Introduce automatic wrapper to run DTensor tests under local tensor mode

The wrapper enable to share test body implementation while eliminating need
test class by hand. As an example, this change converts the whole DTensorTest
to use local tensor mode.

[ghstack-poisoned]

.ci/docker/build.sh

@@ -113,7 +113,6 @@ case "$tag" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     TRITON=yes
-    INSTALL_MINGW=yes
     ;;
   pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11)
     CUDA_VERSION=13.0.0
@@ -362,7 +361,6 @@ docker build \
        --build-arg "OPENBLAS=${OPENBLAS:-}" \
        --build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
        --build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
-       --build-arg "INSTALL_MINGW=${INSTALL_MINGW:-}" \
        -f $(dirname ${DOCKERFILE})/Dockerfile \
        -t "$tmp_tag" \
        "$@" \

.ci/docker/common/install_cpython.sh

@@ -83,6 +83,10 @@ function build_cpython {
         py_suffix=${py_ver::-1}
         py_folder=$py_suffix
     fi
+    # Update to rc2 due to https://github.com/python/cpython/commit/c72699086fe4
+    if [ "$py_suffix" == "3.14.0" ]; then
+        py_suffix="3.14.0rc2"
+    fi
     wget -q $PYTHON_DOWNLOAD_URL/$py_folder/Python-$py_suffix.tgz -O Python-$py_ver.tgz
     do_cpython_build $py_ver Python-$py_suffix
 

.ci/docker/common/install_mingw.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Install MinGW-w64 for Windows cross-compilation
-apt-get update
-apt-get install -y g++-mingw-w64-x86-64-posix
-
-echo "MinGW-w64 installed successfully"
-x86_64-w64-mingw32-g++ --version

.ci/docker/common/install_onnx.sh

@@ -20,7 +20,7 @@ pip_install \
 
 pip_install coloredlogs packaging
 pip_install onnxruntime==1.23.0
-pip_install onnxscript==0.5.4
+pip_install onnxscript==0.5.3
 
 # 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/libtorch/build.sh

@@ -39,13 +39,9 @@ case ${DOCKER_TAG_PREFIX} in
         DOCKER_GPU_BUILD_ARG=""
         ;;
     rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
         if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         BASE_TARGET=rocm
         GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete

.ci/docker/manywheel/build.sh

@@ -75,13 +75,9 @@ case ${image} in
         DOCKERFILE_SUFFIX="_cuda_aarch64"
         ;;
     manylinux2_28-builder:rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
         if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         TARGET=rocm_final
         MANY_LINUX_VERSION="2_28"

.ci/docker/requirements-docs.txt

@@ -1,11 +1,15 @@
-sphinx==7.2.6
+sphinx==5.3.0
 #Description: This is used to generate PyTorch docs
-#Pinned versions: 7.2.6
+#Pinned versions: 5.3.0
 
-pytorch_sphinx_theme2==0.1.0
-#Description: This is needed to generate PyTorch docs
-#Pinned versions: 0.1.0
+standard-imghdr==3.13.0; python_version >= "3.13"
+#Description: This is needed by Sphinx, so it needs to be added here.
+# The reasons are as follows:
+# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
+# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
+# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
 
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.
@@ -32,17 +36,17 @@ tensorboard==2.18.0 ; python_version >= "3.13"
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 2.13.0
 
-breathe==4.36.0
+breathe==4.34.0
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.36.0
+#Pinned versions: 4.34.0
 
-exhale==0.3.7
+exhale==0.2.3
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.3.7
+#Pinned versions: 0.2.3
 
-docutils==0.20
+docutils==0.16
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.20
+#Pinned versions: 0.16
 
 bs4==0.0.1
 #Description: This is used to generate PyTorch C++ docs
@@ -52,13 +56,13 @@ IPython==8.12.0
 #Description: This is used to generate PyTorch functorch docs
 #Pinned versions: 8.12.0
 
-myst-nb==1.3.0
+myst-nb==0.17.2
 #Description: This is used to generate PyTorch functorch and torch.compile docs.
-#Pinned versions: 1.3.0
+#Pinned versions: 0.17.2
 
 # The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
 python-etcd==0.4.5
 sphinx-copybutton==0.5.0
-sphinx-design==0.6.1
+sphinx-design==0.4.0
 sphinxcontrib-mermaid==1.0.0
-myst-parser==4.0.1
+myst-parser==0.18.1

.ci/docker/ubuntu/Dockerfile

@@ -103,11 +103,6 @@ COPY ci_commit_pins/torchbench.txt torchbench.txt
 RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
 RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
 
-ARG INSTALL_MINGW
-COPY ./common/install_mingw.sh install_mingw.sh
-RUN if [ -n "${INSTALL_MINGW}" ]; then bash ./install_mingw.sh; fi
-RUN rm install_mingw.sh
-
 ARG TRITON
 ARG TRITON_CPU
 

.ci/lumen_cli/cli/lib/common/git_helper.py

@@ -57,8 +57,8 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
         logger.info("Successfully cloned %s", target)
         return r, commit
 
-    except GitCommandError:
-        logger.exception("Git operation failed")
+    except GitCommandError as e:
+        logger.error("Git operation failed: %s", e)
         raise
 
 

.ci/manywheel/build_cuda.sh

@@ -187,22 +187,19 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
             export USE_CUFILE=0
         else
             DEPS_LIST+=(
+                "/usr/local/cuda/lib64/libnvToolsExt.so.1"
                 "/usr/local/cuda/lib64/libcublas.so.12"
                 "/usr/local/cuda/lib64/libcublasLt.so.12"
                 "/usr/local/cuda/lib64/libcudart.so.12"
                 "/usr/local/cuda/lib64/libnvrtc.so.12"
                 "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12")
             DEPS_SONAME+=(
+                "libnvToolsExt.so.1"
                 "libcublas.so.12"
                 "libcublasLt.so.12"
                 "libcudart.so.12"
                 "libnvrtc.so.12"
                 "libcupti.so.12")
-
-            if [[ $CUDA_VERSION != 12.9* ]]; then
-                DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
-                DEPS_SONAME+=("libnvToolsExt.so.1")
-            fi
         fi
     else
         echo "Using nvidia libs from pypi."

.ci/pytorch/python_doc_push_script.sh

@@ -102,18 +102,8 @@ if [ "$is_main_doc" = true ]; then
     echo coverage output not found
     exit 1
   elif [ $undocumented -gt 0 ]; then
-    echo "======================================"
-    echo "ERROR: $undocumented undocumented objects found!"
-    echo "======================================"
-    echo ""
-    echo "Full coverage report:"
+    echo undocumented objects found:
     cat build/coverage/python.txt
-    echo ""
-    echo "======================================"
-    echo "Undocumented modules/objects (lines after TOTAL):"
-    tail -n +$((lines - undocumented + 1)) build/coverage/python.txt
-    echo "======================================"
-    echo ""
     echo "Make sure you've updated relevant .rsts in docs/source!"
     echo "You can reproduce locally by running 'cd docs && make coverage && cat build/coverage/python.txt'"
     exit 1

.ci/pytorch/test.sh

@@ -485,22 +485,6 @@ test_inductor_aoti() {
   /usr/bin/env "${TEST_ENVS[@]}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference cpp/test_vec_half_AVX2 -dist=loadfile
 }
 
-test_inductor_aoti_cross_compile_for_windows() {
-
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-
-  # Set WINDOWS_CUDA_HOME environment variable
-  WINDOWS_CUDA_HOME="$(pwd)/win-torch-wheel-extracted"
-  export WINDOWS_CUDA_HOME
-
-  echo "WINDOWS_CUDA_HOME is set to: $WINDOWS_CUDA_HOME"
-  echo "Contents:"
-  ls -lah "$(pwd)/win-torch-wheel-extracted/lib/x64/" || true
-
-  python test/inductor/test_aoti_cross_compile_windows.py -k compile --package-dir "$TEST_REPORTS_DIR" --win-torch-lib-dir "$(pwd)/win-torch-wheel-extracted/torch/lib"
-}
-
 test_inductor_cpp_wrapper_shard() {
   if [[ -z "$NUM_TEST_SHARDS" ]]; then
     echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
@@ -916,7 +900,7 @@ test_inductor_set_cpu_affinity(){
   export LD_PRELOAD="$JEMALLOC_LIB":"$LD_PRELOAD"
   export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"
 
-  if [[ "$(uname -m)" != "aarch64" ]]; then
+  if [[ "${TEST_CONFIG}" != *aarch64* ]]; then
     # Use Intel OpenMP for x86
     IOMP_LIB="$(dirname "$(which python)")/../lib/libiomp5.so"
     export LD_PRELOAD="$IOMP_LIB":"$LD_PRELOAD"
@@ -930,7 +914,7 @@ test_inductor_set_cpu_affinity(){
   cores=$((cpus / thread_per_core))
 
   # Set number of cores to 16 on aarch64 for performance runs
-  if [[ "$(uname -m)" == "aarch64" && $cores -gt 16 ]]; then
+  if [[ "${TEST_CONFIG}" == *aarch64* && $cores -gt 16 ]]; then
     cores=16
   fi
   export OMP_NUM_THREADS=$cores
@@ -1631,7 +1615,6 @@ test_operator_benchmark() {
   TEST_REPORTS_DIR=$(pwd)/test/test-reports
   mkdir -p "$TEST_REPORTS_DIR"
   TEST_DIR=$(pwd)
-  ARCH=$(uname -m)
 
   test_inductor_set_cpu_affinity
 
@@ -1646,7 +1629,7 @@ test_operator_benchmark() {
   pip_install pandas
   python check_perf_csv.py \
       --actual "${TEST_REPORTS_DIR}/operator_benchmark_eager_float32_cpu.csv" \
-      --expected "${ARCH}_expected_ci_operator_benchmark_eager_float32_cpu.csv"
+      --expected "expected_ci_operator_benchmark_eager_float32_cpu.csv"
 }
 
 test_operator_microbenchmark() {
@@ -1683,7 +1666,7 @@ if [[ "${TEST_CONFIG}" == *numpy_2* ]]; then
     python -m pip install --pre numpy==2.0.2 scipy==1.13.1 numba==0.60.0
   fi
   python test/run_test.py --include dynamo/test_functions.py dynamo/test_unspec.py test_binary_ufuncs.py test_fake_tensor.py test_linalg.py test_numpy_interop.py test_tensor_creation_ops.py test_torch.py torch_np/test_basic.py
-elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" == 'default' ]]; then
+elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" != *perf_cpu_aarch64* ]]; then
   test_linux_aarch64
 elif [[ "${TEST_CONFIG}" == *backward* ]]; then
   test_forward_backward_compatibility
@@ -1734,8 +1717,6 @@ elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
   test_inductor_micro_benchmark
-elif [[ "${TEST_CONFIG}" == *aoti_cross_compile_for_windows* ]]; then
-  test_inductor_aoti_cross_compile_for_windows
 elif [[ "${TEST_CONFIG}" == *huggingface* ]]; then
   install_torchvision
   id=$((SHARD_NUMBER-1))

.circleci/scripts/binary_populate_env.sh

@@ -163,13 +163,8 @@ if [[ "$(uname)" != Darwin ]]; then
   MEMORY_LIMIT_MAX_JOBS=12
   NUM_CPUS=$(( $(nproc) - 2 ))
 
-  if [[ "$(uname)" == Linux ]]; then
   # Defaults here for **binary** linux builds so they can be changed in one place
   export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
-  else
-    # For other builds
-    export MAX_JOBS=${NUM_CPUS}
-  fi
 
   cat >>"$envfile" <<EOL
   export MAX_JOBS="${MAX_JOBS}"

.flake8

@@ -7,12 +7,16 @@ max-line-length = 120
 # C408 ignored because we like the dict keyword argument syntax
 # E501 is not flexible enough, we're using B950 instead
 ignore =
-    E203,E305,E402,E501,E704,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,F824,
+    E203,E305,E402,E501,E704,E721,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,F824,
     # shebang has extra meaning in fbcode lints, so I think it's not worth trying
     # to line this up with executable bit
     EXE001,
     # these ignores are from flake8-bugbear; please fix!
     B007,B008,B017,B019,B023,B028,B903,B905,B906,B907,B908,B910
+    # these ignores are from flake8-comprehensions; please fix!
+    C407,
+    # these ignores are from flake8-logging-format; please fix!
+    G100,G101,G200
     # these ignores are from flake8-simplify. please fix or ignore with commented reason
     SIM105,SIM108,SIM110,SIM111,SIM113,SIM114,SIM115,SIM116,SIM117,SIM118,SIM119,SIM12,
     # SIM104 is already covered by pyupgrade ruff

.github/ISSUE_TEMPLATE/ci-sev.md

@@ -8,7 +8,6 @@ assignees: ''
 ---
 
 > NOTE: Remember to label this issue with "`ci: sev`"
->       If you want autorevert to be disabled, keep the ci: disable-autorevert label
 
  <!-- Add the `merge blocking` label to this PR to prevent PRs from being merged while this issue is open -->
 

.github/ISSUE_TEMPLATE/disable-autorevert.md

@@ -1,7 +1,7 @@
 ---
-name: "D❌​\U0001F519​ ISABLE AUTOREVERT"
+name: DISABLE AUTOREVERT
 about: Disables autorevert when open
-title: "[DISABLE AUTOREVERT]"
+title: "❌​\U0001F519​ [DISABLE AUTOREVERT]"
 labels: 'ci: disable-autorevert'
 assignees: ''
 

.github/actions/build-external-packages/action.yml

@@ -65,7 +65,7 @@ runs:
           cd .ci/lumen_cli
           python3 -m pip install -e .
         )
-        MAX_JOBS="$(nproc --ignore=10)"
+        MAX_JOBS="$(nproc --ignore=6)"
         export MAX_JOBS
 
         # Split the comma-separated list and build each target

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-69bbe7363897764f9e758d851cd0340147d27f94
+8ad2aa5d354d1bf432339113860185d5a5d1abbd

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-faffd5cf673615583da6517275e361cb3dbc77e6
+f5c6c2ec6490455e86f67b2a25c10390d60a27f7

.github/labeler.yml

@@ -133,32 +133,3 @@
 
 "ciflow/vllm":
 - .github/ci_commit_pins/vllm.txt
-
-"ciflow/b200":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/**/*cublas*
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm
-
-"ciflow/h100":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/**/*cublas*
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm
-
-"ciflow/rocm":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm

.github/pytorch-probot.yml

@@ -3,7 +3,6 @@ ciflow_tracking_issue: 64124
 ciflow_push_tags:
 - ciflow/b200
 - ciflow/b200-symm-mem
-- ciflow/b200-distributed
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
@@ -16,8 +15,7 @@ ciflow_push_tags:
 - ciflow/inductor-micro-benchmark
 - ciflow/inductor-micro-benchmark-cpu-x86
 - ciflow/inductor-perf-compare
-- ciflow/inductor-perf-test-nightly-rocm-mi300
-- ciflow/inductor-perf-test-nightly-rocm-mi355
+- ciflow/inductor-perf-test-nightly-rocm
 - ciflow/inductor-perf-test-nightly-x86-zen
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm
@@ -33,7 +31,6 @@ ciflow_push_tags:
 - ciflow/rocm
 - ciflow/rocm-mi300
 - ciflow/rocm-mi355
-- ciflow/rocm-navi31
 - ciflow/s390
 - ciflow/slow
 - ciflow/torchbench

.github/scripts/filter_test_configs.py

@@ -512,8 +512,6 @@ def perform_misc_tasks(
         "keep-going",
         branch == MAIN_BRANCH
         or bool(tag and re.match(r"^trunk/[a-f0-9]{40}$", tag))
-        # Pattern for tags created via manual run on HUD
-        or bool(tag and re.match(r"^ciflow/[^/]+/[a-f0-9]{40}$", tag))
         or check_for_setting(labels, pr_body, "keep-going"),
     )
     set_output(

.github/scripts/generate_binary_build_matrix.py

@@ -79,21 +79,21 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
     ),
     "12.9": (
-        "nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | "
-        "nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | "
-        "nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | "
-        "nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | "
-        "nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | "
-        "nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | "
-        "nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | "
-        "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
-        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
-        "nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
-        "nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | "
-        "nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'"
+        "nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'"
     ),
     "13.0": (
         "nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | "
@@ -241,11 +241,7 @@ def generate_libtorch_matrix(
             arches += CUDA_ARCHES
             arches += ROCM_ARCHES
         elif os == "windows":
-            # TODO (huydhn): Only build CUDA 12.9 for Linux. This logic is to be cleaned up
-            # in 2.10
-            windows_cuda_arches = CUDA_ARCHES.copy()
-            windows_cuda_arches.remove("12.9")
-            arches += windows_cuda_arches
+            arches += CUDA_ARCHES
     if libtorch_variants is None:
         libtorch_variants = [
             "shared-with-deps",
@@ -309,11 +305,7 @@ def generate_wheels_matrix(
         if os == "linux":
             arches += CUDA_ARCHES + ROCM_ARCHES + XPU_ARCHES
         elif os == "windows":
-            # TODO (huydhn): Only build CUDA 12.9 for Linux. This logic is to be cleaned up
-            # in 2.10
-            windows_cuda_arches = CUDA_ARCHES.copy()
-            windows_cuda_arches.remove("12.9")
-            arches += windows_cuda_arches + XPU_ARCHES
+            arches += CUDA_ARCHES + XPU_ARCHES
         elif os == "linux-aarch64":
             # Separate new if as the CPU type is different and
             # uses different build/test scripts

.github/scripts/trymerge.py

@@ -1092,7 +1092,7 @@ class GitHubPR:
         editor = node["editor"]
         return GitHubComment(
             body_text=node["bodyText"],
-            created_at=node.get("createdAt", ""),
+            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"],
@@ -2042,6 +2042,10 @@ def validate_revert(
             f"[{', '.join(allowed_reverters)}], but instead is {author_association}."
         )
 
+    # Raises exception if matching rule is not found, but ignores all status checks
+    find_matching_merge_rule(
+        pr, repo, skip_mandatory_checks=True, skip_internal_checks=True
+    )
     commit_sha = get_pr_commit_sha(repo, pr)
     return (author_login, commit_sha)
 

.github/templates/macos_binary_build_workflow.yml.j2

@@ -26,8 +26,9 @@ name: !{{ build_environment }}
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "!{{ py_ver.strip('t') + ('.4' if '3.14' not in py_ver else '.0') }}"
+          python-version: "!{{ (py_ver.strip('t') + '.4') if '3.14' not in py_ver else '3.14.0-rc.2' }}"
           freethreaded: !{{ "true" if py_ver.endswith('t') else "false" }}
 {%- endmacro %}
 

.github/templates/windows_binary_build_workflow.yml.j2

@@ -79,9 +79,9 @@ jobs:
     runs-on: "windows-11-arm64-preview"
     {%- else %}
     {%- if branches == "nightly" %}
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     {%- else %}
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge.nonephemeral"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
     {%- endif %}
     {%- endif %}
     timeout-minutes: !{{ common.timeout_minutes_windows_binary }}

.github/workflows/_linux-build.yml

@@ -37,7 +37,7 @@ on:
       runner:
         required: false
         type: string
-        default: "linux.c7i.2xlarge"
+        default: "linux.2xlarge"
         description: |
           Label of the runner this job should run on.
       test-matrix:

.github/workflows/_linux-test.yml

@@ -224,46 +224,6 @@ jobs:
         continue-on-error: true
         uses: ./.github/actions/download-td-artifacts
 
-      - name: Download Windows torch wheel for cross-compilation
-        if: matrix.win_torch_wheel_artifact != ''
-        uses: seemethere/download-artifact-s3@1da556a7aa0a088e3153970611f6c432d58e80e6 # v4.2.0
-        with:
-          name: ${{ matrix.win_torch_wheel_artifact }}
-          path: win-torch-wheel
-
-      - name: Extract Windows wheel and setup CUDA libraries
-        if: matrix.win_torch_wheel_artifact != ''
-        shell: bash
-        run: |
-          set -x
-
-          # Find the wheel file
-          WHEEL_FILE=$(find win-torch-wheel -name "*.whl" -type f | head -n 1)
-          if [ -z "$WHEEL_FILE" ]; then
-            echo "Error: No wheel file found in win-torch-wheel directory"
-            exit 1
-          fi
-          echo "Found wheel file: $WHEEL_FILE"
-
-          # Unzip the wheel file
-          unzip -q "$WHEEL_FILE" -d win-torch-wheel-extracted
-          echo "Extracted wheel contents"
-
-          # Setup CUDA libraries (cuda.lib and cudart.lib) directory
-          mkdir -p win-torch-wheel-extracted/lib/x64
-          if [ -f "win-torch-wheel/cuda.lib" ]; then
-            mv win-torch-wheel/cuda.lib win-torch-wheel-extracted/lib/x64/
-            echo "Moved cuda.lib to win-torch-wheel-extracted/lib/x64/"
-          fi
-          if [ -f "win-torch-wheel/cudart.lib" ]; then
-            mv win-torch-wheel/cudart.lib win-torch-wheel-extracted/lib/x64/
-            echo "Moved cudart.lib to win-torch-wheel-extracted/lib/x64/"
-          fi
-
-          # Verify CUDA libraries are present
-          echo "CUDA libraries:"
-          ls -la win-torch-wheel-extracted/lib/x64/ || echo "No CUDA libraries found"
-
       - name: Parse ref
         id: parse-ref
         run: .github/scripts/parse_ref.py

