA, MKLDNN };
-enum class TORCH_API Float32Op { ALL, CONV, RNN, MATMUL };
-enum class TORCH_API Float32Precision { NONE, IEEE, TF32, BF16 };
-
-TORCH_API Float32Backend str2backend(const std::string& name);
-TORCH_API Float32Op str2op(const std::string& name);
-TORCH_API Float32Precision str2precision(const std::string& name);
-TORCH_API std::string precision2str(Float32Precision prec);
-
 class TORCH_API Context {
  public:
   Context();
@@ -226,15 +210,15 @@ class TORCH_API Context {
   bool userEnabledMkldnn() const;
   void setUserEnabledMkldnn(bool e);
   bool benchmarkCuDNN() const;
-  void setBenchmarkCuDNN(bool /*b*/);
+  void setBenchmarkCuDNN(bool);
   int benchmarkLimitCuDNN() const;
-  void setBenchmarkLimitCuDNN(int /*b*/);
+  void setBenchmarkLimitCuDNN(int);
   bool immediateMiopen() const;
-  void setImmediateMiopen(bool /*b*/);
+  void setImmediateMiopen(bool);
   bool deterministicCuDNN() const;
-  void setDeterministicCuDNN(bool /*b*/);
+  void setDeterministicCuDNN(bool);
   bool deterministicMkldnn() const;
-  void setDeterministicMkldnn(bool /*b*/);
+  void setDeterministicMkldnn(bool);
   bool userEnabledNNPACK() const;
   void setUserEnabledNNPACK(bool e);
 
@@ -252,32 +236,32 @@ class TORCH_API Context {
   void setSDPPriorityOrder(const std::vector<int64_t>& order);
   std::array<at::SDPBackend, at::num_sdp_backends> sDPPriorityOrder();
 
-  void setSDPUseFlash(bool /*e*/);
+  void setSDPUseFlash(bool);
   bool userEnabledFlashSDP() const;
 
-  void setSDPUseMemEfficient(bool /*e*/);
+  void setSDPUseMemEfficient(bool);
   bool userEnabledMemEfficientSDP() const;
 
-  void setSDPUseMath(bool /*e*/);
+  void setSDPUseMath(bool);
   bool userEnabledMathSDP() const;
 
-  void setSDPUseCuDNN(bool /*e*/);
+  void setSDPUseCuDNN(bool);
   bool userEnabledCuDNNSDP() const;
 
-  void setAllowFP16BF16ReductionMathSDP(bool /*e*/);
+  void setAllowFP16BF16ReductionMathSDP(bool);
   bool allowFP16BF16ReductionMathSDP() const;
 
-  void setSDPUseOverrideable(bool /*e*/);
+  void setSDPUseOverrideable(bool);
   bool userEnabledOverrideableSDP() const;
 
   at::LinalgBackend linalgPreferredBackend() const;
-  void setLinalgPreferredBackend(at::LinalgBackend /*b*/);
+  void setLinalgPreferredBackend(at::LinalgBackend);
 
   at::BlasBackend blasPreferredBackend();
-  void setBlasPreferredBackend(at::BlasBackend /*b*/);
+  void setBlasPreferredBackend(at::BlasBackend);
 
   at::ROCmFABackend getROCmFAPreferredBackend();
-  void setROCmFAPreferredBackend(at::ROCmFABackend /*b*/);
+  void setROCmFAPreferredBackend(at::ROCmFABackend);
 
   // Note [Enabling Deterministic Operations]
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -310,9 +294,9 @@ class TORCH_API Context {
 
   bool deterministicAlgorithms() const;
   bool deterministicAlgorithmsWarnOnly() const;
-  void setDeterministicAlgorithms(bool /*b*/, bool /*warn_only*/);
+  void setDeterministicAlgorithms(bool, bool);
   bool deterministicFillUninitializedMemory() const;
-  void setDeterministicFillUninitializedMemory(bool /*b*/);
+  void setDeterministicFillUninitializedMemory(bool);
 
   // Note [Writing Nondeterministic Operations]
   // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -326,7 +310,13 @@ class TORCH_API Context {
   //
   // * Throw an error when `Context::deterministicAlgorithms()` is true. Most
   //   of the time, this should be accomplished by calling
-  //   `at::globalContext().alertNotDeterminstic().
+  //   `at::globalContext().alertNotDeterminstic()`.  However, if the
+  //   nondeterministic behavior is caused by the CuBLAS workspace
+  //   configuration in CUDA >= 10.2,
+  //   `at::globalContext().alertCuBLASConfigNotDeterministic()` should be
+  //   called instead (in this case, a comment explaining why the operation is
+  //   nondeterministic is not necessary). See below for details on these
+  //   methods.
   //
   // * Have an entry in the list of nondeterministic PyTorch operations in the
   //   docstring of `use_deterministic_algorithms()` in torch/__init__.py
@@ -350,29 +340,33 @@ class TORCH_API Context {
   // Throws an error if `Context::deterministicAlgorithms()` is true
   static void alertNotDeterministic(std::string_view const& caller);
 
+  // Throws an error if `Context::deterministicAlgorithms()` is true, CUDA
+  // >= 10.2, and CUBLAS_WORKSPACE_CONFIG is not set to either ":16:8" or
+  // ":4096:8". For more details:
+  // https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility
+  void alertCuBLASConfigNotDeterministic() const;
+
   void setFloat32MatmulPrecision(const std::string& s);
   void setFloat32Precision(
-      Float32Backend backend,
-      Float32Op op,
-      Float32Precision p);
-  bool allowTF32CuDNN(std::optional<Float32Op> op = std::nullopt) const;
-  void setAllowTF32CuDNN(bool /*b*/);
+      const std::string& backend,
+      const std::string& op,
+      const std::string& s);
+  bool allowTF32CuDNN(const std::string& op = std::string()) const;
+  void setAllowTF32CuDNN(bool);
   bool allowTF32OneDNN() const;
-  void setAllowTF32OneDNN(bool /*b*/);
+  void setAllowTF32OneDNN(bool);
   bool allowTF32CuBLAS() const;
-  void setAllowTF32CuBLAS(bool /*b*/);
+  void setAllowTF32CuBLAS(bool);
   Float32MatmulPrecision float32MatmulPrecision() const;
-  Float32Precision float32Precision(Float32Backend backend, Float32Op op) const;
-  CuBLASReductionOption allowFP16ReductionCuBLAS() const;
-  void setAllowFP16ReductionCuBLAS(
-      bool allow_reduced_precision,
-      bool allow_splitk = true);
-  CuBLASReductionOption allowBF16ReductionCuBLAS() const;
-  void setAllowBF16ReductionCuBLAS(
-      bool allow_reduced_precision,
-      bool allow_splitk = true);
+  std::string float32Precision(
+      const std::string& backend,
+      const std::string& op) const;
+  bool allowFP16ReductionCuBLAS() const;
+  void setAllowFP16ReductionCuBLAS(bool);
+  bool allowBF16ReductionCuBLAS() const;
+  void setAllowBF16ReductionCuBLAS(bool);
   bool allowFP16AccumulationCuBLAS() const;
-  void setAllowFP16AccumulationCuBLAS(bool /*b*/);
+  void setAllowFP16AccumulationCuBLAS(bool);
 
