test fix-ubuntu-distro change for driver installation

This reverts commit bf7ca1e0dd66de725f24905c4d192853bde41ac3.

.bc-linter.yml

@@ -13,4 +13,3 @@ exclude:
   - "**/benchmarks/**"
   - "**/test_*.py"
   - "**/*_test.py"
-  - "tools/**"

.ci/docker/build.sh

@@ -136,6 +136,17 @@ case "$tag" in
     TRITON=yes
     INDUCTOR_BENCHMARKS=yes
     ;;
+  pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks)
+    CUDA_VERSION=13.0.0
+    ANACONDA_PYTHON_VERSION=3.10
+    GCC_VERSION=9
+    VISION=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    TRITON=yes
+    INDUCTOR_BENCHMARKS=yes
+    ;;
   pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-vllm)
     CUDA_VERSION=12.8.1
     ANACONDA_PYTHON_VERSION=3.12
@@ -195,16 +206,13 @@ case "$tag" in
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-jammy-xpu-n-py3 | pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks)
+  pytorch-linux-jammy-xpu-n-py3)
     ANACONDA_PYTHON_VERSION=3.10
     GCC_VERSION=11
     VISION=yes
     XPU_VERSION=2025.2
     NINJA_VERSION=1.9.0
     TRITON=yes
-    if [[ $tag =~ "benchmarks" ]]; then
-      INDUCTOR_BENCHMARKS=yes
-    fi
     ;;
   pytorch-linux-jammy-py3-gcc11-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10

.ci/docker/common/install_acl.sh

@@ -3,7 +3,7 @@
 
 set -eux
 
-ACL_VERSION=${ACL_VERSION:-"v52.6.0"}
+ACL_VERSION=${ACL_VERSION:-"v25.02"}
 ACL_INSTALL_DIR="/acl"
 
 # Clone ACL

.ci/docker/common/install_rocm_magma.sh

@@ -12,8 +12,8 @@ function do_install() {
 
     rocm_version_nodot=${rocm_version//./}
 
-    # post merge of https://github.com/icl-utk-edu/magma/pull/65
-    MAGMA_VERSION=c0792ae825fb36872784892ea643dd6f3456bc5f
+    # https://github.com/icl-utk-edu/magma/pull/65
+    MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
     magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
 
     rocm_dir="/opt/rocm"

.ci/docker/manywheel/build.sh

@@ -97,7 +97,7 @@ case ${image} in
     manylinux2_28-builder:xpu)
         TARGET=xpu_final
         GPU_IMAGE=amd64/almalinux:8
-        DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13"
+        DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
         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==0.2.0
-#Description: This is needed to generate PyTorch docs
-#Pinned versions: 0.2.0
+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-xpu/Dockerfile

@@ -54,15 +54,12 @@ ENV OPENSSL_DIR /opt/openssl
 RUN rm install_openssl.sh
 
 ARG INDUCTOR_BENCHMARKS
-ARG ANACONDA_PYTHON_VERSION
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
 COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
 COPY ./common/common_utils.sh common_utils.sh
 COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
 COPY ci_commit_pins/timm.txt timm.txt
-COPY ci_commit_pins/torchbench.txt torchbench.txt
 RUN if [ -n "${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
+RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt
 
 # Install XPU Dependencies
 ARG XPU_VERSION

.ci/magma-rocm/Makefile

@@ -1,7 +1,7 @@
 SHELL=/usr/bin/env bash
 
 DOCKER_CMD ?= docker
-DESIRED_ROCM ?= 7.1
+DESIRED_ROCM ?= 7.0
 DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
 PACKAGE_NAME = magma-rocm
 # inherit this from underlying docker image, do not pass this env var to docker
@@ -16,7 +16,6 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	magma-rocm/build_magma.sh
 
 .PHONY: all
-all: magma-rocm71
 all: magma-rocm70
 all: magma-rocm64
 
@@ -25,11 +24,6 @@ clean:
 	$(RM) -r magma-*
 	$(RM) -r output
 
-.PHONY: magma-rocm71
-magma-rocm71: DESIRED_ROCM := 7.1
-magma-rocm71:
-	$(DOCKER_RUN)
-
 .PHONY: magma-rocm70
 magma-rocm70: DESIRED_ROCM := 7.0
 magma-rocm70:

.ci/magma-rocm/build_magma.sh

@@ -6,8 +6,8 @@ set -eou pipefail
 # The script expects DESIRED_CUDA and PACKAGE_NAME to be set
 ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
 
-# post merge of https://github.com/icl-utk-edu/magma/pull/65
-MAGMA_VERSION=c0792ae825fb36872784892ea643dd6f3456bc5f
+# https://github.com/icl-utk-edu/magma/pull/65
+MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
 
 # Folders for the build
 PACKAGE_FILES=${ROOT_DIR}/magma-rocm/package_files # metadata
@@ -20,7 +20,7 @@ mkdir -p ${PACKAGE_DIR} ${PACKAGE_OUTPUT}/linux-64 ${PACKAGE_BUILD} ${PACKAGE_RE
 
 # Fetch magma sources and verify checksum
 pushd ${PACKAGE_DIR}
-git clone https://github.com/icl-utk-edu/magma
+git clone https://github.com/jeffdaily/magma
 pushd magma
 git checkout ${MAGMA_VERSION}
 popd

.ci/pytorch/build.sh

@@ -36,6 +36,11 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
   nvcc --version
 fi
 
+if [[ "$BUILD_ENVIRONMENT" == *cuda13* ]]; then
+  # Disable FBGEMM for CUDA 13 builds
+  export USE_FBGEMM=0
+fi
+
 if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
   if [[ "$BUILD_ENVIRONMENT" != *clang* ]]; then
     # TODO: there is a linking issue when building with UCC using clang,
@@ -426,7 +431,7 @@ fi
 if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
   # export test times so that potential sharded tests that'll branch off this build will use consistent data
   # don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
-  PYTHONPATH=. python tools/stats/export_test_times.py
+  python tools/stats/export_test_times.py
 fi
 # don't do this for bazel or s390x or riscv64 as they don't use sccache
 if [[ "$BUILD_ENVIRONMENT" != *s390x* && "$BUILD_ENVIRONMENT" != *riscv64* && "$BUILD_ENVIRONMENT" != *-bazel-* ]]; then

.ci/pytorch/common_utils.sh

@@ -286,7 +286,10 @@ EOF
     rm -rf fbgemm
   else
     pip_build_and_install "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}" dist/torchrec
-    pip_build_and_install "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#subdirectory=fbgemm_gpu" dist/fbgemm_gpu
+    # Skip fbgemm for CUDA 13 as it's not compatible yet
+    if [[ "$BUILD_ENVIRONMENT" != *cuda13* ]]; then
+      pip_build_and_install "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#subdirectory=fbgemm_gpu" dist/fbgemm_gpu
+    fi
   fi
 }
 

.ci/pytorch/python_doc_push_script.sh

@@ -89,23 +89,20 @@ if [ "$is_main_doc" = true ]; then
 
   make coverage
   # Now we have the coverage report, we need to make sure it is empty.
-  # Sphinx 7.2.6+ format: python.txt contains a statistics table with a TOTAL row
-  # showing the undocumented count in the third column.
-  # Example: | TOTAL | 99.83% | 2 |
+  # Count the number of lines in the file and turn that number into a variable
+  # $lines. The `cut -f1 ...` is to only parse the number, not the filename
+  # Skip the report header by subtracting 2: the header will be output even if
+  # there are no undocumented items.
   #
   # Also: see docs/source/conf.py for "coverage_ignore*" items, which should
   # be documented then removed from there.
-
-  # Extract undocumented count from TOTAL row in Sphinx 7.2.6 statistics table
-  # The table format is: | Module | Coverage | Undocumented |
-  # Extract the third column (undocumented count) from the TOTAL row
-  undocumented=$(grep "| TOTAL" build/coverage/python.txt | awk -F'|' '{print $4}' | tr -d ' ')
-
-  if [ -z "$undocumented" ] || ! [[ "$undocumented" =~ ^[0-9]+$ ]]; then
+  lines=$(wc -l build/coverage/python.txt 2>/dev/null |cut -f1 -d' ')
+  undocumented=$((lines - 2))
+  if [ $undocumented -lt 0 ]; then
     echo coverage output not found
     exit 1
-  elif [ "$undocumented" -gt 0 ]; then
-    echo "undocumented objects found:"
+  elif [ $undocumented -gt 0 ]; then
+    echo undocumented objects found:
     cat build/coverage/python.txt
     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'"

.ci/pytorch/test.sh

@@ -572,8 +572,6 @@ fi
 
 if [[ "${TEST_CONFIG}" == *cpu* ]]; then
   DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
-elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
-  DYNAMO_BENCHMARK_FLAGS+=(--device xpu)
 else
   DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
 fi
@@ -667,8 +665,6 @@ test_perf_for_dashboard() {
     device=cuda_b200
   elif [[ "${TEST_CONFIG}" == *rocm* ]]; then
     device=rocm
-  elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
-    device=xpu
   fi
 
   for mode in "${modes[@]}"; do
@@ -838,8 +834,14 @@ test_dynamo_benchmark() {
   local shard_id="$1"
   shift
 
+  # Exclude torchrec_dlrm for CUDA 13 as FBGEMM is not compatible
+  local extra_args=()
+  if [[ "$BUILD_ENVIRONMENT" == *cuda13* ]]; then
+    extra_args=(--exclude-exact torchrec_dlrm)
+  fi
+
   if [[ "${TEST_CONFIG}" == *perf_compare* ]]; then
-    test_single_dynamo_benchmark "training" "$suite" "$shard_id" --training --amp "$@"
+    test_single_dynamo_benchmark "training" "$suite" "$shard_id" --training --amp "${extra_args[@]}" "$@"
   elif [[ "${TEST_CONFIG}" == *perf* ]]; then
     # TODO (huydhn): Just smoke test some sample models
     if [[ "${TEST_CONFIG}" == *b200* ]]; then
@@ -851,7 +853,7 @@ test_dynamo_benchmark() {
         export TORCHBENCH_ONLY_MODELS="BERT_pytorch"
       fi
     fi
-    test_single_dynamo_benchmark "dashboard" "$suite" "$shard_id" "$@"
+    test_single_dynamo_benchmark "dashboard" "$suite" "$shard_id" "${extra_args[@]}" "$@"
   else
     if [[ "${TEST_CONFIG}" == *cpu* ]]; then
       local dt="float32"
@@ -859,17 +861,17 @@ test_dynamo_benchmark() {
         dt="amp"
       fi
       if [[ "${TEST_CONFIG}" == *freezing* ]]; then
-        test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" --freezing "$@"
+        test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" --freezing "${extra_args[@]}" "$@"
       else
-        test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" "$@"
+        test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" "${extra_args[@]}" "$@"
       fi
     elif [[ "${TEST_CONFIG}" == *aot_inductor* ]]; then
-      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
+      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "${extra_args[@]}" "$@"
     elif [[ "${TEST_CONFIG}" == *max_autotune_inductor* ]]; then
-      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
+      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "${extra_args[@]}" "$@"
     else
-      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
-      test_single_dynamo_benchmark "training" "$suite" "$shard_id" --training --amp "$@"
+      test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "${extra_args[@]}" "$@"
+      test_single_dynamo_benchmark "training" "$suite" "$shard_id" --training --amp "${extra_args[@]}" "$@"
     fi
   fi
 }
@@ -1761,7 +1763,7 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
   else
     # Do this after checkout_install_torchbench to ensure we clobber any
     # nightlies that torchbench may pull in
-    if [[ "${TEST_CONFIG}" != *cpu* && "${TEST_CONFIG}" != *xpu* ]]; then
+    if [[ "${TEST_CONFIG}" != *cpu* ]]; then
       install_torchrec_and_fbgemm
     fi
     PYTHONPATH=/torchbench test_dynamo_benchmark torchbench "$id"

.clang-tidy

@@ -60,11 +60,9 @@ performance-*,
 readability-container-size-empty,
 readability-delete-null-pointer,
 readability-duplicate-include,
-readability-named-parameter,
 readability-misplaced-array-index,
 readability-redundant*,
 readability-simplify-subscript-expr,
-readability-static-definition-in-anonymous-namespace
 readability-string-compare,
 -readability-redundant-access-specifiers,
 -readability-redundant-control-flow,

.claude/skills/add-uint-support/SKILL.md

@@ -1,319 +0,0 @@
----
-name: add-uint-support
-description: Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
----
-
-# Add Unsigned Integer (uint) Support to Operators
-
-This skill helps add support for unsigned integer types (uint16, uint32, uint64) to PyTorch operators by updating their AT_DISPATCH macros.
-
-## When to use this skill
-
-Use this skill when:
-- Adding uint16, uint32, or uint64 support to an operator
-- User mentions "unsigned types", "uint support", "barebones unsigned types"
-- Enabling support for kUInt16, kUInt32, kUInt64 in kernels
-- Working with operator implementations that need expanded type coverage
-
-## Quick reference
-
-**Add unsigned types to existing dispatch:**
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES));
-
-// After (method 1: add unsigned types explicitly)
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES));
-
-// After (method 2: use V2 integral types if AT_INTEGRAL_TYPES present)
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
-```
-
-## Type group reference
-
-**Unsigned type groups:**
-- `AT_BAREBONES_UNSIGNED_TYPES`: kUInt16, kUInt32, kUInt64
-- `AT_INTEGRAL_TYPES_V2`: AT_INTEGRAL_TYPES + AT_BAREBONES_UNSIGNED_TYPES
-
-**Relationship:**
-```cpp
-AT_INTEGRAL_TYPES          // kByte, kChar, kInt, kLong, kShort
-AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
-AT_INTEGRAL_TYPES_V2       // INTEGRAL_TYPES + BAREBONES_UNSIGNED_TYPES
-```
-
-## Instructions
-
-### Step 1: Determine if conversion to V2 is needed
-
-Check if the file uses AT_DISPATCH_V2:
-
-**If using old AT_DISPATCH:**
-- First convert to AT_DISPATCH_V2 using the at-dispatch-v2 skill
-- Then proceed with adding uint support
-
-**If already using AT_DISPATCH_V2:**
-- Proceed directly to Step 2
-
-### Step 2: Analyze the current dispatch macro
-
-Identify what type groups are currently in use:
-
-```cpp
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  // body
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-    ^^^^^^^^^^^^^^^^^^^^^^^^^
-    Current type coverage
-```
-
-Common patterns:
-- `AT_EXPAND(AT_ALL_TYPES)` → includes AT_INTEGRAL_TYPES + AT_FLOATING_TYPES
-- `AT_EXPAND(AT_INTEGRAL_TYPES)` → signed integers only
-- `AT_EXPAND(AT_FLOATING_TYPES)` → floating point types
-
-### Step 3: Choose the uint addition method
-
-Two approaches:
-
-**Method 1: Add AT_BAREBONES_UNSIGNED_TYPES explicitly**
-- Use when: You want to be explicit about adding uint support
-- Add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the type list
-
-**Method 2: Substitute AT_INTEGRAL_TYPES with AT_INTEGRAL_TYPES_V2**
-- Use when: The dispatch already uses `AT_EXPAND(AT_INTEGRAL_TYPES)`
-- More concise: replaces one type group with its superset
-- Only applicable if AT_INTEGRAL_TYPES is present
-
-### Step 4: Apply the transformation
-
-**Method 1 example:**
-```cpp
-// Before
-AT_DISPATCH_V2(
-    dtype,
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      kernel_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    kBFloat16, kHalf, kBool
-);
-
-// After (add unsigned types)
-AT_DISPATCH_V2(
-    dtype,
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      kernel_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
-    kBFloat16, kHalf, kBool
-);
-```
-
-**Method 2 example:**
-```cpp
-// Before
-AT_DISPATCH_V2(
-    dtype,
-    "integral_op",
-    AT_WRAP([&]() {
-      kernel<scalar_t>();
-    }),
-    AT_EXPAND(AT_INTEGRAL_TYPES)
-);
-
-// After (substitute with V2)
-AT_DISPATCH_V2(
-    dtype,
-    "integral_op",
-    AT_WRAP([&]() {
-      kernel<scalar_t>();
-    }),
-    AT_EXPAND(AT_INTEGRAL_TYPES_V2)
-);
-```
-
-### Step 5: Handle AT_ALL_TYPES vs individual type groups
-
-If the dispatch uses `AT_EXPAND(AT_ALL_TYPES)`:
-- `AT_ALL_TYPES` = `AT_INTEGRAL_TYPES` + `AT_FLOATING_TYPES`
-- To add uint: add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the list
-
-If the dispatch separately lists INTEGRAL and FLOATING:
-```cpp
-// Before
-AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
-
-// After (Method 2 preferred)
-AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES)
-```
-
-### Step 6: Verify all dispatch sites
-
-Check the file for ALL dispatch macros that need uint support:
-- Some operators have multiple dispatch sites (CPU, CUDA, different functions)
-- Apply the transformation consistently across all sites
-- Ensure each gets the same type coverage updates
-
-### Step 7: Validate the changes
-
-Check that:
-- [ ] AT_DISPATCH_V2 format is used (not old AT_DISPATCH)
-- [ ] Unsigned types are added via one of the two methods
-- [ ] All relevant dispatch sites in the file are updated
-- [ ] Type groups use `AT_EXPAND()`
-- [ ] Arguments are properly formatted and comma-separated
-
-## Common patterns
-
-### Pattern 1: AT_ALL_TYPES + extras
-
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
-```
-
-### Pattern 2: Separate INTEGRAL + FLOATING
-
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
-```
-
-### Pattern 3: Old dispatch needs conversion first
-
-```cpp
-// Before (needs v2 conversion first)
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
-  kernel<scalar_t>();
-});
-
-// After v2 conversion
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-
-// After adding uint support
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
-```
-
-## Multiple dispatch sites example
-
-For a file with multiple functions:
-
-```cpp
-void min_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
-    impl<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
-  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  //                           Added uint support
-}
-
-void min_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
-    gpu_reduce_kernel<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
-  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  //                           Added uint support here too
-}
-```
-
-## Decision tree
-
-Use this decision tree to determine the approach:
-
-```
-Is the file using AT_DISPATCH_V2?
-├─ No → Use at-dispatch-v2 skill first, then continue
-└─ Yes
-   └─ Does it use AT_EXPAND(AT_INTEGRAL_TYPES)?
-      ├─ Yes → Replace with AT_EXPAND(AT_INTEGRAL_TYPES_V2)
-      └─ No → Add AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to type list
-```
-
-## Edge cases
-
-### Case 1: Dispatch with only floating types
-
-If the operator only supports floating point types, don't add uint support:
-
-```cpp
-// Leave as-is - floating point only operator
-AT_DISPATCH_V2(dtype, "float_op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf);
-```
-
-### Case 2: Complex types present
-
-Unsigned types work alongside complex types:
-
-```cpp
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
-    AT_EXPAND(AT_COMPLEX_TYPES),
-    kHalf, kBFloat16);
-```
-
-### Case 3: Already has uint support
-
-Check if uint types are already present:
-- If `AT_INTEGRAL_TYPES_V2` is used → already has uint support
-- If `AT_BAREBONES_UNSIGNED_TYPES` is already in list → already has uint support
-- Skip the file if uint support is already present
-
-## Workflow
-
-When asked to add uint support:
-
-1. Read the target file
-2. Check if using AT_DISPATCH_V2:
-   - If not → use at-dispatch-v2 skill first
-3. Identify all dispatch macro sites
-4. For each dispatch:
-   - Analyze current type groups
-   - Choose method (add BAREBONES_UNSIGNED or upgrade to V2)
-   - Apply transformation with Edit tool
-5. Show the user the changes
-6. Explain what was modified
-
-## Important notes
-
-- Always check if v2 conversion is needed first
-- Apply changes consistently across all dispatch sites in the file
-- Method 2 (AT_INTEGRAL_TYPES_V2) is cleaner when applicable
-- Method 1 (explicit AT_BAREBONES_UNSIGNED_TYPES) is more explicit
-- Unsigned types are: kUInt16, kUInt32, kUInt64 (not kByte which is uint8)
-- Some operators may not semantically support unsigned types - use judgment
-
-## Testing
-
-After adding uint support, the operator should accept uint16, uint32, and uint64 tensors. The user is responsible for functional testing.