.github/workflows/_win-build.yml

@@ -168,31 +168,6 @@ jobs:
         run: |
           .ci/pytorch/win-build.sh
 
-      # Collect Windows torch libs and CUDA libs for cross-compilation
-      - name: Collect Windows CUDA libs for cross-compilation
-        if: steps.build.outcome != 'skipped' && inputs.cuda-version != 'cpu'
-        shell: bash
-        run: |
-          set -ex
-
-          # Create directory structure if does not exist
-          mkdir -p /c/${{ github.run_id }}/build-results
-
-          # Copy CUDA libs
-          CUDA_PATH="/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${{ inputs.cuda-version }}"
-
-          if [ -f "${CUDA_PATH}/lib/x64/cuda.lib" ]; then
-            cp "${CUDA_PATH}/lib/x64/cuda.lib" /c/${{ github.run_id }}/build-results/
-          fi
-
-          if [ -f "${CUDA_PATH}/lib/x64/cudart.lib" ]; then
-            cp "${CUDA_PATH}/lib/x64/cudart.lib" /c/${{ github.run_id }}/build-results/
-          fi
-
-          # List collected files
-          echo "Collected CUDA libs:"
-          ls -lah /c/${{ github.run_id }}/build-results/*.lib
-
       # Upload to github so that people can click and download artifacts
       - name: Upload artifacts to s3
         if: steps.build.outcome != 'skipped'

.github/workflows/b200-distributed.yml

@@ -1,62 +0,0 @@
-name: CI for distributed tests on B200
-
-on:
-  pull_request:
-    paths:
-      - .github/workflows/b200-distributed.yml
-  workflow_dispatch:
-  push:
-    tags:
-      - ciflow/b200-distributed/*
-  schedule:
-    - cron: 46 8 * * *  # about 1:46am PDT
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-
-  get-label-type:
-    if: github.repository_owner == 'pytorch'
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
-  linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-build-distributed-b200
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "distributed", shard: 1, num_shards: 2, runner: "linux.dgx.b200.8" },
-          { config: "distributed", shard: 2, num_shards: 2, runner: "linux.dgx.b200.8" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-cuda12_8-py3_10-gcc11-test-distributed-b200:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-test-b200
-    uses: ./.github/workflows/_linux-test.yml
-    needs:
-      - linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200
-    with:
-      timeout-minutes: 1200
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit

.github/workflows/build-vllm-wheel.yml

@@ -27,8 +27,9 @@ jobs:
       fail-fast: false
       matrix:
         python-version: [ '3.12' ]
+        # TODO (huydhn): Add cu130 after https://github.com/vllm-project/vllm/issues/24464 is resolved
         platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
-        device: [ 'cu128', 'cu129', 'cu130' ]
+        device: [ 'cu128', 'cu129' ]
         include:
           - platform: manylinux_2_28_x86_64
             device: cu128
@@ -38,10 +39,6 @@ jobs:
             device: cu129
             manylinux-image: 'pytorch/manylinux2_28-builder:cuda12.9'
             runner: linux.12xlarge.memory
-          - platform: manylinux_2_28_x86_64
-            device: cu130
-            manylinux-image: 'pytorch/manylinux2_28-builder:cuda13.0'
-            runner: linux.12xlarge.memory
           - platform: manylinux_2_28_aarch64
             device: cu128
             manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.8'
@@ -50,11 +47,6 @@ jobs:
             device: cu129
             manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.9'
             runner: linux.arm64.r7g.12xlarge.memory
-        exclude:
-          # TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
-          # xformers is update to support 13.0
-          - platform: manylinux_2_28_aarch64
-            device: cu130
     name: "Build ${{ matrix.device }} vLLM wheel on ${{ matrix.platform }}"
     runs-on: ${{ matrix.runner }}
     timeout-minutes: 480
@@ -177,12 +169,7 @@ jobs:
       fail-fast: false
       matrix:
         platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
-        device: [ 'cu128', 'cu129', 'cu130' ]
-        exclude:
-          # TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
-          # xformers is update to support 13.0
-          - platform: manylinux_2_28_aarch64
-            device: cu130
+        device: [ 'cu128', 'cu129' ]
     env:
       PLATFORM: ${{ matrix.platform }}
       BUILD_DEVICE: ${{ matrix.device }}

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

@@ -224,7 +224,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -473,7 +473,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -722,7 +722,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -971,7 +971,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1220,7 +1220,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1469,7 +1469,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1718,7 +1718,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       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-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_10-cuda12_9-test:  # Testing
@@ -925,7 +925,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_11-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_11-cuda12_9-test:  # Testing
@@ -1591,7 +1591,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_12-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_12-cuda12_9-test:  # Testing
@@ -2257,7 +2257,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13-cuda12_9-test:  # Testing
@@ -2923,7 +2923,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13t-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13t-cuda12_9-test:  # Testing
@@ -3589,7 +3589,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14-cuda12_9-test:  # Testing
@@ -4255,7 +4255,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14t-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14t-cuda12_9-test:  # Testing

.github/workflows/generated-macos-arm64-binary-libtorch-release-nightly.yml

@@ -63,6 +63,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.10.4"
           freethreaded: false

.github/workflows/generated-macos-arm64-binary-wheel-nightly.yml

@@ -59,6 +59,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.10.4"
           freethreaded: false
@@ -168,6 +169,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.11.4"
           freethreaded: false
@@ -277,6 +279,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.12.4"
           freethreaded: false
@@ -386,6 +389,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.13.4"
           freethreaded: false
@@ -495,6 +499,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.13.4"
           freethreaded: true
@@ -604,8 +609,9 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "3.14.0"
+          python-version: "3.14.0-rc.2"
           freethreaded: false
       - name: Checkout PyTorch
         uses: actions/checkout@v4
@@ -713,8 +719,9 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "3.14.0"
+          python-version: "3.14.0-rc.2"
           freethreaded: true
       - name: Checkout PyTorch
         uses: actions/checkout@v4

.github/workflows/generated-windows-binary-libtorch-debug-nightly.yml

@@ -44,7 +44,7 @@ jobs:
   libtorch-cpu-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -291,7 +291,7 @@ jobs:
   libtorch-cuda12_6-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -541,7 +541,7 @@ jobs:
   libtorch-cuda12_8-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -788,10 +788,260 @@ jobs:
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
+  libtorch-cuda12_9-shared-with-deps-debug-build:
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs: get-label-type
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
+    timeout-minutes: 360
+    env:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      SKIP_ALL_TESTS: 1
+      LIBTORCH_CONFIG: debug
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+    steps:
+      # NOTE: These environment variables are put here so that they can be applied on every job equally
+      #       They are also here because setting them at a workflow level doesn't give us access to the
+      #       runner.temp variable, which we need.
+      - name: Populate binary env
+        shell: bash
+        run: |
+          echo "BINARY_ENV_FILE=${RUNNER_TEMP}/env" >> "${GITHUB_ENV}"
+          echo "PYTORCH_FINAL_PACKAGE_DIR=${RUNNER_TEMP}/artifacts" >> "${GITHUB_ENV}"
+          echo "WIN_PACKAGE_WORK_DIR=${RUNNER_TEMP}"
+      - name: Display EC2 information
+        shell: bash
+        run: |
+          set -euo pipefail
+          function get_ec2_metadata() {
+            # Pulled from instance metadata endpoint for EC2
+            # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
+            category=$1
+            curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
+          }
+          echo "ami-id: $(get_ec2_metadata ami-id)"
+          echo "instance-id: $(get_ec2_metadata instance-id)"
+          echo "instance-type: $(get_ec2_metadata instance-type)"
+          echo "system info $(uname -a)"
+      - name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
+        uses: pytorch/test-infra/.github/actions/setup-ssh@main
+        continue-on-error: true
+        with:
+          github-secret: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable git long paths and symlinks on Windows and disable fsmonitor daemon
+        shell: bash
+        run: |
+          git config --global core.longpaths true
+          git config --global core.symlinks true
+
+          # https://git-scm.com/docs/git-fsmonitor--daemon.  The daemon could lock
+          # the directory on Windows and prevent GHA from checking out as reported
+          # in https://github.com/actions/checkout/issues/1018
+          git config --global core.fsmonitor false
+      # Needed for binary builds, see: https://github.com/pytorch/pytorch/issues/73339#issuecomment-1058981560
+      - name: Enable long paths on Windows
+        shell: powershell
+        run: |
+          Set-ItemProperty -Path "HKLM:\\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1
+      # Since it's just a defensive command, the workflow should continue even the command fails. This step can be
+      # removed once Windows Defender is removed from the AMI
+      - name: Disables Windows Defender scheduled and real-time scanning for files in directories used by PyTorch
+        continue-on-error: true
+        shell: powershell
+        run: |
+          Add-MpPreference -ExclusionPath $(Get-Location).tostring(),$Env:TEMP -ErrorAction Ignore
+          # Let's both exclude the path and disable Windows Defender completely just to be sure
+          # that it doesn't interfere
+          Set-MpPreference -DisableRealtimeMonitoring $True -ErrorAction Ignore
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      - name: Populate binary env
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_populate_env.sh"
+      - name: Build PyTorch binary
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
+      - uses: actions/upload-artifact@v4.4.0
+        if: always()
+        with:
+          name: libtorch-cuda12_9-shared-with-deps-debug
+          retention-days: 14
+          if-no-files-found: error
+          path: "${{ env.PYTORCH_FINAL_PACKAGE_DIR }}"
+      - name: Wait until all sessions have drained
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        timeout-minutes: 120
+        run: |
+          .github\scripts\wait_for_ssh_to_drain.ps1
+      - name: Kill active ssh sessions if still around (Useful if workflow was cancelled)
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        run: |
+          .github\scripts\kill_active_ssh_sessions.ps1
+
+  libtorch-cuda12_9-shared-with-deps-debug-test:  # Testing
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs:
+      - libtorch-cuda12_9-shared-with-deps-debug-build
+      - get-label-type
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.g4dn.xlarge"
+    timeout-minutes: 360
+    env:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      SKIP_ALL_TESTS: 1
+      LIBTORCH_CONFIG: debug
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+    steps:
+      - name: Display EC2 information
+        shell: bash
+        run: |
+          set -euo pipefail
+          function get_ec2_metadata() {
+            # Pulled from instance metadata endpoint for EC2
+            # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
+            category=$1
+            curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
+          }
+          echo "ami-id: $(get_ec2_metadata ami-id)"
+          echo "instance-id: $(get_ec2_metadata instance-id)"
+          echo "instance-type: $(get_ec2_metadata instance-type)"
+          echo "system info $(uname -a)"
+      - name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
+        uses: pytorch/test-infra/.github/actions/setup-ssh@main
+        continue-on-error: true
+        with:
+          github-secret: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable git long paths and symlinks on Windows and disable fsmonitor daemon
+        shell: bash
+        run: |
+          git config --global core.longpaths true
+          git config --global core.symlinks true
+
+          # https://git-scm.com/docs/git-fsmonitor--daemon.  The daemon could lock
+          # the directory on Windows and prevent GHA from checking out as reported
+          # in https://github.com/actions/checkout/issues/1018
+          git config --global core.fsmonitor false
+      # Needed for binary builds, see: https://github.com/pytorch/pytorch/issues/73339#issuecomment-1058981560
+      - name: Enable long paths on Windows
+        shell: powershell
+        run: |
+          Set-ItemProperty -Path "HKLM:\\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1
+      # Since it's just a defensive command, the workflow should continue even the command fails. This step can be
+      # removed once Windows Defender is removed from the AMI
+      - name: Disables Windows Defender scheduled and real-time scanning for files in directories used by PyTorch
+        continue-on-error: true
+        shell: powershell
+        run: |
+          Add-MpPreference -ExclusionPath $(Get-Location).tostring(),$Env:TEMP -ErrorAction Ignore
+          # Let's both exclude the path and disable Windows Defender completely just to be sure
+          # that it doesn't interfere
+          Set-MpPreference -DisableRealtimeMonitoring $True -ErrorAction Ignore
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      # NOTE: These environment variables are put here so that they can be applied on every job equally
+      #       They are also here because setting them at a workflow level doesn't give us access to the
+      #       runner.temp variable, which we need.
+      - name: Populate binary env
+        shell: bash
+        run: |
+          echo "BINARY_ENV_FILE=${RUNNER_TEMP}/env" >> "${GITHUB_ENV}"
+          echo "PYTORCH_FINAL_PACKAGE_DIR=${RUNNER_TEMP}/artifacts" >> "${GITHUB_ENV}"
+          echo "WIN_PACKAGE_WORK_DIR=${RUNNER_TEMP}"
+      - uses: actions/download-artifact@v4.1.7
+        name: Download Build Artifacts
+        with:
+          name: libtorch-cuda12_9-shared-with-deps-debug
+          path: "${{ env.PYTORCH_FINAL_PACKAGE_DIR }}"
+      - name: Populate binary env
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_populate_env.sh"
+      - name: Test PyTorch binary
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_windows_test.sh"
+      - name: Wait until all sessions have drained
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        timeout-minutes: 120
+        run: |
+          .github\scripts\wait_for_ssh_to_drain.ps1
+      - name: Kill active ssh sessions if still around (Useful if workflow was cancelled)
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        run: |
+          .github\scripts\kill_active_ssh_sessions.ps1
+  libtorch-cuda12_9-shared-with-deps-debug-upload:  # Uploading
+    if: ${{ github.repository_owner == 'pytorch' }}
+    permissions:
+      id-token: write
+      contents: read
+    needs: libtorch-cuda12_9-shared-with-deps-debug-test
+    with:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      LIBTORCH_CONFIG: debug
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+      build_name: libtorch-cuda12_9-shared-with-deps-debug
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+    uses: ./.github/workflows/_binary-upload.yml
   libtorch-cuda13_0-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch

.github/workflows/generated-windows-binary-libtorch-release-nightly.yml

@@ -44,7 +44,7 @@ jobs:
   libtorch-cpu-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -291,7 +291,7 @@ jobs:
   libtorch-cuda12_6-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -541,7 +541,7 @@ jobs:
   libtorch-cuda12_8-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -788,10 +788,260 @@ jobs:
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
+  libtorch-cuda12_9-shared-with-deps-release-build:
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs: get-label-type
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
+    timeout-minutes: 360
+    env:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      SKIP_ALL_TESTS: 1
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+    steps:
+      # NOTE: These environment variables are put here so that they can be applied on every job equally
+      #       They are also here because setting them at a workflow level doesn't give us access to the
+      #       runner.temp variable, which we need.
+      - name: Populate binary env
+        shell: bash
+        run: |
+          echo "BINARY_ENV_FILE=${RUNNER_TEMP}/env" >> "${GITHUB_ENV}"
+          echo "PYTORCH_FINAL_PACKAGE_DIR=${RUNNER_TEMP}/artifacts" >> "${GITHUB_ENV}"
+          echo "WIN_PACKAGE_WORK_DIR=${RUNNER_TEMP}"
+      - name: Display EC2 information
+        shell: bash
+        run: |
+          set -euo pipefail
+          function get_ec2_metadata() {
+            # Pulled from instance metadata endpoint for EC2
+            # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
+            category=$1
+            curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
+          }
+          echo "ami-id: $(get_ec2_metadata ami-id)"
+          echo "instance-id: $(get_ec2_metadata instance-id)"
+          echo "instance-type: $(get_ec2_metadata instance-type)"
+          echo "system info $(uname -a)"
+      - name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
+        uses: pytorch/test-infra/.github/actions/setup-ssh@main
+        continue-on-error: true
+        with:
+          github-secret: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable git long paths and symlinks on Windows and disable fsmonitor daemon
+        shell: bash
+        run: |
+          git config --global core.longpaths true
+          git config --global core.symlinks true
+
+          # https://git-scm.com/docs/git-fsmonitor--daemon.  The daemon could lock
+          # the directory on Windows and prevent GHA from checking out as reported
+          # in https://github.com/actions/checkout/issues/1018
+          git config --global core.fsmonitor false
+      # Needed for binary builds, see: https://github.com/pytorch/pytorch/issues/73339#issuecomment-1058981560
+      - name: Enable long paths on Windows
+        shell: powershell
+        run: |
+          Set-ItemProperty -Path "HKLM:\\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1
+      # Since it's just a defensive command, the workflow should continue even the command fails. This step can be
+      # removed once Windows Defender is removed from the AMI
+      - name: Disables Windows Defender scheduled and real-time scanning for files in directories used by PyTorch
+        continue-on-error: true
+        shell: powershell
+        run: |
+          Add-MpPreference -ExclusionPath $(Get-Location).tostring(),$Env:TEMP -ErrorAction Ignore
+          # Let's both exclude the path and disable Windows Defender completely just to be sure
+          # that it doesn't interfere
+          Set-MpPreference -DisableRealtimeMonitoring $True -ErrorAction Ignore
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      - name: Populate binary env
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_populate_env.sh"
+      - name: Build PyTorch binary
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
+      - uses: actions/upload-artifact@v4.4.0
+        if: always()
+        with:
+          name: libtorch-cuda12_9-shared-with-deps-release
+          retention-days: 14
+          if-no-files-found: error
+          path: "${{ env.PYTORCH_FINAL_PACKAGE_DIR }}"
+      - name: Wait until all sessions have drained
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        timeout-minutes: 120
+        run: |
+          .github\scripts\wait_for_ssh_to_drain.ps1
+      - name: Kill active ssh sessions if still around (Useful if workflow was cancelled)
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        run: |
+          .github\scripts\kill_active_ssh_sessions.ps1
+
+  libtorch-cuda12_9-shared-with-deps-release-test:  # Testing
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs:
+      - libtorch-cuda12_9-shared-with-deps-release-build
+      - get-label-type
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.g4dn.xlarge"
+    timeout-minutes: 360
+    env:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      SKIP_ALL_TESTS: 1
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+    steps:
+      - name: Display EC2 information
+        shell: bash
+        run: |
+          set -euo pipefail
+          function get_ec2_metadata() {
+            # Pulled from instance metadata endpoint for EC2
+            # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
+            category=$1
+            curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
+          }
+          echo "ami-id: $(get_ec2_metadata ami-id)"
+          echo "instance-id: $(get_ec2_metadata instance-id)"
+          echo "instance-type: $(get_ec2_metadata instance-type)"
+          echo "system info $(uname -a)"
+      - name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
+        uses: pytorch/test-infra/.github/actions/setup-ssh@main
+        continue-on-error: true
+        with:
+          github-secret: ${{ secrets.GITHUB_TOKEN }}
+      - name: Enable git long paths and symlinks on Windows and disable fsmonitor daemon
+        shell: bash
+        run: |
+          git config --global core.longpaths true
+          git config --global core.symlinks true
+
+          # https://git-scm.com/docs/git-fsmonitor--daemon.  The daemon could lock
+          # the directory on Windows and prevent GHA from checking out as reported
+          # in https://github.com/actions/checkout/issues/1018
+          git config --global core.fsmonitor false
+      # Needed for binary builds, see: https://github.com/pytorch/pytorch/issues/73339#issuecomment-1058981560
+      - name: Enable long paths on Windows
+        shell: powershell
+        run: |
+          Set-ItemProperty -Path "HKLM:\\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1
+      # Since it's just a defensive command, the workflow should continue even the command fails. This step can be
+      # removed once Windows Defender is removed from the AMI
+      - name: Disables Windows Defender scheduled and real-time scanning for files in directories used by PyTorch
+        continue-on-error: true
+        shell: powershell
+        run: |
+          Add-MpPreference -ExclusionPath $(Get-Location).tostring(),$Env:TEMP -ErrorAction Ignore
+          # Let's both exclude the path and disable Windows Defender completely just to be sure
+          # that it doesn't interfere
+          Set-MpPreference -DisableRealtimeMonitoring $True -ErrorAction Ignore
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      # NOTE: These environment variables are put here so that they can be applied on every job equally
+      #       They are also here because setting them at a workflow level doesn't give us access to the
+      #       runner.temp variable, which we need.
+      - name: Populate binary env
+        shell: bash
+        run: |
+          echo "BINARY_ENV_FILE=${RUNNER_TEMP}/env" >> "${GITHUB_ENV}"
+          echo "PYTORCH_FINAL_PACKAGE_DIR=${RUNNER_TEMP}/artifacts" >> "${GITHUB_ENV}"
+          echo "WIN_PACKAGE_WORK_DIR=${RUNNER_TEMP}"
+      - uses: actions/download-artifact@v4.1.7
+        name: Download Build Artifacts
+        with:
+          name: libtorch-cuda12_9-shared-with-deps-release
+          path: "${{ env.PYTORCH_FINAL_PACKAGE_DIR }}"
+      - name: Populate binary env
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_populate_env.sh"
+      - name: Test PyTorch binary
+        shell: bash
+        run: |
+          "${PYTORCH_ROOT}/.circleci/scripts/binary_windows_test.sh"
+      - name: Wait until all sessions have drained
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        timeout-minutes: 120
+        run: |
+          .github\scripts\wait_for_ssh_to_drain.ps1
+      - name: Kill active ssh sessions if still around (Useful if workflow was cancelled)
+        shell: powershell
+        working-directory: pytorch
+        if: always()
+        run: |
+          .github\scripts\kill_active_ssh_sessions.ps1
+  libtorch-cuda12_9-shared-with-deps-release-upload:  # Uploading
+    if: ${{ github.repository_owner == 'pytorch' }}
+    permissions:
+      id-token: write
+      contents: read
+    needs: libtorch-cuda12_9-shared-with-deps-release-test
+    with:
+      PYTORCH_ROOT: ${{ github.workspace }}/pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: cu129
+      GPU_ARCH_VERSION: "12.9"
+      GPU_ARCH_TYPE: cuda
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      # This is a dummy value for libtorch to work correctly with our batch scripts
+      # without this value pip does not get installed for some reason
+      DESIRED_PYTHON: "3.10"
+      build_name: libtorch-cuda12_9-shared-with-deps-release
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+    uses: ./.github/workflows/_binary-upload.yml
   libtorch-cuda13_0-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch

.github/workflows/inductor-perf-test-nightly-rocm-mi300.yml

@@ -1,132 +0,0 @@
-name: inductor-perf-nightly-rocm-mi300
-
-on:
-  push:
-    tags:
-      - ciflow/inductor-perf-test-nightly-rocm-mi300/*
-  schedule:
-    - cron: 15 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
-  workflow_dispatch:
-    inputs:
-      training:
-        description: Run training (on by default)?
-        required: false
-        type: boolean
-        default: true
-      inference:
-        description: Run inference (on by default)?
-        required: false
-        type: boolean
-        default: true
-      default:
-        description: Run inductor_default?
-        required: false
-        type: boolean
-        default: false
-      dynamic:
-        description: Run inductor_dynamic_shapes?
-        required: false
-        type: boolean
-        default: false
-      cppwrapper:
-        description: Run inductor_cpp_wrapper?
-        required: false
-        type: boolean
-        default: false
-      cudagraphs:
-        description: Run inductor_cudagraphs?
-        required: false
-        type: boolean
-        default: true
-      freezing_cudagraphs:
-        description: Run inductor_cudagraphs with freezing for inference?
-        required: false
-        type: boolean
-        default: false
-      aotinductor:
-        description: Run aot_inductor for inference?
-        required: false
-        type: boolean
-        default: false
-      maxautotune:
-        description: Run inductor_max_autotune?
-        required: false
-        type: boolean
-        default: false
-      benchmark_configs:
-        description: The list of configs used the benchmark
-        required: false
-        type: string
-        default: inductor_huggingface_perf_rocm_mi300,inductor_timm_perf_rocm_mi300,inductor_torchbench_perf_rocm_mi300
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions: read-all
-
-jobs:
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-      opt_out_experiments: lf
-
-  linux-jammy-rocm-py3_10-inductor-benchmark-build:
-    if: github.repository_owner == 'pytorch'
-    name: rocm-py3_10-inductor-benchmark-build
-    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: "inductor_huggingface_perf_rocm_mi300", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-inductor-benchmark-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: rocm-py3_10-inductor-benchmark-test
-    uses: ./.github/workflows/_rocm-test.yml
-    needs: linux-jammy-rocm-py3_10-inductor-benchmark-build
-    with:
-      build-environment: linux-jammy-rocm-py3_10
-      dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-cudagraphs_low_precision-true
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.test-matrix }}
-      timeout-minutes: 720
-      # Disable monitor in perf tests for more investigation
-      disable-monitor: true
-      monitor-log-interval: 10
-      monitor-data-collect-interval: 2
-    secrets: inherit

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

@@ -1,11 +1,11 @@
-name: inductor-perf-nightly-rocm-mi355
+name: inductor-perf-nightly-rocm
 
 on:
   push:
     tags:
-      - ciflow/inductor-perf-test-nightly-rocm-mi355/*
+      - ciflow/inductor-perf-test-nightly-rocm/*
   schedule:
-    - cron: 15 0 * * *
+    - cron: 0 7 * * 0,3
   # 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
   workflow_dispatch:
@@ -59,7 +59,7 @@ on:
         description: The list of configs used the benchmark
         required: false
         type: string
-        default: inductor_huggingface_perf_rocm_mi355,inductor_timm_perf_rocm_mi355,inductor_torchbench_perf_rocm_mi355
+        default: inductor_huggingface_perf_rocm,inductor_timm_perf_rocm,inductor_torchbench_perf_rocm
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
@@ -88,27 +88,23 @@ jobs:
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
       test-matrix: |
         { include: [
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.mi355.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 1, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 2, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 3, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 4, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 1, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 2, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 3, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 4, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 5, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 6, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 7, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 8, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
         ]}
     secrets: inherit
 

.github/workflows/lint.yml

@@ -118,9 +118,9 @@ jobs:
         CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
         echo "Running all other linters"
         if [ "$CHANGED_FILES" = '*' ]; then
-          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY --all-files" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT --all-files" .github/scripts/lintrunner.sh
         else
-          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT ${CHANGED_FILES}" .github/scripts/lintrunner.sh
         fi
 
   quick-checks:

.github/workflows/operator_benchmark.yml

@@ -7,11 +7,9 @@ on:
   workflow_dispatch:
     inputs:
       test_mode:
-        type: choice
-        options:
-          - 'short'
-          - 'long'
-          - 'all'
+        required: false
+        type: string
+        default: 'short'
         description: tag filter for operator benchmarks, options from long, short, all
   schedule:
     # Run at 07:00 UTC every Sunday
@@ -30,49 +28,38 @@ permissions:
   contents: read
 
 jobs:
-  x86-opbenchmark-build:
+  opbenchmark-build:
     if: github.repository_owner == 'pytorch'
-    name: x86-opbenchmark-build
+    name: opbenchmark-build
     uses: ./.github/workflows/_linux-build.yml
     with:
       build-environment: linux-jammy-py3.10-gcc11-build
       docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
       test-matrix: |
         { include: [
-          { config: "cpu_operator_benchmark_${{ inputs.test_mode || 'short' }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
+          { config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
         ]}
     secrets: inherit
 
-  x86-opbenchmark-test:
-    name: x86-opbenchmark-test
-    uses: ./.github/workflows/_linux-test.yml
-    needs: x86-opbenchmark-build
-    with:
-      build-environment: linux-jammy-py3.10-gcc11-build
-      docker-image: ${{ needs.x86-opbenchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.x86-opbenchmark-build.outputs.test-matrix }}
-    secrets: inherit
-
-  aarch64-opbenchmark-build:
-    if: github.repository_owner == 'pytorch'
-    name: aarch64-opbenchmark-build
+  opbenchmark-on-demand-build:
+    if: ${{ github.event_name == 'workflow_dispatch' && github.repository_owner == 'pytorch' }}
+    name: opbenchmark-on-demand-build
     uses: ./.github/workflows/_linux-build.yml
     with:
-      build-environment: linux-jammy-aarch64-py3.10
-      runner: linux.arm64.m7g.4xlarge
-      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
+      build-environment: linux-jammy-py3.10-gcc11-build
+      docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
       test-matrix: |
         { include: [
-          { config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.arm64.m8g.4xlarge" },
+          { config: "cpu_operator_benchmark_${{ inputs.test_mode }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
         ]}
     secrets: inherit
 
-  aarch64-opbenchmark-test:
-    name: aarch64-opbenchmark-test
+  opbenchmark-test:
+    name: opbenchmark-test
     uses: ./.github/workflows/_linux-test.yml
-    needs: aarch64-opbenchmark-build
+    needs: opbenchmark-build
     with:
-      build-environment: linux-jammy-aarch64-py3.10
-      docker-image: ${{ needs.aarch64-opbenchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.aarch64-opbenchmark-build.outputs.test-matrix }}
+      build-environment: linux-jammy-py3.10-gcc11-build
+      docker-image: ${{ needs.opbenchmark-build.outputs.docker-image }}
+      test-matrix: ${{ needs.opbenchmark-build.outputs.test-matrix }}
     secrets: inherit

.github/workflows/rocm-mi355.yml

@@ -45,12 +45,12 @@ jobs:
       sync-tag: rocm-build
       test-matrix: |
         { include: [
-          { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
-          { config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },
+          { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.mi355.2" },
         ]}
     secrets: inherit
 

.github/workflows/rocm-navi31.yml

@@ -1,63 +0,0 @@
-name: rocm-navi31
-
-on:
-  push:
-    tags:
-      - ciflow/rocm-navi31/*
-  workflow_dispatch:
-  schedule:
-    # We have several schedules so jobs can check github.event.schedule to activate only for a fraction of the runs.
-    # Also run less frequently on weekends.
-    - cron: 45 */2 * * 1-5
-    - cron: 45 4,12 * * 0,6
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions: read-all
-
-jobs:
-  target-determination:
-    if: github.repository_owner == 'pytorch'
-    name: before-test
-    uses: ./.github/workflows/target_determination.yml
-    permissions:
-      id-token: write
-      contents: read
-
-  linux-jammy-rocm-py3_10-build:
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    name: linux-jammy-rocm-py3.10
-    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
-      sync-tag: rocm-build
-      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" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    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: >-
-         ${{ github.event_name == 'schedule' && '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/rocm.yml

@@ -59,3 +59,29 @@ 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/trunk.yml

@@ -180,50 +180,16 @@ jobs:
       disable-monitor: false
     secrets: inherit
 
-  win-vs2022-cuda12_8-py3-build:
-    name: win-vs2022-cuda12.8-py3
+  win-vs2022-cuda12_6-py3-build:
+    name: win-vs2022-cuda12.6-py3
     uses: ./.github/workflows/_win-build.yml
     needs: get-label-type
     with:
-      build-environment: win-vs2022-cuda12.8-py3
-      cuda-version: "12.8"
+      build-environment: win-vs2022-cuda12.6-py3
+      cuda-version: "12.6"
       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" },
-        ]}
-    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"
-    secrets: inherit
-
   inductor-build:
     name: inductor-build
     uses: ./.github/workflows/_linux-build.yml
@@ -234,23 +200,6 @@ jobs:
       cuda-arch-list: '8.0'
     secrets: inherit
 
-  # Test cross-compiled models with Windows libs extracted from wheel
-  cross-compile-linux-test:
-    name: cross-compile-linux-test
-    uses: ./.github/workflows/_linux-test.yml
-    needs:
-      - linux-jammy-cuda12_8-py3_10-gcc11-build
-      - get-label-type
-      - win-vs2022-cuda12_8-py3-build
-    with:
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build.outputs.docker-image }}
-      test-matrix: |
-        { include: [
-          { config: "aoti_cross_compile_for_windows", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", win_torch_wheel_artifact: "win-vs2022-cuda12.8-py3" },
-        ]}
-    secrets: inherit
-
   verify-cachebench-cpu-build:
     name: verify-cachebench-cpu-build
     uses: ./.github/workflows/_linux-build.yml

.gitignore

@@ -374,7 +374,6 @@ third_party/ruy/
 third_party/glog/
 
 # Virtualenv
-.venv/
 venv/
 
 # Log files
@@ -396,4 +395,3 @@ android/pytorch_android_torchvision/.cxx
 CLAUDE.local.md
 /test_*.py
 /debug_*.py
-CLAUDE_CONTEXT/

.lintrunner.toml

@@ -209,46 +209,6 @@ command = [
     '@{{PATHSFILE}}'
 ]
 
-
-[[linter]]
-code = 'PYREFLY'
-include_patterns = [
-    'torch/**/*.py',
-    'torch/**/*.pyi',
-    'torchgen/**/*.py',
-    'torchgen/**/*.pyi',
-    'functorch/**/*.py',
-    'functorch/**/*.pyi',
-]
-exclude_patterns = []
-command = [
-    'python3',
-    'tools/linter/adapters/pyrefly_linter.py',
-    '--config=pyrefly.toml',
-]
-init_command = [
-    'python3',
-    'tools/linter/adapters/pip_init.py',
-    '--dry-run={{DRYRUN}}',
-    'numpy==2.1.0 ; python_version >= "3.12"',
-    'expecttest==0.3.0',
-    'pyrefly==0.36.2',
-    'sympy==1.13.3',
-    'types-requests==2.27.25',
-    'types-pyyaml==6.0.2',
-    'types-tabulate==0.8.8',
-    'types-protobuf==5.29.1.20250403',
-    'types-setuptools==79.0.0.20250422',
-    'types-jinja2==2.11.9',
-    'types-colorama==0.4.6',
-    'filelock==3.18.0',
-    'junitparser==2.1.1',
-    'rich==14.1.0',
-    'optree==0.17.0',
-    'types-openpyxl==3.1.5.20250919',
-    'types-python-dateutil==2.9.0.20251008'
-]
-
 [[linter]]
 code = 'CLANGTIDY'
 include_patterns = [

CODEOWNERS

@@ -201,17 +201,3 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
 /torch/csrc/stable/ @janeyx99 @mikaylagawarecki
 /torch/headeronly/ @janeyx99
 /torch/header_only_apis.txt @janeyx99
-
-# FlexAttention
-/torch/nn/attention/flex_attention.py @drisspg
-/torch/_higher_order_ops/flex_attention.py @drisspg
-/torch/_inductor/kernel/flex/ @drisspg
-/torch/_inductor/codegen/cpp_flex_attention_template.py @drisspg
-/test/inductor/test_flex_attention.py @drisspg
-/test/inductor/test_flex_decoding.py @drisspg
-
-# Low Precision GEMMs
-/aten/src/ATen/native/cuda/Blas.cpp @drisspg @slayton58
-/aten/src/ATen/cuda/CUDABlas.cpp @drisspg @slayton58
-/aten/src/ATen/cuda/CUDABlas.h @drisspg @slayton58
-/test/test_scaled_matmul_cuda.py @drisspg @slayton58

aten/src/ATen/CMakeLists.txt

@@ -256,7 +256,6 @@ endif()
 IF(USE_FBGEMM_GENAI)
   set(FBGEMM_THIRD_PARTY ${PROJECT_SOURCE_DIR}/third_party/fbgemm/external/)
   set(FBGEMM_GENAI_SRCS ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize)
-
   if(USE_CUDA)
     # To avoid increasing the build time/binary size unnecessarily, use an allow-list of kernels to build.
     # If you want to integrate a kernel from FBGEMM into torch, you have to add it here.
@@ -293,18 +292,15 @@ IF(USE_FBGEMM_GENAI)
       "${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
     )
 
-    target_include_directories(fbgemm_genai PRIVATE
+    target_include_directories(fbgemm_genai PUBLIC
       ${FBGEMM_THIRD_PARTY}/cutlass/include
       ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
       ${fbgemm_genai_mx8mx8bf16_grouped}
       ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
       ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
     )
-
-    # Add FBGEMM_GENAI include directories for torch_ops.h
-    list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
-    list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
-  elseif(USE_ROCM)
+  else()
+    if(USE_ROCM)
       # Only include the kernels we want to build to avoid increasing binary size.
       file(GLOB_RECURSE fbgemm_genai_native_rocm_hip
         "${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/kernels/fp8_rowwise_grouped*.hip"
@@ -313,14 +309,13 @@ IF(USE_FBGEMM_GENAI)
 
       # Add additional HIPCC compiler flags for performance
       set(FBGEMM_GENAI_EXTRA_HIPCC_FLAGS
+        -mllvm
+        -amdgpu-coerce-illegal-types=1
         -mllvm
         -enable-post-misched=0
         -mllvm
         -greedy-reverse-local-assignment=1
         -fhip-new-launch-api)
-    if(DEFINED ROCM_VERSION_DEV AND ROCM_VERSION_DEV VERSION_LESS "7.2.0")
-        list(PREPEND FBGEMM_GENAI_EXTRA_HIPCC_FLAGS -mllvm -amdgpu-coerce-illegal-types=1)
-      endif()
 
       # Only compile for gfx942 for now.
       # This is rather hacky, I could not figure out a clean solution :(
@@ -339,7 +334,7 @@ IF(USE_FBGEMM_GENAI)
       set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
       target_compile_definitions(fbgemm_genai PRIVATE FBGEMM_GENAI_NO_EXTENDED_SHAPES)
 
-    target_include_directories(fbgemm_genai PRIVATE
+      target_include_directories(fbgemm_genai PUBLIC
         # FBGEMM version of Composable Kernel is used due to some customizations
         ${FBGEMM_THIRD_PARTY}/composable_kernel/include
         ${FBGEMM_THIRD_PARTY}/composable_kernel/library/include
@@ -348,10 +343,7 @@ IF(USE_FBGEMM_GENAI)
         ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
         ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
       )
-
-    # Add FBGEMM_GENAI include directories for torch_ops.h
-    list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
-    list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
+    endif()
   endif()
 endif()
 
@@ -700,6 +692,12 @@ if(USE_CUDA AND NOT USE_ROCM)
   list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/include)
   list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/tools/util/include)
 
+  # Add FBGEMM_GENAI include directories for torch_ops.h
+  if(USE_FBGEMM_GENAI)
+    list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
+    list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
+  endif()
+
   if($ENV{ATEN_STATIC_CUDA})
     if(CUDA_VERSION VERSION_LESS_EQUAL 12.9)
       list(APPEND ATen_CUDA_DEPENDENCY_LIBS

aten/src/ATen/DLConvertor.cpp

@@ -389,16 +389,37 @@ void fillVersion<DLManagedTensorVersioned>(
 // constructed out of ATen tensor
 template <class T>
 T* toDLPackImpl(const Tensor& src) {
+  auto view = src;
+
+  // Detect whether there is need to normalize the strides
+  // Background: gh-83069
+  //
+  // However, normalizing strides can come at a high-cost
+  // to slow down toDLPack conversion 3x, so we
+  // only normalize if needed.
+  //
+  // The following code detects whether the src follows
+  // a continuous pattern. If the src follows such pattern (common-case)
+  // then we do not need to normalize the strides.
+  bool need_normalize_strides = src.dim() == 1 && src.size(0) == 1 && src.stride(0) != 1;
+  // less common case, try normalizing the strides
+  if (need_normalize_strides) {
+    // create a new tensor with possibly normalized strides
+    // gh-83069
+    auto shape = src.sizes();
+    view = src.as_strided(shape, {1}, src.storage_offset());
+  }
+
   ATenDLMTensor<T>* atDLMTensor(new ATenDLMTensor<T>);
-  atDLMTensor->handle = src;
+  atDLMTensor->handle = view;
   atDLMTensor->tensor.manager_ctx = atDLMTensor;
   atDLMTensor->tensor.deleter = &deleter<T>;
-  atDLMTensor->tensor.dl_tensor.data = src.data_ptr();
+  atDLMTensor->tensor.dl_tensor.data = view.data_ptr();
   atDLMTensor->tensor.dl_tensor.device = torchDeviceToDLDevice(src.device());
   atDLMTensor->tensor.dl_tensor.ndim = static_cast<int32_t>(src.dim());
   atDLMTensor->tensor.dl_tensor.dtype = getDLDataType(src);
-  atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(src.sizes().data());
-  atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(src.strides().data());
+  atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(view.sizes().data());
+  atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(view.strides().data());
   atDLMTensor->tensor.dl_tensor.byte_offset = 0;
   fillVersion(&atDLMTensor->tensor);
 

aten/src/ATen/DLConvertor.h

@@ -52,16 +52,16 @@ struct DLPackTraits {};
 
 template <>
 struct DLPackTraits<DLManagedTensor> {
-  inline static constexpr const char* capsule = "dltensor";
-  inline static constexpr const char* used = "used_dltensor";
+  inline static const char* capsule = "dltensor";
+  inline static const char* used = "used_dltensor";
   inline static auto toDLPack = at::toDLPack;
   inline static auto fromDLPack = at::fromDLPack;
 };
 
 template <>
 struct DLPackTraits<DLManagedTensorVersioned> {
-  inline static constexpr const char* capsule = "dltensor_versioned";
-  inline static constexpr const char* used = "used_dltensor_versioned";
+  inline static const char* capsule = "dltensor_versioned";
+  inline static const char* used = "used_dltensor_versioned";
   inline static auto toDLPack = at::toDLPackVersioned;
   inline static auto fromDLPack = at::fromDLPackVersioned;
 };

aten/src/ATen/PythonTorchFunctionTLS.cpp

@@ -42,14 +42,8 @@ const PythonTorchFunctionTLS& PythonTorchFunctionTLS::get_state() {
 }
 
 bool torch_function_mode_enabled() {
-  // Manually flatten because gcc is refusing to inline here.  Note
-  // that we are still calling __tls_get_addr twice here with GCC,
-  // presumably because of
-  // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81501 (which says
-  // the fix ships in GCC 16), but forcing inlining still improves
-  // performance.
-  const auto& ptfs = pythonTorchFunctionState;
-  return ptfs.disabled_state_ != TorchFunctionDisabledState::ALL_DISABLED && !ptfs.stack_.empty();
+  return PythonTorchFunctionTLS::get_disabled_state() != TorchFunctionDisabledState::ALL_DISABLED &&
+         PythonTorchFunctionTLS::stack_len() > 0;
 }
 
 // This is needed to disambiguate the ternary torch function disabled states

aten/src/ATen/PythonTorchFunctionTLS.h

@@ -27,7 +27,6 @@ struct TORCH_API PythonTorchFunctionTLS {
   TorchFunctionDisabledState disabled_state_ =
       TorchFunctionDisabledState::ENABLED;
   std::vector<std::shared_ptr<c10::SafePyObject>> stack_;
-  friend TORCH_API bool torch_function_mode_enabled();
 };
 
 TORCH_API bool torch_function_mode_enabled();

aten/src/ATen/core/CachingHostAllocator.h

@@ -39,7 +39,7 @@ struct HostBlock {
 };
 
 template <typename B>
-struct alignas(hardware_destructive_interference_size) FreeBlockList {
+struct alignas(64) FreeBlockList {
   std::mutex mutex_;
   std::deque<B*> list_;
 };
@@ -122,7 +122,7 @@ struct TORCH_API HostStats {
 // Struct containing memory allocator summary statistics for host, as they
 // are staged for reporting. This is a temporary struct that is used to
 // avoid locking the allocator while collecting stats.
-struct alignas(hardware_destructive_interference_size) HostStatsStaged {
+struct alignas(64) HostStatsStaged {
   std::mutex timing_mutex_;
   // COUNT: total allocations (active + free)
   // LOCK: access to this stat is protected by the allocator's blocks_mutex_
@@ -669,7 +669,7 @@ struct CachingHostAllocatorImpl {
     TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for query_event");
   }
 