   // Matmuls can use a so-called "persistent" kernel which launches one CUDA
   // block for each SM on the GPU, and each block then iterates over multiple
@@ -384,7 +378,7 @@ class TORCH_API Context {
   // to make matmuls target only a subset of the SMs, so they can fully schedule
   // even next to a comms kernel, and only be a few percent slower.
   std::optional<int32_t> _SMCarveout_EXPERIMENTAL() const;
-  void _setSMCarveout_EXPERIMENTAL(std::optional<int32_t> /*c*/);
+  void _setSMCarveout_EXPERIMENTAL(std::optional<int32_t>);
 
   at::QEngine qEngine() const;
   void setQEngine(at::QEngine e);
@@ -405,7 +399,7 @@ class TORCH_API Context {
   void setDefaultMobileCPUAllocator();
   void unsetDefaultMobileCPUAllocator();
   bool allowFP16ReductionCPU() const;
-  void setAllowFP16ReductionCPU(bool /*b*/);
+  void setAllowFP16ReductionCPU(bool);
 
   // Preserved for BC
   void lazyInitCUDA() {
@@ -435,6 +429,7 @@ class TORCH_API Context {
   }
 
  private:
+  static bool checkCuBLASConfigDeterministic();
   std::array<c10::once_flag, at::COMPILE_TIME_MAX_DEVICE_TYPES> init_;
   bool enabled_cudnn = true;
   bool deterministic_cudnn = false;
@@ -462,10 +457,8 @@ class TORCH_API Context {
       : at::Float32MatmulPrecision::HIGHEST;
   int benchmark_limit_cudnn = 10;
   bool allow_tf32_cudnn = true;
-  CuBLASReductionOption allow_fp16_reduction_cublas =
-      CuBLASReductionOption::AllowReducedPrecisionWithSplitK;
-  CuBLASReductionOption allow_bf16_reduction_cublas =
-      CuBLASReductionOption::AllowReducedPrecisionWithSplitK;
+  bool allow_fp16_reduction_cublas = true;
+  bool allow_bf16_reduction_cublas = true;
   bool allow_fp16_accumulation_cublas = false;
   std::optional<int32_t> sm_carveout = std::nullopt;
   bool enabled_mkldnn = true;
@@ -495,20 +488,21 @@ class TORCH_API Context {
   bool enable_sparse_tensor_invariant_checks = false;
   bool allow_fp16_reduction_cpu = false;
 
-  using Key = std::pair<Float32Backend, Float32Op>;
-  std::unordered_map<Key, Float32Precision, c10::hash<Key>> fp32_precision = {
-      {{Float32Backend::GENERIC, Float32Op::ALL}, Float32Precision::NONE},
-      {{Float32Backend::MKLDNN, Float32Op::ALL}, Float32Precision::NONE},
-      {{Float32Backend::MKLDNN, Float32Op::CONV}, Float32Precision::NONE},
-      {{Float32Backend::MKLDNN, Float32Op::RNN}, Float32Precision::NONE},
-      {{Float32Backend::MKLDNN, Float32Op::MATMUL}, Float32Precision::NONE},
-      {{Float32Backend::CUDA, Float32Op::ALL}, Float32Precision::NONE},
-      {{Float32Backend::CUDA, Float32Op::CONV}, Float32Precision::TF32},
-      {{Float32Backend::CUDA, Float32Op::RNN}, Float32Precision::TF32},
-      {{Float32Backend::CUDA, Float32Op::MATMUL},
-       float32_matmul_precision == at::Float32MatmulPrecision::HIGHEST
-           ? Float32Precision::NONE
-           : Float32Precision::TF32},
+  std::map<std::string, std::map<std::string, std::string>> fp32_precision = {
+      {"generic", {{"all", "none"}}},
+      {"mkldnn",
+       {{"matmul", "none"},
+        {"conv", "none"},
+        {"rnn", "none"},
+        {"all", "none"}}},
+      {"cuda",
+       {{"matmul",
+         float32_matmul_precision == at::Float32MatmulPrecision::HIGHEST
+             ? "none"
+             : "tf32"},
+        {"conv", "tf32"},
+        {"rnn", "tf32"},
+        {"all", "none"}}},
   };
 
   Allocator* prev_allocator_ptr_{nullptr};
@@ -690,4 +684,5 @@ struct TORCH_API ROCmBackwardPassGuard {
   ~ROCmBackwardPassGuard();
   static bool is_backward_pass();
 };
+
 } // namespace at

aten/src/ATen/MapAllocator.cpp

@@ -62,7 +62,7 @@ constexpr const char* unknown_eventname = "eventname not specified";
 #endif
 }  // namespace (anonymous)
 
-MapAllocator::MapAllocator(WithFd /*unused*/, std::string_view filename, int fd, int flags, size_t size)
+MapAllocator::MapAllocator(WithFd, std::string_view filename, int fd, int flags, size_t size)
   : filename_(filename.empty() ? unknown_filename : filename)
   , size_(0) // to be filled later
 #ifdef _WIN32
@@ -292,28 +292,6 @@ MapAllocator::MapAllocator(WithFd /*unused*/, std::string_view filename, int fd,
           if (ftruncate(fd, static_cast<off_t>(size)) == -1) {
             TORCH_CHECK(false, "unable to resize file <", filename_, "> to the right size: ", c10::utils::str_error(errno), " (", errno, ")");
           }
-
-#ifdef HAVE_POSIX_FALLOCATE
-          if (flags_ & ALLOCATOR_MAPPED_SHAREDMEM) {
-            for (;;) {
-              if (posix_fallocate(fd, 0, static_cast<off_t>(size)) == 0) {
-                break;
-              }
-
-              if (errno == EINTR) {
-                continue;
-              }
-
-              if (errno == EINVAL || errno == EOPNOTSUPP) {
-                // the underlying filesystem does not support the operation
-                break;
-              }
-
-              TORCH_CHECK(false, "unable to allocate shared memory(shm) for file <", filename_, ">: ", c10::utils::str_error(errno), " (", errno, ")");
-            }
-          }
-#endif
-
           if (fstat(fd, &file_stat) == -1 || file_stat.st_size < static_cast<int64_t>(size)) {
 #ifndef STRIP_ERROR_MESSAGES
             int last_err = errno;
@@ -494,7 +472,7 @@ RefcountedMapAllocator::RefcountedMapAllocator(const char *filename, int flags,
 
     initializeAlloc();
 }
-RefcountedMapAllocator::RefcountedMapAllocator(WithFd /*unused*/, const char *filename, int fd, int flags, size_t size)
+RefcountedMapAllocator::RefcountedMapAllocator(WithFd, const char *filename, int fd, int flags, size_t size)
   : RefcountedMapAllocatorArgCheck(flags)
   , MapAllocator(WITH_FD, filename, flags, fd, size + map_alloc_alignment) {
 
@@ -614,7 +592,7 @@ at::DataPtr MapAllocator::makeDataPtr(std::string_view filename, int flags, size
   return {context->data(), context, &deleteMapAllocator, at::DeviceType::CPU};
 }
 
-at::DataPtr MapAllocator::makeDataPtr(WithFd /*unused*/, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out) {
+at::DataPtr MapAllocator::makeDataPtr(WithFd, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out) {
   auto* context = new MapAllocator(WITH_FD, filename, fd, flags, size);
   if (actual_size_out) *actual_size_out = context->size();
   return {context->data(), context, &deleteMapAllocator, at::DeviceType::CPU};
@@ -626,7 +604,7 @@ at::DataPtr RefcountedMapAllocator::makeDataPtr(const char *filename, int flags,
   return {context->data(), context, &deleteRefcountedMapAllocator, at::DeviceType::CPU};
 }
 