.claude/skills/at-dispatch-v2/SKILL.md

@@ -1,305 +0,0 @@
----
-name: at-dispatch-v2
-description: Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
----
-
-# AT_DISPATCH to AT_DISPATCH_V2 Converter
-
-This skill helps convert PyTorch's legacy AT_DISPATCH macros to the new AT_DISPATCH_V2 format, as defined in `aten/src/ATen/Dispatch_v2.h`.
-
-## When to use this skill
-
-Use this skill when:
-- Converting AT_DISPATCH_* macros to AT_DISPATCH_V2
-- Porting ATen kernels to use the new dispatch API
-- Working with files in `aten/src/ATen/native/` that use dispatch macros
-- User mentions "AT_DISPATCH", "dispatch v2", "Dispatch_v2.h", or macro conversion
-
-## Quick reference
-
-**Old format:**
-```cpp
-AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, dtype, "kernel_name", [&]() {
-  // lambda body
-});
-```
-
-**New format:**
-```cpp
-AT_DISPATCH_V2(dtype, "kernel_name", AT_WRAP([&]() {
-  // lambda body
-}), AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool);
-```
-
-## Key transformations
-
-1. **Reorder arguments**: `scalar_type` and `name` come first, then lambda, then types
-2. **Wrap the lambda**: Use `AT_WRAP(lambda)` to handle internal commas
-3. **Expand type groups**: Use `AT_EXPAND(AT_ALL_TYPES)` instead of implicit expansion
-4. **List individual types**: Add extra types (kHalf, kBFloat16, etc.) after expanded groups
-5. **Add include**: `#include <ATen/Dispatch_v2.h>` near other Dispatch includes
-
-## Instructions
-
-### Step 1: Add the Dispatch_v2.h include
-
-Add the v2 header near the existing `#include <ATen/Dispatch.h>`:
-
-```cpp
-#include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
-```
-
-Keep the old Dispatch.h include for now (other code may still need it).
-
-### Step 2: Identify the old dispatch pattern
-
-Common patterns to convert:
-
-- `AT_DISPATCH_ALL_TYPES_AND{2,3,4}(type1, type2, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_FLOATING_TYPES_AND{2,3}(type1, type2, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND{2,3}(type1, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND{2,3}(type1, ..., scalar_type, name, lambda)`
-
-### Step 3: Map the old macro to type groups
-
-Identify which type group macro corresponds to the base types:
-
-| Old macro base | AT_DISPATCH_V2 type group |
-|----------------|---------------------------|
-| `ALL_TYPES` | `AT_EXPAND(AT_ALL_TYPES)` |
-| `FLOATING_TYPES` | `AT_EXPAND(AT_FLOATING_TYPES)` |
-| `INTEGRAL_TYPES` | `AT_EXPAND(AT_INTEGRAL_TYPES)` |
-| `COMPLEX_TYPES` | `AT_EXPAND(AT_COMPLEX_TYPES)` |
-| `ALL_TYPES_AND_COMPLEX` | `AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX)` |
-
-For combined patterns, use multiple `AT_EXPAND()` entries:
-```cpp
-// Old: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(...)
-// New: AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES), type1, type2
-```
-
-### Step 4: Extract the individual types
-
-From `AT_DISPATCH_*_AND2(type1, type2, ...)` or `AT_DISPATCH_*_AND3(type1, type2, type3, ...)`, extract the individual types (type1, type2, etc.).
-
-These become the trailing arguments after the type group:
-```cpp
-AT_DISPATCH_V2(..., AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool)
-                                             ^^^^^^^^^^^^^^^^^^^^^^^^
-                                             Individual types from AND3
-```
-
-### Step 5: Transform to AT_DISPATCH_V2
-
-Apply the transformation:
-
-**Pattern:**
-```cpp
-AT_DISPATCH_V2(
-  scalar_type,           // 1st: The dtype expression
-  "name",                // 2nd: The debug string
-  AT_WRAP(lambda),       // 3rd: The lambda wrapped in AT_WRAP
-  type_groups,           // 4th+: Type groups with AT_EXPAND()
-  individual_types       // Last: Individual types
-)
-```
-
-**Example transformation:**
-```cpp
-// BEFORE
-AT_DISPATCH_ALL_TYPES_AND3(
-    kBFloat16, kHalf, kBool,
-    iter.dtype(),
-    "min_values_cuda",
-    [&]() {
-      min_values_kernel_cuda_impl<scalar_t>(iter);
-    }
-);
-
-// AFTER
-AT_DISPATCH_V2(
-    iter.dtype(),
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      min_values_kernel_cuda_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    kBFloat16, kHalf, kBool
-);
-```
-
-### Step 6: Handle multi-line lambdas
-
-For lambdas with internal commas or complex expressions, AT_WRAP is essential:
-
-```cpp
-AT_DISPATCH_V2(
-    dtype,
-    "complex_kernel",
-    AT_WRAP([&]() {
-      gpu_reduce_kernel<scalar_t, scalar_t>(
-        iter,
-        MinOps<scalar_t>{},
-        thrust::pair<scalar_t, int64_t>(upper_bound(), 0)  // Commas inside!
-      );
-    }),
-    AT_EXPAND(AT_ALL_TYPES)
-);
-```
-
-### Step 7: Verify the conversion
-
-Check that:
-- [ ] `AT_WRAP()` wraps the entire lambda
-- [ ] Type groups use `AT_EXPAND()`
-- [ ] Individual types don't have `AT_EXPAND()` (just `kBFloat16`, not `AT_EXPAND(kBFloat16)`)
-- [ ] Argument order is: scalar_type, name, lambda, types
-- [ ] Include added: `#include <ATen/Dispatch_v2.h>`
-
-## Type group reference
-
-Available type group macros (use with `AT_EXPAND()`):
-
-```cpp
-AT_INTEGRAL_TYPES      // kByte, kChar, kInt, kLong, kShort
-AT_FLOATING_TYPES      // kDouble, kFloat
-AT_COMPLEX_TYPES       // kComplexDouble, kComplexFloat
-AT_QINT_TYPES         // kQInt8, kQUInt8, kQInt32
-AT_ALL_TYPES          // INTEGRAL_TYPES + FLOATING_TYPES
-AT_ALL_TYPES_AND_COMPLEX  // ALL_TYPES + COMPLEX_TYPES
-AT_INTEGRAL_TYPES_V2  // INTEGRAL_TYPES + unsigned types
-AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
-AT_FLOAT8_TYPES       // Float8 variants
-```
-
-## Common patterns
-
-### Pattern: AT_DISPATCH_ALL_TYPES_AND2
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
-  kernel<scalar_t>(data);
-});
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>(data);
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-```
-
-### Pattern: AT_DISPATCH_FLOATING_TYPES_AND3
-
-```cpp
-// Before
-AT_DISPATCH_FLOATING_TYPES_AND3(kHalf, kBFloat16, kFloat8_e4m3fn,
-    tensor.scalar_type(), "float_op", [&] {
-  process<scalar_t>(tensor);
-});
-
-// After
-AT_DISPATCH_V2(tensor.scalar_type(), "float_op", AT_WRAP([&] {
-  process<scalar_t>(tensor);
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn);
-```
-
-### Pattern: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
-    kComplexHalf, kHalf,
-    self.scalar_type(),
-    "complex_op",
-    [&] {
-      result = compute<scalar_t>(self);
-    }
-);
-
-// After
-AT_DISPATCH_V2(
-    self.scalar_type(),
-    "complex_op",
-    AT_WRAP([&] {
-      result = compute<scalar_t>(self);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_COMPLEX_TYPES),
-    kComplexHalf,
-    kHalf
-);
-```
-
-## Edge cases
-
-### Case 1: No extra types (rare)
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES(dtype, "op", [&]() { kernel<scalar_t>(); });
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES));
-```
-
-### Case 2: Many individual types (AND4, AND5, etc.)
-
-```cpp
-// Before
-AT_DISPATCH_FLOATING_TYPES_AND4(kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2,
-    dtype, "float8_op", [&]() { kernel<scalar_t>(); });
-
-// After
-AT_DISPATCH_V2(dtype, "float8_op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2);
-```
-
-### Case 3: Lambda with no captures
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBool, dtype, "op", []() {
-  static_kernel<scalar_t>();
-});
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([]() {
-  static_kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBool);
-```
-
-## Benefits of AT_DISPATCH_V2
-
-1. **No arity in macro name**: Don't need different macros for AND2, AND3, AND4
-2. **Composable type sets**: Mix and match type groups with `AT_EXPAND()`
-3. **Extensible**: Easy to add more types without hitting macro limits
-4. **Clearer**: Type groups are explicit, not implicit in macro name
-
-## Important notes
-
-- Keep `#include <ATen/Dispatch.h>` - other code may need it
-- The `AT_WRAP()` is mandatory - prevents comma parsing issues in the lambda
-- Type groups need `AT_EXPAND()`, individual types don't
-- The v2 API is in `aten/src/ATen/Dispatch_v2.h` - refer to it for full docs
-- See the header file for the Python script to regenerate the macro implementation
-
-## Workflow
-
-When asked to convert AT_DISPATCH macros:
-
-1. Read the file to identify all AT_DISPATCH uses
-2. Add `#include <ATen/Dispatch_v2.h>` if not present
-3. For each dispatch macro:
-   - Identify the pattern and extract components
-   - Map the base type group
-   - Extract individual types
-   - Construct the AT_DISPATCH_V2 call
-   - Apply with Edit tool
-4. Show the user the complete converted file
-5. Explain what was changed
-
-Do NOT compile or test the code - focus on accurate conversion only.

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-cfbc5c2f1c798991715a6b06bb3ce46478c4487c
+218d2ab791d437309f91e0486eb9fa7f00badc17

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-c8b09f5f77d6bf6fb7ed7a9aa83e5d8156b3a5e9
+df6798dfb931ce7c7fe5bed2447cd1092a5981af

.github/pytorch-probot.yml

@@ -19,7 +19,6 @@ ciflow_push_tags:
 - ciflow/inductor-perf-test-nightly-rocm-mi300
 - ciflow/inductor-perf-test-nightly-rocm-mi355
 - ciflow/inductor-perf-test-nightly-x86-zen
-- ciflow/inductor-perf-test-nightly-xpu
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm
 - ciflow/linux-aarch64

.github/scripts/generate_binary_build_matrix.py

@@ -11,17 +11,11 @@ architectures:
     * Latest XPU
 """
 
-import json
 import os
-import re
-from pathlib import Path
 from typing import Optional
 
 
-SCRIPT_DIR = Path(__file__).absolute().parent
-REPO_ROOT = SCRIPT_DIR.parent.parent
-
-
+# NOTE: Please also update the CUDA sources in `PIP_SOURCES` in tools/nightly.py when changing this
 CUDA_ARCHES = ["12.6", "12.8", "12.9", "13.0"]
 CUDA_STABLE = "12.8"
 CUDA_ARCHES_FULL_VERSION = {
@@ -37,7 +31,8 @@ CUDA_ARCHES_CUDNN_VERSION = {
     "13.0": "9",
 }
 
-ROCM_ARCHES = ["7.0", "7.1"]
+# NOTE: Please also update the ROCm sources in `PIP_SOURCES` in tools/nightly.py when changing this
+ROCM_ARCHES = ["6.4", "7.0"]
 
 XPU_ARCHES = ["xpu"]
 
@@ -142,48 +137,9 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
 }
 
 
-# Used by tools/nightly.py
-PYTORCH_NIGHTLY_PIP_INDEX_URL = "https://download.pytorch.org/whl/nightly"
-NIGHTLY_SOURCE_MATRIX = {
-    "cpu": dict(
-        name="cpu",
-        index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/cpu",
-        supported_platforms=["Linux", "macOS", "Windows"],
-        accelerator="cpu",
-    )
-}
-CUDA_NIGHTLY_SOURCE_MATRIX = {
-    f"cuda-{major}.{minor}": dict(
-        name=f"cuda-{major}.{minor}",
-        index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/cu{major}{minor}",
-        supported_platforms=["Linux", "Windows"],
-        accelerator="cuda",
-    )
-    for major, minor in (map(int, version.split(".")) for version in CUDA_ARCHES)
-}
-ROCM_NIGHTLY_SOURCE_MATRIX = {
-    f"rocm-{major}.{minor}": dict(
-        name=f"rocm-{major}.{minor}",
-        index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/rocm{major}.{minor}",
-        supported_platforms=["Linux"],
-        accelerator="rocm",
-    )
-    for major, minor in (map(int, version.split(".")) for version in ROCM_ARCHES)
-}
-XPU_NIGHTLY_SOURCE_MATRIX = {
-    "xpu": dict(
-        name="xpu",
-        index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/xpu",
-        supported_platforms=["Linux"],
-        accelerator="xpu",
-    )
-}
-NIGHTLY_SOURCE_MATRIX.update(CUDA_NIGHTLY_SOURCE_MATRIX)
-NIGHTLY_SOURCE_MATRIX.update(ROCM_NIGHTLY_SOURCE_MATRIX)
-NIGHTLY_SOURCE_MATRIX.update(XPU_NIGHTLY_SOURCE_MATRIX)
-
-
 def get_nccl_wheel_version(arch_version: str) -> str:
+    import re
+
     requirements = map(
         str.strip, re.split("[;|]", PYTORCH_EXTRA_INSTALL_REQUIREMENTS[arch_version])
     )
@@ -191,14 +147,17 @@ def get_nccl_wheel_version(arch_version: str) -> str:
 
 
 def read_nccl_pin(arch_version: str) -> str:
-    nccl_pin_path = (
-        REPO_ROOT
-        / ".ci"
-        / "docker"
-        / "ci_commit_pins"
-        / f"nccl-cu{arch_version[:2]}.txt"
+    from pathlib import Path
+
+    nccl_pin_path = os.path.join(
+        Path(__file__).absolute().parents[2],
+        ".ci",
+        "docker",
+        "ci_commit_pins",
+        f"nccl-cu{arch_version[:2]}.txt",
     )
-    return nccl_pin_path.read_text().strip()
+    with open(nccl_pin_path) as f:
+        return f.read().strip()
 
 
 def validate_nccl_dep_consistency(arch_version: str) -> None:
@@ -206,8 +165,7 @@ def validate_nccl_dep_consistency(arch_version: str) -> None:
     wheel_ver = get_nccl_wheel_version(arch_version)
     if not nccl_release_tag.startswith(f"v{wheel_ver}"):
         raise RuntimeError(
-            f"{arch_version} NCCL release tag version {nccl_release_tag} "
-            f"does not correspond to wheel version {wheel_ver}"
+            f"{arch_version} NCCL release tag version {nccl_release_tag} does not correspond to wheel version {wheel_ver}"
         )
 