-  alignas(hardware_destructive_interference_size) std::mutex blocks_mutex_;
+  alignas(64) std::mutex blocks_mutex_;
   ska::flat_hash_set<B*> blocks_; // block list
   ska::flat_hash_map<void*, B*> ptr_to_block_;
 
@@ -677,17 +677,17 @@ struct CachingHostAllocatorImpl {
   // size. This allows us to quickly find a free block of the right size.
   // We use deque to store per size free list and guard the list with its own
   // mutex.
-  alignas(hardware_destructive_interference_size) std::vector<FreeBlockList<B>> free_list_ =
+  alignas(64) std::vector<FreeBlockList<B>> free_list_ =
       std::vector<FreeBlockList<B>>(MAX_SIZE_INDEX);
 
-  alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
+  alignas(64) std::mutex events_mutex_;
   std::deque<std::pair<E, B*>> events_; // event queue paired with block
 
   // Indicates whether the object is active.
   // Set to false in the destructor to signal background threads to stop.
   std::atomic<bool> active_{true};
 protected:
-  alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
+  alignas(64) HostStatsStaged stats_;
 };
 
 struct TORCH_API HostAllocator : public at::Allocator {

aten/src/ATen/core/PhiloxRNGEngine.h

@@ -229,10 +229,10 @@ private:
   }
 
 
-  static constexpr uint32_t kPhilox10A = 0x9E3779B9;
-  static constexpr uint32_t kPhilox10B = 0xBB67AE85;
-  static constexpr uint32_t kPhiloxSA = 0xD2511F53;
-  static constexpr uint32_t kPhiloxSB = 0xCD9E8D57;
+  static const uint32_t kPhilox10A = 0x9E3779B9;
+  static const uint32_t kPhilox10B = 0xBB67AE85;
+  static const uint32_t kPhiloxSA = 0xD2511F53;
+  static const uint32_t kPhiloxSB = 0xCD9E8D57;
 };
 
 typedef philox_engine Philox4_32;

aten/src/ATen/core/ivalue.h

@@ -624,14 +624,7 @@ struct TORCH_API IValue final {
   IValue(const c10::SymBool& i) {
     if (auto mi = i.maybe_as_bool()) {
       tag = Tag::Bool;
-#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
       payload.u.as_int = *mi;
-#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-      /* due to byteorder if value assigned as_int, as_bool actually is not set correctly */
-      payload.u.as_bool = *mi;
-#else
-#error Unexpected or undefined __BYTE_ORDER__
-#endif
     } else {
       tag = Tag::SymBool;
       payload.u.as_intrusive_ptr = i.toSymNodeImpl().release();

aten/src/ATen/cpu/vec/vec128/vec128.h

@@ -8,7 +8,6 @@
 #include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_float_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_half_neon.h>
-#include <ATen/cpu/vec/vec128/vec128_int_aarch64.h>
 #endif
 
 #include <ATen/cpu/vec/vec128/vec128_convert.h>

aten/src/ATen/cpu/vec/vec128/vec128_int_aarch64.h

@@ -1,794 +0,0 @@
-#pragma once
-
-#include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/vec_base.h>
-#include <c10/macros/Macros.h>
-#include <c10/util/irange.h>
-
-namespace at::vec {
-// Note [CPU_CAPABILITY namespace]
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// This header, and all of its subheaders, will be compiled with
-// different architecture flags for each supported set of vector
-// intrinsics. So we need to make sure they aren't inadvertently
-// linked together. We do this by declaring objects in an `inline
-// namespace` which changes the name mangling, but can still be
-// accessed as `at::vec`.
-inline namespace CPU_CAPABILITY {
-
-#define VEC_INT_NEON_TEMPLATE(vl, bit)                                        \
-  template <>                                                                 \
-  struct is_vec_specialized_for<int##bit##_t> : std::bool_constant<true> {};  \
-                                                                              \
-  template <>                                                                 \
-  class Vectorized<int##bit##_t> {                                            \
-    using neon_type = int##bit##x##vl##_t;                                    \
-                                                                              \
-   private:                                                                   \
-    neon_type values;                                                         \
-                                                                              \
-   public:                                                                    \
-    using value_type = int##bit##_t;                                          \
-    using size_type = int;                                                    \
-    static constexpr size_type size() {                                       \
-      return vl;                                                              \
-    }                                                                         \
-    Vectorized() {                                                            \
-      values = vdupq_n_s##bit(0);                                             \
-    }                                                                         \
-    Vectorized(neon_type v) : values(v) {}                                    \
-    Vectorized(int##bit##_t val);                                             \
-    template <                                                                \
-        typename... Args,                                                     \
-        typename = std::enable_if_t<(sizeof...(Args) == size())>>             \
-    Vectorized(Args... vals) {                                                \
-      __at_align__ int##bit##_t buffer[size()] = {vals...};                   \
-      values = vld1q_s##bit(buffer);                                          \
-    }                                                                         \
-    operator neon_type() const {                                              \
-      return values;                                                          \
-    }                                                                         \
-    static Vectorized<int##bit##_t> loadu(                                    \
-        const void* ptr,                                                      \
-        int64_t count = size());                                              \
-    void store(void* ptr, int64_t count = size()) const;                      \
-    template <int64_t mask>                                                   \
-    static Vectorized<int##bit##_t> blend(                                    \
-        const Vectorized<int##bit##_t>& a,                                    \
-        const Vectorized<int##bit##_t>& b);                                   \
-    static Vectorized<int##bit##_t> blendv(                                   \
-        const Vectorized<int##bit##_t>& a,                                    \
-        const Vectorized<int##bit##_t>& b,                                    \
-        const Vectorized<int##bit##_t>& mask_) {                              \
-      return vbslq_s##bit(vreinterpretq_u##bit##_s##bit(mask_.values), b, a); \
-    }                                                                         \
-    template <typename step_t>                                                \
-    static Vectorized<int##bit##_t> arange(                                   \
-        value_type base = 0,                                                  \
-        step_t step = static_cast<step_t>(1));                                \
-    static Vectorized<int##bit##_t> set(                                      \
-        const Vectorized<int##bit##_t>& a,                                    \
-        const Vectorized<int##bit##_t>& b,                                    \
-        int64_t count = size());                                              \
-    const int##bit##_t& operator[](int idx) const = delete;                   \
-    int##bit##_t& operator[](int idx) = delete;                               \
-    Vectorized<int##bit##_t> abs() const {                                    \
-      return vabsq_s##bit(values);                                            \
-    }                                                                         \
-    Vectorized<int##bit##_t> real() const {                                   \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<int##bit##_t> imag() const {                                   \
-      return vdupq_n_s##bit(0);                                               \
-    }                                                                         \
-    Vectorized<int##bit##_t> conj() const {                                   \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<int##bit##_t> neg() const {                                    \
-      return vnegq_s##bit(values);                                            \
-    }                                                                         \
-    int##bit##_t reduce_add() const {                                         \
-      return vaddvq_s##bit(values);                                           \
-    }                                                                         \
-    int##bit##_t reduce_max() const;                                          \
-    Vectorized<int##bit##_t> operator==(                                      \
-        const Vectorized<int##bit##_t>& other) const {                        \
-      return Vectorized<value_type>(                                          \
-          vreinterpretq_s##bit##_u##bit(vceqq_s##bit(values, other.values))); \
-    }                                                                         \
-    Vectorized<int##bit##_t> operator!=(                                      \
-        const Vectorized<int##bit##_t>& other) const;                         \
-    Vectorized<int##bit##_t> operator<(                                       \
-        const Vectorized<int##bit##_t>& other) const {                        \
-      return Vectorized<value_type>(                                          \
-          vreinterpretq_s##bit##_u##bit(vcltq_s##bit(values, other.values))); \
-    }                                                                         \
-    Vectorized<int##bit##_t> operator<=(                                      \
-        const Vectorized<int##bit##_t>& other) const {                        \
-      return Vectorized<value_type>(                                          \
-          vreinterpretq_s##bit##_u##bit(vcleq_s##bit(values, other.values))); \
-    }                                                                         \
-    Vectorized<int##bit##_t> operator>(                                       \
-        const Vectorized<int##bit##_t>& other) const {                        \
-      return Vectorized<value_type>(                                          \
-          vreinterpretq_s##bit##_u##bit(vcgtq_s##bit(values, other.values))); \
-    }                                                                         \
-    Vectorized<int##bit##_t> operator>=(                                      \
-        const Vectorized<int##bit##_t>& other) const {                        \
-      return Vectorized<value_type>(                                          \
-          vreinterpretq_s##bit##_u##bit(vcgeq_s##bit(values, other.values))); \
-    }                                                                         \
-    Vectorized<int##bit##_t> eq(const Vectorized<int##bit##_t>& other) const; \
-    Vectorized<int##bit##_t> ne(const Vectorized<int##bit##_t>& other) const; \
-    Vectorized<int##bit##_t> gt(const Vectorized<int##bit##_t>& other) const; \
-    Vectorized<int##bit##_t> ge(const Vectorized<int##bit##_t>& other) const; \
-    Vectorized<int##bit##_t> lt(const Vectorized<int##bit##_t>& other) const; \
-    Vectorized<int##bit##_t> le(const Vectorized<int##bit##_t>& other) const; \
-  };                                                                          \
-  template <>                                                                 \
-  Vectorized<int##bit##_t> inline operator+(                                  \
-      const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
-    return vaddq_s##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<int##bit##_t> inline operator-(                                  \
-      const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
-    return vsubq_s##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<int##bit##_t> inline operator&(                                  \
-      const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
-    return vandq_s##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<int##bit##_t> inline operator|(                                  \
-      const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
-    return vorrq_s##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<int##bit##_t> inline operator^(                                  \
-      const Vectorized<int##bit##_t>& a, const Vectorized<int##bit##_t>& b) { \
-    return veorq_s##bit(a, b);                                                \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::eq(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this == other) & Vectorized<int##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::ne(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this != other) & Vectorized<int##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::gt(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this > other) & Vectorized<int##bit##_t>(1);                     \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::ge(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this >= other) & Vectorized<int##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::lt(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this < other) & Vectorized<int##bit##_t>(1);                     \
-  }                                                                           \
-  Vectorized<int##bit##_t> inline Vectorized<int##bit##_t>::le(               \
-      const Vectorized<int##bit##_t>& other) const {                          \
-    return (*this <= other) & Vectorized<int##bit##_t>(1);                    \
-  }
-
-VEC_INT_NEON_TEMPLATE(2, 64)
-VEC_INT_NEON_TEMPLATE(4, 32)
-VEC_INT_NEON_TEMPLATE(8, 16)
-VEC_INT_NEON_TEMPLATE(16, 8)
-
-inline int32_t Vectorized<int32_t>::reduce_max() const {
-  return vmaxvq_s32(values);
-}
-
-inline int16_t Vectorized<int16_t>::reduce_max() const {
-  return vmaxvq_s16(values);
-}
-
-inline int8_t Vectorized<int8_t>::reduce_max() const {
-  return vmaxvq_s8(values);
-}
-
-template <>
-Vectorized<int32_t> inline operator*(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  return vmulq_s32(a, b);
-}
-
-template <>
-Vectorized<int16_t> inline operator*(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  return vmulq_s16(a, b);
-}
-
-template <>
-Vectorized<int8_t> inline operator*(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  return vmulq_s8(a, b);
-}
-
-template <>
-inline Vectorized<int64_t> operator~(const Vectorized<int64_t>& a) {
-  int64x2_t val = a;
-  return ~val;
-}
-
-template <>
-inline Vectorized<int32_t> operator~(const Vectorized<int32_t>& a) {
-  return vmvnq_s32(a);
-}
-
-template <>
-inline Vectorized<int16_t> operator~(const Vectorized<int16_t>& a) {
-  return vmvnq_s16(a);
-}
-
-template <>
-inline Vectorized<int8_t> operator~(const Vectorized<int8_t>& a) {
-  return vmvnq_s8(a);
-}
-
-inline Vectorized<int64_t> Vectorized<int64_t>::operator!=(
-    const Vectorized<int64_t>& other) const {
-  return ~(*this == other);
-}
-
-inline Vectorized<int32_t> Vectorized<int32_t>::operator!=(
-    const Vectorized<int32_t>& other) const {
-  return ~(*this == other);
-}
-
-inline Vectorized<int16_t> Vectorized<int16_t>::operator!=(
-    const Vectorized<int16_t>& other) const {
-  return ~(*this == other);
-}
-
-inline Vectorized<int8_t> Vectorized<int8_t>::operator!=(
-    const Vectorized<int8_t>& other) const {
-  return ~(*this == other);
-}
-
-template <>
-Vectorized<int32_t> inline minimum(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  return vminq_s32(a, b);
-}
-
-template <>
-Vectorized<int16_t> inline minimum(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  return vminq_s16(a, b);
-}
-
-template <>
-Vectorized<int8_t> inline minimum(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  return vminq_s8(a, b);
-}
-
-template <>
-Vectorized<int32_t> inline maximum(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  return vmaxq_s32(a, b);
-}
-
-template <>
-Vectorized<int16_t> inline maximum(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  return vmaxq_s16(a, b);
-}
-
-template <>
-Vectorized<int8_t> inline maximum(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  return vmaxq_s8(a, b);
-}
-
-template <int64_t mask>
-Vectorized<int64_t> Vectorized<int64_t>::blend(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint64x2_t maskArray = {
-      (mask & 1LL) ? 0xFFFFFFFFFFFFFFFF : 0,
-      (mask & 2LL) ? 0xFFFFFFFFFFFFFFFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_s64(maskArray, b.values, a.values);
-}
-
-template <int64_t mask>
-Vectorized<int32_t> Vectorized<int32_t>::blend(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint32x4_t maskArray = {
-      (mask & 1LL) ? 0xFFFFFFFF : 0,
-      (mask & 2LL) ? 0xFFFFFFFF : 0,
-      (mask & 4LL) ? 0xFFFFFFFF : 0,
-      (mask & 8LL) ? 0xFFFFFFFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_s32(maskArray, b.values, a.values);
-}
-
-template <int64_t mask>
-Vectorized<int16_t> Vectorized<int16_t>::blend(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint16x8_t maskArray = {
-      (mask & 1LL) ? 0xFFFF : 0,
-      (mask & 2LL) ? 0xFFFF : 0,
-      (mask & 4LL) ? 0xFFFF : 0,
-      (mask & 8LL) ? 0xFFFF : 0,
-      (mask & 16LL) ? 0xFFFF : 0,
-      (mask & 32LL) ? 0xFFFF : 0,
-      (mask & 64LL) ? 0xFFFF : 0,
-      (mask & 128LL) ? 0xFFFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_s16(maskArray, b.values, a.values);
-}
-
-template <int64_t mask>
-Vectorized<int8_t> Vectorized<int8_t>::blend(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint8x16_t maskArray = {
-      (mask & 1LL) ? 0xFF : 0,
-      (mask & 2LL) ? 0xFF : 0,
-      (mask & 4LL) ? 0xFF : 0,
-      (mask & 8LL) ? 0xFF : 0,
-      (mask & 16LL) ? 0xFF : 0,
-      (mask & 32LL) ? 0xFF : 0,
-      (mask & 64LL) ? 0xFF : 0,
-      (mask & 128LL) ? 0xFF : 0,
-      (mask & 256LL) ? 0xFF : 0,
-      (mask & 512LL) ? 0xFF : 0,
-      (mask & 1024LL) ? 0xFF : 0,
-      (mask & 2048LL) ? 0xFF : 0,
-      (mask & 4096LL) ? 0xFF : 0,
-      (mask & 8192LL) ? 0xFF : 0,
-      (mask & 16384LL) ? 0xFF : 0,
-      (mask & 32768LL) ? 0xFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_s8(maskArray, b.values, a.values);
-}
-
-#define VEC_INT_NEON_OPS(vl, bit)                                             \
-  inline Vectorized<int##bit##_t>::Vectorized(int##bit##_t val) {             \
-    values = vdupq_n_s##bit(val);                                             \
-  }                                                                           \
-  inline Vectorized<int##bit##_t> Vectorized<int##bit##_t>::loadu(            \
-      const void* ptr, int64_t count) {                                       \
-    if (count == size()) {                                                    \
-      return vld1q_s##bit(reinterpret_cast<const int##bit##_t*>(ptr));        \
-    } else {                                                                  \
-      __at_align__ int##bit##_t tmp_values[size()];                           \
-      for (const auto i : c10::irange(size())) {                              \
-        tmp_values[i] = 0;                                                    \
-      }                                                                       \
-      std::memcpy(                                                            \
-          tmp_values,                                                         \
-          reinterpret_cast<const int##bit##_t*>(ptr),                         \
-          count * sizeof(int##bit##_t));                                      \
-      return vld1q_s##bit(reinterpret_cast<const int##bit##_t*>(tmp_values)); \
-    }                                                                         \
-  }                                                                           \
-  inline void Vectorized<int##bit##_t>::store(void* ptr, int64_t count)       \
-      const {                                                                 \
-    if (count == size()) {                                                    \
-      vst1q_s##bit(reinterpret_cast<int##bit##_t*>(ptr), values);             \
-    } else {                                                                  \
-      int##bit##_t tmp_values[size()];                                        \
-      vst1q_s##bit(reinterpret_cast<int##bit##_t*>(tmp_values), values);      \
-      std::memcpy(ptr, tmp_values, count * sizeof(int##bit##_t));             \
-    }                                                                         \
-  }
-
-VEC_INT_NEON_OPS(2, 64)
-VEC_INT_NEON_OPS(4, 32)
-VEC_INT_NEON_OPS(8, 16)
-VEC_INT_NEON_OPS(16, 8)
-
-template <>
-Vectorized<int64_t> inline operator*(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t x = a;
-  int64x2_t y = b;
-  return x * y;
-}
-
-template <>
-Vectorized<int64_t> inline operator/(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t x = a;
-  int64x2_t y = b;
-  return x / y;
-}
-
-template <>
-Vectorized<int32_t> inline operator/(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  int32x4_t x = a;
-  int32x4_t y = b;
-  return x / y;
-}
-
-inline int64_t Vectorized<int64_t>::reduce_max() const {
-  return std::max(values[0], values[1]);
-}
-
-template <>
-Vectorized<int64_t> inline minimum(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t x = a;
-  int64x2_t y = b;
-  return {std::min(x[0], y[0]), std::min(x[1], y[1])};
-}
-
-template <>
-Vectorized<int64_t> inline maximum(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t x = a;
-  int64x2_t y = b;
-  return {std::max(x[0], y[0]), std::max(x[1], y[1])};
-}
-
-template <typename step_t>
-inline Vectorized<int64_t> Vectorized<int64_t>::arange(
-    int64_t base,
-    step_t step) {
-  const Vectorized<int64_t> base_vec(base);
-  const Vectorized<int64_t> step_vec(step);
-  const int64x2_t step_sizes = {0, 1};
-  return base_vec.values + step_sizes * step_vec.values;
-}
-
-template <typename step_t>
-inline Vectorized<int32_t> Vectorized<int32_t>::arange(
-    int32_t base,
-    step_t step) {
-  const Vectorized<int32_t> base_vec(base);
-  const Vectorized<int32_t> step_vec(step);
-  const int32x4_t step_sizes = {0, 1, 2, 3};
-  return vmlaq_s32(base_vec, step_sizes, step_vec);
-}
-
-template <typename step_t>
-inline Vectorized<int16_t> Vectorized<int16_t>::arange(
-    int16_t base,
-    step_t step) {
-  const Vectorized<int16_t> base_vec(base);
-  const Vectorized<int16_t> step_vec(step);
-  const int16x8_t step_sizes = {0, 1, 2, 3, 4, 5, 6, 7};
-  return vmlaq_s16(base_vec, step_sizes, step_vec);
-}
-
-template <typename step_t>
-inline Vectorized<int8_t> Vectorized<int8_t>::arange(int8_t base, step_t step) {
-  const Vectorized<int8_t> base_vec(base);
-  const Vectorized<int8_t> step_vec(step);
-  const int8x16_t step_sizes = {
-      0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
-  return vmlaq_s8(base_vec, step_sizes, step_vec);
-}
-
-template <>
-Vectorized<int64_t> inline operator>>(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t x = a;
-  int64x2_t y = b;
-  uint64x2_t u = vreinterpretq_u64_s64(y);
-  uint64x2_t z = {std::min(u[0], (uint64_t)63), std::min(u[1], (uint64_t)63)};
-  return x >> vreinterpretq_s64_u64(z);
-}
-
-template <>
-Vectorized<int32_t> inline operator>>(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  int32x4_t x = a;
-  int32x4_t y = b;
-  uint32x4_t bound = vdupq_n_u32(31);
-  uint32x4_t z = vminq_u32(vreinterpretq_u32_s32(y), bound);
-  return x >> vreinterpretq_s32_u32(z);
-}
-
-template <>
-Vectorized<int16_t> inline operator>>(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  int16x8_t x = a;
-  int16x8_t y = b;
-  uint16x8_t bound = vdupq_n_u16(15);
-  uint16x8_t z = vminq_u16(vreinterpretq_u16_s16(y), bound);
-  return x >> vreinterpretq_s16_u16(z);
-}
-
-template <>
-Vectorized<int8_t> inline operator>>(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  int8x16_t x = a;
-  int8x16_t y = b;
-  uint8x16_t bound = vdupq_n_u8(7);
-  int8x16_t z = vreinterpretq_s8_u8(vminq_u8(vreinterpretq_u8_s8(y), bound));
-  return x >> z;
-}
-
-template <>
-Vectorized<int64_t> inline operator<<(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b) {
-  int64x2_t y = b;
-  uint64x2_t u = vreinterpretq_u64_s64(y);
-  uint64x2_t z = {std::min(u[0], (uint64_t)64), std::min(u[1], (uint64_t)64)};
-  return vshlq_s64(a, vreinterpretq_s64_u64(z));
-}
-
-template <>
-Vectorized<int32_t> inline operator<<(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b) {
-  int32x4_t y = b;
-  uint32x4_t bound = vdupq_n_u32(32);
-  uint32x4_t z = vminq_u32(vreinterpretq_u32_s32(y), bound);
-  return vshlq_s32(a, vreinterpretq_s32_u32(z));
-}
-
-template <>
-Vectorized<int16_t> inline operator<<(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  int16x8_t y = b;
-  uint16x8_t bound = vdupq_n_u16(16);
-  uint16x8_t z = vminq_u16(vreinterpretq_u16_s16(y), bound);
-  return vshlq_s16(a, vreinterpretq_s16_u16(z));
-}
-
-template <>
-Vectorized<int8_t> inline operator<<(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  int8x16_t y = b;
-  uint8x16_t bound = vdupq_n_u8(8);
-  int8x16_t z = vreinterpretq_s8_u8(vminq_u8(vreinterpretq_u8_s8(y), bound));
-  return vshlq_s8(a, z);
-}
-
-inline Vectorized<int64_t> Vectorized<int64_t>::set(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& b,
-    int64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 2) {
-    return b;
-  } else {
-    int64x2_t c = {b.values[0], a.values[1]};
-    return c;
-  }
-}
-
-inline Vectorized<int32_t> Vectorized<int32_t>::set(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& b,
-    int64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 4) {
-    return b;
-  } else {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint32x4_t maskArray = {
-        (count >= 1LL) ? 0xFFFFFFFF : 0,
-        (count >= 2LL) ? 0xFFFFFFFF : 0,
-        (count >= 3LL) ? 0xFFFFFFFF : 0,
-        0};
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_s32(maskArray, b.values, a.values);
-  }
-}
-
-inline Vectorized<int16_t> Vectorized<int16_t>::set(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b,
-    int64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 8) {
-    return b;
-  } else {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint16x8_t maskArray = {
-        static_cast<uint16_t>((count >= 1LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 2LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 3LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 4LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 5LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 6LL) ? 0xFFFF : 0),
-        static_cast<uint16_t>((count >= 7LL) ? 0xFFFF : 0),
-        0};
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_s16(maskArray, b.values, a.values);
-  }
-}
-
-inline Vectorized<int8_t> Vectorized<int8_t>::set(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b,
-    int64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 16) {
-    return b;
-  } else {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint8x16_t maskArray = {
-        static_cast<uint8_t>((count >= 1LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 2LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 3LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 4LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 5LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 6LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 7LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 8LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 9LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 10LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 11LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 12LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 13LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 14LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 15LL) ? 0xFF : 0),
-        0};
-
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_s8(maskArray, b.values, a.values);
-  }
-}
-
-template <>
-Vectorized<int16_t> inline operator/(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& b) {
-  Vectorized<int32_t> highBitsA = vmovl_high_s16(a);
-  Vectorized<int32_t> highBitsB = vmovl_high_s16(b);
-  Vectorized<int32_t> lowBitsA = vmovl_s16(vget_low_s16(a));
-  Vectorized<int32_t> lowBitsB = vmovl_s16(vget_low_s16(b));
-  int32x4_t highBitsResult = highBitsA / highBitsB;
-  int32x4_t lowBitsResult = lowBitsA / lowBitsB;
-  return vuzp1q_s16(
-      vreinterpretq_s16_s32(lowBitsResult),
-      vreinterpretq_s16_s32(highBitsResult));
-}
-
-template <>
-Vectorized<int8_t> inline operator/(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& b) {
-  Vectorized<int16_t> highBitsA = vmovl_high_s8(a);
-  Vectorized<int16_t> highBitsB = vmovl_high_s8(b);
-  Vectorized<int16_t> lowBitsA = vmovl_s8(vget_low_s8(a));
-  Vectorized<int16_t> lowBitsB = vmovl_s8(vget_low_s8(b));
-  int16x8_t highBitsResult = highBitsA / highBitsB;
-  int16x8_t lowBitsResult = lowBitsA / lowBitsB;
-  return vuzp1q_s8(
-      vreinterpretq_s8_s16(lowBitsResult),
-      vreinterpretq_s8_s16(highBitsResult));
-}
-
-template <>
-Vectorized<int64_t> inline clamp(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& min,
-    const Vectorized<int64_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<int32_t> inline clamp(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& min,
-    const Vectorized<int32_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<int16_t> inline clamp(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& min,
-    const Vectorized<int16_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<int8_t> inline clamp(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& min,
-    const Vectorized<int8_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<int64_t> inline clamp_max(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<int32_t> inline clamp_max(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<int16_t> inline clamp_max(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<int8_t> inline clamp_max(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<int64_t> inline clamp_min(
-    const Vectorized<int64_t>& a,
-    const Vectorized<int64_t>& min) {
-  return maximum(min, a);
-}
-
-template <>
-Vectorized<int32_t> inline clamp_min(
-    const Vectorized<int32_t>& a,
-    const Vectorized<int32_t>& min) {
-  return maximum(min, a);
-}
-
-template <>
-Vectorized<int16_t> inline clamp_min(
-    const Vectorized<int16_t>& a,
-    const Vectorized<int16_t>& min) {
-  return maximum(min, a);
-}
-
-template <>
-Vectorized<int8_t> inline clamp_min(
-    const Vectorized<int8_t>& a,
-    const Vectorized<int8_t>& min) {
-  return maximum(min, a);
-}
-
-} // namespace CPU_CAPABILITY
-} // namespace at::vec