-at::DataPtr RefcountedMapAllocator::makeDataPtr(WithFd /*unused*/, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out) {
+at::DataPtr RefcountedMapAllocator::makeDataPtr(WithFd, const char *filename, int fd, int flags, size_t size, size_t* actual_size_out) {
   auto* context = new RefcountedMapAllocator(WITH_FD, filename, fd, flags, size);
   if (actual_size_out) *actual_size_out = context->size() - map_alloc_alignment;
   return {context->data(), context, &deleteRefcountedMapAllocator, at::DeviceType::CPU};

aten/src/ATen/MapAllocator.h

@@ -25,7 +25,7 @@ class TORCH_API MapAllocator {
  public:
   MapAllocator(std::string_view filename, int flags, size_t size);
   MapAllocator(
-      WithFd /*unused*/,
+      WithFd,
       std::string_view filename,
       int fd,
       int flags,
@@ -59,14 +59,14 @@ class TORCH_API MapAllocator {
     return flags_;
   }
 
-  static MapAllocator* fromDataPtr(const at::DataPtr& /*dptr*/);
+  static MapAllocator* fromDataPtr(const at::DataPtr&);
   static at::DataPtr makeDataPtr(
       std::string_view filename,
       int flags,
       size_t size,
       size_t* actual_size_out);
   static at::DataPtr makeDataPtr(
-      WithFd /*unused*/,
+      WithFd,
       const char* filename,
       int fd,
       int flags,
@@ -105,13 +105,13 @@ class TORCH_API RefcountedMapAllocator : private RefcountedMapAllocatorArgCheck,
  public:
   RefcountedMapAllocator(const char* filename, int flags, size_t size);
   RefcountedMapAllocator(
-      WithFd /*unused*/,
+      WithFd,
       const char* filename,
       int fd,
       int flags,
       size_t size);
 
-  static RefcountedMapAllocator* fromDataPtr(const at::DataPtr& /*dptr*/);
+  static RefcountedMapAllocator* fromDataPtr(const at::DataPtr&);
   RefcountedMapAllocator(const RefcountedMapAllocator&) = delete;
   RefcountedMapAllocator(RefcountedMapAllocator&&) = delete;
   RefcountedMapAllocator& operator=(const RefcountedMapAllocator&) = delete;
@@ -122,7 +122,7 @@ class TORCH_API RefcountedMapAllocator : private RefcountedMapAllocatorArgCheck,
       size_t size,
       size_t* actual_size_out);
   static at::DataPtr makeDataPtr(
-      WithFd /*unused*/,
+      WithFd,
       const char* filename,
       int fd,
       int flags,

aten/src/ATen/NamedTensorUtils.cpp

@@ -179,7 +179,7 @@ void propagate_names_except(const Tensor& result, const Tensor& src, IntArrayRef
     return;
   }
   const auto src_names = src.names();
-  const auto result_dim = result.dim();
+  const auto result_dim = static_cast<int64_t>(result.dim());
   const auto src_dim = static_cast<int64_t>(src_names.size());
   const auto excluded_dim = static_cast<int64_t>(excluded_idxs.size());
   TORCH_INTERNAL_ASSERT(src_dim - excluded_dim == result_dim);

aten/src/ATen/NestedTensorImpl.cpp

@@ -273,7 +273,7 @@ c10::SymInt NestedTensorImpl::sym_numel_custom() const {
   return NestedTensorImpl::numel_custom();
 }
 
-c10::SymBool NestedTensorImpl::sym_is_contiguous_custom(MemoryFormat /*memory_format*/) const {
+c10::SymBool NestedTensorImpl::sym_is_contiguous_custom(MemoryFormat) const {
   return nested_tensor_impl_is_contiguous(this);
 }
 IntArrayRef NestedTensorImpl::sizes_custom() const {

aten/src/ATen/NestedTensorImpl.h

@@ -115,8 +115,7 @@ struct TORCH_API NestedTensorImpl : public c10::TensorImpl {
   // with real implementations
   int64_t numel_custom() const override;
   c10::SymInt sym_numel_custom() const override;
-  c10::SymBool sym_is_contiguous_custom(
-      MemoryFormat /*memory_format*/) const override;
+  c10::SymBool sym_is_contiguous_custom(MemoryFormat) const override;
   int64_t size_custom(int64_t d) const override {
     return this->size(d);
   }

aten/src/ATen/Parallel.h

@@ -14,7 +14,7 @@ inline int64_t divup(int64_t x, int64_t y) {
 TORCH_API void init_num_threads();
 
 // Sets the number of threads to be used in parallel region
-TORCH_API void set_num_threads(int /*nthreads*/);
+TORCH_API void set_num_threads(int);
 
 // Returns the maximum number of threads that may be used in a parallel region
 TORCH_API int get_num_threads();
@@ -37,7 +37,7 @@ inline void lazy_init_num_threads() {
   }
 }
 
-TORCH_API void set_thread_num(int /*id*/);
+TORCH_API void set_thread_num(int);
 
 class TORCH_API ThreadIdGuard {
  public:
@@ -130,7 +130,7 @@ inline scalar_t parallel_reduce(
 TORCH_API std::string get_parallel_info();
 
 // Sets number of threads used for inter-op parallelism
-TORCH_API void set_num_interop_threads(int /*nthreads*/);
+TORCH_API void set_num_interop_threads(int);
 
 // Returns the number of threads used for inter-op parallelism
 TORCH_API size_t get_num_interop_threads();

aten/src/ATen/SparseCsrTensorImpl.cpp

@@ -252,7 +252,7 @@ void SparseCsrTensorImpl::set_stride(int64_t dim, int64_t new_stride) {
 void SparseCsrTensorImpl::set_storage_offset(int64_t storage_offset) {
   TORCH_CHECK(false, "Sparse ", at::sparse_csr::layoutToString(layout_, /*upper=*/true), " tensors do not have set_storage_offset.");
 }
-c10::SymBool SparseCsrTensorImpl::sym_is_contiguous_custom(MemoryFormat /*memory_format*/) const {
+c10::SymBool SparseCsrTensorImpl::sym_is_contiguous_custom(MemoryFormat) const {
   TORCH_CHECK(false, "Sparse ", at::sparse_csr::layoutToString(layout_, /*upper=*/true), " tensors do not have is_contiguous");
 }
 } // namespace at

aten/src/ATen/SparseCsrTensorImpl.h

@@ -32,10 +32,10 @@ struct TORCH_API SparseCsrTensorImpl : public TensorImpl {
 
  public:
   explicit SparseCsrTensorImpl(
-      at::DispatchKeySet /*key_set*/,
+      at::DispatchKeySet,
       at::Device device,
       Layout layout,
-      const caffe2::TypeMeta /*data_type*/);
+      const caffe2::TypeMeta);
 
   void resize_(int64_t nnz, IntArrayRef size);
   void resize_and_clear_(
@@ -86,8 +86,7 @@ struct TORCH_API SparseCsrTensorImpl : public TensorImpl {
  protected:
   IntArrayRef strides_custom() const override;
   SymIntArrayRef sym_strides_custom() const override;
-  SymBool sym_is_contiguous_custom(
-      MemoryFormat /*memory_format*/) const override;
+  SymBool sym_is_contiguous_custom(MemoryFormat) const override;
 
  public:
   void set_size(int64_t dim, int64_t new_size) override;

aten/src/ATen/SparseTensorImpl.h

@@ -46,9 +46,7 @@ struct TORCH_API SparseTensorImpl : public TensorImpl {
 
  public:
   // Public for now...
-  explicit SparseTensorImpl(
-      at::DispatchKeySet /*key_set*/,
-      const caffe2::TypeMeta /*data_type*/);
+  explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta);
 
   void release_resources() override;
 