 
@@ -454,14 +412,7 @@ def generate_wheels_matrix(
     return ret
 
 
-arch_version = ""
-for arch_version in CUDA_ARCHES:
-    validate_nccl_dep_consistency(arch_version)
-del arch_version
-
-
-if __name__ == "__main__":
-    # Used by tools/nightly.py
-    (SCRIPT_DIR / "nightly_source_matrix.json").write_text(
-        json.dumps(NIGHTLY_SOURCE_MATRIX, indent=4) + "\n"
-    )
+validate_nccl_dep_consistency("13.0")
+validate_nccl_dep_consistency("12.9")
+validate_nccl_dep_consistency("12.8")
+validate_nccl_dep_consistency("12.6")

.github/workflows/_linux-test.yml

@@ -167,10 +167,10 @@ jobs:
 
       - name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
         id: install-nvidia-driver
-        uses: pytorch/test-infra/.github/actions/setup-nvidia@main
+        uses: tinglvv/test-infra/.github/actions/setup-nvidia@fix-ubuntu-driver
         with:
           driver-version: ${{ matrix.config == 'legacy_nvidia_driver' && '525.105.17' || '580.82.07' }}
-        if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' && !contains(matrix.runner, 'b200') }}
+        if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && (steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' || contains(inputs.build-environment, 'cuda13')) && !contains(matrix.runner, 'b200') }}
 
       - name: Setup GPU_FLAG for docker run
         id: setup-gpu-flag

.github/workflows/_xpu-test.yml

@@ -38,10 +38,6 @@ on:
         default: ""
         description: |
           List of tests to include (empty string implies default list)
-      dashboard-tag:
-        required: false
-        type: string
-        default: ""
       disable-monitor:
         description: |
           [Experimental] Disable utilization monitoring for tests.
@@ -62,11 +58,6 @@ on:
         required: false
         type: number
         default: 1
-    secrets:
-      HUGGING_FACE_HUB_TOKEN:
-        required: false
-        description: |
-          HF Auth token to avoid rate limits when downloading models or datasets from hub
 permissions:
   id-token: write
   contents: read
@@ -205,8 +196,6 @@ jobs:
           PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }}
           PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }}
           TESTS_TO_INCLUDE: ${{ inputs.tests-to-include }}
-          DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
-          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
         timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }}
         run: |
           # Fetch aws credential from IMDs
@@ -257,8 +246,6 @@ jobs:
             -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
             -e TESTS_TO_INCLUDE \
             -e ZE_AFFINITY_MASK \
-            -e HUGGING_FACE_HUB_TOKEN \
-            -e DASHBOARD_TAG \
             --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
             --ulimit stack=10485760:83886080 \
             --ulimit core=0 \

.github/workflows/build-almalinux-images.yml

@@ -36,7 +36,7 @@ jobs:
     runs-on: linux.9xlarge.ephemeral
     strategy:
       matrix:
-        tag: ["cuda12.6", "cuda12.8", "cuda12.9", "cuda13.0", "rocm7.0", "rocm7.1", "cpu"]
+        tag: ["cuda12.6", "cuda12.8", "cuda12.9", "cuda13.0", "rocm6.4", "rocm7.0", "cpu"]
     steps:
       - name: Build docker image
         uses: pytorch/pytorch/.github/actions/binary-docker-build@main

.github/workflows/build-libtorch-images.yml

@@ -52,8 +52,8 @@ jobs:
           { tag: "cuda12.9" },
           { tag: "cuda12.8" },
           { tag: "cuda12.6" },
+          { tag: "rocm6.4"  },
           { tag: "rocm7.0"  },
-          { tag: "rocm7.1"  },
           { tag: "cpu"      },
         ]
     steps:

.github/workflows/build-magma-rocm-linux.yml

@@ -34,7 +34,7 @@ jobs:
       id-token: write
     strategy:
       matrix:
-        rocm_version: ["71", "70"]
+        rocm_version: ["70", "64"]
     steps:
       - name: Checkout PyTorch
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

.github/workflows/build-manywheel-images.yml

@@ -54,8 +54,8 @@ jobs:
           { name: "manylinuxaarch64-builder",       tag: "cuda12.9",          runner: "linux.arm64.2xlarge.ephemeral" },
           { name: "manylinuxaarch64-builder",       tag: "cuda12.8",          runner: "linux.arm64.2xlarge.ephemeral" },
           { name: "manylinuxaarch64-builder",       tag: "cuda12.6",          runner: "linux.arm64.2xlarge.ephemeral" },
+          { name: "manylinux2_28-builder",          tag: "rocm6.4",           runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "rocm7.0",           runner: "linux.9xlarge.ephemeral" },
-          { name: "manylinux2_28-builder",          tag: "rocm7.1",           runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "cpu",               runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28_aarch64-builder",  tag: "cpu-aarch64",       runner: "linux.arm64.2xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "xpu",               runner: "linux.9xlarge.ephemeral" },

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

@@ -55,7 +55,7 @@ jobs:
         docker-image: ["pytorch/manylinux2_28-builder:cpu"]
         include:
           - device: "rocm"
-            rocm_version: "7.1"
+            rocm_version: "7.0"
             runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
           - device: "cuda"
             rocm_version: ""
@@ -159,7 +159,12 @@ jobs:
             WITH_CLANG_LDD="--with-clang-ldd"
           fi
 
-          docker exec -t "${container_name}" bash -c "${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE $WITH_CLANG_LDD"
+          if [[ "${BUILD_DEVICE}" == xpu ]]; then
+            docker exec -t "${container_name}" bash -c "dnf install -y gcc-toolset-13-gcc-c++"
+            docker exec -t "${container_name}" bash -c "source /opt/rh/gcc-toolset-13/enable && ${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE"
+          else
+            docker exec -t "${container_name}" bash -c "${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE $WITH_CLANG_LDD"
+          fi
 
           if [[ ("${{ matrix.device }}" == "cuda" || "${{ matrix.device }}" == "xpu") ]]; then
             docker exec -t "${container_name}"  bash -c "auditwheel repair --plat ${PLATFORM} //artifacts/*.whl"

.github/workflows/docker-builds.yml

@@ -53,6 +53,7 @@ jobs:
           pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11,
           pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-vllm,
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks,
+          pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks,
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
           pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
           pytorch-linux-jammy-py3.10-clang12,
@@ -67,7 +68,6 @@ jobs:
           pytorch-linux-jammy-py3.12-halide,
           pytorch-linux-jammy-xpu-n-1-py3,
           pytorch-linux-jammy-xpu-n-py3,
-          pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,

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

@@ -384,6 +384,124 @@ jobs:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
 
+  libtorch-rocm6_4-shared-with-deps-release-build:
+    if: ${{ github.repository_owner == 'pytorch' }}
+    uses: ./.github/workflows/_binary-build-linux.yml
+    needs: get-label-type
+    with:
+      PYTORCH_ROOT: /pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: rocm6.4
+      GPU_ARCH_VERSION: "6.4"
+      GPU_ARCH_TYPE: rocm
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.4
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      timeout-minutes: 300
+      build_name: libtorch-rocm6_4-shared-with-deps-release
+      build_environment: linux-binary-libtorch
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+  libtorch-rocm6_4-shared-with-deps-release-test:  # Testing
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs:
+      - libtorch-rocm6_4-shared-with-deps-release-build
+      - get-label-type
+    runs-on: linux.rocm.gpu.mi250
+    timeout-minutes: 240
+    env:
+      PYTORCH_ROOT: /pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: rocm6.4
+      GPU_ARCH_VERSION: "6.4"
+      GPU_ARCH_TYPE: rocm
+      SKIP_ALL_TESTS: 1
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.4
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - name: Setup ROCm
+        uses: ./.github/actions/setup-rocm
+      - uses: actions/download-artifact@v4.1.7
+        name: Download Build Artifacts
+        with:
+          name: libtorch-rocm6_4-shared-with-deps-release
+          path: "${{ runner.temp }}/artifacts/"
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      - name: ROCm set GPU_FLAG
+        run: |
+          echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
+      - name: configure aws credentials
+        id: aws_creds
+        if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
+          aws-region: us-east-1
+          role-duration-seconds: 18000
+      - name: Calculate docker image
+        id: calculate-docker-image
+        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
+        with:
+          docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
+          docker-image-name: libtorch-cxx11-builder
+          custom-tag-prefix: rocm6.4
+          docker-build-dir: .ci/docker
+          working-directory: pytorch
+      - name: Pull Docker image
+        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
+        with:
+          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
+      - name: Test Pytorch binary
+        uses: ./pytorch/.github/actions/test-pytorch-binary
+        env:
+          DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
+      - name: Teardown ROCm
+        uses: ./.github/actions/teardown-rocm
+  libtorch-rocm6_4-shared-with-deps-release-upload:  # Uploading
+    if: ${{ github.repository_owner == 'pytorch' }}
+    permissions:
+      id-token: write
+      contents: read
+    needs: libtorch-rocm6_4-shared-with-deps-release-test
+    with:
+      PYTORCH_ROOT: /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: rocm6.4
+      GPU_ARCH_VERSION: "6.4"
+      GPU_ARCH_TYPE: rocm
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.4
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      build_name: libtorch-rocm6_4-shared-with-deps-release
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+    uses: ./.github/workflows/_binary-upload.yml
+
   libtorch-rocm7_0-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     uses: ./.github/workflows/_binary-build-linux.yml
@@ -501,121 +619,3 @@ jobs:
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
-
-  libtorch-rocm7_1-shared-with-deps-release-build:
-    if: ${{ github.repository_owner == 'pytorch' }}
-    uses: ./.github/workflows/_binary-build-linux.yml
-    needs: get-label-type
-    with:
-      PYTORCH_ROOT: /pytorch
-      PACKAGE_TYPE: libtorch
-      # TODO: This is a legacy variable that we eventually want to get rid of in
-      #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: rocm7.1
-      GPU_ARCH_VERSION: "7.1"
-      GPU_ARCH_TYPE: rocm
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.1
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
-      build_name: libtorch-rocm7_1-shared-with-deps-release
-      build_environment: linux-binary-libtorch
-    secrets:
-      github-token: ${{ secrets.GITHUB_TOKEN }}
-  libtorch-rocm7_1-shared-with-deps-release-test:  # Testing
-    if: ${{ github.repository_owner == 'pytorch' }}
-    needs:
-      - libtorch-rocm7_1-shared-with-deps-release-build
-      - get-label-type
-    runs-on: linux.rocm.gpu.mi250
-    timeout-minutes: 240
-    env:
-      PYTORCH_ROOT: /pytorch
-      PACKAGE_TYPE: libtorch
-      # TODO: This is a legacy variable that we eventually want to get rid of in
-      #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: rocm7.1
-      GPU_ARCH_VERSION: "7.1"
-      GPU_ARCH_TYPE: rocm
-      SKIP_ALL_TESTS: 1
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.1
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - name: Setup ROCm
-        uses: ./.github/actions/setup-rocm
-      - uses: actions/download-artifact@v4.1.7
-        name: Download Build Artifacts
-        with:
-          name: libtorch-rocm7_1-shared-with-deps-release
-          path: "${{ runner.temp }}/artifacts/"
-      - name: Checkout PyTorch
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-          submodules: recursive
-          path: pytorch
-          show-progress: false
-      - name: Clean PyTorch checkout
-        run: |
-          # Remove any artifacts from the previous checkouts
-          git clean -fxd
-        working-directory: pytorch
-      - name: ROCm set GPU_FLAG
-        run: |
-          echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
-      - name: configure aws credentials
-        id: aws_creds
-        if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-          aws-region: us-east-1
-          role-duration-seconds: 18000
-      - name: Calculate docker image
-        id: calculate-docker-image
-        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
-        with:
-          docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
-          docker-image-name: libtorch-cxx11-builder
-          custom-tag-prefix: rocm7.1
-          docker-build-dir: .ci/docker
-          working-directory: pytorch
-      - name: Pull Docker image
-        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
-        with:
-          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
-      - name: Test Pytorch binary
-        uses: ./pytorch/.github/actions/test-pytorch-binary
-        env:
-          DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
-      - name: Teardown ROCm
-        uses: ./.github/actions/teardown-rocm
-  libtorch-rocm7_1-shared-with-deps-release-upload:  # Uploading
-    if: ${{ github.repository_owner == 'pytorch' }}
-    permissions:
-      id-token: write
-      contents: read
-    needs: libtorch-rocm7_1-shared-with-deps-release-test
-    with:
-      PYTORCH_ROOT: /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: rocm7.1
-      GPU_ARCH_VERSION: "7.1"
-      GPU_ARCH_TYPE: rocm
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.1
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-      build_name: libtorch-rocm7_1-shared-with-deps-release
-    secrets:
-      github-token: ${{ secrets.GITHUB_TOKEN }}
-    uses: ./.github/workflows/_binary-upload.yml

.github/workflows/inductor-micro-benchmark.yml

@@ -55,3 +55,30 @@ jobs:
       test-matrix: ${{ needs.build.outputs.test-matrix }}
       timeout-minutes: 720
     secrets: inherit
+
+  build-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-build.yml
+    needs:
+      - get-default-label-prefix
+    with:
+      runner_prefix: "${{ needs.get-default-label-prefix.outputs.label-type }}"
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.0'
+      test-matrix: |
+        { include: [
+          { config: "inductor-micro-benchmark", shard: 1, num_shards: 1, runner: "linux.aws.a100", owners: ["oncall:pt2"] },
+        ]}
+    secrets: inherit
+
+  test-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 720
+    secrets: inherit

.github/workflows/inductor-perf-compare.yml

@@ -59,3 +59,37 @@ jobs:
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
+
+  build-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-build.yml
+    needs:
+      - get-default-label-prefix
+    with:
+      runner_prefix: "${{ needs.get-default-label-prefix.outputs.label-type }}"
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.0'
+      test-matrix: |
+        { include: [
+          { config: "inductor_huggingface_perf_compare", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf_compare", shard: 1, num_shards: 2, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf_compare", shard: 2, num_shards: 2, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf_compare", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
+        ]}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  test-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      # disable monitor in perf tests for more investigation
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit

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

@@ -178,3 +178,98 @@ jobs:
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
+
+  build-cuda13:
+    name: build-cuda13
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      # Use a bigger runner here because CUDA_ARCH 9.0 is only built for H100
+      # or newer GPUs, so it doesn't benefit much from existing compiler cache
+      # from trunk. Also use a memory-intensive runner here because memory is
+      # usually the bottleneck
+      runner: linux.12xlarge.memory
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm90
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '9.0'
+      test-matrix: |
+        { include: [
+          { config: "inductor_huggingface_perf_cuda_h100", shard: 1, num_shards: 5, runner: "linux.aws.h100" },
+          { config: "inductor_huggingface_perf_cuda_h100", shard: 2, num_shards: 5, runner: "linux.aws.h100" },
+          { config: "inductor_huggingface_perf_cuda_h100", shard: 3, num_shards: 5, runner: "linux.aws.h100" },
+          { config: "inductor_huggingface_perf_cuda_h100", shard: 4, num_shards: 5, runner: "linux.aws.h100" },
+          { config: "inductor_huggingface_perf_cuda_h100", shard: 5, num_shards: 5, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 1, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 2, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 3, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 4, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 5, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 6, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_timm_perf_cuda_h100", shard: 7, num_shards: 7, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 1, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 2, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 3, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 4, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 5, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 6, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 7, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 8, num_shards: 9, runner: "linux.aws.h100" },
+          { config: "inductor_torchbench_perf_cuda_h100", shard: 9, num_shards: 9, runner: "linux.aws.h100" },
+        ]}
+      selected-test-configs: ${{ inputs.benchmark_configs }}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  test-periodically-cuda13:
+    name: test-periodically-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    if: github.event.schedule == '15 0,12 * * 1-6'
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm90
+      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.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 720
+      # disable monitor in perf tests, next step is to enable it
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit
+
+  test-weekly-cuda13:
+    name: test-weekly-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    if: github.event.schedule == '0 7 * * 0'
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm90
+      dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-maxautotune-true-freeze_autotune_cudagraphs-true-cudagraphs_low_precision-true
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 1440
+      # disable monitor in perf tests, next step is to enable it
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit
+
+  test-cuda13:
+    name: test-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    # The pull_request trigger is used in PR to bump transformers pin which always
+    # needs one round of benchmark
+    if: ${{ github.event_name == 'workflow_dispatch' || github.event_name == 'pull_request' }}
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm90
+      dashboard-tag: training-${{ inputs.training || 'true' }}-inference-${{ inputs.inference || 'true' }}-default-${{ inputs.default || 'true' }}-dynamic-${{ inputs.dynamic || 'true' }}-cudagraphs-${{ inputs.cudagraphs || 'true' }}-cppwrapper-${{ inputs.cppwrapper || 'false' }}-aotinductor-${{ inputs.aotinductor || 'false' }}-maxautotune-${{ inputs.maxautotune || 'false' }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs || 'false' }}-cudagraphs_low_precision-${{ inputs.cudagraphs || 'false' }}
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 720
+      # disable monitor in perf tests for more investigation
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit

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

@@ -1,148 +0,0 @@
-name: inductor-perf-nightly-xpu
-
-on:
-  push:
-    tags:
-      - ciflow/inductor-perf-test-nightly-xpu/*
-  schedule:
-    - cron: 30 17 * * *
-  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: false
-      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,inductor_timm_perf,inductor_torchbench_perf,cachebench
-
-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: ${{ github.ref_type }}
-      opt_out_experiments: lf
-
-  xpu-n-py3_10-inductor-benchmark-build:
-    name: xpu-n-py3.10-inductor-benchmark
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks
-      runner: linux.c7i.12xlarge
-      test-matrix: |
-        { include: [
-          { config: "inductor_huggingface_perf_xpu", shard: 1, num_shards: 5, runner: "linux.idc.xpu" },
-          { config: "inductor_huggingface_perf_xpu", shard: 2, num_shards: 5, runner: "linux.idc.xpu" },
-          { config: "inductor_huggingface_perf_xpu", shard: 3, num_shards: 5, runner: "linux.idc.xpu" },
-          { config: "inductor_huggingface_perf_xpu", shard: 4, num_shards: 5, runner: "linux.idc.xpu" },
-          { config: "inductor_huggingface_perf_xpu", shard: 5, num_shards: 5, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 2, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 3, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 4, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 5, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_timm_perf_xpu", shard: 6, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 2, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 3, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 4, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 5, num_shards: 6, runner: "linux.idc.xpu" },
-          { config: "inductor_torchbench_perf_xpu", shard: 6, num_shards: 6, runner: "linux.idc.xpu" },
-        ]}
-    secrets: inherit
-
-  xpu-n-py3_10-inductor-benchmark-test-nightly:
-    permissions:
-      id-token: write
-      contents: read
-    if: github.event_name != 'workflow_dispatch'
-    name: xpu-n-py3.10-inductor-benchmark
-    uses: ./.github/workflows/_xpu-test.yml
-    needs: xpu-n-py3_10-inductor-benchmark-build
-    with:
-      build-environment: linux-jammy-xpu-n-py3.10
-      dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-false-cppwrapper-true-aotinductor-true-freezing_cudagraphs-false-cudagraphs_low_precision-false
-      docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
-      timeout-minutes: 720
-      # Disable monitor in perf tests for more investigation
-      disable-monitor: true
-      monitor-log-interval: 10
-      monitor-data-collect-interval: 2
-    secrets: inherit
-
-  xpu-n-py3_10-inductor-benchmark-test:
-    permissions:
-      id-token: write
-      contents: read
-    if: github.event_name == 'workflow_dispatch'
-    name: xpu-n-py3.10-inductor-test
-    uses: ./.github/workflows/_xpu-test.yml
-    needs: xpu-n-py3_10-inductor-benchmark-build
-    with:
-      build-environment: linux-jammy-xpu-n-py3.10
-      dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
-      docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
-      timeout-minutes: 720
-      disable-monitor: false
-      monitor-log-interval: 15
-      monitor-data-collect-interval: 4
-    secrets: inherit

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

@@ -164,3 +164,89 @@ jobs:
       monitor-log-interval: 15
       monitor-data-collect-interval: 4
     secrets: inherit
+
+  build-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      # Every bit to make perf run faster helps
+      runner: linux.12xlarge.memory
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.0'
+      test-matrix: |
+        { include: [
+          { config: "inductor_huggingface_perf", shard: 1, num_shards: 5, runner: "linux.aws.a100" },
+          { config: "inductor_huggingface_perf", shard: 2, num_shards: 5, runner: "linux.aws.a100" },
+          { config: "inductor_huggingface_perf", shard: 3, num_shards: 5, runner: "linux.aws.a100" },
+          { config: "inductor_huggingface_perf", shard: 4, num_shards: 5, runner: "linux.aws.a100" },
+          { config: "inductor_huggingface_perf", shard: 5, num_shards: 5, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 1, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 2, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 3, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 4, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 5, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_timm_perf", shard: 6, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 1, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 2, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 3, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 4, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 5, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "inductor_torchbench_perf", shard: 6, num_shards: 6, runner: "linux.aws.a100" },
+          { config: "cachebench", shard: 1, num_shards: 2, runner: "linux.aws.a100" },
+          { config: "cachebench", shard: 2, num_shards: 2, runner: "linux.aws.a100" },
+        ]}
+      selected-test-configs: ${{ inputs.benchmark_configs }}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  test-nightly-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    if: github.event.schedule == '0 7 * * 1-6'
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      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.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 720
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit
+
+  test-weekly-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    if: github.event.schedule == '0 7 * * 0'
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-maxautotune-true-freeze_autotune_cudagraphs-true-cudagraphs_low_precision-true
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 1440
+      # disable monitor in perf tests, next step is to enable it
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit
+
+  test-cuda13:
+    name: cuda13.0-py3.10-gcc9-sm80
+    uses: ./.github/workflows/_linux-test.yml
+    needs: build-cuda13
+    if: github.event_name == 'workflow_dispatch'
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
+      docker-image: ${{ needs.build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.build-cuda13.outputs.test-matrix }}
+      timeout-minutes: 720
+      disable-monitor: false
+      monitor-log-interval: 15
+      monitor-data-collect-interval: 4
+    secrets: inherit

.github/workflows/inductor-periodic.yml

@@ -81,6 +81,56 @@ jobs:
       test-matrix: ${{ needs.periodic-dynamo-benchmarks-build.outputs.test-matrix }}
     secrets: inherit
 
+  periodic-dynamo-benchmarks-build-cuda13:
+    name: periodic-dynamo-benchmarks-build-cuda13
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-default-label-prefix
+    with:
+      runner_prefix: "${{ needs.get-default-label-prefix.outputs.label-type }}"
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm86
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.0;8.6'
+      test-matrix: |
+        { include: [
+          { config: "dynamo_eager_torchbench", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamo_eager_torchbench", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamo_eager_huggingface", shard: 1, num_shards: 1, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamo_eager_timm", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamo_eager_timm", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_eager_torchbench", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_eager_torchbench", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_eager_huggingface", shard: 1, num_shards: 1, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_eager_timm", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_eager_timm", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_aot_eager_torchbench", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_aot_eager_torchbench", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_aot_eager_huggingface", shard: 1, num_shards: 1, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_aot_eager_timm", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_aot_eager_timm", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_inductor_huggingface", shard: 1, num_shards: 1, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_inductor_timm", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_inductor_timm", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_inductor_torchbench", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "dynamic_inductor_torchbench", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_inductor_huggingface", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
+          { config: "aot_inductor_timm", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_inductor_timm", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_inductor_torchbench", shard: 1, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+          { config: "aot_inductor_torchbench", shard: 2, num_shards: 2, runner: "linux.g5.4xlarge.nvidia.gpu" },
+        ]}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  periodic-dynamo-benchmarks-test-cuda13:
+    name: periodic-dynamo-benchmarks-test-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: periodic-dynamo-benchmarks-build-cuda13
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm86
+      docker-image: ${{ needs.periodic-dynamo-benchmarks-build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.periodic-dynamo-benchmarks-build-cuda13.outputs.test-matrix }}
+    secrets: inherit
+
   rocm-periodic-dynamo-benchmarks-build:
     if: github.repository_owner == 'pytorch'
     name: rocm-periodic-dynamo-benchmarks-build
@@ -158,6 +208,33 @@ jobs:
       test-matrix: ${{ needs.inductor-smoke-build.outputs.test-matrix }}
     secrets: inherit
 
+  inductor-smoke-build-cuda13:
+    name: inductor-smoke-build-cuda13
+    uses: ./.github/workflows/_linux-build.yml
+    needs:
+      - get-default-label-prefix
+    with:
+      runner_prefix: "${{ needs.get-default-label-prefix.outputs.label-type }}"
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.0'
+      test-matrix: |
+        { include: [
+          { config: "inductor_torchbench_smoketest_perf", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
+        ]}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  inductor-smoke-test-cuda13:
+    name: inductor-smoke-test-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: inductor-smoke-build-cuda13
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm80
+      docker-image: ${{ needs.inductor-smoke-build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.inductor-smoke-build-cuda13.outputs.test-matrix }}
+    secrets: inherit
+
   periodic-dynamo-benchmarks-cpu-build:
     name: periodic-dynamo-benchmarks-cpu-build
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/inductor.yml

@@ -74,6 +74,36 @@ jobs:
       test-matrix: ${{ needs.inductor-build.outputs.test-matrix }}
     secrets: inherit
 
+  inductor-build-cuda13:
+    name: inductor-build-cuda13
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm86
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '8.6'
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      test-matrix: |
+        { include: [
+          { config: "inductor_huggingface", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.4xlarge.nvidia.gpu" },
+          { config: "inductor_timm", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.4xlarge.nvidia.gpu" },
+          { config: "inductor_timm", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.4xlarge.nvidia.gpu" },
+          { config: "inductor_torchbench", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.4xlarge.nvidia.gpu" },
+          { config: "inductor_torchbench", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.4xlarge.nvidia.gpu" },
+        ]}
+      build-additional-packages: "vision audio torchao"
+    secrets: inherit
+
+  inductor-test-cuda13:
+    name: inductor-test-cuda13
+    uses: ./.github/workflows/_linux-test.yml
+    needs: inductor-build-cuda13
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm86
+      docker-image: ${{ needs.inductor-build-cuda13.outputs.docker-image }}
+      test-matrix: ${{ needs.inductor-build-cuda13.outputs.test-matrix }}
+    secrets: inherit
+
   inductor-cpu-build:
     name: inductor-cpu-build
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/lint.yml

@@ -76,12 +76,11 @@ jobs:
 
   # NOTE: mypy needs its own job because it depends on --all-files, without assessing all files it sometimes
   #       fails to find types when it should
-  # NOTE: We should be able to disable this and consolidate with Pyrefly
-  lintrunner-pyrefly:
+  lintrunner-mypy:
     uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
-    name: lintrunner-pyrefly-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
+    name: lintrunner-mypy-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
     needs: [get-label-type, get-changed-files]
-    # Only run if there are changed files relevant to pyrefly
+    # Only run if there are changed files relevant to mypy
     if: |
       github.repository_owner == 'pytorch' && (
         needs.get-changed-files.outputs.changed-files == '*' ||
@@ -99,8 +98,8 @@ jobs:
       ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
       script: |
         CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
-        echo "Running pyrefly"
-        ADDITIONAL_LINTRUNNER_ARGS="--take PYREFLY --all-files" .github/scripts/lintrunner.sh
+        echo "Running mypy"
+        ADDITIONAL_LINTRUNNER_ARGS="--take MYPY,MYPYSTRICT --all-files" .github/scripts/lintrunner.sh
 
   lintrunner-noclang:
     uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
@@ -119,9 +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,PYREFLY --all-files" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY --all-files" .github/scripts/lintrunner.sh
         else
-          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
+          ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
         fi
 
   quick-checks:

.github/workflows/pull.yml

@@ -342,6 +342,31 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-inductor-build.outputs.test-matrix }}
     secrets: inherit
 
+  linux-jammy-cuda13_0-py3_10-gcc9-inductor-build:
+    name: cuda13.0-py3.10-gcc9-sm75
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm75
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      cuda-arch-list: '7.5'
+      test-matrix: |
+        { include: [
+          { config: "pr_time_benchmarks", shard: 1, num_shards: 1, runner: "linux.g4dn.metal.nvidia.gpu" },
+        ]}
+    secrets: inherit
+
+  linux-jammy-cuda13_0-py3_10-gcc9-inductor-test:
+    name: cuda13.0-py3.10-gcc9-sm75
+    uses: ./.github/workflows/_linux-test.yml
+    needs: linux-jammy-cuda13_0-py3_10-gcc9-inductor-build
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.10-gcc9-sm75
+      docker-image: ${{ needs.linux-jammy-cuda13_0-py3_10-gcc9-inductor-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-cuda13_0-py3_10-gcc9-inductor-build.outputs.test-matrix }}
+    secrets: inherit
+
   linux-jammy-xpu-n-py3_10-build:
     name: linux-jammy-xpu-n-py3.10
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/trunk.yml

@@ -234,6 +234,16 @@ jobs:
       cuda-arch-list: '8.0'
     secrets: inherit
 
+  inductor-build-cuda13:
+    name: inductor-build-cuda13
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      build-environment: linux-jammy-cuda13.0-py3.12-gcc9-sm80
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc9-inductor-benchmarks
+      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

.gitignore

@@ -143,7 +143,6 @@ scripts/release_notes/*.json
 sccache-stats*.json
 lint.json
 merge_record.json
-.github/scripts/nightly_source_matrix.json
 
 # These files get copied over on invoking setup.py
 torchgen/packaged/*
@@ -398,4 +397,3 @@ CLAUDE.local.md
 /test_*.py
 /debug_*.py
 CLAUDE_CONTEXT/
-/.claude/settings.local.json

.lintrunner.toml

@@ -121,6 +121,94 @@ command = [
 ]
 is_formatter = true
 
+[[linter]]
+code = 'MYPY'
+include_patterns = [
+    'setup.py',
+    'functorch/dim/**/*.py',
+    'torch/**/*.py',
+    'torch/**/*.pyi',
+    'caffe2/**/*.py',
+    'caffe2/**/*.pyi',
+    'test/test_bundled_images.py',
+    'test/test_bundled_inputs.py',
+    'test/test_complex.py',
+    'test/test_datapipe.py',
+    'test/test_futures.py',
+    'test/test_numpy_interop.py',
+    'test/test_torch.py',
+    'test/test_type_hints.py',
+    'test/test_type_info.py',
+    'test/test_utils.py',
+]
+exclude_patterns = [
+    '**/fb/**',
+]
+command = [
+    'python3',
+    'tools/linter/adapters/mypy_linter.py',
+    '--config=mypy.ini',
+    '--',
+    '@{{PATHSFILE}}'
+]
+init_command = [
+    'python3',
+    'tools/linter/adapters/pip_init.py',
+    '--dry-run={{DRYRUN}}',
+    'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
+    'numpy==2.1.0 ; python_version >= "3.12"',
+    'expecttest==0.3.0',
+    'mypy==1.16.0',
+    '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',
+    'pyyaml==6.0.2',
+    'optree==0.13.0',
+    'dataclasses-json==0.6.7',
+    'pandas==2.2.3',
+]
+
+[[linter]]
+code = 'MYPYSTRICT'
+include_patterns = [
+    '.github/**/*.py',
+    'benchmarks/instruction_counts/**/*.py',
+    'tools/**/*.py',
+    'torchgen/**/*.py',
+    'torch/utils/_pytree.py',
+    'torch/utils/_cxx_pytree.py',
+    'torch/utils/benchmark/utils/common.py',
+    'torch/utils/benchmark/utils/timer.py',
+    'torch/utils/benchmark/utils/valgrind_wrapper/**/*.py',
+]
+exclude_patterns = [
+    # (linbinyu) copied from internal repo
+    '**/fb/**',
+    'tools/code_analyzer/gen_operators_yaml.py',
+    'tools/dynamo/verify_dynamo.py',
+    'tools/gen_vulkan_spv.py',
+    'tools/test/gen_operators_yaml_test.py',
+    'tools/test/gen_oplist_test.py',
+    'tools/test/test_selective_build.py',
+    'tools/experimental/torchfuzz/**',
+]
+command = [
+    'python3',
+    'tools/linter/adapters/mypy_linter.py',
+    '--config=mypy-strict.ini',
+    '--code=MYPYSTRICT',
+    '--',
+    '@{{PATHSFILE}}'
+]
+
 
 [[linter]]
 code = 'PYREFLY'
@@ -142,7 +230,6 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
     'numpy==2.1.0 ; python_version >= "3.12"',
     'expecttest==0.3.0',
     'pyrefly==0.36.2',

CONTRIBUTING.md

@@ -11,6 +11,7 @@ aspects of contributing to PyTorch.
 <!-- toc -->
 
 - [Developing PyTorch](#developing-pytorch)
+  - [Setup the development environment](#setup-the-development-environment)
   - [Tips and Debugging](#tips-and-debugging)
 - [Nightly Checkout & Pull](#nightly-checkout--pull)
 - [Codebase structure](#codebase-structure)
@@ -66,6 +67,23 @@ aspects of contributing to PyTorch.
 