aten/src/ATen/cpu/vec/vec256/vec256_qint.h

@@ -1377,7 +1377,7 @@ Vectorized<c10::quint8> inline maximum(
 #if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
 std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
     at::vec::Vectorized<int8_t> src) {
-  auto s8x8 = vget_low_s8(src);
+  auto s8x8 = vld1_s8(src.operator const int8_t*());
   auto s16x8 = vmovl_s8(s8x8);
 
   auto s32x4_hi = vmovl_s16(vget_high_s16(s16x8));
@@ -1402,7 +1402,7 @@ std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
 
 Vectorized<float> inline convert_int8_half_register_to_float(
     at::vec::Vectorized<int8_t> src) {
-  auto s8x8 = vget_low_s8(src);
+  auto s8x8 = vld1_s8(src.operator const int8_t*());
   auto s16x8 = vmovl_s8(s8x8);
 
   auto s32x4_lo = vmovl_s16(vget_low_s16(s16x8));

aten/src/ATen/cuda/CUDABlas.cpp

@@ -16,8 +16,6 @@
 #include <c10/util/irange.h>
 #include <c10/core/ScalarType.h>
 
-#include <ATen/cuda/detail/BLASConstants.h>
-
 #ifdef USE_ROCM
 #include <c10/cuda/CUDAStream.h>
 #include <hipblaslt/hipblaslt-ext.hpp>
@@ -1956,15 +1954,13 @@ void scaled_gemm(
     const void *result_scale_ptr,
     int64_t result_ld,
     ScalarType result_dtype,
-    bool use_fast_accum,
-    const std::optional<Tensor>& alpha) {
+    bool use_fast_accum) {
   // Note: see `cublasCommonArgs` for various non-intuitive manupulations
   // of input arguments to this function.
   const auto computeType = CUBLAS_COMPUTE_32F;
   const auto scaleType = CUDA_R_32F;
-  // Note: alpha_val may change later depending on user-passed argument
-  float alpha_val = 1.0;
-  float beta_val = 0.0;
+  const float alpha_val = 1.0;
+  const float beta_val = 0.0;
   CuBlasLtMatmulDescriptor computeDesc(computeType, scaleType);
   computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSA, _cublasOpFromChar(transa));
   computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSB, _cublasOpFromChar(transb));
@@ -2035,33 +2031,6 @@ void scaled_gemm(
     computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_EPILOGUE, CUBLASLT_EPILOGUE_BIAS);
     computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, ScalarTypeToCudaDataType(bias_dtype));
   }
-
-  // Handle user-passed alpha
-  float *alpha_ptr = &alpha_val;
-  float *beta_ptr = &beta_val;
-
-  if (alpha.has_value()) {
-    auto& a = alpha.value();
-
-    // if device-tensor
-    if (a.is_cuda()) {
-      // NOTE: there are lifetime requirements on device-side pointers for alpha/beta -- the value must be
-      //       valid & correct until the cublas call finishes (not is scheduled like host-side values). Thus
-      //       we need to use allocations for alpha/beta that have some guarantees on lifetime - a statically
-      //       managed 4B buffer for alpha that we'll copy the passed alpha value into, and constant memory
-      //       for beta respectively.
-      float *user_alpha_ptr = at::cuda::detail::get_user_alpha_ptr();
-      at::Tensor user_alpha = at::from_blob(user_alpha_ptr, {1}, TensorOptions().device(kCUDA).dtype(kFloat));
-      user_alpha.copy_(a);
-      // Tell cublasLt we're using device-side pointers for alpha/beta
-      auto pointer_mode = CUBLASLT_POINTER_MODE_DEVICE;
-      computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_POINTER_MODE, pointer_mode);
-      alpha_ptr = user_alpha.data_ptr<float>();
-      beta_ptr = at::cuda::detail::get_cublas_device_zero();
-    } else {
-      alpha_val = a.item<float>();
-    }
-  }
     // For other data types, use the get_scale_mode function based on scaling type
     // The SCALE_MODE attrs only exist in cuBLAS 12.8+/ROCm 7.0 or in recent hipblaslt,
     // but we must invoke get_scale_mode anyways to trigger the version checks.
@@ -2079,7 +2048,6 @@ void scaled_gemm(
   cublasLtMatmulHeuristicResult_t heuristicResult = {};
   int returnedResult = 0;
   cublasLtHandle_t ltHandle = at::cuda::getCurrentCUDABlasLtHandle();
-
   TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
       ltHandle,
       computeDesc.descriptor(),
@@ -2120,10 +2088,10 @@ void scaled_gemm(
         auto is_valid_status = hipblaslt_ext::matmulIsAlgoSupported(
                 ltHandle,
                 computeDesc.descriptor(),
-                alpha_ptr,
+                &alpha_val,
                 Adesc.descriptor(),
                 Bdesc.descriptor(),
-                beta_ptr,
+                &beta_val,
                 Cdesc.descriptor(),
                 Ddesc.descriptor(),
                 all_algos[i].algo,
@@ -2142,14 +2110,17 @@ void scaled_gemm(
   cublasStatus_t cublasStatus = cublasLtMatmul(
       ltHandle,
       computeDesc.descriptor(),
-      alpha_ptr,
+      &alpha_val,
       mat1_ptr,
       Adesc.descriptor(),
       mat2_ptr,
       Bdesc.descriptor(),
-      beta_ptr,
-      // NOTE: always use result_ptr here, because cuBLASLt w/device beta=0 can't handle nullptr either
+      &beta_val,
+#ifdef USE_ROCM
       result_ptr, // unused, since beta_val is 0, but hipblaslt can't handle nullptr
+#else
+      nullptr,
+#endif // ifdef USE_ROCM
       Cdesc.descriptor(),
       result_ptr,
       Ddesc.descriptor(),

aten/src/ATen/cuda/CUDABlas.h

@@ -161,8 +161,7 @@ void scaled_gemm(
     const void* result_scale_ptr,
     int64_t result_ld,
     ScalarType result_dtype,
-    bool use_fast_accum,
-    const std::optional<Tensor>& alpha);
+    bool use_fast_accum);
 
 #define CUDABLAS_BGEMM_ARGTYPES(Dtype)  CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(Dtype, Dtype)
 

aten/src/ATen/cuda/CUDAGeneratorImpl.cpp

@@ -325,9 +325,9 @@ uint64_t CUDAGeneratorImpl::seed() {
  */
 c10::intrusive_ptr<c10::TensorImpl> CUDAGeneratorImpl::get_state() const {
   // The RNG state comprises the seed, and an offset used for Philox.
-  constexpr size_t seed_size = sizeof(uint64_t);
-  constexpr size_t offset_size = sizeof(int64_t);
-  constexpr size_t total_size = seed_size + offset_size;
+  static const size_t seed_size = sizeof(uint64_t);
+  static const size_t offset_size = sizeof(int64_t);
+  static const size_t total_size = seed_size + offset_size;
 
   auto state_tensor = at::detail::empty_cpu({(int64_t)total_size}, ScalarType::Byte, std::nullopt, std::nullopt, std::nullopt, std::nullopt);
   auto rng_state = state_tensor.data_ptr<uint8_t>();
@@ -346,9 +346,9 @@ c10::intrusive_ptr<c10::TensorImpl> CUDAGeneratorImpl::get_state() const {
  * and size of the internal state.
  */
 void CUDAGeneratorImpl::set_state(const c10::TensorImpl& new_state) {
-  constexpr size_t seed_size = sizeof(uint64_t);
-  constexpr size_t offset_size = sizeof(int64_t);
-  constexpr size_t total_size = seed_size + offset_size;
+  static const size_t seed_size = sizeof(uint64_t);
+  static const size_t offset_size = sizeof(int64_t);
+  static const size_t total_size = seed_size + offset_size;
 
   detail::check_rng_state(new_state);
 

aten/src/ATen/cuda/CachingHostAllocator.cpp

@@ -183,6 +183,11 @@ struct CUDACachingHostAllocatorImpl
     return true;
   }
 
+  bool pinned_use_background_threads() override {
+    return c10::cuda::CUDACachingAllocator::CUDAAllocatorConfig::
+        pinned_use_background_threads();
+  }
+
   EventPool::Event create_event_internal(DeviceIndex idx) {
     // Leak the event pool to avoid shutdown issue.
     static auto* event_pool = new EventPool();

aten/src/ATen/cuda/cub.cuh

@@ -177,6 +177,7 @@ inline void segmented_sort_pairs(
   }
 }
 
+#if CUB_SUPPORTS_UNIQUE_BY_KEY()
 template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT, typename NumSelectedIteratorT>
 inline void unique_by_key(
   KeysInputIteratorT keys_in, ValuesInputIteratorT values_in,
@@ -192,6 +193,7 @@ inline void unique_by_key(
   CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceSelect::UniqueByKey,
     keys_in, values_in, keys_out_, values_out, num_selected, num_input_items, c10::cuda::getCurrentCUDAStream());
 }
+#endif
 
 namespace impl {
 
@@ -577,6 +579,7 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
 #endif
 }
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 
 template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT>
 inline void inclusive_sum_by_key(KeysInputIteratorT keys, ValuesInputIteratorT input, ValuesOutputIteratorT output, int64_t num_items) {
@@ -604,6 +607,7 @@ inline void inclusive_scan_by_key(KeysInputIteratorT keys, ValuesInputIteratorT
 #endif
 }
 
+#endif
 
 template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT>
 void unique(InputIteratorT input, OutputIteratorT output,

aten/src/ATen/cuda/cub_definitions.cuh

@@ -28,6 +28,22 @@
 #define USE_GLOBAL_CUB_WRAPPED_NAMESPACE() false
 #endif
 
+// cub support for UniqueByKey is added to cub 1.16 in:
+// https://github.com/NVIDIA/cub/pull/405
+#if CUB_VERSION >= 101600
+#define CUB_SUPPORTS_UNIQUE_BY_KEY() true
+#else
+#define CUB_SUPPORTS_UNIQUE_BY_KEY() false
+#endif
+
+// cub support for scan by key is added to cub 1.15
+// in https://github.com/NVIDIA/cub/pull/376
+#if CUB_VERSION >= 101500
+#define CUB_SUPPORTS_SCAN_BY_KEY() 1
+#else
+#define CUB_SUPPORTS_SCAN_BY_KEY() 0
+#endif
+
 // cub support for cub::FutureValue is added to cub 1.15 in:
 // https://github.com/NVIDIA/cub/pull/305
 #if CUB_VERSION >= 101500

aten/src/ATen/cuda/detail/BLASConstants.cu

@@ -1,54 +0,0 @@
-#include <ATen/Functions.h>
-#include <ATen/Tensor.h>
-#include <ATen/cuda/Exceptions.h>
-
-#include <mutex>
-
-namespace at {
-namespace cuda {
-namespace detail {
-
-__device__ __constant__ float cublas_one_device;
-__device__ __constant__ float cublas_zero_device;
-
-float *get_cublas_device_one() {
-  static c10::once_flag init_flag;
-
-  c10::call_once(init_flag, []() {
-    const float one = 1.f;
-    AT_CUDA_CHECK(cudaMemcpyToSymbol(cublas_one_device, &one, sizeof(float)));
-  });
-
-  float *ptr;
-  AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_one_device));
-  return ptr;
-}
-
-float *get_cublas_device_zero() {
-  static c10::once_flag init_flag;
-
-  c10::call_once(init_flag, []() {
-    const float zero = 0.f;
-    AT_CUDA_CHECK(cudaMemcpyToSymbol(cublas_zero_device, &zero, sizeof(float)));
-  });
-
-  float *ptr;
-  AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_zero_device));
-  return ptr;
-}
-
-float *get_user_alpha_ptr() {
-  static float *alpha_ptr;
-
-  static c10::once_flag init_flag;
-
-  c10::call_once(init_flag, []() {
-    AT_CUDA_CHECK(cudaMalloc(&alpha_ptr, sizeof(float)));
-  });
-
-  return alpha_ptr;
-}
-
-} // namespace detail
-} // namespace cuda
-} // namespace at

aten/src/ATen/cuda/detail/BLASConstants.h

@@ -1,11 +0,0 @@
-#pragma once
-
-#include <ATen/core/TensorBase.h>
-
-namespace at::cuda::detail {
-
-float *get_cublas_device_one();
-float *get_cublas_device_zero();
-float *get_user_alpha_ptr();
-
-} // namespace at::cuda::detail

aten/src/ATen/cuda/tunable/GemmCommon.h

@@ -13,7 +13,6 @@
 #include <c10/core/ScalarType.h>
 
 #include <ATen/cuda/tunable/TunableOp.h>
-#include <ATen/cuda/tunable/Tunable.h>
 #include <ATen/cuda/CUDABlas.h>
 #include <ATen/cuda/Exceptions.h>
 #include <c10/util/StringUtil.h>
@@ -151,7 +150,6 @@ inline std::string ScalarTypeToBLASType(c10::ScalarType scalar_type) {
       BLASType = "unknown";
   }
   return BLASType;
-
 }
 
 // Similar to Compute Type in GemmRocblas.h
@@ -246,25 +244,33 @@ inline std::string to_string_epilogue(const at::cuda::blas::GEMMAndBiasActivatio
 
 namespace detail {
 
-static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size, const NumericalCheckConfig& config) {
-
-  if (!config.enabled) {
-    return true; // skip when disabled
-  }
-
+static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size) {
   auto options = at::TensorOptions().dtype(dtype).device(at::kCUDA);
+  // comparison done as 1D tensor
   at::Tensor ref = at::from_blob(c,       {size}, options);
   at::Tensor oth = at::from_blob(other_c, {size}, options);
   at::Tensor ref_float = ref.to(at::kFloat);
   at::Tensor oth_float = oth.to(at::kFloat);
-
-  const bool ok = at::allclose(ref_float, oth_float, config.rtol, config.atol);
-  if (ok) {
-    TUNABLE_LOG3("├──verify numerics: PASSED with atol=", config.atol, ", rtol=", config.rtol);
-  } else {
-    TUNABLE_LOG3("├──verify numerics: FAILED with atol=", config.atol, ", rtol=", config.rtol);
+  std::vector<double> atols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
+  std::vector<double> rtols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
+  double last_succeed_atol = 1;
+  double last_succeed_rtol = 1;
+  for (auto& atol : atols) {
+    for (auto& rtol : rtols) {
+      if (at::allclose(ref_float, oth_float, rtol, atol)) {
+        last_succeed_atol = atol;
+        last_succeed_rtol = rtol;
       }
-  return ok;
+    }
+  }
+  if (last_succeed_atol == 1) {
+    return false;
+  }
+  else {
+    TUNABLE_LOG3("├──verify numerics: atol=", last_succeed_atol, ", rtol=", last_succeed_rtol);
+  }
+
+  return true;
 }
 
 }
@@ -349,10 +355,8 @@ struct GemmParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<T>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -445,10 +449,8 @@ struct GemmAndBiasParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmAndBiasParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<T>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -544,10 +546,8 @@ struct GemmStridedBatchedParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmStridedBatchedParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<C_Dtype>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -663,9 +663,7 @@ struct ScaledGemmParams : OpParams {
   }
 