@@ -231,14 +229,14 @@ struct TORCH_API SparseTensorImpl : public TensorImpl {
   }
 
   void resize_(int64_t sparse_dim, int64_t dense_dim, ArrayRef<int64_t> size) {
-    _resize_(sparse_dim, dense_dim, size);
+    return _resize_(sparse_dim, dense_dim, size);
   }
 
   void resize_(
       int64_t sparse_dim,
       int64_t dense_dim,
       ArrayRef<c10::SymInt> size) {
-    _resize_(sparse_dim, dense_dim, size);
+    return _resize_(sparse_dim, dense_dim, size);
   }
 
   // NOTE: this function will resize the sparse tensor and also set `indices`
@@ -386,8 +384,8 @@ struct TORCH_API SparseTensorImpl : public TensorImpl {
 
  private:
   explicit SparseTensorImpl(
-      at::DispatchKeySet /*key_set*/,
-      const caffe2::TypeMeta /*data_type*/,
+      at::DispatchKeySet,
+      const caffe2::TypeMeta,
       at::Tensor indices,
       at::Tensor values);
 

aten/src/ATen/TensorIndexing.cpp

@@ -59,7 +59,7 @@ static inline void set_item(const Tensor& self, ArrayRef<TensorIndex> indices, c
     }
   }
 
-  set_item(self, indices, value);
+  return set_item(self, indices, value);
 }
 
 } // namespace indexing

aten/src/ATen/TensorIndexing.h

@@ -112,10 +112,10 @@ TORCH_API std::ostream& operator<<(std::ostream& stream, const Slice& slice);
 // `torch.tensor([1, 2])`) | `torch::tensor({1, 2})`
 struct TORCH_API TensorIndex final {
   // Case 1: `at::indexing::None`
-  TensorIndex(std::nullopt_t /*unused*/) : type_(TensorIndexType::None) {}
+  TensorIndex(std::nullopt_t) : type_(TensorIndexType::None) {}
 
   // Case 2: "..." / `at::indexing::Ellipsis`
-  TensorIndex(at::indexing::EllipsisIndexType /*unused*/)
+  TensorIndex(at::indexing::EllipsisIndexType)
       : type_(TensorIndexType::Ellipsis) {}
   TensorIndex(const char* str) : TensorIndex(at::indexing::Ellipsis) {
     TORCH_CHECK_VALUE(
@@ -214,7 +214,7 @@ inline Tensor applySlice(
       "step must be greater than zero");
 
   // See NOTE [nested tensor size for indexing]
-  if (self_sizes.has_value() && !self_sizes.value().empty()) {
+  if (self_sizes.has_value() && self_sizes.value().size() > 0) {
     // Skip this optimization if we are tracing, as the trace may be polymorphic
     // over the shape of the `self` tensor, and we still want to record
     // the slice.

aten/src/ATen/TensorIterator.cpp

@@ -765,8 +765,7 @@ void TensorIteratorBase::for_each(loop2d_t loop, int64_t grain_size) {
   if (numel == 0) {
     return;
   } else if (numel < grain_size || at::get_num_threads() == 1) {
-    serial_for_each(loop, {0, numel});
-    return;
+    return serial_for_each(loop, {0, numel});
   } else {
     at::parallel_for(0, numel, grain_size, [&](int64_t begin, int64_t end) {
       serial_for_each(loop, {begin, end});

aten/src/ATen/TensorIterator.h

@@ -250,7 +250,7 @@ struct TORCH_API TensorIteratorBase : public impl::MetaBase {
   using PtrVector = SmallVector<char*, 4>;
   using StrideVector = SmallVector<int64_t, 6>;
 
-  void build(TensorIteratorConfig& /*config*/);
+  void build(TensorIteratorConfig&);
 
   // The inner-loop function operates on the fastest moving dimension. It
   // implements element-wise operations in terms of 1-d strided tensors.
@@ -618,20 +618,20 @@ struct TORCH_API TensorIteratorBase : public impl::MetaBase {
 #undef TORCH_DISALLOW_TEMPORARIES
  protected:
   // Mutable reference as it moves tensors out of TensorIteratorConfig
-  void populate_operands(TensorIteratorConfig& /*config*/);
+  void populate_operands(TensorIteratorConfig&);
   void mark_outputs();
-  void mark_resize_outputs(const TensorIteratorConfig& /*config*/);
-  void compute_mem_overlaps(const TensorIteratorConfig& /*config*/);
-  void compute_shape(const TensorIteratorConfig& /*config*/);
-  void compute_strides(const TensorIteratorConfig& /*config*/);
+  void mark_resize_outputs(const TensorIteratorConfig&);
+  void compute_mem_overlaps(const TensorIteratorConfig&);
+  void compute_shape(const TensorIteratorConfig&);
+  void compute_strides(const TensorIteratorConfig&);
   void reorder_dimensions();
   void permute_dimensions(IntArrayRef perm);
-  void compute_types(const TensorIteratorConfig& /*config*/);
+  void compute_types(const TensorIteratorConfig&);
   ScalarType compute_common_dtype();
   void allocate_or_resize_outputs();
-  bool fast_set_up(const TensorIteratorConfig& /*config*/);
-  FastSetupType compute_fast_setup_type(const TensorIteratorConfig& /*config*/);
-  void compute_names(const TensorIteratorConfig& /*config*/);
+  bool fast_set_up(const TensorIteratorConfig&);
+  FastSetupType compute_fast_setup_type(const TensorIteratorConfig&);
+  void compute_names(const TensorIteratorConfig&);
   void propagate_names_to_outputs();
   void coalesce_dimensions();
 

aten/src/ATen/TensorUtils.cpp

@@ -273,11 +273,11 @@ void checkLayout(CheckedFrom c, at::ArrayRef<Tensor> tensors, at::Layout layout)
 }
 
 void * maybe_data_ptr(const Tensor& tensor) {
-  return tensor.defined() ? tensor.data_ptr() : nullptr;
+  return tensor.defined() ? (void *)tensor.data_ptr() : nullptr;
 }
 
 void * maybe_data_ptr(const TensorArg& tensor) {
-  return tensor->defined() ? tensor->data_ptr() : nullptr;
+  return tensor->defined() ? (void *)tensor->data_ptr() : nullptr;
 }
 
 void check_dim_size(

aten/src/ATen/Utils.h

@@ -20,7 +20,7 @@
 
 namespace at {
 
-TORCH_API int _crash_if_asan(int /*arg*/);
+TORCH_API int _crash_if_asan(int);
 
 // Converts a TensorList (i.e. ArrayRef<Tensor> to vector of TensorImpl*)
 // NB: This is ONLY used by legacy TH bindings, and ONLY used by cat.

aten/src/ATen/autocast_mode.cpp

@@ -148,7 +148,7 @@ Tensor cached_cast(at::ScalarType to_type, const Tensor& arg, DeviceType device_
 Banned functions
 *******************************/
 