 Follow the instructions for [installing PyTorch from source](https://github.com/pytorch/pytorch#from-source). If you get stuck when developing PyTorch on your machine, check out the [tips and debugging](#tips-and-debugging) section below for common solutions.
 
+### Setup the development environment
+
+First, you need to [fork the PyTorch project on GitHub](https://github.com/pytorch/pytorch/fork) and follow the instructions at [Connecting to GitHub with SSH](https://docs.github.com/en/authentication/connecting-to-github-with-ssh) to setup your SSH authentication credentials.
+
+Then clone the PyTorch project and setup the development environment:
+
+```bash
+git clone git@github.com:<USERNAME>/pytorch.git
+cd pytorch
+git remote add upstream git@github.com:pytorch/pytorch.git
+
+make setup-env
+# Or run `make setup-env-cuda` for pre-built CUDA binaries
+# Or run `make setup-env-rocm` for pre-built ROCm binaries
+source venv/bin/activate  # or `. .\venv\Scripts\activate` on Windows
+```
+
 ### Tips and Debugging
 
 * If you want to have no-op incremental rebuilds (which are fast), see [Make no-op build fast](#make-no-op-build-fast) below.

aten/src/ATen/CPUGeneratorImpl.cpp

@@ -181,7 +181,7 @@ c10::intrusive_ptr<c10::TensorImpl> CPUGeneratorImpl::get_state() const {
   static const size_t size = sizeof(CPUGeneratorImplState);
   static_assert(std::is_standard_layout_v<CPUGeneratorImplState>, "CPUGeneratorImplState is not a PODType");
 
-  auto state_tensor = at::detail::empty_cpu({static_cast<int64_t>(size)}, ScalarType::Byte, std::nullopt, std::nullopt, std::nullopt, std::nullopt);
+  auto state_tensor = at::detail::empty_cpu({(int64_t)size}, ScalarType::Byte, std::nullopt, std::nullopt, std::nullopt, std::nullopt);
   auto rng_state = state_tensor.data_ptr();
 
   // accumulate generator data to be copied into byte tensor

aten/src/ATen/Context.cpp

@@ -223,7 +223,7 @@ void Context::setSDPPriorityOrder(const std::vector<int64_t>& order) {
     "setSDPPriority order expected ", sdp_priority_order.size() - 1, " but got ",
     at::num_sdp_backends, " unique backends specified in priority order.");
   for (uint32_t i = 0; i < order.size(); i++) {
-    sdp_priority_order[i] = static_cast<at::SDPBackend>(order[i]);
+    sdp_priority_order[i] = (at::SDPBackend) order[i];
   }
 }
 
@@ -825,14 +825,6 @@ void Context::setDisplayVmapFallbackWarnings(bool enabled) {
   display_vmap_fallback_warnings_ = enabled;
 }
 
-bool Context::warnOnAccumulateGradStreamMismatch() const {
-  return warn_on_accumulate_grad_stream_mismatch_;
-}
-
-void Context::setWarnOnAccumulateGradStreamMismatch(bool enabled) {
-  warn_on_accumulate_grad_stream_mismatch_ = enabled;
-}
-
 bool Context::isDefaultMobileCPUAllocatorSet() {
   return prev_allocator_ptr_ != nullptr;
 }

aten/src/ATen/Context.h

@@ -404,9 +404,6 @@ class TORCH_API Context {
   void setDisplayVmapFallbackWarnings(bool enabled);
   bool areVmapFallbackWarningsEnabled() const;
 
-  void setWarnOnAccumulateGradStreamMismatch(bool enabled);
-  bool warnOnAccumulateGradStreamMismatch() const;
-
   bool isDefaultMobileCPUAllocatorSet();
   void setDefaultMobileCPUAllocator();
   void unsetDefaultMobileCPUAllocator();
@@ -497,7 +494,6 @@ class TORCH_API Context {
   bool release_original_weights = false;
 #endif
   bool display_vmap_fallback_warnings_ = false;
-  bool warn_on_accumulate_grad_stream_mismatch_ = true;
   std::atomic<at::QEngine> quantized_engine = at::QEngine::NoQEngine;
   bool enable_sparse_tensor_invariant_checks = false;
   bool allow_fp16_reduction_cpu = false;

aten/src/ATen/Dispatch.h

@@ -197,7 +197,6 @@ inline at::ScalarType scalar_type(at::ScalarType s) {
     /* don't use TYPE again in case it is an expensive or side-effect op */ \
     at::ScalarType _st = ::detail::scalar_type(the_type);                   \
     RECORD_KERNEL_FUNCTION_DTYPE(at_dispatch_name, _st);                    \
-    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")             \
     switch (_st) {                                                          \
       __VA_ARGS__                                                           \
       default:                                                              \
@@ -209,7 +208,6 @@ inline at::ScalarType scalar_type(at::ScalarType s) {
             toString(_st),                                                  \
             "'");                                                           \
     }                                                                       \
-    C10_DIAGNOSTIC_POP()                                                    \
   }()
 
 #define AT_DISPATCH_CASE_FLOATING_TYPES(...)            \

aten/src/ATen/MapAllocator.cpp

@@ -252,13 +252,13 @@ MapAllocator::MapAllocator(WithFd /*unused*/, std::string_view filename, int fd,
     if (!(flags_ & ALLOCATOR_MAPPED_FROMFD)) {
       if (flags_ & ALLOCATOR_MAPPED_SHARED) {
         // NOLINTNEXTLINE(bugprone-assignment-in-if-condition)
-        if ((fd = open(filename_.c_str(), flags, static_cast<mode_t>(0600))) == -1) {
+        if ((fd = open(filename_.c_str(), flags, (mode_t)0600)) == -1) {
           TORCH_CHECK(false, "unable to open file <", filename_, "> in read-write mode: ", c10::utils::str_error(errno), " (", errno, ")");
         }
       } else if (flags_ & ALLOCATOR_MAPPED_SHAREDMEM) {
 #ifdef HAVE_SHM_OPEN
         // NOLINTNEXTLINE(bugprone-assignment-in-if-condition)
-        if((fd = shm_open(filename_.c_str(), flags, static_cast<mode_t>(0600))) == -1) {
+        if((fd = shm_open(filename_.c_str(), flags, (mode_t)0600)) == -1) {
           TORCH_CHECK(false, "unable to open shared memory object <", filename_, "> in read-write mode: ", c10::utils::str_error(errno), " (", errno, ")");
         }
 #else
@@ -503,7 +503,7 @@ RefcountedMapAllocator::RefcountedMapAllocator(WithFd /*unused*/, const char *fi
 
 void RefcountedMapAllocator::initializeAlloc() {
   TORCH_CHECK(base_ptr_, "base_ptr_ is null");
-  MapInfo *map_info = static_cast<MapInfo*>(base_ptr_);
+  MapInfo *map_info = (MapInfo*)base_ptr_;
 
 #ifdef _WIN32
   ReleaseContext* r_ctx = new ReleaseContext;
@@ -539,7 +539,7 @@ void RefcountedMapAllocator::close() {
   }
 #else /* _WIN32 */
 
-  MapInfo *info = static_cast<MapInfo*>(data);
+  MapInfo *info = (MapInfo*)(data);
   if (--info->refcount == 0) {
 #ifdef HAVE_SHM_UNLINK
     if (shm_unlink(filename_.c_str()) == -1) {

aten/src/ATen/TensorIterator.cpp

@@ -862,7 +862,7 @@ void TensorIteratorBase::narrow(int dim, int64_t start, int64_t size) {
   shape_[dim] = size;
   view_offsets_[dim] += start;
   for (auto& op : operands_) {
-    op.data = (static_cast<char*>(op.data)) + op.stride_bytes[dim] * start;
+    op.data = ((char*)op.data) + op.stride_bytes[dim] * start;
   }
   if (size == 1 && !is_reduction_) {
     coalesce_dimensions();
@@ -873,7 +873,7 @@ void TensorIteratorBase::select_all_keeping_dim(int start_dim, IntArrayRef indic
   TORCH_INTERNAL_ASSERT(start_dim <= ndim());
   for (const auto i : c10::irange(start_dim, ndim())) {
     for (auto& op : operands_) {
-      op.data = (static_cast<char*>(op.data)) + op.stride_bytes[i] * indices[i - start_dim];
+      op.data = ((char*)op.data) + op.stride_bytes[i] * indices[i - start_dim];
     }
     shape_[i] = 1;
   }

aten/src/ATen/TensorIteratorInternal.h

@@ -41,7 +41,7 @@ inline void serial_for_each(
     IntArrayRef strides,
     char** base_ptrs,
     size_t ntensors,
-    TensorIteratorBase::loop2d_t loop,
+    typename TensorIteratorBase::loop2d_t loop,
     Range range) {
   const auto ndim = shape.size();
   TORCH_INTERNAL_ASSERT_DEBUG_ONLY(

aten/src/ATen/core/IListRef.h

@@ -190,14 +190,12 @@ class IListRef;
  * it to a function (e.g. `ImplT::<dispatch-function>(this_)`).
  */
 #define TORCH_ILISTREF_UNWRAP(TAG, BODY)                         \
-  C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")    \
   switch (TAG) {                                                 \
     TORCH_ILISTREF_FORALL_TAGS(TORCH_ILISTREF_UNWRAP_CASE, BODY) \
     break;                                                       \
     default:                                                     \
       TORCH_INTERNAL_ASSERT(false, "invalid IListRef tag.");     \
-  } \
-  C10_DIAGNOSTIC_POP()
+  }
 
 enum class IListRefTag {
 #define DEFINE_TAG(tag, ...) tag,

aten/src/ATen/core/TransformationHelper.h

@@ -56,7 +56,7 @@ C10_HOST_DEVICE inline T uniform_int_full_range(V val) {
  * in this overloaded version
  */
 template <typename T, typename V>
-C10_HOST_DEVICE inline std::enable_if_t<!std::is_floating_point_v<T>, T>uniform_int(V val) {
+C10_HOST_DEVICE inline std::enable_if_t<!(std::is_floating_point_v<T>), T>uniform_int(V val) {
   if constexpr (std::is_same_v<T, bool>) {
     return static_cast<bool>(val & 1);
   } else if constexpr (std::is_same_v<T, int64_t>) {

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

@@ -114,25 +114,25 @@ inline typename remove_symint<T>::type unpackSymInt(T x) {
 }
 
 template <>
-inline remove_symint<c10::SymInt>::type unpackSymInt(c10::SymInt x) {
+inline typename remove_symint<c10::SymInt>::type unpackSymInt(c10::SymInt x) {
   return x.guard_int(__FILE__, __LINE__);
 }
 
 template <>
-inline remove_symint<c10::SymIntArrayRef>::type unpackSymInt(
+inline typename remove_symint<c10::SymIntArrayRef>::type unpackSymInt(
     c10::SymIntArrayRef x) {
   return C10_AS_INTARRAYREF_SLOW(x);
 }
 
 template <>
-inline remove_symint<std::optional<c10::SymInt>>::type unpackSymInt(
+inline typename remove_symint<std::optional<c10::SymInt>>::type unpackSymInt(
     std::optional<c10::SymInt> x) {
   return x.has_value() ? std::make_optional(x->guard_int(__FILE__, __LINE__))
                        : std::nullopt;
 }
 
 template <>
-inline remove_symint<at::OptionalSymIntArrayRef>::type unpackSymInt(
+inline typename remove_symint<at::OptionalSymIntArrayRef>::type unpackSymInt(
     at::OptionalSymIntArrayRef x) {
   return x.has_value() ? std::make_optional(C10_AS_INTARRAYREF_SLOW(*x))
                        : std::nullopt;

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

@@ -631,8 +631,8 @@ call_functor_with_args_from_stack_(
     Stack* stack,
     std::index_sequence<ivalue_arg_indices...> /*unused*/,
     guts::typelist::typelist<ArgTypes...>* /*unused*/) {
-  (void)stack; // when sizeof...(ivalue_arg_indices) == 0, this argument would
-               // be unused and we have to silence the compiler warning.
+  (void)(stack); // when sizeof...(ivalue_arg_indices) == 0, this argument would
+                 // be unused and we have to silence the compiler warning.
 
   // We're explicitly filtering out DispatchKeySet from the argument list.
   // Some kernels take a DispatchKeySet as their first argument in order to

aten/src/ATen/core/enum_type.h

@@ -18,7 +18,6 @@ struct TORCH_API EnumType : public NamedType {
       TypePtr value,
       std::vector<EnumNameValue> enum_names_values,
       std::weak_ptr<::torch::jit::CompilationUnit> cu) {
-    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")
     switch (value->kind()) {
       case TypeKind::IntType:
       case TypeKind::FloatType:
@@ -35,7 +34,6 @@ struct TORCH_API EnumType : public NamedType {
             value->str(),
             "', only int, float and string are supported");
     }
-    C10_DIAGNOSTIC_POP()
   }
 
   std::string str() const override {

aten/src/ATen/core/ivalue.cpp

@@ -601,8 +601,8 @@ std::ostream& IValue::repr(
       double d = v.toDouble();
       int c = std::fpclassify(d);
       if ((c == FP_NORMAL || c == FP_ZERO ) && std::abs(d) < 1e10) {
-        int64_t i = static_cast<int64_t>(d);
-        if (static_cast<double>(i) == d) {
+        int64_t i = int64_t(d);
+        if (double(i) == d) {
           // -0.0 (signed zero) needs to be parsed as -0.
           if (i == 0 && std::signbit(d)) {
             return out << "-" << i << ".";
@@ -799,8 +799,8 @@ std::ostream& operator<<(std::ostream & out, const IValue & v) {
       double d = v.toDouble();
       int c = std::fpclassify(d);
       if (c == FP_NORMAL || c == FP_ZERO) {
-        int64_t i = static_cast<int64_t>(d);
-        if (static_cast<double>(i) == d) {
+        int64_t i = int64_t(d);
+        if (double(i) == d) {
           return out << i << ".";
         }
       }

aten/src/ATen/core/jit_type.h

@@ -41,7 +41,7 @@ void standardizeVectorForUnion(std::vector<TypePtr>* to_flatten);
 inline bool is_contiguous_strides(
     const IntArrayRef sizes,
     const IntArrayRef strides) {
-  size_t n_dim = sizes.size();
+  int n_dim = static_cast<int>(sizes.size());
   if (n_dim == 0) {
     return true;
   }
@@ -50,7 +50,7 @@ inline bool is_contiguous_strides(
     return false;
   }
 
-  for (int i = static_cast<int>(n_dim) - 2; i >= 0; i--) {
+  for (int i = n_dim - 2; i >= 0; i--) {
     if (strides[i] != strides[i + 1] * sizes[i + 1]) {
       return false;
     }
@@ -922,7 +922,6 @@ struct TORCH_API DictType : public SharedType {
     if (auto dyn = key->castRaw<DynamicType>()) {
       kind = dyn->dynamicKind();
     }
-    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")
     switch (kind) {
       case TypeKind::AnyType:
       case TypeKind::IntType:
@@ -939,7 +938,6 @@ struct TORCH_API DictType : public SharedType {
             key->str(),
             "', only int, float, complex, Tensor, device and string keys are supported");
     }
-    C10_DIAGNOSTIC_POP()
   }
 
   // aligned with the format in FunctionSchema
@@ -2373,7 +2371,7 @@ private:
 };
 
 template<>
-inline detail::CastReturnType<NamedType>::type Type::cast<NamedType>() {
+inline typename detail::CastReturnType<NamedType>::type Type::cast<NamedType>() {
   if (kind() == TypeKind::TupleType || kind() == TypeKind::FunctionType ||
       kind() == TypeKind::ClassType || kind() == TypeKind::InterfaceType) {
     return std::static_pointer_cast<NamedType>(static_cast<NamedType *>(this)->shared_from_this());
@@ -2382,7 +2380,7 @@ inline detail::CastReturnType<NamedType>::type Type::cast<NamedType>() {
 }
 
 template<>
-inline detail::CastConstReturnType<NamedType>::type Type::cast<NamedType>() const {
+inline typename detail::CastConstReturnType<NamedType>::type Type::cast<NamedType>() const {
   if (kind() == TypeKind::TupleType || kind() == TypeKind::FunctionType ||
       kind() == TypeKind::ClassType || kind() == TypeKind::InterfaceType) {
     return std::static_pointer_cast<const NamedType>(static_cast<const NamedType *>(this)->shared_from_this());

aten/src/ATen/cpu/vec/vec128/vec128_bfloat16_neon.h

@@ -19,13 +19,6 @@ inline namespace CPU_CAPABILITY {
 #error "Big endian is not supported."
 #endif
 