   TuningStatus NumericalCheck(ScaledGemmParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};

aten/src/ATen/cuda/tunable/README.md

@@ -145,7 +145,7 @@ programmatically since the settings become fixed. Use the C++ or Python APIs ins
 | PYTORCH_TUNABLEOP_VERBOSE | Default is 0. Set to 1 to enable basic logging. 2 for basic tuning status. 3 for full trace. |
 | PYTORCH_TUNABLEOP_VERBOSE_FILENAME | Default is "err" for stderr. Set to "out" for stdout or a filename for capturing verbose logging. |
 | PYTORCH_TUNABLEOP_FILENAME | Default is 'tunableop_results.csv'. |
-| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is off. Set 'atol_rtol' to enable, for example "1e-5_1e-5". |
+| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is 0. Set to 1 to enable. |
 | PYTORCH_TUNABLEOP_ROCBLAS_ENABLED | Default is 1. Set to 0 to disable rocblas being considered during tuning. |
 | PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED | Default is 1. Set to 0 to disable hipblaslt being considered during tuning. |
 | PYTORCH_TUNABLEOP_MAX_TUNING_DURATION_MS | Default is 30. Unit is milliseconds. |
@@ -173,9 +173,10 @@ All python APIs exist in the `torch.cuda.tunable` module.
 | get_max_tuning_iterations() -> int | |
 | set_filename(filename: str, insert_device_ordinal: bool = False) -> None | |
 | get_filename() -> str | |
-| set_numerical_check_tolerances(enable: bool, atol: float, rtol: float) -> None | Enable or disable numerical checking; atol and rtol default to 1e-5.
 | get_results() -> Tuple[str, str, str, float] | |
 | get_validators() -> Tuple[str, str] | |
+| write_file_on_exit(val: bool) -> None | Default is True. |
+| write_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
 | read_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
 | tune_gemm_in_file(filename: str) -> None | read an untuned file and tune GEMMs in it. |
 | mgpu_tune_gemm_in_file(filename_pattern: str, num_gpus: int) -> None: -> None | read one or more untuned files and tune all unique GEMMs on one or more GPUs. |

aten/src/ATen/cuda/tunable/Tunable.cpp

@@ -107,30 +107,14 @@ void TuningResultsManager::AddImpl(const std::string& op_signature,
 }
 
 void TuningResultsManager::Add(const std::string& op_signature, const std::string& params_signature, ResultEntry best) {
-  bool is_new = false;
-  ResultEntry inserted = ResultEntry::Null();
-
-  // ---- mutate maps under results lock ----
-  {
   std::scoped_lock l{lock_};
-    auto& km = results_[op_signature];  // creates if missing
-    is_new = (km.find(params_signature) == km.end());
-    AddImpl(op_signature, params_signature, std::move(best), km);
-    if (is_new) {
-      inserted = km.at(params_signature);  // snapshot for I/O after unlocking
-    }
-  }
-   if (!is_new) return;  // only write once per unique (op, params)
 
-   TuningContext* ctx = getTuningContext();
-  if (ctx->IsTuningEnabled() && !ctx->IsRecordUntunedEnabled()) {
-    InitRealtimeAppend(ctx->GetFilename(), ctx->GetTuningResultsValidator().GetAllValidators());
-
-    if (is_new && realtime_out_ && realtime_out_->good()) {
-      AppendResultLine(op_signature, params_signature, inserted);
-    }
+  auto it = results_.find(op_signature);
+  if (it == results_.end()) {
+    it = results_.insert({op_signature, {}}).first;
   }
 
+  AddImpl(op_signature, params_signature, std::move(best), it->second);
 }
 
 void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
@@ -166,77 +150,6 @@ void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std
   }
 }
 
-void TuningResultsManager::InitRealtimeAppend(const std::string& filename, const std::unordered_map<std::string, std::string>& validators) {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-  if (realtime_out_ && realtime_out_->good() && realtime_filename_ == filename) {
-    return;
-  }
-
-  if (realtime_out_ && realtime_filename_ != filename) {
-    realtime_out_->flush();
-    realtime_out_->close();
-    realtime_out_.reset();
-    validators_written_ = false;
-  }
-
-  bool file_exists = false;
-  bool file_empty = true;
-
-  {
-    std::ifstream check_file(filename);
-    if (check_file.good()) {
-      file_exists = true;
-      file_empty = (check_file.peek() == std::ifstream::traits_type::eof());
-    }
-  }
-
-  realtime_out_ = std::make_unique<std::ofstream>(filename, std::ios::out | std::ios::app);
-
-  if (!realtime_out_->good()) {
-    TORCH_WARN("TunableOp realtime append: failed to open '", filename,"'");
-    realtime_out_.reset();
-    return;
-  }
-
-  if(!file_exists || file_empty) {
-    for(const auto& [key, val] : validators) {
-      (*realtime_out_) << "Validator," << key << "," << val << std::endl;
-      realtime_out_->flush();
-    }
-    validators_written_ = true;
-
-    TUNABLE_LOG2("Wrote validators to realtime output file");
-  }
-
-  realtime_filename_ = filename;
-}
-
-void TuningResultsManager::AppendResultLine(const std::string& op_sig, const std::string& param_sig, const ResultEntry& result) {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-  if(!realtime_out_ || !realtime_out_->good()) {
-    return;
-  }
-
-  (*realtime_out_) << op_sig << "," << param_sig << "," << result << std::endl;
-  realtime_out_->flush(); //ensure immediate write to disk
-
-  TUNABLE_LOG3("Realtime append: ", op_sig, "(", param_sig, ") -> ", result);
-}
-
-void TuningResultsManager::CloseRealtimeAppend() {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-
-  if(realtime_out_) {
-    realtime_out_->flush();
-    realtime_out_->close();
-    realtime_out_.reset();
-    TUNABLE_LOG2("Closed realtime output file");
-  }
-}
-
 void TuningResultsManager::Delete(const std::string& op_signature, const std::string& params_signature) {
   std::scoped_lock l{lock_};
 
@@ -483,6 +396,7 @@ TuningContext::TuningContext() :
     tuning_enable_{true},
     record_untuned_enable_{false},
     manager_initialized_{false},
+    write_file_on_exit_{true},
     numerics_check_enable_{false},
     max_tuning_duration_ms_{30},
     max_tuning_iterations_{100},
@@ -503,8 +417,20 @@ TuningContext::~TuningContext() {
     // but doesn't do any computation itself.
     return;
   }
-  TUNABLE_LOG1("Closing File");
-  GetTuningResultsManager().CloseRealtimeAppend(); // Since, we do instant logging by default now.
+  auto filename = GetFilename();
+  if (IsTunableOpEnabled() && IsTuningEnabled() && !filename.empty() && write_file_on_exit_) {
+    if (results_count_from_input_file_ < GetTuningResultsManager().GetSize()) {
+      if (results_count_from_input_file_ > 0) {
+        TUNABLE_LOG1("additional tuning results available, rewriting file ", filename);
+      }
+      else {
+        TUNABLE_LOG1("writing file ", filename);
+      }
+      if (!WriteFile(filename)) {
+        TUNABLE_LOG1("failed to write file ", filename);
+      }
+    }
+  }
 
   if (untuned_file_.good()) {
     untuned_file_.close();
@@ -585,54 +511,20 @@ std::ofstream& TuningContext::GetUntunedFile(){
   return untuned_file_;
 }
 
+void TuningContext::WriteFileOnExit(bool value) {
+  write_file_on_exit_ = value;
+}
 
 void TuningContext::EnableNumericsCheck(bool value) {
   numerics_check_enable_ = value;
 }
 
-NumericalCheckConfig TuningContext::GetNumericalCheckConfig() const {
-  const auto env_opt = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
-
-  if (!env_opt.has_value()) {
-    return numerics_cfg_;
-  }
-
-  const std::string& env = env_opt.value();
-
-  if (env == "0") {
-    return NumericalCheckConfig(false, 1e-5, 1e-5);
-  }
-
-  const size_t underscore = env.find('_');
-
-  TORCH_CHECK(
-      underscore != std::string::npos,
-      "Invalid PYTORCH_TUNABLEOP_NUMERICAL_CHECK format. "
-      "Expected 'atol_rtol', got: ",
-      env);
-
-  double atol = 0.0;
-  double rtol = 0.0;
-
-  try {
-    atol = std::stod(env.substr(0, underscore));
-    rtol = std::stod(env.substr(underscore + 1));
-  } catch (const std::exception& e) {
-    TORCH_CHECK(false, "Failed to parse PYTORCH_TUNABLEOP_NUMERICAL_CHECK: ", e.what());
-  }
-
-  TORCH_CHECK( atol > 0.0 && rtol > 0.0, "Tolerance values must be positive. atol=", atol, ", rtol=", rtol);
-  return NumericalCheckConfig(true, atol, rtol);
-}
-
-void TuningContext::SetNumericalCheckConfig(bool enabled, double atol, double rtol) {
-  TORCH_CHECK(atol > 0.0 && rtol > 0.0, "Numerical check tolerances must be positive");
-  numerics_cfg_ = {enabled, atol, rtol};
-}
-
 bool TuningContext::IsNumericsCheckEnabled() const {
-  const auto cfg = GetNumericalCheckConfig();
-  return cfg.enabled || numerics_check_enable_;
+  const auto env = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
+  if (env == "1") {
+    return true;
+  }
+  return numerics_check_enable_;
 }
 
 void TuningContext::SetMaxTuningDurationMs(int max_duration_ms) {
@@ -742,6 +634,11 @@ TuningResultsManager& TuningContext::GetTuningResultsManager() {
     auto filename = GetFilename();
     if (!filename.empty() && !IsRecordUntunedEnabled()) {
       ReadFile(filename);
+      // attempt immediately to open file for writing to catch errors early
+      std::ofstream file(filename, std::ios::out | std::ios::app);
+      if (!file.good()) {
+        TORCH_WARN("failed to open file '", filename, "' for writing; your tuning results will not be saved");
+      }
     }
   });
   return manager_;
@@ -847,6 +744,27 @@ bool TuningContext::ReadFile(const std::string& filename_) {
   return true;
 }
 
+bool TuningContext::WriteFile(const std::string& filename_) {
+  std::string filename = filename_.empty() ? GetFilename() : filename_;
+  std::ofstream file(filename, std::ios::out | std::ios::trunc);
+  if (!file.good()) {
+    TUNABLE_LOG1("error opening tuning results file for writing ", filename);
+    return false;
+  }
+  auto validators = GetTuningResultsValidator().GetAllValidators();
+  for (const auto& [key, val] : validators) {
+    file << "Validator," << key << "," << val << std::endl;
+  }
+  auto results = GetTuningResultsManager().Dump();
+  for (const auto& [op_sig, kernelmap] : results) {
+    for (const auto& [param_sig, result] : kernelmap) {
+      file << op_sig << "," << param_sig << "," << result << std::endl;
+    }
+  }
+  file.close();
+  return true;
+}
+
 namespace {
 
 struct MaybeDelete {

aten/src/ATen/cuda/tunable/Tunable.h

@@ -103,24 +103,10 @@ class TORCH_CUDA_CPP_API TuningResultsManager {
 
     void RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
       const std::string& params_signature, const std::string& blas_signature);
-
-    void InitRealtimeAppend(
-        const std::string& filename,
-        const std::unordered_map<std::string, std::string>& validators);
-
-    void AppendResultLine(const std::string& op_sig,
-                         const std::string& param_sig,
-                         const ResultEntry& result);
-
-    void CloseRealtimeAppend();  // For clean shutdown
   private:
     std::mutex lock_;
-    std::mutex realtime_file_mutex_;
-    std::unique_ptr<std::ofstream> realtime_out_;
-    std::string realtime_filename_;
     ResultsMap results_;
     UntunedMap untuned_results_;
-    bool validators_written_ = false;
 
 };
 
@@ -148,16 +134,6 @@ class TORCH_CUDA_CPP_API TuningResultsValidator {
     GetValidateFuncs validators_;
 };
 
-struct NumericalCheckConfig {
-  bool   enabled{false};
-  double atol{1e-5};
-  double rtol{1e-5};
-
-  NumericalCheckConfig() = default;
-  NumericalCheckConfig(bool e, double a, double r) : enabled(e), atol(a), rtol(r) {}
-};
-
-
 class TORCH_CUDA_CPP_API TuningContext {
   public:
     TuningContext();
@@ -179,8 +155,6 @@ class TORCH_CUDA_CPP_API TuningContext {
 
     void EnableNumericsCheck(bool value);
     bool IsNumericsCheckEnabled() const;
-    void SetNumericalCheckConfig(bool enabled, double atol, double rtol);
-    NumericalCheckConfig GetNumericalCheckConfig() const;
 
     void SetMaxTuningDurationMs(int max_duration_ms);
     int GetMaxTuningDurationMs() const;
@@ -211,7 +185,10 @@ class TORCH_CUDA_CPP_API TuningContext {
     void SetFilename(const std::string& filename, bool insert_device_ordinal=false);
     std::string GetFilename() const;
 
+    void WriteFileOnExit(bool value);
+
     bool ReadFile(const std::string& filename={});
+    bool WriteFile(const std::string& filename={});
 
     template<class... Types>
     void Log(int level, Types... args) {
@@ -230,6 +207,7 @@ class TORCH_CUDA_CPP_API TuningContext {
     bool tuning_enable_;
     bool record_untuned_enable_;
     bool manager_initialized_;
+    bool write_file_on_exit_;
     bool numerics_check_enable_;
     int max_tuning_duration_ms_;
     int max_tuning_iterations_;
@@ -244,8 +222,6 @@ class TORCH_CUDA_CPP_API TuningContext {
     std::ofstream untuned_file_;
     size_t results_count_from_input_file_;
     bool is_shutting_down_;
-
-    NumericalCheckConfig numerics_cfg_{};
 };
 
 TORCH_CUDA_CPP_API TuningContext* getTuningContext();

aten/src/ATen/cuda/tunable/TunableGemm.h

@@ -109,8 +109,7 @@ class DefaultScaledGemmOp : public Callable<ScaledGemmParams<T>> {
           params->c_scale_ptr,
           params->ldc,
           params->c_dtype,
-          params->use_fast_accum,
-          std::nullopt /* alpha */);
+          params->use_fast_accum);
       return OK;
     }
 };

aten/src/ATen/cuda/tunable/TunableOp.h

@@ -267,11 +267,28 @@ class TunableOp {
       for (size_t i = 0; i < op_names_.size(); i++) {
         auto* candidate = ops_[op_names_[i]].get(); // borrow pointer
 
+        if (do_numerics_check) {
+          ParamsT* numerical_params = params->DeepCopy(false);
+          auto status = candidate->Call(numerical_params);
+          if (status != OK) {
+            numerical_params->Delete();
+            TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
+            continue;
+          }
+          status = reference_params->NumericalCheck(numerical_params);
+          numerical_params->Delete();
+          if (status != OK) {
+            TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
+            continue;
+          }
+        }
+        else {
           auto status = candidate->Call(reusable_params[0]);
           if (status != OK) {
             TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
             continue;
           }
+        }
 
         // collect a small profile
         int approx_num_iter = 3;
@@ -293,22 +310,6 @@ class TunableOp {
           continue;
         }
 
-        if (do_numerics_check) {
-          ParamsT* numerical_params = params->DeepCopy(false);
-          auto status = candidate->Call(numerical_params);
-          if (status != OK) {
-            numerical_params->Delete();
-            TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
-            continue;
-          }
-          status = reference_params->NumericalCheck(numerical_params);
-          numerical_params->Delete();
-          if (status != OK) {
-            TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
-            continue;
-          }
-        }
-
         // for warmup does user set max duration, max iters, or both?
         // warmup is skipped by default, i.e. warmup_iter = 0
         // warmup will be set to the non-zero value of max_warmup_duration

aten/src/ATen/functorch/BatchRulesModules.cpp

@@ -213,22 +213,40 @@ static cudnn_grid_sample_backward_batch_rule(
   return grid_sample_backward_helper_out(std::move(bw_out), 0, 0, bdim_size);
 }
 
-// uses functional formulation for one_hot under vmap to be compatible with
-// fakeTensor/dynamic shapes and compiled functorch transforms.
-// mirrors the meta path in aten/src/ATen/native/Onehot.cpp,
-// but requires explicit positive num_classes under vmap to avoid
-// data-dependent output shapes.
+// TODO: replace with targetable functionalization
 static Tensor one_hot_decomposition_hack(const Tensor &self, int64_t num_classes) {
     TORCH_CHECK(self.dtype() == kLong, "one_hot is only applicable to index tensor.");
+    auto shape = self.sym_sizes().vec();
+
+    // empty tensor could be converted to one hot representation,
+    // but shape inference is not possible.
+    if (self.sym_numel() == 0) {
+        if (num_classes <= 0) {
+            TORCH_CHECK(false, "Can not infer total number of classes from empty tensor.");
+        } else {
+            shape.emplace_back(num_classes);
+            return at::empty_symint(shape, self.options());
+        }
+    }
 
-    // disallow implicit inference under vmap; this would be data-dependent
-    // and is intentionally guarded by Dynamo in torch/_dynamo/variables/torch.py.
     TORCH_CHECK(num_classes > 0, "When vmap-ing torch.nn.functional.one_hot, please "
         "provide an explicit positive num_classes argument.");
 
-    const auto options = self.options();
-    at::Tensor index = at::arange(num_classes, options);
-    return at::eq(self.unsqueeze(-1), index).to(at::kLong);
+    // Disabling all of the following checks. This is OK because scatter has checks too.
+    // Maybe one_hot should be a primitive wrt autograd so we don't have to deal with this.
+    // // non-empty tensor
+    // if (self.device().type() != at::kCUDA) {
+    //   //for cuda, rely on device assert thrown by scatter
+    //   TORCH_CHECK(self.min().item().toLong() >= 0, "Class values must be non-negative.");
+    // }
+    // if (self.device().type() != at::kCUDA) {
+    //   //rely on device asserts from scatter to avoid sync here
+    //   TORCH_CHECK(num_classes > self.max().item().toLong(), "Class values must be smaller than num_classes.");
+    // }
+
+    shape.emplace_back(num_classes);
+    Tensor ret = at::zeros_symint(shape, self.options());
+    return ret.scatter(-1, self.unsqueeze(-1), 1);
 }
 
 template <typename A, A a, typename C>

aten/src/ATen/functorch/BatchedTensorImpl.h

@@ -160,10 +160,6 @@ constexpr DispatchKeySet kKeysToPropagateToWrapper({
   DispatchKey::CUDA,
   DispatchKey::CPU,
   DispatchKey::PrivateUse1,
-  DispatchKey::SparseCPU,
-  DispatchKey::SparseCUDA,
-  DispatchKey::SparseCsrCPU,
-  DispatchKey::SparseCsrCUDA,
 });
 
 inline DispatchKeySet getKeysToPropagateToWrapper(const Tensor& tensor, DispatchKeySet to_propagate=kKeysToPropagateToWrapper) {

aten/src/ATen/native/Activation.cpp

@@ -240,8 +240,8 @@ TORCH_META_FUNC(gelu_backward) (
 
 namespace at::native {
 
-static constexpr double SELU_ALPHA = 1.6732632423543772848170429916717;
-static constexpr double SELU_SCALE = 1.0507009873554804934193349852946;
+static const double SELU_ALPHA = 1.6732632423543772848170429916717;
+static const double SELU_SCALE = 1.0507009873554804934193349852946;
 
 DEFINE_DISPATCH(elu_stub);
 DEFINE_DISPATCH(elu_backward_stub);

aten/src/ATen/native/BlasKernel.cpp

@@ -286,7 +286,7 @@ template void scal_fast_path<scalar_t>(int *n, scalar_t *a, scalar_t *x, int *in
 #if AT_BUILD_WITH_BLAS()
 template <>
 bool scal_use_fast_path<double>(int64_t n, int64_t incx) {
-  auto constexpr intmax = std::numeric_limits<int>::max();
+  auto intmax = std::numeric_limits<int>::max();
   return n <= intmax && incx <= intmax;
 }
 
@@ -315,7 +315,7 @@ bool gemv_use_fast_path<float>(
     int64_t incx,
     [[maybe_unused]] float beta,
     int64_t incy) {
-  auto constexpr intmax = std::numeric_limits<int>::max();
+  auto intmax = std::numeric_limits<int>::max();
   return (m <= intmax) && (n <= intmax) && (lda <= intmax) &&
          (incx > 0) && (incx <= intmax) && (incy > 0) && (incy <= intmax);
 }

aten/src/ATen/native/Convolution.cpp

@@ -658,7 +658,6 @@ static void check_shape_forward(const at::Tensor& input,
   TORCH_CHECK(!params.is_output_padding_neg(), "negative output_padding is not supported");
   TORCH_CHECK(!params.is_stride_nonpos(), "non-positive stride is not supported");
   TORCH_CHECK(!params.is_dilation_neg(), "dilation should be greater than zero");
-  TORCH_CHECK(groups > 0, "expected groups to be greater than 0, but got groups=", groups);
 