-static Tensor binary_cross_entropy_banned(const Tensor & /*unused*/, const Tensor & /*unused*/, const std::optional<Tensor>& /*unused*/, int64_t /*unused*/) {
+static Tensor binary_cross_entropy_banned(const Tensor &, const Tensor &, const std::optional<Tensor>&, int64_t) {
   TORCH_CHECK(false, "torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are unsafe to autocast.\n"
            "Many models use a sigmoid layer right before the binary cross entropy layer.\n"
            "In this case, combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits\n"

aten/src/ATen/core/CachingHostAllocator.h

@@ -50,57 +50,19 @@ namespace {
   constexpr size_t MAX_SIZE_INDEX = 64;
 }
 
-// A large reserved pinned memory segment that is created in advance which is used
-// to allocate small pinned memory requests to avoid calling into expensive APIs.
-// We never free this memory and move up the pointer as we allocate new blocks
-// and when blocks are freed, they are cached in the free lists.
-struct PinnedReserveSegment {
-  PinnedReserveSegment(void *start, size_t size) : start_(start), size_(size),
-    current_ptr_(start_), initialized_(true) {}
-
-  PinnedReserveSegment() : start_(nullptr), size_(0), current_ptr_(nullptr), initialized_(false) {}
-
-  bool initialized() {
-    return initialized_;
-  }
-
-  void* allocate(size_t bytes) {
-    std::lock_guard<std::mutex> guard(mutex_);
-
-    // Round up the requested size to 4KB boundary for all including the small ones.
-    size_t rounded_bytes = (bytes + 4096 - 1) & ~(4096 - 1);
-
-    if (((uint8_t*)current_ptr_ + rounded_bytes) > ((uint8_t*)start_ + size_)) {
-      return nullptr;
-    }
-
-    void* ptr = current_ptr_;
-    current_ptr_ = (uint8_t*)current_ptr_ + rounded_bytes;
-    return ptr;
-  }
-
-  bool owns(void* ptr) {
-    return ptr >= start_ && ptr < (uint8_t*)start_ + size_;
-  }
-
-  std::mutex mutex_;
-  void* start_;
-  size_t size_;
-  void* current_ptr_;
-  bool initialized_;
-};
-
 // Struct containing memory allocator summary statistics for host.
 struct TORCH_API HostStats {
-  // COUNT: total allocations (active)
-  Stat active_requests;
-  // SUM: bytes allocated/reserved by this memory alocator. (active)
-  Stat active_bytes;
-  // COUNT: total allocations (active + free)
-  Stat allocations;
-  // SUM: bytes allocated/reserved by this memory alocator. This accounts
-  // for both free and in-use blocks.
+  // COUNT: allocations requested by client code. Note that active
+  // count can be extracted by looking at current allocations
+  Stat allocation;
+  // COUNT: number of allocated segments from host memory allocation.
+  Stat segment;
+
+  // SUM: bytes allocated by this memory alocator. Note that active bytes
+  // can be extracted by looking at current bytes allocated
   Stat allocated_bytes;
+  // SUM: bytes reserved by this memory allocator (both free and used)
+  Stat reserved_bytes;
 
   // SUM: time spent in cudaHostAlloc/cudaHostRegister in microseconds
   DurationStat host_alloc_time;
@@ -115,7 +77,7 @@ struct TORCH_API HostStats {
   // COUNT: number of times cudaHostFree/cudaHostUnregister was called.
   int64_t num_host_free = 0; // This is derived from segment or timing
 
-  // Count of cudaHostAlloc/cudaHostRegister per bucket
+  // Count of cudaHostFree/cudaHostUnregister per bucket
   std::vector<int64_t> bucket_allocation = std::vector<int64_t>(MAX_SIZE_INDEX);
 };
 
@@ -124,22 +86,17 @@ struct TORCH_API HostStats {
 // avoid locking the allocator while collecting stats.
 struct alignas(64) HostStatsStaged {
   std::mutex timing_mutex_;
-  // COUNT: total allocations (active + free)
+  // COUNT: allocations requested by client code resulting in a new segment/block allocation
+  // LOCK: access to this stat is protected by the allocator's blocks_mutex_
+  Stat allocation;
+  // SUM: bytes within active memory blocks, including blocks that are
+  // currently in the free list.
   // LOCK: access to this stat is protected by the allocator's blocks_mutex_
-  Stat allocations;
-  // SUM: bytes allocated/reserved by this memory alocator. This accounts
-  // for both free and in-use blocks.
   Stat allocated_bytes;
-  // COUNT: number of allocations per bucket (active)
-  // LOCK: access to this stat is protected by the per bucket free_list_[index].mutex_
-  std::vector<Stat> active_bucket_stats = std::vector<Stat>(MAX_SIZE_INDEX);
-  // SUM: bytes of allocation per bucket (active)
-  // LOCK: access to this stat is protected by the per bucket free_list_[index].mutex_
-  std::vector<Stat> active_bytes_bucket_stats = std::vector<Stat>(MAX_SIZE_INDEX);
-  // COUNT: number of allocations per bucket (active + free)
+  // COUNT: number of allocations per bucket
   // LOCK: access to this stat is protected by the per bucket free_list_[index].mutex_
   std::vector<Stat> allocation_bucket_stats = std::vector<Stat>(MAX_SIZE_INDEX);
-  // SUM: bytes of allocation per bucket (active + free)
+  // SUM: bytes of allocation per bucket
   // LOCK: access to this stat is protected by the per bucket free_list_[index].mutex_
   std::vector<Stat> allocated_bytes_bucket_stats = std::vector<Stat>(MAX_SIZE_INDEX);
   // SUM: time spent in cudaHostAlloc/cudaHostRegister
@@ -243,21 +200,7 @@ struct CachingHostAllocatorImpl {
     // background.
     if (!pinned_use_background_threads()) {
       process_events();
-    }
-
-    // Round up the allocation to the nearest power of two to improve reuse.
-    // These power of two sizes are also used to index into the free list.
-    size_t roundSize = c10::llvm::PowerOf2Ceil(size);
-
-    // First, try to allocate from the free list
-    auto* block = get_free_block(roundSize);
-    if (block) {
-      return {block->ptr_, reinterpret_cast<void*>(block)};
-    }
-
-    // Check in the recently freed blocks with pending events to see if we
-    // can reuse them. Call get_free_block again after processing events
-    if (pinned_use_background_threads()) {
+    } else {
       // Launch the background thread and process events in a loop.
       static bool background_thread_flag [[maybe_unused]] = [this] {
         getBackgroundThreadPool()->run([&]() {
@@ -270,6 +213,16 @@ struct CachingHostAllocatorImpl {
       }();
     }
 
+    // Round up the allocation to the nearest power of two to improve reuse.
+    // These power of two sizes are also used to index into the free list.
+    size_t roundSize = c10::llvm::PowerOf2Ceil(size);
+
+    // First, try to allocate from the free list
+    auto* block = get_free_block(roundSize);
+    if (block) {
+      return {block->ptr_, reinterpret_cast<void*>(block)};
+    }
+
     // Slow path: if we can't allocate from the cached free list, we need
     // to create a new block.
     void* ptr = nullptr;
@@ -380,7 +333,7 @@ struct CachingHostAllocatorImpl {
         ptr_to_block_.erase(block->ptr_);
         auto index = size_index(block->size_);
         free_block(block);
-        stats_.allocations.decrease(1);
+        stats_.allocation.decrease(1);
         stats_.allocated_bytes.decrease(block->size_);
         stats_.allocation_bucket_stats[index].decrease(1);
         stats_.allocated_bytes_bucket_stats[index].decrease(block->size_);
@@ -430,16 +383,16 @@ struct CachingHostAllocatorImpl {
       // per bucket (we pick index 0 arbitrarily). These are also all the host
       // allocations, not taking into account caching and free lists.
       if (i == 0) {
-        stats.allocations = stats_.allocations;
-        stats.allocated_bytes = stats_.allocated_bytes;
-        stats.num_host_alloc = stats.allocations.allocated;
-        stats.num_host_free = stats.allocations.freed;
+        stats.segment = stats_.allocation;
+        stats.reserved_bytes = stats_.allocated_bytes;
+        stats.num_host_alloc = stats.segment.allocated;
+        stats.num_host_free = stats.segment.freed;
       }
 