-// GCC does not properly optimize bf16 operators
-#if defined(__ARM_FEATURE_BF16) && (__clang_major__ >= 19)
-#define BF16_ARITHMETIC_SUPPORTED() 1
-#else
-#define BF16_ARITHMETIC_SUPPORTED() 0
-#endif
-
 // Unlike the float16_t family of types, bfloat16_t is not available
 // when we're not targeting bfloat16 hardware support on some
 // platforms (but not Mac, so we have to be careful not to shadow the
@@ -359,72 +352,18 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
         other, &Vectorized<float>::name);                        \
   }
 
+  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(abs)
   Vectorized frac() const;
+  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(trunc)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(sqrt)
-
-#ifdef __ARM_FEATURE_BF16
-  // Flip sign bit
-  Vectorized<c10::BFloat16> neg() const {
-    return vreinterpretq_bf16_s16(vreinterpretq_s16_bf16(values) ^ (-32768));
-  }
-  // Fast reciprocal is fine because we are truncating results
-  Vectorized<c10::BFloat16> reciprocal() const {
-    auto x = vcvtq_low_f32_bf16(values);
-    auto y = vcvtq_high_f32_bf16(values);
-    x = vrecpeq_f32(x);
-    y = vrecpeq_f32(y);
-    return vcvtq_high_bf16_f32(vcvtq_low_bf16_f32(x), y);
-  }
-  // Clearing the sign bit
-  Vectorized<c10::BFloat16> abs() const {
-    return vreinterpretq_bf16_u16(vreinterpretq_u16_bf16(values) & 0x7FFF);
-  }
-#else
-  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(abs)
-  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(reciprocal)
-#endif
-
-// These functions are optimized on clang-21+
-#if BF16_ARITHMETIC_SUPPORTED() && (__clang_major__ >= 21)
-  Vectorized<c10::BFloat16> operator==(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values == other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator!=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values != other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values < other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values <= other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values > other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values >= other.values;
-  }
-#else
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator==)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator!=)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator<)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator<=)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>=)
-#endif
 
 #undef DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
 #undef DEFINE_BINARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
@@ -473,52 +412,28 @@ template <>
 Vectorized<c10::BFloat16> inline operator+(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x + y;
-#else
   return binary_operator_via_float(std::plus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator-(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x - y;
-#else
   return binary_operator_via_float(std::minus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator*(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x * y;
-#else
   return binary_operator_via_float(std::multiplies<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator/(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x / y;
-#else
   return binary_operator_via_float(std::divides<Vectorized<float>>(), a, b);
-#endif
 }
 
 // frac. Implement this here so we can use subtraction
@@ -629,19 +544,12 @@ Vectorized<c10::BFloat16> inline fmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y + z;
-#else
   // NOTE [BF16 FMA]: There isn't an FMA that accumulates into BF16!  Also,
   // vbfmlalbq_f32 and vbfmlaltq_f32 take the even and odd-numbered
   // elements, not the bottom and top half, so they don't seem
   // particularly useful here. Ideally we would include dot product in
   // the Vectorized interface...
   return a * b + c;
-#endif
 }
 
 template <>
@@ -649,15 +557,8 @@ Vectorized<c10::BFloat16> inline fnmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y + z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b + c;
-#endif
 }
 
 template <>
@@ -665,15 +566,8 @@ Vectorized<c10::BFloat16> inline fmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return a * b - c;
-#endif
 }
 
 template <>
@@ -681,15 +575,8 @@ Vectorized<c10::BFloat16> inline fnmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#if BF16_ARITHMETIC_SUPPORTED()
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b - c;
-#endif
 }
 
 #endif // !defined(C10_MOBILE) && defined(__aarch64__)

aten/src/ATen/cpu/vec/vec128/vec128_convert.h

@@ -6,9 +6,9 @@ namespace at::vec {
 inline namespace CPU_CAPABILITY {
 #if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
 
-// Enable auto-vectorization for clang-17+
+// Enable auto-vectorization for GCC-13+ and clang-17+
 // GCC-12 has a bug: gcc.gnu.org/bugzilla/show_bug.cgi?id=117001
-#if defined(__clang__) && (__clang_major__ >= 17)
+#if __GNUC__ > 12 || (defined(__clang__) && (__clang_major__ >= 17))
 
 template <typename from_type, typename to_type>
 inline void convertImpl(

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

@@ -514,7 +514,7 @@ struct Vectorized<c10::qint8> : public Vectorizedqi {
 
   using float_vec_return_type = std::array<Vectorized<float>, kFloatNumVecs>;
   using int_vec_return_type = std::array<Vectorized<c10::qint32>, kIntNumVecs>;
-  using value_type = c10::qint8::underlying;
+  using value_type = typename c10::qint8::underlying;
 
  public:
   using Vectorizedqi::Vectorizedqi;
@@ -727,7 +727,7 @@ struct Vectorized<c10::quint8> : public Vectorizedqi {
 
   using float_vec_return_type = std::array<Vectorized<float>, kFloatNumVecs>;
   using int_vec_return_type = std::array<Vectorized<c10::qint32>, kIntNumVecs>;
-  using value_type = c10::quint8::underlying;
+  using value_type = typename c10::quint8::underlying;
 
  public:
   using Vectorizedqi::Vectorizedqi;

aten/src/ATen/cpu/vec/vec512/vec512_qint.h

@@ -567,7 +567,7 @@ struct Vectorized<c10::qint8> : public Vectorizedqi {
 
   using float_vec_return_type = std::array<Vectorized<float>, 4>;
   using int_vec_return_type = std::array<Vectorized<c10::qint32>, 4>;
-  using value_type = c10::qint8::underlying;
+  using value_type = typename c10::qint8::underlying;
 
  public:
   using Vectorizedqi::Vectorizedqi;
@@ -804,7 +804,7 @@ struct Vectorized<c10::quint8> : public Vectorizedqi {
 
   using float_vec_return_type = std::array<Vectorized<float>, 4>;
   using int_vec_return_type = std::array<Vectorized<c10::qint32>, 4>;
-  using value_type = c10::quint8::underlying;
+  using value_type = typename c10::quint8::underlying;
 
  public:
   using Vectorizedqi::Vectorizedqi;

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

@@ -672,7 +672,7 @@ struct Vectorized {
     return map(std::sqrt);
   }
   Vectorized<T> reciprocal() const {
-    return map([](T x) { return (T)1 / x; });
+    return map([](T x) { return (T)(1) / x; });
   }
   Vectorized<T> rsqrt() const {
     return map([](T x) { return (T)1 / std::sqrt(x); });

aten/src/ATen/cpu/vml.h

@@ -46,7 +46,7 @@ inline void vrsqrt(scalar_t* out, scalar_t* in, int64_t size) {
   parallel_for(0, size, 2048, [out, in](int64_t begin, int64_t end) {
     map(
         [](const Vectorized<scalar_t>& x) {
-          return Vectorized<scalar_t>((scalar_t)1) / x.sqrt();
+          return Vectorized<scalar_t>((scalar_t)(1)) / x.sqrt();
         },
         out + begin,
         in + begin,

aten/src/ATen/cuda/CUDAGeneratorImpl.cpp

@@ -194,8 +194,8 @@ void CUDAGeneratorState::unregister_graph(cuda::CUDAGraph* graph) {
 void CUDAGeneratorState::capture_prologue() {
   capturing_ = true;
   offset_intragraph_ = 0;
-  seed_extragraph_.fill_(static_cast<int64_t>(seed_));
-  offset_extragraph_.fill_(0);
+  seed_extragraph_.fill_(int64_t(seed_));
+  offset_extragraph_.fill_(int64_t(0));
 }
 
 /**
@@ -216,8 +216,8 @@ void CUDAGeneratorState::replay_prologue(uint64_t wholegraph_increment) {
   at::cuda::assertNotCapturing(
       "Cannot prepare for replay during capturing stage.");
   if (wholegraph_increment) {
-      seed_extragraph_.fill_(static_cast<int64_t>(seed_));
-      offset_extragraph_.fill_(static_cast<int64_t>(philox_offset_per_thread_));
+      seed_extragraph_.fill_(int64_t(seed_));
+      offset_extragraph_.fill_(int64_t(philox_offset_per_thread_));
       // Applies the total increment achieved during previous captures to update the
       // offset.
       increase(wholegraph_increment);
@@ -329,7 +329,7 @@ c10::intrusive_ptr<c10::TensorImpl> CUDAGeneratorImpl::get_state() const {
   constexpr size_t offset_size = sizeof(int64_t);
   constexpr size_t total_size = seed_size + offset_size;
 
-  auto state_tensor = at::detail::empty_cpu({static_cast<int64_t>(total_size)}, ScalarType::Byte, std::nullopt, std::nullopt, std::nullopt, std::nullopt);
+  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>();
   auto current_seed = this->current_seed();
   auto offset = static_cast<int64_t>(this->philox_offset_per_thread()); // Note that old THCGeneratorState had offset as std::atomic<int64_t>

aten/src/ATen/cuda/CUDAGreenContext.cpp

@@ -1,90 +1,78 @@
 #include <ATen/cuda/CUDAGreenContext.h>
 
-#if defined(CUDA_VERSION) && (CUDA_VERSION >= 12030) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
-#include <c10/cuda/driver_api.h>
-#include <stdexcept>
-#include <vector>
-#define HAS_CUDA_GREEN_CONTEXT() 1
-#else
-#define HAS_CUDA_GREEN_CONTEXT() 0
-// Suppress unsued private field warnings as this class is not supposed to be called
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-private-field")
-#endif
-
 namespace at::cuda {
+  GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
+#if CUDA_HAS_GREEN_CONTEXT
+    int driver_version;
+    C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
+    TORCH_CHECK(
+        driver_version >= 12080, "cuda driver too old to use green context!");
+    CUcontext pctx = nullptr;
+    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
+    if (C10_UNLIKELY(!pctx)) {
+      TORCH_WARN(
+          "Attempted to create a green context but"
+          " there was no primary context! Creating a primary context...");
 
-GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
-#if HAS_CUDA_GREEN_CONTEXT()
-  int driver_version;
-  C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
-  TORCH_CHECK(
-      driver_version >= 12080, "cuda driver too old to use green context!");
-  CUcontext pctx = nullptr;
-  C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
-  if (C10_UNLIKELY(!pctx)) {
-    TORCH_WARN(
-        "Attempted to create a green context but"
-        " there was no primary context! Creating a primary context...");
+      cudaFree(0);
+    }
 
-    cudaFree(0);
-  }
+    CUdevice device;
+    device_id_ = device_id;
+    C10_CUDA_DRIVER_CHECK(
+        c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
 
-   CUdevice device;
-  device_id_ = device_id;
-  C10_CUDA_DRIVER_CHECK(
-      c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
+    // Get device resources
+    CUdevResource device_resource;
+    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
+        device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
 
-  // Get device resources
-  CUdevResource device_resource;
-  C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
-      device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
+    // Split resources
+    std::vector<CUdevResource> result(1);
+    auto result_data = result.data();
+    unsigned int nb_groups = 1;
+    CUdevResource remaining;
 
-  // Split resources
-  std::vector<CUdevResource> result(1);
-  auto result_data = result.data();
-  unsigned int nb_groups = 1;
-  CUdevResource remaining;
+    C10_CUDA_DRIVER_CHECK(
+        c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
+            result_data,
+            &nb_groups,
+            &device_resource,
+            &remaining,
+            0, // default flags
+            num_sms));
 
-  C10_CUDA_DRIVER_CHECK(
-      c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
-          result_data,
-          &nb_groups,
-          &device_resource,
-          &remaining,
-          0, // default flags
-          num_sms));
+    TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
 
-  TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
+    // Generate resource descriptor
+    CUdevResourceDesc desc;
+    C10_CUDA_DRIVER_CHECK(
+        c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
+            &desc, result_data, 1));
 
-  // Generate resource descriptor
-  CUdevResourceDesc desc;
-  C10_CUDA_DRIVER_CHECK(
-      c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
-          &desc, result_data, 1));
+    // Create green context
+    // CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
+    // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
+    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
+        &green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
 
-  // Create green context
-  // CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
-  // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
-  C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
-      &green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
-
-  // Convert to regular context
-  C10_CUDA_DRIVER_CHECK(
-      c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
-  TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
+    // Convert to regular context
+    C10_CUDA_DRIVER_CHECK(
+        c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
+    TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
 #else
-  TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
+    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
 #endif
   }
 
   std::unique_ptr<GreenContext> GreenContext::create(
       uint32_t num_sms,
       std::optional<uint32_t> device_id) {
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     if (!device_id.has_value()) {
       device_id = at::cuda::current_device();
     }
-    return std::unique_ptr<GreenContext>(new GreenContext(device_id.value(), num_sms));
+    return std::make_unique<GreenContext>(device_id.value(), num_sms);
 #else
     TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
 #endif
@@ -92,7 +80,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
 
   // Implement move operations
   GreenContext::GreenContext(GreenContext&& other) noexcept{
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     device_id_ = std::exchange(other.device_id_, -1);
     green_ctx_ = std::exchange(other.green_ctx_, nullptr);
     context_ = std::exchange(other.context_, nullptr);
@@ -103,7 +91,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
   }
 
   GreenContext& GreenContext::operator=(GreenContext&& other) noexcept{
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     if (this != &other) {
       // Clean up current resources
       if (green_ctx_) {
@@ -132,7 +120,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
   }
 
   GreenContext::~GreenContext() noexcept{
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     C10_CUDA_DRIVER_CHECK(
         c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
 #else
@@ -140,9 +128,25 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
 #endif
   }
 
+  // Get the underlying CUDA context
+  CUcontext GreenContext::getContext() const {
+#if CUDA_HAS_GREEN_CONTEXT
+    return context_;
+#else
+    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
+#endif
+  }
+
+  // Get the underlying green context
+#if CUDA_HAS_GREEN_CONTEXT
+  CUgreenCtx GreenContext::getGreenContext() const {
+    return green_ctx_;
+  }
+#endif
+
   // Make this context current
   void GreenContext::setContext() {
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     auto current_stream = c10::cuda::getCurrentCUDAStream();
     parent_stream_ = current_stream.stream();
 
@@ -171,7 +175,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
   }
 
   void GreenContext::popContext() {
-#if HAS_CUDA_GREEN_CONTEXT()
+#if CUDA_HAS_GREEN_CONTEXT
     // see above note about stream being hardcoded to the default stream
     at::cuda::CUDAEvent ev;
     ev.record(c10::cuda::getCurrentCUDAStream());

aten/src/ATen/cuda/CUDAGreenContext.h

@@ -1,38 +1,53 @@
 #pragma once
 #include <ATen/cuda/CUDAEvent.h>
-#include <cuda.h>
 
-// Forward declare green context as opaque ptr
-typedef struct CUgreenCtx_st* CUgreenCtx;
+#if defined(CUDA_VERSION) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
+#include <c10/cuda/driver_api.h>
+#include <cuda.h>
+#include <memory>
+#include <stdexcept>
+#include <vector>
+#define CUDA_HAS_GREEN_CONTEXT 1
+#else
+#define CUDA_HAS_GREEN_CONTEXT 0
+#endif
 
 namespace at::cuda {
 
 class TORCH_CUDA_CPP_API GreenContext {
  public:
-  // Green context creation
-  static std::unique_ptr<GreenContext> create(
-      uint32_t num_sms,
-      std::optional<uint32_t> device_id);
-  ~GreenContext() noexcept;
+  GreenContext(uint32_t device_id, uint32_t num_sms);
+
+  static std::unique_ptr<GreenContext> create(uint32_t num_sms, std::optional<uint32_t> device_id);
 
   // Delete copy constructor and assignment
   GreenContext(const GreenContext&) = delete;
   GreenContext& operator=(const GreenContext&) = delete;
 