   TORCH_CHECK(weight_dim == k,
            "Expected ", weight_dim, "-dimensional input for ", weight_dim,

aten/src/ATen/native/Distributions.h

@@ -1,6 +1,5 @@
 #pragma once
 
-#include <array>
 #include <ATen/native/Math.h>
 #include <c10/macros/Macros.h>
 #include <c10/util/MathConstants.h>
@@ -128,7 +127,7 @@ C10_DEVICE scalar_t sample_gamma(scalar_t alpha, BaseSampler<accscalar_t, unifor
 
 template<typename scalar_t>
 C10_DEVICE scalar_t stirling_approx_tail(scalar_t k) {
-  constexpr static scalar_t kTailValues[] = {
+  const static scalar_t kTailValues[] = {
     0.0810614667953272,
     0.0413406959554092,
     0.0276779256849983,
@@ -140,7 +139,7 @@ C10_DEVICE scalar_t stirling_approx_tail(scalar_t k) {
     0.00925546218271273,
     0.00833056343336287
   };
-  if (k < std::size(kTailValues)) {
+  if (k <= 9) {
     return kTailValues[static_cast<size_t>(k)];
   }
   scalar_t kp1sq = (k + 1) * (k + 1);

aten/src/ATen/native/LinearAlgebra.cpp

@@ -3620,7 +3620,7 @@ Tensor& _int_mm_out_cpu(const Tensor& self, const Tensor& mat2, Tensor& result)
     try {
       mkldnn_matmul_i8i8i32(self, mat2, result);
       dispatched = true;
-    } catch ([[maybe_unused]] const std::exception& e) {
+    } catch (const std::exception& e) {
       TORCH_WARN(func_name, " failed, switching to BLAS gemm: ", e.what());
     }
   }

aten/src/ATen/native/Math.h

@@ -581,7 +581,7 @@ scalar_t ratevl(scalar_t x, const scalar_t num[], int64_t M,
 template <typename scalar_t>
 static scalar_t lanczos_sum_expg_scaled(scalar_t x) {
   // lanczos approximation
-  static constexpr scalar_t lanczos_sum_expg_scaled_num[13] = {
+  static const scalar_t lanczos_sum_expg_scaled_num[13] = {
     0.006061842346248906525783753964555936883222,
     0.5098416655656676188125178644804694509993,
     19.51992788247617482847860966235652136208,
@@ -596,7 +596,7 @@ static scalar_t lanczos_sum_expg_scaled(scalar_t x) {
     103794043.1163445451906271053616070238554,
     56906521.91347156388090791033559122686859
   };
-  static constexpr scalar_t lanczos_sum_expg_scaled_denom[13] = {
+  static const scalar_t lanczos_sum_expg_scaled_denom[13] = {
     1.,
     66.,
     1925.,
@@ -712,7 +712,7 @@ static scalar_t _igamc_helper_series(scalar_t a, scalar_t x) {
 template <typename scalar_t>
 static scalar_t _igam_helper_asymptotic_series(scalar_t a, scalar_t x, bool igam) {
   // Compute igam/igamc using DLMF 8.12.3/8.12.4 [igam1]
-  static constexpr scalar_t d[25][25] =
+  static const scalar_t d[25][25] =
     {{-3.3333333333333333e-1, 8.3333333333333333e-2, -1.4814814814814815e-2,
       1.1574074074074074e-3, 3.527336860670194e-4, -1.7875514403292181e-4,
       3.9192631785224378e-5, -2.1854485106799922e-6, -1.85406221071516e-6,

aten/src/ATen/native/Normalization.cpp

@@ -62,7 +62,7 @@
 #include <utility>
 #include <vector>
 
-static constexpr int MIOPEN_DIM_MAX = 5;
+static const int MIOPEN_DIM_MAX = 5;
 
 namespace at::meta {
 

aten/src/ATen/native/Onehot.cpp

@@ -34,16 +34,16 @@ Tensor one_hot(const Tensor &self, int64_t num_classes) {
         }
     }
 
-    auto shape = self.sym_sizes().vec();
+    auto shape = self.sizes().vec();
 
     // empty tensor could be converted to one hot representation,
     // but shape inference is not possible.
-    if (self.sym_numel() == 0) {
+    if (self.numel() == 0) {
         if (num_classes <= 0) {
             TORCH_CHECK(false, "Can not infer total number of classes from empty tensor.");
         } else {
-            shape.emplace_back(num_classes);
-            return at::empty_symint(shape, self.options());
+            shape.push_back(num_classes);
+            return at::empty(shape, self.options());
         }
     }
 
@@ -66,8 +66,8 @@ Tensor one_hot(const Tensor &self, int64_t num_classes) {
         }
     }
 
-    shape.emplace_back(num_classes);
-    Tensor ret = at::zeros_symint(shape, self.options());
+    shape.push_back(num_classes);
+    Tensor ret = at::zeros(shape, self.options());
     ret.scatter_(-1, self.unsqueeze(-1), 1);
     return ret;
 }

aten/src/ATen/native/TensorAdvancedIndexing.cpp

@@ -1906,9 +1906,11 @@ Tensor& index_fill_(
         "This also applies to advanced indexing e.g. tensor[mask] = scalar");
   }
 
+  if (!self.is_complex() && source.isComplex()) {
     TORCH_CHECK(
-      self.is_complex() || !source.isComplex(),
+        false,
         "index_fill_(): Converting complex Scalar to non-complex type is not supported");
+  }
 
   // Handle the case when `self` is 0-dim
   Tensor self_nonzero_dim = (self.dim() == 0) ? self.unsqueeze(-1) : self;

aten/src/ATen/native/TensorAdvancedIndexingUtils.h

@@ -77,7 +77,7 @@ inline AdvancedIndex make_info(Tensor self, IOptTensorListRef orig) {
   // next broadcast all index tensors together
   try {
     indices = expand_outplace(indices);
-  } catch (std::exception&) {
+  } catch (std::exception& e) {
     TORCH_CHECK_INDEX(
         false,
         "shape mismatch: indexing tensors could not be broadcast together"

aten/src/ATen/native/cpu/PowKernel.cpp

@@ -120,7 +120,7 @@ static void pow_tensor_scalar_kernel(
   } else if (dtype == ScalarType::Half) {
     [&]() {
       using scalar_t =
-          c10::impl::ScalarTypeToCPPTypeT<ScalarType::Half>;
+          decltype(c10::impl::ScalarTypeToCPPType<ScalarType::Half>::t);
       const auto exp = exp_scalar.to<scalar_t>();
       using Vec = Vectorized<scalar_t>;
       cpu_kernel_vec(iter,

aten/src/ATen/native/cpu/UpSampleKernel.cpp

@@ -1038,7 +1038,7 @@ struct HelperInterpNearest : public HelperInterpBase {
   // We keep this structure for BC and consider as deprecated.
   // See HelperInterpNearestExact as replacement
 
-  static constexpr int interp_size = 1;
+  static const int interp_size = 1;
 
   static inline void init_indices_weights(
     at::ScalarType output_type,
@@ -1155,7 +1155,7 @@ struct HelperInterpNearestExact : public HelperInterpNearest {
 
 struct HelperInterpLinear : public HelperInterpBase {
 
-  static constexpr int interp_size = 2;
+  static const int interp_size = 2;
 
   // Compute indices and weights for each interpolated dimension
   // indices_weights = {
@@ -1275,7 +1275,7 @@ struct HelperInterpLinear : public HelperInterpBase {
 
 struct HelperInterpCubic : public HelperInterpBase {
 
-  static constexpr int interp_size = 4;
+  static const int interp_size = 4;
 
   // Compute indices and weights for each interpolated dimension
   // indices_weights = {

aten/src/ATen/native/cuda/CUDALoops.cuh

@@ -856,13 +856,9 @@ struct type_specialized_kernel_launcher {
       out_calc_t output_offset_calculator,
       loader_t loader,
       storer_t storer) {
-    constexpr ScalarType sret_t = rt_binary_specializations[arg_index][0];
-    constexpr ScalarType sarg0_t = rt_binary_specializations[arg_index][1];
-    constexpr ScalarType sarg1_t = rt_binary_specializations[arg_index][2];
-    if (ret_t == sret_t && arg0_t == sarg0_t && arg1_t == sarg1_t) {
-      using cret_t = c10::impl::ScalarTypeToCPPTypeT<sret_t>;
-      using carg0_t = c10::impl::ScalarTypeToCPPTypeT<sarg0_t>;
-      using carg1_t = c10::impl::ScalarTypeToCPPTypeT<sarg1_t>;
+    if (ret_t == rt_binary_specializations[arg_index][0] &&
+        arg0_t == rt_binary_specializations[arg_index][1] &&
+        arg1_t == rt_binary_specializations[arg_index][2])
       launch_vectorized_templated_kernel<
           func_t,
           array_t,
@@ -870,9 +866,12 @@ struct type_specialized_kernel_launcher {
           out_calc_t,
           loader_t,
           storer_t,
-          cret_t,
-          carg0_t,
-          carg1_t>(
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][0]>::t),
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][1]>::t),
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][2]>::t)>(
           numel,
           f,
           data,
@@ -881,7 +880,6 @@ struct type_specialized_kernel_launcher {
           loader,
           storer);
   }
-  }
 };
 
 } // namespace

aten/src/ATen/native/cuda/DilatedMaxPool2d.cu

@@ -38,41 +38,12 @@ __device__ inline int min(int a, int b) {
 #define BLOCK_STRIDE_BWD 2 // increasing block_stride to lower # of blocks launched
 #endif
 
-template <typename index_t>
-static __device__ inline index_t p_start(index_t size, int pad, int kernel, int dilation, int stride) {
-  const auto kernel_extent = static_cast<index_t>((kernel - 1) * dilation + 1);
-  return (size + pad < kernel_extent) ? index_t(0) : (size + pad - kernel_extent) / stride + 1;
+static __device__ inline int p_start(int size, int pad, int kernel, int dilation, int stride) {
+  return (size + pad < ((kernel - 1) * dilation + 1)) ? 0 : (size + pad - ((kernel - 1) * dilation + 1)) / stride + 1;
 }
 
-template <typename index_t>
-static __device__ inline index_t p_end(index_t size, int pad, index_t pooled_size, int stride) {
-  return std::min((size + pad) / stride + 1, pooled_size);
-}
-
-static inline bool can_use_int32_nhwc(
-    int64_t nbatch, int64_t channels,
-    int64_t height, int64_t width,
-    int64_t pooled_height, int64_t pooled_width,
-    int64_t in_stride_n, int64_t in_stride_c,
-    int64_t in_stride_h, int64_t in_stride_w)
-{
-  constexpr int64_t int_max = std::numeric_limits<int>::max();
-
-  int64_t max_intra_batch =
-      (height ? (height - 1) * in_stride_h : 0) +
-      (width ? (width - 1) * in_stride_w : 0) +
-      (channels? (channels - 1) * in_stride_c : 0);
-
-  int64_t max_input_offset = (nbatch ? (nbatch - 1) * in_stride_n : 0) + max_intra_batch;
-
-  if (max_input_offset > int_max) return false;
-
-  int64_t out_batch_stride = pooled_height * pooled_width * channels;
-  if ((nbatch ? (nbatch - 1) * out_batch_stride : 0) > int_max) return false;
-
-  if (height * width > int_max) return false;
-
-  return true;
+static __device__ inline int p_end(int size, int pad, int pooled_size, int stride) {
+  return min((size + pad) / stride + 1, pooled_size);
 }
 
 // kernels borrowed from Caffe
@@ -114,25 +85,21 @@ __global__ void max_pool_forward_nchw(const int nthreads, const scalar_t* bottom
   }
 }
 
-template <typename scalar_t, typename index_t>
+template <typename scalar_t>
 C10_LAUNCH_BOUNDS_1(CUDA_MAX_THREADS)
-__global__ void max_pool_forward_nhwc(
-    const scalar_t* bottom_data,
-    const int nbatch,
-    const index_t channels, const index_t height, const index_t width,
-    const index_t pooled_height, const index_t pooled_width,
+__global__ void max_pool_forward_nhwc(const scalar_t* bottom_data, const int nbatch,
+                                   const int64_t channels, const int64_t height,
+                                   const int64_t width, const int pooled_height, const int pooled_width,
                                    const int kernel_h, const int kernel_w, const int stride_h,
                                    const int stride_w, const int pad_h, const int pad_w,
                                    const int dilation_h, const int dilation_w,
-    const index_t in_stride_n, const index_t in_stride_c,
-    const index_t in_stride_h, const index_t in_stride_w,
+                                   const int in_stride_n, const int in_stride_c,
+                                   const int in_stride_h, const int in_stride_w,
                                    const int kernel_stride_C, const int kernel_size_C,
                                    scalar_t* top_data, int64_t* top_mask) {
-
-  extern __shared__ unsigned char smem_raw[];
-  index_t *out_mask_cached = reinterpret_cast<index_t*>(smem_raw);
-  scalar_t *out_cached = reinterpret_cast<scalar_t*>(
-      out_mask_cached + kernel_size_C*blockDim.x*blockDim.y*blockDim.z);
+  extern __shared__ int smem[];
+  int *out_mask_cached = smem;
+  scalar_t *out_cached = reinterpret_cast<scalar_t*>(&out_mask_cached[kernel_size_C*blockDim.x*blockDim.y*blockDim.z]);
 
   // flattening cta for pre-computation & smem initialization;
   int thread_id = threadIdx.x + blockDim.x * (threadIdx.y + blockDim.y * threadIdx.z);
@@ -151,26 +118,26 @@ __global__ void max_pool_forward_nhwc(
   int channel_id = blockIdx.x / nbatch;
   int channel_offset = threadIdx.x + channel_id * blockDim.x;
 
-  top_data = top_data + static_cast<index_t>(batch_id) * (pooled_height * pooled_width * channels);
-  top_mask = top_mask + static_cast<index_t>(batch_id) * (pooled_height * pooled_width * channels);
-  bottom_data = bottom_data + static_cast<index_t>(batch_id) * in_stride_n;
+  top_data = top_data + batch_id * pooled_height * pooled_width * channels;
+  top_mask = top_mask + batch_id * pooled_height * pooled_width * channels;
+  bottom_data = bottom_data + batch_id * in_stride_n;
 
-  out_cached += (threadIdx.z * blockDim.y + threadIdx.y) * kernel_size_C*blockDim.x;
-  out_mask_cached  += (threadIdx.z * blockDim.y + threadIdx.y) * kernel_size_C*blockDim.x;
+  out_cached = &out_cached[(threadIdx.z * blockDim.y + threadIdx.y) * kernel_size_C*blockDim.x];
+  out_mask_cached = &out_mask_cached[(threadIdx.z * blockDim.y + threadIdx.y) * kernel_size_C*blockDim.x];
 
-  int oH = (static_cast<int>(pooled_height) + gridDim.z - 1) / gridDim.z;
-  int oW = (static_cast<int>(pooled_width)  + gridDim.y - 1) / gridDim.y;
+  int oH = (pooled_height + gridDim.z-1) / gridDim.z;
+  int oW = (pooled_width + gridDim.y-1) / gridDim.y;
   int ostartH = threadIdx.z + blockIdx.z*oH;
-  int oendH = ::min(ostartH+oH, static_cast<int>(pooled_height));
+  int oendH = ::min(ostartH+oH, pooled_height);
   int ostartW = threadIdx.y + blockIdx.y*oW;
-  int oendW = ::min(ostartW+oW, static_cast<int>(pooled_width));
+  int oendW = ::min(ostartW+oW, pooled_width);
 
   for (int oh = ostartH; oh < oendH; oh+=blockDim.z) {
-    index_t hstart = static_cast<index_t>(oh) * stride_h - pad_h;
-    index_t hend = std::min(hstart + static_cast<index_t>((kernel_h - 1) * dilation_h + 1), height);
+    int hstart = oh * stride_h - pad_h;
+    int hend = min(hstart + (kernel_h - 1) * dilation_h + 1, height);
     for (int ow = ostartW; ow < oendW; ow+=blockDim.y) {
-      index_t wstart = static_cast<index_t>(ow) * stride_w - pad_w;
-      index_t wend = std::min(wstart + static_cast<index_t>((kernel_w - 1) * dilation_w + 1), width);
+      int wstart = ow * stride_w - pad_w;
+      int wend = min(wstart + (kernel_w - 1) * dilation_w + 1, width);
       while(hstart < 0)
         hstart += dilation_h;
       while(wstart < 0)
@@ -218,11 +185,11 @@ __global__ void max_pool_forward_nhwc(
       // Else do it Non-Prefetch...
       else
 #endif
-      for (index_t ih = hstart; ih < hend; ih += dilation_h) {
-        for (index_t iw = wstart; iw < wend; iw += dilation_w) {
+      for (int ih = hstart; ih < hend; ih += dilation_h) {
+        for (int iw = wstart; iw < wend; iw += dilation_w) {
           int cached_index = threadIdx.x;
           const scalar_t *ptr_input = bottom_data + ih * in_stride_h + iw * in_stride_w;
-          for (index_t c = channel_offset; c < channels; c += static_cast<index_t>(blockDim.x) * kernel_stride_C) {
+          for(int c = channel_offset; c < channels; c+= blockDim.x*kernel_stride_C) {
             scalar_t val = ptr_input[c*in_stride_c];
             if ((val > out_cached[cached_index]) || at::_isnan(val)) {
               out_cached[cached_index] = val;
@@ -233,15 +200,15 @@ __global__ void max_pool_forward_nhwc(
         }
       }
 
-      scalar_t *ptr_output_data = top_data + (static_cast<index_t>(oh) * pooled_width + ow) * channels;
-      int64_t *ptr_output_mask = top_mask + (static_cast<index_t>(oh) * pooled_width + ow) * channels;
+      scalar_t *ptr_output_data = top_data + (oh * pooled_width + ow) * channels;
+      int64_t *ptr_output_mask = top_mask + (oh * pooled_width + ow) * channels;
 
       int cached_index = threadIdx.x;
-      for (index_t c = channel_offset; c < channels; c += static_cast<index_t>(blockDim.x) * kernel_stride_C) {
+      for(int c = channel_offset; c < channels; c+= blockDim.x*kernel_stride_C) {
         ptr_output_data[c] = out_cached[cached_index];
-        ptr_output_mask[c] = static_cast<int64_t>(out_mask_cached[cached_index]);
+        ptr_output_mask[c] = out_mask_cached[cached_index];
         out_cached[cached_index] = at::numeric_limits<scalar_t>::lower_bound();
-        out_mask_cached[cached_index] = index_t(0);
+        out_mask_cached[cached_index] = 0;
         cached_index += blockDim.x;
       }
     }
@@ -249,7 +216,7 @@ __global__ void max_pool_forward_nhwc(
 }
 
 
-static constexpr int BLOCK_THREADS = 256;
+static const int BLOCK_THREADS = 256;
 
 template <typename scalar_t, typename accscalar_t>
 #if defined (USE_ROCM)
@@ -495,11 +462,6 @@ const Tensor& indices) {
               maxThreadsDim[0], std::min<int>(lastPow2(nInputPlane), max_threads / block_y / block_z));
           const dim3 block(block_x, block_y, block_z);
 
-          bool use_int32 = can_use_int32_nhwc(
-              nbatch, nInputPlane, inputHeight, inputWidth,
-              outputHeight, outputWidth,
-              in_stride_n, in_stride_c, in_stride_h, in_stride_w);
-
           int kernel_stride_C = ceil_div(
               safe_downcast<int, int64_t>(nInputPlane), block_x * 4);
           int kernel_size_C = ceil_div(
@@ -514,41 +476,18 @@ const Tensor& indices) {
               ceil_div(safe_downcast<int, int64_t>(outputHeight), block_z*BLOCK_STRIDE_FWD));
           const dim3 grid(grid_x, grid_y, grid_z);
 
-          size_t shmem_size;
-          size_t mask_elems = static_cast<size_t>(kernel_size_C) * block_x * block_y * block_z;
+          size_t shmem_size = (kernel_size_C * block_x*block_y*block_z) * (sizeof(int) + sizeof(scalar_t));
+          AT_ASSERT(shmem_size <= at::cuda::getCurrentDeviceProperties()->sharedMemPerBlock);
 
-          if (use_int32) {
-            shmem_size = mask_elems * (sizeof(int32_t) + sizeof(scalar_t));
-            TORCH_CHECK(shmem_size <= at::cuda::getCurrentDeviceProperties()->sharedMemPerBlock,
-                        "shared memory too small");
-            max_pool_forward_nhwc<scalar_t, int32_t>
+          max_pool_forward_nhwc<scalar_t>
           <<<grid, block, shmem_size, at::cuda::getCurrentCUDAStream()>>>(
-                input_data, static_cast<int>(nbatch),
-                static_cast<int32_t>(nInputPlane),
-                static_cast<int32_t>(inputHeight),
-                static_cast<int32_t>(inputWidth),
-                static_cast<int32_t>(outputHeight),
-                static_cast<int32_t>(outputWidth),
-                kH, kW, dH, dW, padH, padW, dilationH, dilationW,
-                static_cast<int32_t>(in_stride_n),
-                static_cast<int32_t>(in_stride_c),
-                static_cast<int32_t>(in_stride_h),
-                static_cast<int32_t>(in_stride_w),
-                kernel_stride_C, kernel_size_C,
-                output_data, indices_data);
-          } else {
-            shmem_size = mask_elems * (sizeof(int64_t) + sizeof(scalar_t));
-            TORCH_CHECK(shmem_size <= at::cuda::getCurrentDeviceProperties()->sharedMemPerBlock,
-                        "shared memory too small");
-            max_pool_forward_nhwc<scalar_t, int64_t>
-              <<<grid, block, shmem_size, at::cuda::getCurrentCUDAStream()>>>(
-                input_data, static_cast<int>(nbatch),
+              input_data, nbatch,
                   nInputPlane, inputHeight, inputWidth, outputHeight, outputWidth,
                   kH, kW, dH, dW, padH, padW, dilationH, dilationW,
-                in_stride_n, in_stride_c, in_stride_h, in_stride_w,
+                  in_stride_n, in_stride_c,
+                  in_stride_h, in_stride_w,
                   kernel_stride_C, kernel_size_C,
                   output_data, indices_data);
-          }
           C10_CUDA_KERNEL_LAUNCH_CHECK();
           break;
         }

aten/src/ATen/native/cuda/Embedding.cu

@@ -15,7 +15,9 @@
 #include <ATen/native/cuda/block_reduce.cuh>
 #include <ATen/native/cuda/thread_constants.h>
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 #include <thrust/iterator/reverse_iterator.h>
+#endif
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -34,9 +36,9 @@ namespace at::native {
 namespace {
 