       // Bucket stats need to be merged with the slow-path stats. We do this in
       // a best effort manner, since we can't really replay the cached events per bucket.
-      add_bucket_stats(stats.active_requests, stats_.active_bucket_stats[i]);
-      add_bucket_stats(stats.active_bytes, stats_.active_bytes_bucket_stats[i]);
+      add_bucket_stats(stats.allocation, stats_.allocation_bucket_stats[i]);
+      add_bucket_stats(stats.allocated_bytes, stats_.allocated_bytes_bucket_stats[i]);
       stats.bucket_allocation[i] = stats_.allocation_bucket_stats[i].allocated;
     }
 
@@ -464,11 +417,9 @@ struct CachingHostAllocatorImpl {
       std::lock_guard<std::mutex> gb(blocks_mutex_, std::adopt_lock);
 
       if (i == 0) {
-        stats_.allocations.reset_accumulated();
+        stats_.allocation.reset_accumulated();
         stats_.allocated_bytes.reset_accumulated();
       }
-      stats_.active_bucket_stats[i].reset_accumulated();
-      stats_.active_bytes_bucket_stats[i].reset_accumulated();
       stats_.allocation_bucket_stats[i].reset_accumulated();
       stats_.allocated_bytes_bucket_stats[i].reset_accumulated();
     }
@@ -491,11 +442,9 @@ struct CachingHostAllocatorImpl {
       std::lock_guard<std::mutex> gb(blocks_mutex_, std::adopt_lock);
 
       if (i == 0) {
-        stats_.allocations.reset_peak();
+        stats_.allocation.reset_peak();
         stats_.allocated_bytes.reset_peak();
       }
-      stats_.active_bucket_stats[i].reset_peak();
-      stats_.active_bytes_bucket_stats[i].reset_peak();
       stats_.allocation_bucket_stats[i].reset_peak();
       stats_.allocated_bytes_bucket_stats[i].reset_peak();
     }
@@ -512,7 +461,7 @@ struct CachingHostAllocatorImpl {
   virtual void add_allocated_block(B* block) {
     std::lock_guard<std::mutex> g(blocks_mutex_);
     blocks_.insert(block);
-    stats_.allocations.increase(1);
+    stats_.allocation.increase(1);
     stats_.allocated_bytes.increase(block->size_);
     ptr_to_block_.insert({block->ptr_, block});
 
@@ -525,8 +474,6 @@ struct CachingHostAllocatorImpl {
       std::lock_guard<std::mutex> g(free_list_[index].mutex_);
       stats_.allocation_bucket_stats[index].increase(1);
       stats_.allocated_bytes_bucket_stats[index].increase(size);
-      stats_.active_bucket_stats[index].increase(1);
-      stats_.active_bytes_bucket_stats[index].increase(size);
     }
   }
 
@@ -537,8 +484,6 @@ struct CachingHostAllocatorImpl {
       B* block = free_list_[index].list_.back();
       free_list_[index].list_.pop_back();
       block->allocated_ = true;
-      stats_.active_bucket_stats[index].increase(1);
-      stats_.active_bytes_bucket_stats[index].increase(size);
       return block;
     }
     return nullptr;
@@ -632,8 +577,6 @@ struct CachingHostAllocatorImpl {
         auto index = size_index(block->size_);
         std::lock_guard<std::mutex> g(free_list_[index].mutex_);
         free_list_[index].list_.push_back(block);
-        stats_.active_bucket_stats[index].decrease(1);
-        stats_.active_bytes_bucket_stats[index].decrease(size);
         if (size != -1) {
           return;
         }

aten/src/ATen/core/NamedTensor.cpp

@@ -49,7 +49,7 @@ static void check_unique_names(DimnameList names) {
 }
 
 void check_names_valid_for(const TensorBase& tensor, DimnameList names) {
-  impl::check_names_valid_for(tensor.unsafeGetTensorImpl(), names);
+  return impl::check_names_valid_for(tensor.unsafeGetTensorImpl(), names);
 }
 
 void check_names_valid_for(size_t tensor_dim, DimnameList names) {

aten/src/ATen/core/NamedTensor.h

@@ -27,11 +27,11 @@ struct TORCH_API NamedTensorMeta final : public c10::NamedTensorMetaInterface {
     HasNonWildcard
   };
 
-  explicit NamedTensorMeta(HAS_NON_WILDCARD /*unused*/, DimnameList names)
+  explicit NamedTensorMeta(HAS_NON_WILDCARD, DimnameList names)
     : names_(names.vec()) {
     check_invariants();
   }
-  explicit NamedTensorMeta(HAS_NON_WILDCARD /*unused*/, std::vector<Dimname>&& names)
+  explicit NamedTensorMeta(HAS_NON_WILDCARD, std::vector<Dimname>&& names)
     : names_(std::move(names)) {
     check_invariants();
   }
@@ -52,13 +52,13 @@ struct TORCH_API NamedTensorMeta final : public c10::NamedTensorMetaInterface {
       std::any_of(names_.begin(), names_.end(), [](const Dimname& n) { return !n.isWildcard(); }));
   }
 
-  void set_names(HAS_NON_WILDCARD /*unused*/, DimnameList new_names) {
+  void set_names(HAS_NON_WILDCARD, DimnameList new_names) {
     TORCH_INTERNAL_ASSERT(new_names.size() == names_.size());
     std::copy(new_names.begin(), new_names.end(), names_.begin());
     check_invariants();
   }
 
-  void set_names(HAS_NON_WILDCARD /*unused*/, std::vector<Dimname>&& new_names) {
+  void set_names(HAS_NON_WILDCARD, std::vector<Dimname>&& new_names) {
     TORCH_INTERNAL_ASSERT(new_names.size() == names_.size());
     names_ = std::move(new_names);
     check_invariants();

aten/src/ATen/core/PythonOpRegistrationTrampoline.h

@@ -13,7 +13,7 @@ class TORCH_API PythonOpRegistrationTrampoline final {
 public:
   //  Returns true if you successfully registered yourself (that means
   //  you are in the hot seat for doing the operator registrations!)
-  static bool registerInterpreter(c10::impl::PyInterpreter* /*interp*/);
+  static bool registerInterpreter(c10::impl::PyInterpreter*);
 
   // Returns nullptr if no interpreter has been registered yet.
   static c10::impl::PyInterpreter* getInterpreter();

aten/src/ATen/core/Tensor.cpp

@@ -138,7 +138,7 @@ void Tensor::_backward(TensorList inputs,
         const std::optional<Tensor>& gradient,
         std::optional<bool> keep_graph,
         bool create_graph) const {
-  impl::GetVariableHooks()->_backward(*this, inputs, gradient, keep_graph, create_graph);
+  return impl::GetVariableHooks()->_backward(*this, inputs, gradient, keep_graph, create_graph);
 }
 
 const TensorBase& TensorBase::requires_grad_(bool _requires_grad) const {
@@ -173,12 +173,4 @@ unsigned TensorBase::_register_hook(std::function<TensorBase(const TensorBase&)>
   return impl::GetVariableHooks()->_register_hook(*this, std::move(hook));
 }
 
-std::optional<ScalarType> TensorBase::grad_dtype() const {
-  return impl::GetVariableHooks()->grad_dtype(*this);
-}
-
-void TensorBase::set_grad_dtype(const std::optional<ScalarType>& grad_dtype) const {
-  return impl::GetVariableHooks()->set_grad_dtype(*this, grad_dtype);
-}
-
 } // namespace at

aten/src/ATen/core/TensorBase.h

@@ -100,7 +100,7 @@ class TORCH_API TensorBase {
   // Create a Tensor with a +0 reference count. Special care must be
   // taken to avoid decrementing this reference count at destruction
   // time. Intended to support MaybeOwnedTraits<Tensor>.
-  explicit TensorBase(unsafe_borrow_t /*unused*/, const TensorBase& rhs)
+  explicit TensorBase(unsafe_borrow_t, const TensorBase& rhs)
       : impl_(c10::intrusive_ptr<at::TensorImpl, UndefinedTensorImpl>(rhs.impl_.get(), c10::raw::DontIncreaseRefcount{})) {}
   friend MaybeOwnedTraits<TensorBase>;
 
@@ -930,10 +930,6 @@ public:
 
   const TensorBase& requires_grad_(bool _requires_grad=true) const;
 
-  std::optional<ScalarType> grad_dtype() const;
-
-  void set_grad_dtype(const std::optional<ScalarType>& grad_dtype) const;
-
   // View Variables
   //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -954,7 +950,7 @@ protected:
   c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> impl_;
 
 private:
-  TensorBase __dispatch_contiguous(c10::MemoryFormat /*memory_format*/) const;
+  TensorBase __dispatch_contiguous(c10::MemoryFormat) const;
 };
 
 inline DeviceIndex get_device(const TensorBase& self) {

aten/src/ATen/core/TransformationHelper.h

@@ -117,7 +117,7 @@ C10_HOST_DEVICE inline T cauchy(T val, T median, T sigma) {
 template <>
 C10_HOST_DEVICE inline double cauchy(double val, double median, double sigma) {
   // https://en.wikipedia.org/wiki/Cauchy_distribution#Cumulative_distribution_function
-  return median + sigma * at::tan(c10::pi<double> * (val - 0.5));
+  return median + sigma * at::tan(c10::pi<double> * (val - static_cast<double>(0.5)));
 }
 
 /**

aten/src/ATen/core/VariableHooksInterface.h

@@ -68,8 +68,6 @@ struct TORCH_API VariableHooksInterface {
       const c10::OperatorHandle& op,
       c10::DispatchKeySet dispatch_keys,
       torch::jit::Stack* stack) const = 0;
-  virtual std::optional<c10::ScalarType> grad_dtype(const TensorBase&) const = 0;
-  virtual void set_grad_dtype(const TensorBase&, const std::optional<c10::ScalarType>&) const = 0;
 };
 
 TORCH_API void SetVariableHooks(VariableHooksInterface* hooks);

aten/src/ATen/core/boxing/BoxedKernel.h

@@ -18,10 +18,10 @@ class KernelFunction;
 // implementation notes; notably, this does NOT actually go through the
 // boxing/unboxing codepath.
 TORCH_API void fallthrough_kernel(
-    OperatorKernel* /*unused*/,
-    const OperatorHandle& /*unused*/,
-    DispatchKeySet /*unused*/,
-    Stack* /*unused*/);
+    OperatorKernel*,
+    const OperatorHandle&,
+    DispatchKeySet,
+    Stack*);
 
 // Note [Ambiguity in AutogradOther kernel]
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -62,10 +62,10 @@ TORCH_API void fallthrough_kernel(
 // than arbitrarily pick one or the other, we just register a kernel that raises
 // an error and let the user decide how to proceed.
 TORCH_API void ambiguous_autogradother_kernel(
-    OperatorKernel* /*unused*/,
-    const OperatorHandle& /*op*/,
-    DispatchKeySet /*unused*/,
-    Stack* /*unused*/);
+    OperatorKernel*,
+    const OperatorHandle&,
+    DispatchKeySet,
+    Stack*);
 
 // Note [named_not_supported_kernel]
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -75,10 +75,10 @@ TORCH_API void ambiguous_autogradother_kernel(
 // give a good error message in cases when boxing is not supported).  When
 // boxing is universally supported this can be removed.
 [[noreturn]] TORCH_API void named_not_supported_kernel(
-    OperatorKernel* /*unused*/,
-    const OperatorHandle& /*op*/,
-    DispatchKeySet /*unused*/,
-    Stack* /*unused*/);
+    OperatorKernel*,
+    const OperatorHandle&,
+    DispatchKeySet,
+    Stack*);
 
 /**
  * BoxedKernel is similar to a std::function storing a boxed kernel.
@@ -185,16 +185,16 @@ class TORCH_API BoxedKernel final {
 
   template <BoxedKernelFunction* func>
   static void make_boxed_function(
-      OperatorKernel* /*unused*/,
+      OperatorKernel*,
       const OperatorHandle& opHandle,
-      DispatchKeySet /*unused*/,
+      DispatchKeySet,
       Stack* stack);
 
   template <BoxedKernelFunction_withDispatchKeys* func>
   static void make_boxed_function(
-      OperatorKernel* /*unused*/,
+      OperatorKernel*,
       const OperatorHandle& opHandle,
-      DispatchKeySet /*ks*/,
+      DispatchKeySet,
       Stack* stack);
 
   explicit BoxedKernel(

aten/src/ATen/core/boxing/BoxedKernel_impl.h

@@ -2,7 +2,7 @@
 
 namespace c10 {
 
-inline BoxedKernel::BoxedKernel() : boxed_kernel_func_(nullptr) {}
+inline BoxedKernel::BoxedKernel() : functor_(), boxed_kernel_func_(nullptr) {}
 
 inline BoxedKernel::BoxedKernel(
     std::unique_ptr<OperatorKernel> functor,
@@ -11,9 +11,9 @@ inline BoxedKernel::BoxedKernel(
 
 template <BoxedKernel::BoxedKernelFunction* func>
 inline void BoxedKernel::make_boxed_function(
-    OperatorKernel* /*unused*/,
+    OperatorKernel*,
     const OperatorHandle& opHandle,
-    DispatchKeySet /*unused*/,
+    DispatchKeySet,
     Stack* stack) {
   // Note that we're dropping the DispatchKeySet argument.
   // See Note [Plumbing Keys Through The Dispatcher 2] for details.
@@ -22,7 +22,7 @@ inline void BoxedKernel::make_boxed_function(
 
 template <BoxedKernel::BoxedKernelFunction_withDispatchKeys* func>
 inline void BoxedKernel::make_boxed_function(
-    OperatorKernel* /*unused*/,
+    OperatorKernel*,
     const OperatorHandle& opHandle,
     DispatchKeySet ks,
     Stack* stack) {

aten/src/ATen/core/boxing/KernelFunction.cpp

@@ -10,7 +10,7 @@ namespace c10 {
 // be handled specially.  Its semantics is that it redispatches to the
 // *next* dispatch key that would have been processed, skipping the current
 // one.
-void fallthrough_kernel(OperatorKernel* /*unused*/, const OperatorHandle& /*unused*/, DispatchKeySet /*unused*/, Stack* /*unused*/) {
+void fallthrough_kernel(OperatorKernel*, const OperatorHandle&, DispatchKeySet, Stack*) {
   TORCH_INTERNAL_ASSERT(0,
     "fallthrough_kernel was executed but it should have been short-circuited by the dispatcher. "
     "This could occur if you registered a fallthrough kernel as a override for a specific operator "
@@ -19,7 +19,7 @@ void fallthrough_kernel(OperatorKernel* /*unused*/, const OperatorHandle& /*unus
     "let us know in the bug tracker.");
 }
 
-void ambiguous_autogradother_kernel(OperatorKernel* /*unused*/, const OperatorHandle& op, DispatchKeySet /*unused*/, Stack* /*unused*/) {
+void ambiguous_autogradother_kernel(OperatorKernel*, const OperatorHandle& op, DispatchKeySet, Stack*) {
   TORCH_INTERNAL_ASSERT(0,
     op.operator_name(), " has kernels registered to both CompositeImplicitAutograd and a backend mapped to AutogradOther. "
     "This makes the backend kernel unreachable; the dispatcher will always prefer the CompositeImplicitAutograd lowering "
@@ -32,7 +32,7 @@ void ambiguous_autogradother_kernel(OperatorKernel* /*unused*/, const OperatorHa
     "\nCanonical state\n~~~~~~~~~~~\n", op.dumpState(), "\n\n");
 }
 