 #if defined(USE_ROCM)
-static constexpr int BLOCKDIMY = 16;
+static const int BLOCKDIMY = 16;
 #else
-static constexpr int BLOCKDIMY = 32;
+static const int BLOCKDIMY = 32;
 #endif
 
 template
@@ -238,6 +240,10 @@ __global__ void renorm_kernel(
 
 } // anonymous namespace
 
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count);
+#endif
 
 Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indices_,
                                int64_t num_weights, int64_t padding_idx,
@@ -300,6 +306,7 @@ Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indice
 
   if (scale_grad_by_freq) {
     count = at::empty_like(indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+#if CUB_SUPPORTS_SCAN_BY_KEY()
     AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_dense_backward_cuda", [&] () {
       cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
@@ -326,6 +333,11 @@ Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indice
         num_indices
       );
     });
+#else
+    AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_dense_backward_cuda", [&] () {
+      embedding_dense_backward_cuda_scan<index_t>(sorted_indices, count);
+    });
+#endif
   }
 
   return embedding_backward_cuda_kernel(grad, orig_indices,

aten/src/ATen/native/cuda/EmbeddingBackwardKernel.cu

@@ -10,7 +10,9 @@
 
 #include <c10/macros/Macros.h>
 
+#if CUB_SUPPORTS_UNIQUE_BY_KEY()
 #include <thrust/iterator/counting_iterator.h>
+#endif
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -194,9 +196,18 @@ __global__ void compute_num_of_partial_segments(const index_t *partials_per_segm
             partials_per_segment_offset[num_of_segments-1];
 }
 
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+__global__ void write_num_of_segments_for_legacy_thrust_path(int64_t *num_of_segments_ptr, int64_t num_of_segments) {
+  *num_of_segments_ptr = num_of_segments;
+}
+#endif
 
 } // anon namespace
 
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+template<typename index_t>
+int64_t embedding_backward_cuda_kernel_unique_by_key(const Tensor &sorted_indices, Tensor &segment_offsets);
+#endif
 
 Tensor embedding_backward_cuda_kernel(
         const Tensor &grad,
@@ -223,12 +234,20 @@ Tensor embedding_backward_cuda_kernel(
   auto segment_offsets = at::empty({numel}, orig_indices.options());
   auto num_of_segments_tensor = at::empty({}, grad.options().dtype(kLong));
   int64_t *num_of_segments_ptr = num_of_segments_tensor.mutable_data_ptr<int64_t>();
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+  AT_DISPATCH_INDEX_TYPES(orig_indices.scalar_type(), "embedding_backward_cuda_kernel", [&] () {
+    int64_t num_of_segments = embedding_backward_cuda_kernel_unique_by_key<index_t>(sorted_indices, segment_offsets);
+    write_num_of_segments_for_legacy_thrust_path<<<1, 1, 0, c10::cuda::getCurrentCUDAStream()>>>(num_of_segments_ptr, num_of_segments);
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
+  });
+#else
   AT_DISPATCH_INDEX_TYPES(orig_indices.scalar_type(), "embedding_backward_cuda_kernel", [&] () {
     cuda::cub::unique_by_key(
       sorted_indices.const_data_ptr<index_t>(), thrust::make_counting_iterator(0),
       segment_offsets.mutable_data_ptr<index_t>(),
       num_of_segments_ptr, sorted_indices.numel());
   });
+#endif
 
   int64_t max_segments = std::min<int64_t>(numel, num_weights);
 

aten/src/ATen/native/cuda/EmbeddingBag.cu

@@ -31,10 +31,16 @@
 
 #include <c10/macros/Macros.h>
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 #include <thrust/iterator/reverse_iterator.h>
+#endif
 
 namespace at::native {
 
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count);
+#endif
 
 namespace {
 
@@ -193,6 +199,7 @@ Tensor embedding_bag_backward_cuda_sum_avg(
 
   if (scale_grad_by_freq) {
     count = at::empty_like(indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+#if CUB_SUPPORTS_SCAN_BY_KEY()
     AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_backward_cuda_sum_avg", [&] () {
       cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
@@ -219,6 +226,11 @@ Tensor embedding_bag_backward_cuda_sum_avg(
         num_indices
       );
     });
+#else
+    AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_backward_cuda_sum_avg", [&] () {
+      embedding_dense_backward_cuda_scan<index_t>(sorted_indices, count);
+    });
+#endif
   }
   return embedding_backward_cuda_kernel(grad, orig_indices, sorted_indices,
       count, num_weights, padding_idx, mode == EmbeddingBagMode::MEAN, offset2bag,

aten/src/ATen/native/cuda/IGammaKernel.cu

@@ -82,7 +82,7 @@ __host__ __device__ scalar_t lanczos_sum_expg_scaled(scalar_t x) {
   // lanczos approximation
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
 
-  constexpr accscalar_t lanczos_sum_expg_scaled_num[13] = {
+  static const accscalar_t lanczos_sum_expg_scaled_num[13] = {
     0.006061842346248906525783753964555936883222,
     0.5098416655656676188125178644804694509993,
     19.51992788247617482847860966235652136208,
@@ -97,7 +97,7 @@ __host__ __device__ scalar_t lanczos_sum_expg_scaled(scalar_t x) {
     103794043.1163445451906271053616070238554,
     56906521.91347156388090791033559122686859
   };
-  constexpr accscalar_t lanczos_sum_expg_scaled_denom[13] = {
+  static const accscalar_t lanczos_sum_expg_scaled_denom[13] = {
     1.,
     66.,
     1925.,
@@ -126,10 +126,10 @@ __host__ __device__ scalar_t _igam_helper_fac(scalar_t a, scalar_t x) {
 
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
   accscalar_t ax, fac, res, num, numfac;
-  constexpr accscalar_t MAXLOG = std::is_same_v<accscalar_t,double> ?
+  static const accscalar_t MAXLOG = std::is_same_v<accscalar_t,double> ?
     7.09782712893383996843E2 : 88.72283905206835;
-  constexpr accscalar_t EXP1 = 2.718281828459045;
-  constexpr accscalar_t lanczos_g = 6.024680040776729583740234375;
+  static const accscalar_t EXP1 = 2.718281828459045;
+  static const accscalar_t lanczos_g = 6.024680040776729583740234375;
 
   if (::fabs(a - x) > 0.4 * ::fabs(a)) {
     ax = a * ::log(x) - x - ::lgamma(a);
@@ -158,9 +158,9 @@ __host__ __device__ scalar_t _igam_helper_series(scalar_t a, scalar_t x) {
   // Compute igam using DLMF 8.11.4. [igam1]
 
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
-  constexpr accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
+  static const accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
     1.11022302462515654042E-16 : 5.9604644775390625E-8;
-  constexpr int MAXITER = 2000;
+  static const int MAXITER = 2000;
 
   int i;
   accscalar_t ans, ax, c, r;
@@ -196,8 +196,8 @@ __host__ __device__ scalar_t _igamc_helper_series(scalar_t a, scalar_t x) {
   accscalar_t fac = 1;
   accscalar_t sum = 0;
   accscalar_t term, logx;
-  constexpr int MAXITER = 2000;
-  constexpr accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
+  static const int MAXITER = 2000;
+  static const accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
     1.11022302462515654042E-16 : 5.9604644775390625E-8;
 
   for (n = 1; n < MAXITER; n++) {
@@ -219,7 +219,7 @@ __host__ __device__ scalar_t _igam_helper_asymptotic_series(scalar_t a, scalar_t
   // Compute igam/igamc using DLMF 8.12.3/8.12.4 [igam1]
 
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
-  constexpr accscalar_t d[25][25] =
+  static const accscalar_t d[25][25] =
     {{-3.3333333333333333e-1, 8.3333333333333333e-2, -1.4814814814814815e-2, 1.1574074074074074e-3, 3.527336860670194e-4, -1.7875514403292181e-4, 3.9192631785224378e-5, -2.1854485106799922e-6, -1.85406221071516e-6, 8.296711340953086e-7, -1.7665952736826079e-7, 6.7078535434014986e-9, 1.0261809784240308e-8, -4.3820360184533532e-9, 9.1476995822367902e-10, -2.551419399494625e-11, -5.8307721325504251e-11, 2.4361948020667416e-11, -5.0276692801141756e-12, 1.1004392031956135e-13, 3.3717632624009854e-13, -1.3923887224181621e-13, 2.8534893807047443e-14, -5.1391118342425726e-16, -1.9752288294349443e-15},
     {-1.8518518518518519e-3, -3.4722222222222222e-3, 2.6455026455026455e-3, -9.9022633744855967e-4, 2.0576131687242798e-4, -4.0187757201646091e-7, -1.8098550334489978e-5, 7.6491609160811101e-6, -1.6120900894563446e-6, 4.6471278028074343e-9, 1.378633446915721e-7, -5.752545603517705e-8, 1.1951628599778147e-8, -1.7543241719747648e-11, -1.0091543710600413e-9, 4.1627929918425826e-10, -8.5639070264929806e-11, 6.0672151016047586e-14, 7.1624989648114854e-12, -2.9331866437714371e-12, 5.9966963656836887e-13, -2.1671786527323314e-16, -4.9783399723692616e-14, 2.0291628823713425e-14, -4.13125571381061e-15},
     {4.1335978835978836e-3, -2.6813271604938272e-3, 7.7160493827160494e-4, 2.0093878600823045e-6, -1.0736653226365161e-4, 5.2923448829120125e-5, -1.2760635188618728e-5, 3.4235787340961381e-8, 1.3721957309062933e-6, -6.298992138380055e-7, 1.4280614206064242e-7, -2.0477098421990866e-10, -1.4092529910867521e-8, 6.228974084922022e-9, -1.3670488396617113e-9, 9.4283561590146782e-13, 1.2872252400089318e-10, -5.5645956134363321e-11, 1.1975935546366981e-11, -4.1689782251838635e-15, -1.0940640427884594e-12, 4.6622399463901357e-13, -9.905105763906906e-14, 1.8931876768373515e-17, 8.8592218725911273e-15},
@@ -248,7 +248,7 @@ __host__ __device__ scalar_t _igam_helper_asymptotic_series(scalar_t a, scalar_t
 
   int k, n, sgn;
   int maxpow = 0;
-  constexpr accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
+  static const accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
     1.11022302462515654042E-16 : 5.9604644775390625E-8;
   accscalar_t lambda = x / a;
   accscalar_t sigma = (x - a) / a;
@@ -314,12 +314,12 @@ __host__ __device__ scalar_t _igamc_helper_continued_fraction(scalar_t a, scalar
   int i;
   accscalar_t ans, ax, c, yc, r, t, y, z;
   accscalar_t pk, pkm1, pkm2, qk, qkm1, qkm2;
-  constexpr int MAXITER = 2000;
-  constexpr accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
+  static const int MAXITER = 2000;
+  static const accscalar_t MACHEP = std::is_same_v<accscalar_t, double> ?
     1.11022302462515654042E-16 : 5.9604644775390625E-8;
-  constexpr accscalar_t BIG = std::is_same_v<accscalar_t,double> ?
+  static const accscalar_t BIG = std::is_same_v<accscalar_t,double> ?
     4.503599627370496e15 : 16777216.;
-  constexpr accscalar_t BIGINV = std::is_same_v<accscalar_t,double> ?
+  static const accscalar_t BIGINV = std::is_same_v<accscalar_t,double> ?
     2.22044604925031308085e-16 : 5.9604644775390625E-8;
 
   ax = _igam_helper_fac(a, x);
@@ -385,10 +385,10 @@ __noinline__ __host__ __device__ scalar_t calc_igammac(scalar_t a, scalar_t x) {
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
   accscalar_t absxma_a;
 
-  constexpr accscalar_t SMALL = 20.0;
-  constexpr accscalar_t LARGE = 200.0;
-  constexpr accscalar_t SMALLRATIO = 0.3;
-  constexpr accscalar_t LARGERATIO = 4.5;
+  static const accscalar_t SMALL = 20.0;
+  static const accscalar_t LARGE = 200.0;
+  static const accscalar_t SMALLRATIO = 0.3;
+  static const accscalar_t LARGERATIO = 4.5;
 
   if ((x < 0) || (a < 0)) {
     // out of defined-region of the function
@@ -467,10 +467,10 @@ __noinline__ __host__ __device__ scalar_t calc_igamma(scalar_t a, scalar_t x) {
 
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
   accscalar_t absxma_a;
-  constexpr accscalar_t SMALL = 20.0;
-  constexpr accscalar_t LARGE = 200.0;
-  constexpr accscalar_t SMALLRATIO = 0.3;
-  constexpr accscalar_t LARGERATIO = 4.5;
+  static const accscalar_t SMALL = 20.0;
+  static const accscalar_t LARGE = 200.0;
+  static const accscalar_t SMALLRATIO = 0.3;
+  static const accscalar_t LARGERATIO = 4.5;
 
   // boundary values following SciPy
   if ((x < 0) || (a < 0)) {

aten/src/ATen/native/cuda/LegacyThrustHelpers.cu

@@ -0,0 +1,90 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+#include <ATen/core/Tensor.h>
+#include <ATen/native/cuda/SortingCommon.cuh>
+#include <ATen/cuda/cub_definitions.cuh>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/empty_like.h>
+#endif
+
+#include <ATen/cuda/ThrustAllocator.h>
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/unique.h>
+#include <thrust/device_ptr.h>
+#include <thrust/iterator/constant_iterator.h>
+
+namespace at::native {
+
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count) {
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  at::cuda::ThrustAllocator allocator;
+  auto policy = thrust::cuda::par(allocator).on(stream);
+
+  auto num_indices = count.numel();
+
+  // Compute an increasing sequence per unique item in sortedIndices:
+  // sorted: 2 5 5 5 7 7 8 9 9
+  //  count: 1 1 2 3 1 2 1 1 2
+  auto sorted_data = thrust::device_ptr<const index_t>(sorted_indices.const_data_ptr<index_t>());
+  auto count_data = thrust::device_ptr<index_t>(count.mutable_data_ptr<index_t>());
+  thrust::inclusive_scan_by_key(
+    policy,
+    sorted_data,
+    sorted_data + num_indices,
+    thrust::make_constant_iterator(1),
+    count_data
+  );
+
+  // Take the maximum of each count per unique key in reverse:
+  // sorted: 2 5 5 5 7 7 8 9 9
+  //  count: 1 3 3 3 2 2 1 2 2
+  thrust::inclusive_scan_by_key(
+    policy,
+    thrust::make_reverse_iterator(sorted_data + num_indices),
+    thrust::make_reverse_iterator(sorted_data),
+    thrust::make_reverse_iterator(count_data + num_indices),
+    thrust::make_reverse_iterator(count_data + num_indices),
+    thrust::equal_to<index_t>(),
+    thrust::maximum<index_t>()
+  );
+}
+
+template
+void embedding_dense_backward_cuda_scan<int>(Tensor &sorted_indices, Tensor &count);
+template
+void embedding_dense_backward_cuda_scan<int64_t>(Tensor &sorted_indices, Tensor &count);
+
+#endif
+
+template<typename index_t>
+int64_t embedding_backward_cuda_kernel_unique_by_key(const Tensor &sorted_indices, Tensor &segment_offsets) {
+  auto stream = at::cuda::getCurrentCUDAStream();
+  at::cuda::ThrustAllocator allocator;
+  auto policy = thrust::cuda::par(allocator).on(stream);
+  const ptrdiff_t numel = sorted_indices.numel();
+  auto sorted_indices_dev = thrust::device_ptr<const index_t>(sorted_indices.const_data_ptr<index_t>());
+  auto dummy = at::empty_like(sorted_indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+  auto dummy_dev = thrust::device_ptr<index_t>(dummy.mutable_data_ptr<index_t>());
+  auto ends = thrust::unique_by_key_copy(
+          policy,
+          sorted_indices_dev,
+          sorted_indices_dev + numel,
+          thrust::make_counting_iterator(0),
+          dummy_dev,
+          thrust::device_ptr<index_t>(segment_offsets.mutable_data_ptr<index_t>()));
+  return thrust::get<0>(ends) - dummy_dev;
+}
+
+template
+int64_t embedding_backward_cuda_kernel_unique_by_key<int>(const Tensor &sorted_indices, Tensor &segment_offsets);
+template
+int64_t embedding_backward_cuda_kernel_unique_by_key<int64_t>(const Tensor &sorted_indices, Tensor &segment_offsets);
+
+} // namespace at::native

aten/src/ATen/native/cuda/Math.cuh

@@ -231,7 +231,7 @@ const auto lcm_string = jiterator_stringify(
 const auto digamma_string = jiterator_stringify(
   template <typename T>
   T digamma(T x) {
-    static constexpr double PI_f64 = 3.14159265358979323846;
+    static const double PI_f64 = 3.14159265358979323846;
 
     // Short-circuits if x is +/- 0 and returns -/+ ∞ per the C++ standard
     if (x == 0) {
@@ -3072,9 +3072,9 @@ template <typename scalar_t>
 static inline C10_HOST_DEVICE scalar_t calc_digamma(scalar_t in) {
   // [C++ Standard Reference: Gamma Function] https://en.cppreference.com/w/cpp/numeric/math/tgamma
   using accscalar_t = at::acc_type<scalar_t, /*is_cuda=*/true>;
-  static constexpr double PI_f64 = 3.14159265358979323846;
-  constexpr accscalar_t PSI_10 = 2.25175258906672110764;
-  constexpr accscalar_t A[] = {
+  static const double PI_f64 = 3.14159265358979323846;
+  const accscalar_t PSI_10 = 2.25175258906672110764;
+  const accscalar_t A[] = {
       8.33333333333333333333E-2,
       -2.10927960927960927961E-2,
       7.57575757575757575758E-3,

aten/src/ATen/native/cuda/NLLLoss2d.cu

@@ -146,7 +146,6 @@ __global__ void nll_loss2d_backward_no_reduce_kernel(
   int64_t batch_size = target.size(0);
   int64_t H = target.size(1);
   int64_t W = target.size(2);
-  int64_t n_classes = grad_input.size(1);
 
   CUDA_KERNEL_LOOP(index, n_threads) {
     const int64_t b = index % batch_size;
@@ -157,7 +156,6 @@ __global__ void nll_loss2d_backward_no_reduce_kernel(
     if (cur_target == ignore_index) {
       continue;
     }
-    CUDA_KERNEL_ASSERT(cur_target >= 0 && cur_target < n_classes);
     scalar_t value = -(weight != nullptr ? weight[cur_target] : static_cast<scalar_t>(1));
     grad_input[b][cur_target][h][w] = value * grad_output[b][h][w];
   }

aten/src/ATen/native/cuda/Reduce.cuh

@@ -413,12 +413,14 @@ struct ReduceOp {
       value = thread_reduce<output_vec_size>(input_slice);
     }
 
-    if (config.should_block_x_reduce()) {
-      value = block_x_reduce<output_vec_size>(value, shared_memory);
-    }
     if (config.should_block_y_reduce()) {
       value = block_y_reduce<output_vec_size>(value, shared_memory);
     }
+    __syncthreads();
+    if (config.should_block_x_reduce()) {
+      value = block_x_reduce<output_vec_size>(value, shared_memory);
+    }
+
     using out_ptr_vec_t = std::array<out_scalar_t*, output_vec_size>;
     using offset_vec_t = std::array<index_t, output_vec_size>;
     offset_vec_t base_offsets;
@@ -653,14 +655,8 @@ struct ReduceOp {
     }
 
     __syncthreads();
-    // Intra-warp reduction, fix CUDA to have offset decreasing for better numerics
-    // matching Triton, etc.
-    // TODO(PaulZhang12): AMD and internal
-    #if defined(USE_ROCM) || defined(FBCODE_CAFFE2)
+
     for (int offset = 1; offset < dim_x; offset <<= 1) {
-    #else
-    for (int offset = dim_x >> 1; offset > 0; offset >>= 1) {
-    #endif
       #pragma unroll
       for (int i = 0; i < output_vec_size; i++) {
         arg_t other = ops.warp_shfl_down(value[i], offset);
@@ -1095,7 +1091,11 @@ ReduceConfig setReduceConfig(const TensorIterator& iter){
   // threads with different threadIdx.x are independent and will produce results for different outputs.
   // In such case, values in each loaded vector always correspond to different outputs.
   if (fastest_moving_stride == sizeof(scalar_t)) {
+#ifdef USE_ROCM
     if (reduction_on_fastest_striding_dimension && dim0 >= 128 && iter.num_reduce_dims() == 1) {
+#else
+    if (reduction_on_fastest_striding_dimension && dim0 > 128 && iter.num_reduce_dims() == 1 && vt0 >= input_vec_size) {
+#endif
       // Case 1: "vectorize along input"
       // Note that if vt0 < ReduceConfig::vec_size, then this means the register pressure could be high, in such case,
       // we should avoid vectorization.

aten/src/ATen/native/cuda/ReduceMomentKernel.cu

@@ -39,15 +39,10 @@ static void std_var_kernel_cuda(TensorIterator& iter, double correction, bool ta
 template <typename scalar_t, typename acc_t=scalar_t, typename out_t=scalar_t>
 void mean_kernel_impl(TensorIterator& iter) {
   //  returns acc_t for all non-complex dtypes and returns T for c10::complex<T>
-  constexpr bool is_16_bits = sizeof(scalar_t) == 2;
   using factor_t = typename c10::scalar_value_type<acc_t>::type;
   factor_t factor = static_cast<factor_t>(iter.num_output_elements()) / iter.numel();
-  if constexpr (is_16_bits) {
-    gpu_reduce_kernel<scalar_t, out_t, /*vt0=*/4, /*input_vec_size=*/8>(iter, MeanOps<scalar_t, acc_t, factor_t, out_t> {factor});
-  } else {
   gpu_reduce_kernel<scalar_t, out_t>(iter, MeanOps<scalar_t, acc_t, factor_t, out_t> {factor});
 }
-}
 
 static void mean_kernel_cuda(TensorIterator& iter) {
   if (iter.dtype() == kHalf) {