-void named_not_supported_kernel(OperatorKernel* /*unused*/, const OperatorHandle& op, DispatchKeySet /*unused*/, Stack* /*unused*/) {
+void named_not_supported_kernel(OperatorKernel*, const OperatorHandle& op, DispatchKeySet, Stack*) {
   // DO NOT LOOK AT STACK, YOU HAVE SHORT CIRCUITED BOXING
   // See Note [named_not_supported_kernel]
   TORCH_CHECK(0,

aten/src/ATen/core/boxing/KernelFunction.h

@@ -229,7 +229,7 @@ class TORCH_API KernelFunction final {
    * &unboxed_func>();
    */
   template <class FuncPtr, bool AllowLegacyTypes = false>
-  static KernelFunction makeFromUnboxedFunction(FuncPtr /*func_ptr*/);
+  static KernelFunction makeFromUnboxedFunction(FuncPtr);
 
   /**
    * Create a KernelFunction from an unboxed function.
@@ -271,7 +271,7 @@ class TORCH_API KernelFunction final {
 
   std::string dumpState() const;
   // For testing internal invariants only
-  bool _equalsBoxedAndUnboxed(const KernelFunction& /*other*/) const;
+  bool _equalsBoxedAndUnboxed(const KernelFunction&) const;
 
   // Register a token to be invalidated when this KernelFunction is destroyed
   void registerToken(std::weak_ptr<KernelToken> token) const;

aten/src/ATen/core/boxing/KernelFunction_impl.h

@@ -20,7 +20,9 @@ make_unique_base(Args&&... args) {
 } // namespace detail
 
 inline KernelFunction::KernelFunction()
-    : unboxed_kernel_func_(nullptr), sym_unboxed_kernel_func_(nullptr) {}
+    : boxed_kernel_func_(),
+      unboxed_kernel_func_(nullptr),
+      sym_unboxed_kernel_func_(nullptr) {}
 
 inline KernelFunction::~KernelFunction() {
   if (tokens_) {

aten/src/ATen/core/boxing/impl/boxing.h

@@ -131,7 +131,7 @@ C10_ALWAYS_INLINE_UNLESS_MOBILE void boxToStack(
   new (dest++) IValue(options.pinned_memory());
 }
 
-inline void boxArgsToStack(IValue*& /*unused*/) {}
+inline void boxArgsToStack(IValue*&) {}
 
 template <typename T, typename... Args>
 C10_ALWAYS_INLINE_UNLESS_MOBILE void boxArgsToStack(
@@ -185,7 +185,7 @@ struct PopResult<std::tuple<Types...>> final {
   template <size_t... indices>
   static Result pop_to_tuple_impl(
       Stack& stack,
-      std::index_sequence<indices...> /*unused*/) {
+      std::index_sequence<indices...>) {
     return std::make_tuple((std::move(stack[indices]).template to<Types>())...);
   }
 };

aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h

@@ -561,7 +561,7 @@ struct wrap_kernel_functor_unboxed_<
   // doesn't use &&
   static ReturnType call(
       OperatorKernel* functor,
-      DispatchKeySet /*unused*/,
+      DispatchKeySet,
       ParameterTypes... args) {
     KernelFunctor* functor_ = static_cast<KernelFunctor*>(functor);
     // Note [Plumbing Keys Through The Dispatcher 2]
@@ -629,8 +629,8 @@ call_functor_with_args_from_stack_(
     OperatorKernel* functor,
     DispatchKeySet dispatchKeySet,
     Stack* stack,
-    std::index_sequence<ivalue_arg_indices...> /*unused*/,
-    guts::typelist::typelist<ArgTypes...>* /*unused*/) {
+    std::index_sequence<ivalue_arg_indices...>,
+    guts::typelist::typelist<ArgTypes...>*) {
   (void)(stack); // when sizeof...(ivalue_arg_indices) == 0, this argument would
                  // be unused and we have to silence the compiler warning.
 
@@ -708,7 +708,7 @@ struct push_outputs<std::tuple<OutputTypes...>, AllowDeprecatedTypes> final {
   static void call_(
       std::tuple<OutputTypes...>&& output,
       Stack* stack,
-      std::index_sequence<indices...> /*unused*/) {
+      std::index_sequence<indices...>) {
     torch::jit::push(
         *stack,
         return_to_ivalue<OutputTypes, AllowDeprecatedTypes>::call(
@@ -718,7 +718,7 @@ struct push_outputs<std::tuple<OutputTypes...>, AllowDeprecatedTypes> final {
   static void copy_(
       const std::tuple<OutputTypes...>& output,
       Stack* stack,
-      std::index_sequence<indices...> /*unused*/) {
+      std::index_sequence<indices...>) {
     torch::jit::push(
         *stack,
         return_to_ivalue<OutputTypes, AllowDeprecatedTypes>::copy(
@@ -741,7 +741,7 @@ struct make_boxed_from_unboxed_functor final {
 
   static void call(
       OperatorKernel* functor,
-      const OperatorHandle& /*unused*/,
+      const OperatorHandle&,
       DispatchKeySet dispatchKeySet,
       Stack* stack) {
     using ReturnType =

aten/src/ATen/core/builtin_function.h

@@ -63,13 +63,13 @@ struct BuiltinOpFunction : public Function {
 
   bool call(
       Stack& stack,
-      std::optional<size_t> /*unused*/,
-      c10::function_ref<void(const Code&)> /*unused*/) override {
+      std::optional<size_t>,
+      c10::function_ref<void(const Code&)>) override {
     run(stack);
     return false;
   }
 
-  bool call(Stack& stack, c10::function_ref<void(const mobile::Code&)> /*unused*/)
+  bool call(Stack& stack, c10::function_ref<void(const mobile::Code&)>)
       override {
     run(stack);
     return false;

aten/src/ATen/core/dispatch/DispatchKeyExtractor.h

@@ -80,8 +80,7 @@ struct MultiDispatchKeySet : at::IterArgs<MultiDispatchKeySet> {
       ts = ts | x.key_set();
     }
   }
-  [[noreturn]] void operator()(
-      at::ArrayRef<std::optional<at::Tensor>> /*unused*/) {
+  [[noreturn]] void operator()(at::ArrayRef<std::optional<at::Tensor>>) {
     // Just checking that the handling of Tensor?[] didn't change.
     TORCH_INTERNAL_ASSERT(false);
   }
@@ -96,7 +95,7 @@ struct MultiDispatchKeySet : at::IterArgs<MultiDispatchKeySet> {
     }
   }
   template <typename T>
-  void operator()(const T& /*unused*/) {
+  void operator()(const T&) {
     // do nothing
   }
 };

aten/src/ATen/core/dispatch/Dispatcher.cpp

@@ -76,7 +76,13 @@ void _print_dispatch_trace(const std::string& label, const std::string& op_name,
 
 OpRegistrationListener::~OpRegistrationListener()= default;
 
-Dispatcher::Dispatcher(): backendFallbackKernels_(), listeners_(std::make_unique<detail::RegistrationListenerList>()), guard_(std::make_shared<Guard>())
+Dispatcher::Dispatcher()
+: operators_()
+, operatorLookupTable_()
+, backendFallbackKernels_()
+, listeners_(std::make_unique<detail::RegistrationListenerList>())
+, cond_var_()
+, guard_(std::make_shared<Guard>())
 {}
 
 Dispatcher::~Dispatcher() {