+  // Implement move operations
+  GreenContext(GreenContext&& other) noexcept;
+  GreenContext& operator=(GreenContext&& other) noexcept;
+  ~GreenContext() noexcept;
+
+  // Get the underlying CUDA context
+  CUcontext getContext() const;
+
+  // Get the underlying green context
+#if CUDA_HAS_GREEN_CONTEXT
+  CUgreenCtx getGreenContext() const;
+#endif
+
   // Make this context current
   void setContext();
 
   void popContext();
 
  private:
-  GreenContext(uint32_t device_id, uint32_t num_sms);
-  // Implement move operations
-  GreenContext(GreenContext&& other) noexcept;
-  GreenContext& operator=(GreenContext&& other) noexcept;
-
+#if CUDA_HAS_GREEN_CONTEXT
   int32_t device_id_ = -1;
   CUgreenCtx green_ctx_ = nullptr;
   CUcontext context_ = nullptr;
   cudaStream_t parent_stream_ = nullptr;
+#endif
 };
 } // namespace at::cuda

aten/src/ATen/cuda/CUDASparse.h

@@ -7,6 +7,17 @@
 #endif
 
 
+#if defined(USE_ROCM)
+// hipSparse const API added in v2.4.0
+#if HIPSPARSE_VERSION >= 200400
+#define AT_USE_HIPSPARSE_GENERIC_API() 1
+#else
+#define AT_USE_HIPSPARSE_GENERIC_API() 1
+#endif
+#else // USE_ROCM
+#define AT_USE_HIPSPARSE_GENERIC_API() 0
+#endif // USE_ROCM
+
 // cuSparse Generic API spsv function was added in CUDA 11.3.0
 #if defined(CUDART_VERSION) && defined(CUSPARSE_VERSION) && (CUSPARSE_VERSION >= 11500)
 #define AT_USE_CUSPARSE_GENERIC_SPSV() 1

aten/src/ATen/cuda/CublasHandlePool.cpp

@@ -155,8 +155,8 @@ size_t parseChosenWorkspaceSize() {
     while (next != end) {
       std::smatch match = *next;
       TORCH_CHECK(match.size() == 3, "Expected CUBLAS_WORKSPACE_SPACE_CONFIG match of size 3 (Format :SIZE:COUNT)");
-      size_t curr_size = std::stoull(match.str(1));
-      size_t count = std::stoull(match.str(2));
+      size_t curr_size = (size_t) std::stoi(match.str(1));
+      size_t count = (size_t) std::stoi(match.str(2));
       total_size += curr_size * 1024 * count;
       next++;
     }

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

@@ -2,6 +2,8 @@
 #include <ATen/Tensor.h>
 #include <ATen/cuda/Exceptions.h>
 
+#include <mutex>
+
 namespace at {
 namespace cuda {
 namespace detail {
@@ -10,36 +12,39 @@ __device__ __constant__ float cublas_one_device;
 __device__ __constant__ float cublas_zero_device;
 
 float *get_cublas_device_one() {
-  static float *ptr = nullptr;
-  static auto init_flag = [&]() {
+  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)));
-    AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_one_device));
-    return true;
-  }();
+  });
 
+  float *ptr;
+  AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_one_device));
   return ptr;
 }
 
 float *get_cublas_device_zero() {
-  static float *ptr = nullptr;
-  static auto init_flag = [&]() {
+  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)));
-    AT_CUDA_CHECK(cudaGetSymbolAddress(reinterpret_cast<void**>(&ptr), cublas_zero_device));
-    return true;
-  }();
+  });
 
+  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 bool init_flag [[maybe_unused]] = []() {
+  static c10::once_flag init_flag;
+
+  c10::call_once(init_flag, []() {
     AT_CUDA_CHECK(cudaMalloc(&alpha_ptr, sizeof(float)));
-    return true;
-  }();
+  });
 
   return alpha_ptr;
 }

aten/src/ATen/cudnn/Descriptors.cpp

@@ -3,7 +3,6 @@
 #include <ATen/ATen.h>
 #include <c10/util/irange.h>
 
-#include <array>
 #include <iostream>
 #include <sstream>
 
@@ -137,9 +136,9 @@ void FilterDescriptor::set(const at::Tensor &t, const at::MemoryFormat memory_fo
     "Weight strides: ", t.strides(), "\n",
     "cuDNN suggested memory_format: ", memory_format);
 
-  std::array<int, CUDNN_DIM_MAX> size;
+  int size[CUDNN_DIM_MAX];
   for (const auto i : c10::irange(dim)) {
-    size[i] = static_cast<int>(t.size(i));
+    size[i] = (int) t.size(i);
   }
   for (const auto i : c10::irange(dim, pad)) {
     size[i] = 1;
@@ -157,7 +156,7 @@ void FilterDescriptor::set(const at::Tensor &t, const at::MemoryFormat memory_fo
     default:
       TORCH_INTERNAL_ASSERT(false, "unsupported memory_format for cuDNN filters");
   }
-  set(getDataType(t), static_cast<int>(dim), size.data(), filter_format);
+  set(getDataType(t), static_cast<int>(dim), size, filter_format);
 }
 
 std::string cudnnMemoryFormatToString(cudnnTensorFormat_t tformat) {

aten/src/ATen/detail/MTIAHooksInterface.h

@@ -1,6 +1,5 @@
 #pragma once
 
-#include <c10/core/CachingDeviceAllocator.h>
 #include <c10/core/Device.h>
 #include <c10/util/Exception.h>
 
@@ -152,36 +151,6 @@ struct TORCH_API MTIAHooksInterface : AcceleratorHooksInterface {
   }
 
   virtual bool isAvailable() const override;
-
-  /* MTIAGraph related APIs */
-  virtual int64_t mtiagraphCreate(bool keep_graph = false) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-    return -1;
-  }
-
-  virtual void mtiagraphCaptureBegin(int64_t handle, MempoolId_t pool) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
-
-  virtual void mtiagraphCaptureEnd(int64_t handle) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
-
-  virtual void mtiagraphInstantiate(int64_t handle) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
-
-  virtual void mtiagraphReplay(int64_t handle) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
-
-  virtual void mtiagraphReset(int64_t handle) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
-
-  virtual MempoolId_t mtiagraphPool(int64_t handle) const {
-    FAIL_MTIAHOOKS_FUNC(__func__);
-  }
 };
 
 struct TORCH_API MTIAHooksArgs {};

aten/src/ATen/functorch/ADInterpreters.cpp

@@ -198,7 +198,7 @@ static void autogradBasedTransformSendToNext(
   }
 
   // Step 6
-  stack->erase(stack->end() - static_cast<std::ptrdiff_t>(args_size + ret_size), stack->end() - static_cast<std::ptrdiff_t>(ret_size));
+  stack->erase(stack->end() - std::ptrdiff_t(args_size + ret_size), stack->end() - std::ptrdiff_t(ret_size));
 }
 
 void GradInterpreterPtr::processImpl(

aten/src/ATen/functorch/BatchRulesNorm.cpp

@@ -443,14 +443,14 @@ static bool has_same_shape(
   if (!tensor.defined()) {
     return true;
   }
-  if (rankWithoutBatchDim(tensor, tensor_bdim) != static_cast<int64_t>(normalized_shape.size())) {
+  if (rankWithoutBatchDim(tensor, tensor_bdim) != (int64_t) normalized_shape.size()) {
     return false;
   }
   const auto tensor_shape = tensor.sizes();
   for (const auto i : c10::irange(normalized_shape.size())) {
     auto j = i;
     // (0, 1, 2), 1 -> (0, 2, 3)
-    if (tensor_bdim.has_value() && static_cast<int64_t>(i) >= tensor_bdim.value()) {
+    if (tensor_bdim.has_value() && (int64_t)i >= tensor_bdim.value()) {
       j = j + 1;
     }
     if (normalized_shape[i] != tensor_shape[j]) {

aten/src/ATen/functorch/BatchRulesReduceOps.cpp

@@ -135,7 +135,7 @@ static void boxed_reduction_batch_rule(const c10::OperatorHandle& op, torch::jit
     reduction_case = ReductionCase::DimArray;
     dims = arguments[dim_arg_pos].toIntList().vec();
     if (dims.empty()) {
-      auto all_dims = range(0, std::max(static_cast<int64_t>(1), logical_dim));
+      auto all_dims = range(0, std::max((int64_t)1, logical_dim));
       dims = std::vector<int64_t>(all_dims.begin(), all_dims.end());
     }
   } else if (arguments[dim_arg_pos].isInt()) {

aten/src/ATen/functorch/BatchRulesScatterOps.cpp

@@ -432,7 +432,7 @@ namespace {
     // Eg. Given `indexed_shape.size()` is 5 and
     // shape of `values` is (N, 2, 3), then following block
     // will reshape `values` to (N, 1, 1, 2, 3).
-    if ( static_cast<int64_t>(indexed_shape.size()) > values_.dim()) {
+    if ( (int64_t) indexed_shape.size() > values_.dim()) {
       auto values_sizes = values_.sym_sizes();
 
       // number of unit dims (for broadcasting value to indexed_shape)

aten/src/ATen/functorch/BatchRulesViews.cpp

@@ -109,7 +109,7 @@ std::tuple<Tensor, std::optional<int64_t>> repeat_batch_rule(
   SymDimVector sizes_with_bdim = { sizes.begin(), sizes.end() };
   sizes_with_bdim.insert(sizes_with_bdim.begin(), 1);
   auto self_ = moveBatchDimToFront(self, self_bdim);
-  while (self_.dim() < static_cast<int64_t>(sizes_with_bdim.size())) {
+  while (self_.dim() < (int64_t)sizes_with_bdim.size()) {
     self_ = self_.unsqueeze(1);
   }
   return std::make_tuple(self_.repeat_symint(sizes_with_bdim), 0);
@@ -534,20 +534,20 @@ Tensor trace_decomp(const Tensor& tensor) {
 std::tuple<Tensor, std::optional<int64_t>> tril_batch_rule(
     const Tensor& self,
     std::optional<int64_t> self_bdim,
-    c10::SymInt diagonal = 0) {
+    int64_t diagonal = 0) {
   TORCH_CHECK(self.dim() >= 2, "tril: The input tensor must have at least 2 dimensions.");
   auto self_ = moveBatchDimToFront(self, self_bdim);
-  auto result = at::tril_symint(self_, std::move(diagonal));
+  auto result = at::tril(self_, diagonal);
   return std::make_tuple(std::move(result), 0);
 }
 
 std::tuple<Tensor, std::optional<int64_t>> triu_batch_rule(
     const Tensor& self,
     std::optional<int64_t> self_bdim,
-    c10::SymInt diagonal = 0) {
+    int64_t diagonal = 0) {
   TORCH_CHECK(self.dim() >= 2, "triu: The input tensor must have at least 2 dimensions.");
   auto self_ = moveBatchDimToFront(self, self_bdim);
-  auto result = at::triu_symint(self_, std::move(diagonal));
+  auto result = at::triu(self_, diagonal);
   return std::make_tuple(std::move(result), 0);
 }
 

aten/src/ATen/functorch/BatchedFallback.cpp

@@ -191,7 +191,7 @@ static void batchedTensorInplaceForLoopFallback(const c10::OperatorHandle& op, t
       // simplicity. When that is not the case, this code should be updated.
       const auto& argument = (*stack)[arguments_begin + arg_idx];
       if (batched_tensor_inputs_pos_iter == batched_tensor_inputs_position.end()
-          || static_cast<int64_t>(arg_idx) != *batched_tensor_inputs_pos_iter) {
+          || (int64_t)arg_idx != *batched_tensor_inputs_pos_iter) {
         // argument isn't a BatchedTensor
         torch::jit::push(stack, argument);
         continue;
@@ -345,7 +345,7 @@ void batchedTensorForLoopFallback(const c10::OperatorHandle& op, torch::jit::Sta
       // simplicity. When that is not the case, this code should be updated.
       const auto& argument = (*stack)[arguments_begin + arg_idx];
       if (batched_tensor_inputs_pos_iter == batched_tensor_inputs_position.end()
-          || static_cast<int64_t>(arg_idx) != *batched_tensor_inputs_pos_iter) {
+          || (int64_t)arg_idx != *batched_tensor_inputs_pos_iter) {
         // argument isn't a BatchedTensor
         torch::jit::push(stack, argument);
         continue;
@@ -473,7 +473,7 @@ void batchedNestedTensorForLoopFallback(const c10::OperatorHandle& op, torch::ji
       // simplicity. When that is not the case, this code should be updated.
       const auto& argument = (*stack)[arguments_begin + arg_idx];
       if (batched_tensor_inputs_pos_iter == batched_tensor_inputs_position.end()
-          || static_cast<int64_t>(arg_idx) != *batched_tensor_inputs_pos_iter) {
+          || (int64_t)arg_idx != *batched_tensor_inputs_pos_iter) {
         // argument isn't a BatchedTensor
         torch::jit::push(stack, argument);
         continue;

aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp

@@ -157,7 +157,7 @@ Tensor& squeeze__batching_rule(Tensor& self) {
   const auto physical_shape = batched->value().sizes();
   auto how_many_dims_of_size_1_before_bdim = 0;
   for (const auto i : c10::irange(0, physical_shape.size())) {
-    if (static_cast<int64_t>(i) == bdim) {
+    if ((int64_t)i == bdim) {
       break;
     }
     if (physical_shape[i] == 1) {
@@ -573,7 +573,7 @@ Tensor cat_batching_rule(const ITensorListRef& tensors, int64_t dim) {
   }
 
   auto new_dim = bdim_size.has_value() ? dim + 1 : dim;
-  std::optional<int64_t> new_bdim = bdim_size.has_value() ? std::make_optional(static_cast<int64_t>(0)) : std::nullopt;
+  std::optional<int64_t> new_bdim = bdim_size.has_value() ? std::make_optional((int64_t)0) : std::nullopt;
   auto result = at::cat(tensors_to_cat, new_dim);
   return makeBatched(result, new_bdim, get_current_level());
 }

aten/src/ATen/mps/MPSDevice.mm

@@ -1,5 +1,7 @@
 //  Copyright © 2022 Apple Inc.
 
+#include <c10/util/CallOnce.h>
+
 #include <ATen/mps/IndexKernels.h>
 #include <ATen/mps/MPSAllocatorInterface.h>
 #include <ATen/mps/MPSDevice.h>
@@ -8,6 +10,9 @@
 
 namespace at::mps {
 
+static std::unique_ptr<MPSDevice> mps_device;
+static c10::once_flag mpsdev_init;
+
 static inline MTLLanguageVersion getMetalLanguageVersion(const id<MTLDevice>& device) {
   // MPS Advanced Indexing needs at least Metal 2.0 (support for Argument Buffers and function constants)
   // host_name attribute needs at least Metal 2.2 and ulong needs Metal 2.3 (supported on MacOS 11+
@@ -16,8 +21,8 @@ static inline MTLLanguageVersion getMetalLanguageVersion(const id<MTLDevice>& de
 }
 
 MPSDevice* MPSDevice::getInstance() {
-  static MPSDevice mps_device;
-  return &mps_device;
+  c10::call_once(mpsdev_init, [] { mps_device = std::unique_ptr<MPSDevice>(new MPSDevice()); });
+  return mps_device.get();
 }
 
 MPSDevice::~MPSDevice() {

aten/src/ATen/native/AveragePool2d.cpp

@@ -25,19 +25,18 @@ TORCH_PRECOMPUTE_META_FUNC(avg_pool2d)
   // #20866, #22032: Guarantee this for the official C++ API?
   TORCH_CHECK(kernel_size.size() == 1 || kernel_size.size() == 2,
     "avg_pool2d: kernel_size must either be a single int, or a tuple of two ints");
-  const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
-  const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
+  const int64_t kH = kernel_size[0];
+  const int64_t kW = kernel_size.size() == 1 ? kH : kernel_size[1];
 
   TORCH_CHECK(stride.empty() || stride.size() == 1 || stride.size() == 2,
     "avg_pool2d: stride must either be omitted, a single int, or a tuple of two ints");
-  const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
-  const int dW = stride.empty() ? kW :
-                 stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
+  const int64_t dH = stride.empty() ? kH : stride[0];
+  const int64_t dW = stride.empty() ? kW : stride.size() == 1 ? dH : stride[1];
 
   TORCH_CHECK(padding.size() == 1 || padding.size() == 2,
     "avg_pool2d: padding must either be a single int, or a tuple of two ints");
-  const int padH = safe_downcast<int, int64_t>(padding[0]);
-  const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
+  const int64_t padH = padding[0];
+  const int64_t padW = padding.size() == 1 ? padH : padding[1];
 
   TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0,
     "divisor must be not zero");

aten/src/ATen/native/AveragePool3d.cpp

@@ -198,9 +198,9 @@ void avg_pool3d_out_frame(
             int64_t hend = std::min(hstart + kH, iheight + padH);
             int64_t wend = std::min(wstart + kW, iwidth + padW);
             int64_t pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart);
-            tstart = std::max(tstart, static_cast<int64_t>(0));
-            hstart = std::max(hstart, static_cast<int64_t>(0));
-            wstart = std::max(wstart, static_cast<int64_t>(0));
+            tstart = std::max(tstart, (int64_t) 0);
+            hstart = std::max(hstart, (int64_t) 0);
+            wstart = std::max(wstart, (int64_t) 0);
             tend = std::min(tend, itime);
             hend = std::min(hend, iheight);
             wend = std::min(wend, iwidth);
@@ -377,9 +377,9 @@ void avg_pool3d_backward_out_frame(
             int64_t hend = std::min(hstart + kH, iheight + padH);
             int64_t wend = std::min(wstart + kW, iwidth + padW);
             int64_t pool_size = (tend -tstart) * (hend - hstart) * (wend - wstart);
-            tstart = std::max(tstart, static_cast<int64_t>(0));
-            hstart = std::max(hstart, static_cast<int64_t>(0));
-            wstart = std::max(wstart, static_cast<int64_t>(0));
+            tstart = std::max(tstart, (int64_t) 0);
+            hstart = std::max(hstart, (int64_t) 0);
+            wstart = std::max(wstart, (int64_t) 0);
             tend = std::min(tend, itime);
             hend = std::min(hend, iheight);
             wend = std::min(wend, iwidth);

aten/src/ATen/native/BatchLinearAlgebraKernel.cpp

@@ -946,10 +946,10 @@ void apply_lu_factor(const Tensor& input, const Tensor& pivots, const Tensor& in
     }
   };
   // avoid overflow
-  auto matrix_rank = std::min(m, n);
+  float matrix_rank = float(std::min(m, n));
   // A heuristic tested on a 32 core/socket ICX system
   // https://github.com/pytorch/pytorch/pull/93037#discussion_r1090112948
-  int64_t chunk_size_per_thread = static_cast<int64_t>(
+  int64_t chunk_size_per_thread = int64_t(
       std::min(1.0, 3200.0 / (matrix_rank * matrix_rank * matrix_rank)));
   int64_t grain_size = chunk_size_per_thread * at::get_num_threads();
   at::parallel_for(0, batch_size, grain_size, loop);

aten/src/ATen/native/Blas.cpp

@@ -267,7 +267,7 @@ _scaled_mm_out_cpu_emulated(const Tensor& mat1, const Tensor& mat2,
 
   float input_scale = scale_a.item<float>();
   float weight_scale = scale_b.item<float>();
-  float output_scale = 1.0f;
+  float output_scale = float(1.0);
   if (scale_result.has_value() &&
       (*out_dtype == ScalarType::Float8_e4m3fn ||
        *out_dtype == ScalarType::Float8_e5m2)) {

aten/src/ATen/native/BlasKernel.cpp

@@ -331,7 +331,7 @@ bool gemv_use_fast_path<double>(
     [[maybe_unused]] double beta,
     int64_t incy) {
   return gemv_use_fast_path<float>(
-      trans, m, n, static_cast<float>(alpha), lda, incx, static_cast<float>(beta), incy);
+      trans, m, n, (float)alpha, lda, incx, (float)beta, incy);
 }
 
 template <>
@@ -523,8 +523,8 @@ static inline void scal(int64_t n, scalar_t a, scalar_t *x, int64_t incx)
   if (n == 1) incx = 1;
 #if AT_BUILD_WITH_BLAS()
   if (blas_impl::scal_use_fast_path<scalar_t>(n, incx)) {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
     blas_impl::scal_fast_path<scalar_t>(&i_n, &a, x, &i_incx);
     return;
   }
@@ -545,11 +545,11 @@ void gemv(char trans, int64_t m, int64_t n, scalar_t alpha, const scalar_t *a, i
 #if AT_BUILD_WITH_BLAS()
   if (blas_impl::gemv_use_fast_path<scalar_t>(trans, m, n, alpha, lda, incx, beta, incy)) {
     TORCH_CHECK(lda >= std::max<int64_t>(1L, m), "lda should be at least max(1,", m, "), but have ", lda);
-    int i_m = static_cast<int>(m);
-    int i_n = static_cast<int>(n);
-    int i_lda = static_cast<int>(lda);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_m = (int)m;
+    int i_n = (int)n;
+    int i_lda = (int)lda;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     blas_impl::gemv_fast_path<scalar_t>(&trans, &i_m, &i_n, &alpha, a, &i_lda, x, &i_incx, &beta, y, &i_incy);
     return;
   }

aten/src/ATen/native/CPUBlas.cpp

@@ -680,9 +680,9 @@ void axpy(int64_t n, double a, const double *x, int64_t incx, double *y, int64_t
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
   {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_daxpy(i_n, a, x, i_incx, y, i_incy);
     #else
@@ -705,9 +705,9 @@ void axpy(int64_t n, float a, const float *x, int64_t incx, float *y, int64_t in
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
   {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_saxpy(i_n, a, x, i_incx, y, i_incy);
     #else
@@ -730,9 +730,9 @@ void axpy(int64_t n, c10::complex<double> a, const c10::complex<double> *x, int6
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
   {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_zaxpy(i_n, &a, x, i_incx, y, i_incy);
     #else
@@ -755,9 +755,9 @@ void axpy(int64_t n, c10::complex<float> a, const c10::complex<float> *x, int64_
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) )
   {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_caxpy(i_n, &a, x, i_incx, y, i_incy);
     #else
@@ -781,9 +781,9 @@ void copy(int64_t n, const double *x, int64_t incx, double *y, int64_t incy) {
   }
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_dcopy(i_n, x, i_incx, y, i_incy);
     #else
@@ -805,9 +805,9 @@ void copy(int64_t n, const float *x, int64_t incx, float *y, int64_t incy) {
   }
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_scopy(i_n, x, i_incx, y, i_incy);
     #else
@@ -829,9 +829,9 @@ void copy(int64_t n, const c10::complex<double> *x, int64_t incx, c10::complex<d
   }
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_zcopy(i_n, x, i_incx, y, i_incy);
     #else
@@ -853,9 +853,9 @@ void copy(int64_t n, const c10::complex<float> *x, int64_t incx, c10::complex<fl
   }
   #if AT_BUILD_WITH_BLAS()
   if( (n <= INT_MAX) && (incx <= INT_MAX) && (incy <= INT_MAX) ) {
-    int i_n = static_cast<int>(n);
-    int i_incx = static_cast<int>(incx);
-    int i_incy = static_cast<int>(incy);
+    int i_n = (int)n;
+    int i_incx = (int)incx;
+    int i_incy = (int)incy;
     #if C10_IOS
     cblas_ccopy(i_n, &x, i_incx, y, i_incy);
     #else
@@ -1082,7 +1082,7 @@ struct Brgemm : public KernelCache <BrgemmKey, GemmHelper> {
         M,
         N,
         K,
-        1,
+        int64_t(1),
         ld_a,
         ld_b,
         ld_c,
@@ -1096,7 +1096,7 @@ struct Brgemm : public KernelCache <BrgemmKey, GemmHelper> {
           M,
           N,
           K,
-          1,
+          int64_t(1),
           ld_a,
           ld_b,
           ld_c,

aten/src/ATen/native/GridSampler.cpp

@@ -487,17 +487,17 @@ static Tensor _grid_sampler_2d_cpu_quantized(
   int64_t out_sC = output.stride(1);
   int64_t out_sH = output.stride(2);
   int64_t out_sW = output.stride(3);
-  const uint8_t* inp_ptr = input.const_data_ptr<uint8_t>();
-  uint8_t* out_ptr = output.data_ptr<uint8_t>();
-  const float* grid_ptr = grid.const_data_ptr<float>();
+  uint8_t* inp_ptr = (uint8_t*)input.data_ptr<quint8>();
+  uint8_t* out_ptr = (uint8_t*)output.data_ptr<quint8>();
+  float* grid_ptr = grid.data_ptr<float>();
   at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
     for (const auto n : c10::irange(start, end)) {
-      const float* grid_ptr_N = grid_ptr + n * grid_sN;
-      const uint8_t* inp_ptr_N = inp_ptr + n * inp_sN;
+      float* grid_ptr_N = grid_ptr + n * grid_sN;
+      uint8_t* inp_ptr_N = inp_ptr + n * inp_sN;
       for (const auto h : c10::irange(out_H)) {
         for (const auto w : c10::irange(out_W)) {
           // get the corresponding input x, y, z coordinates from grid
-          const float* grid_ptr_NHW = grid_ptr_N + h * grid_sH + w * grid_sW;
+          float* grid_ptr_NHW = grid_ptr_N + h * grid_sH + w * grid_sW;
           float x = *grid_ptr_NHW;
           float y = grid_ptr_NHW[grid_sCoor];
 
@@ -527,7 +527,7 @@ static Tensor _grid_sampler_2d_cpu_quantized(
           float se = (ix - ix_nw) * (iy - iy_nw);
 
           // calculate bilinear weighted pixel value and set output pixel
-          const uint8_t* inp_ptr_NC = inp_ptr_N;
+          uint8_t* inp_ptr_NC = inp_ptr_N;
           uint8_t* out_ptr_NCHW =
               out_ptr + n * out_sN + h * out_sH + w * out_sW;
           for (int64_t c = 0; c < C;

aten/src/ATen/native/Histogram.cpp

@@ -318,7 +318,7 @@ static std::vector<Tensor>& histogramdd_bin_edges_out(const Tensor& self, IntArr
 
     const int64_t N = self.size(-1);
     const int64_t M = std::accumulate(self.sizes().begin(), self.sizes().end() - 1,
-            static_cast<int64_t>(1), std::multiplies<int64_t>());
+            (int64_t)1, std::multiplies<int64_t>());
     Tensor reshaped_self = self.reshape({ M, N });
 
     auto outer_bin_edges = select_outer_bin_edges(reshaped_self, range);

aten/src/ATen/native/Integration.cpp

@@ -40,7 +40,7 @@ Tensor do_trapezoid(const Tensor& y, const Tensor& dx, int64_t dim) {
 // When dx is constant, the above formula simplifies
 // to dx * [(\sum_{i=1}^n y_i) - (y_1 + y_n)/2]
 Tensor do_trapezoid(const Tensor& y, double dx, int64_t dim) {
-    return (y.sum(dim) - (y.select(dim, 0) + y.select(dim, -1)) * 0.5) * dx;
+    return (y.sum(dim) - (y.select(dim, 0) + y.select(dim, -1)) * (0.5)) * dx;
 }
 
 Tensor zeros_like_except(const Tensor& y, int64_t dim) {

aten/src/ATen/native/Linear.cpp

@@ -201,7 +201,7 @@ static Tensor sumproduct_pair(const Tensor& left_, const Tensor& right_, IntArra
   out_size.reserve(out_num_dim);
   for (auto& d : lro) out_size.push_back(left.sym_size(d));
   for (auto& d : lo) out_size.push_back(left.sym_size(d));
-  for (auto& d : sum_dims_) { out_size.emplace_back(1); (void)d; }; // avoid warning about not using d
+  for (auto& d : sum_dims_) { out_size.emplace_back(1); (void)(d); }; // avoid warning about not using d
   for (auto& d : ro) out_size.push_back(right.sym_size(d));
 
   std::vector<int64_t> lpermutation(lro);
@@ -640,7 +640,7 @@ Tensor einsum(std::string_view equation, TensorList operands, at::OptionalIntArr
     }
   }
 
-  return std::move(ops[0]);
+  return ops[0];
 }
 
 // _trilinear computes a trilinear einstein sum with an unrolled dimension
@@ -805,7 +805,7 @@ Tensor tensordot(const Tensor& input1, const Tensor& input2, IntArrayRef dims1,
   std::vector<SymInt> rsizes;  // rsizes: sizes of the result
   p1.reserve(input1.dim());
   p2.reserve(input2.dim());
-  rsizes.reserve(input1.dim() + input2.dim() - static_cast<int64_t>(dims1.size()));
+  rsizes.reserve(input1.dim() + input2.dim() - (int64_t) dims1.size());
   SymInt size1 = 1; // number of non-contracted elements in input1
   SymInt size2 = 1; // number of non-contracted elements in input2
 

aten/src/ATen/native/LinearAlgebra.cpp

@@ -1655,7 +1655,7 @@ static inline void baddbmm_cpu_kernel(const Tensor& result, const Tensor& self,
   auto s0 = self.accessor<const scalar_t, 3>();
   auto m0 = mat2.accessor<const scalar_t, 3>();
 
-  int64_t grain_size = std::max(internal::GRAIN_SIZE / (is * js * ks), static_cast<int64_t>(1));
+  int64_t grain_size = std::max(internal::GRAIN_SIZE / (is * js * ks), (int64_t)1);
   using opmath_t = at::opmath_type<scalar_t>;
   parallel_for(0, bs, grain_size, [&](int64_t b_begin, int64_t b_end) {
       for (const auto b : c10::irange(b_begin, b_end)) {

aten/src/ATen/native/LossNLL.cpp

@@ -235,7 +235,7 @@ void nll_loss_out_frame(
 
   constexpr int64_t cascade_sum_num_levels = 8;
   const int64_t level_power =
-      std::max(static_cast<int64_t>(4), utils::CeilLog2(batch_size) / cascade_sum_num_levels);
+      std::max(int64_t(4), utils::CeilLog2(batch_size) / cascade_sum_num_levels);
   const int64_t level_step = (1 << level_power);
   const int64_t level_mask = level_step - 1;
 

aten/src/ATen/native/LossNLL2d.cpp

@@ -129,7 +129,7 @@ void nll_loss2d_forward_out_frame(
       for (const auto b : c10::irange(start, end)) {
         for (const auto h : c10::irange(H)) {
           for (const auto w : c10::irange(W)) {
-            const int64_t cur_target = target_acc[b][h][w];
+            const int64_t cur_target = (int64_t)target_acc[b][h][w];
 
             if (cur_target == ignore_index) {
               output_acc[b][h][w] = static_cast<scalar_t>(0);
@@ -188,7 +188,7 @@ void nll_loss2d_forward_out_frame(
   // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
   scalar_t loss_partial_sums[cascade_sum_num_levels] = {0};
   const int64_t level_power =
-      std::max(static_cast<int64_t>(4), utils::CeilLog2(numiter) / cascade_sum_num_levels);
+      std::max(int64_t(4), utils::CeilLog2(numiter) / cascade_sum_num_levels);
   const int64_t level_step = (1 << level_power);
   const int64_t level_mask = level_step - 1;
 

aten/src/ATen/native/Math.h

@@ -192,7 +192,7 @@ Date:  February 1996
   x = x - (std::erf(x) - y) / ((static_cast<T>(2.0)/static_cast<T>(std::sqrt(c10::pi<double>)))*std::exp(-x*x));
   x = x - (std::erf(x) - y) / ((static_cast<T>(2.0)/static_cast<T>(std::sqrt(c10::pi<double>)))*std::exp(-x*x));
 
-  return x;
+  return(x);
 }
 
 #undef CENTRAL_RANGE
@@ -3819,7 +3819,7 @@ inline C10_HOST_DEVICE T shifted_chebyshev_polynomial_v_forward(T x, int64_t n)
 
     if ((n > 6) && (std::abs(x + x - T(1.0)) < T(1.0))) {
         if (std::sin(std::acos(x + x - T(1.0)) / T(2.0)) != T(1.0)) {
-            return std::cos((n + T(0.5)) * std::acos(x + x - T(1.0))) / std::cos(std::acos(x + x - T(1.0)) / T(2.0));
+            return std::cos(((n) + T(0.5)) * std::acos(x + x - T(1.0))) / std::cos(std::acos(x + x - T(1.0)) / T(2.0));
         }
 
         if (n % 2 == 0) {

aten/src/ATen/native/NNPACK.cpp

@@ -193,22 +193,22 @@ Tensor _nnpack_spatial_convolution(
   const size_t input_channels = input.size(1);
   const size_t output_channels = weight.size(0);
   const struct nnp_size input_size = {
-      .width = static_cast<size_t>(input.size(3)),
-      .height = static_cast<size_t>(input.size(2)),
+      .width = (size_t)input.size(3),
+      .height = (size_t)input.size(2),
   };
   const struct nnp_padding input_padding = {
-      .top = static_cast<size_t>(padding[0]),
-      .right = static_cast<size_t>(padding[1]),
-      .bottom = static_cast<size_t>(padding[0]),
-      .left = static_cast<size_t>(padding[1]),
+      .top = (size_t)padding[0],
+      .right = (size_t)padding[1],
+      .bottom = (size_t)padding[0],
+      .left = (size_t)padding[1],
   };
   const struct nnp_size kernel_size = {
-      .width = static_cast<size_t>(weight.size(3)),
-      .height = static_cast<size_t>(weight.size(2)),
+      .width = (size_t)weight.size(3),
+      .height = (size_t)weight.size(2),
   };
   const struct nnp_size output_size = {
-      .width = static_cast<size_t>(output.size(3)),
-      .height = static_cast<size_t>(output.size(2)),
+      .width = (size_t)output.size(3),
+      .height = (size_t)output.size(2),
   };
   const nnp_size output_subsample = {
       .width = static_cast<std::size_t>(stride[1]),

aten/src/ATen/native/NaiveConvolutionTranspose3d.cpp

@@ -248,8 +248,8 @@ void slow_conv_transpose3d_out_cpu_template(
   Tensor weight = weight_.contiguous();
   Tensor bias = bias_.defined() ? bias_.contiguous() : bias_;
 
-  const auto n_input_plane = weight.size(0);
-  const auto n_output_plane = weight.size(1);
+  const int n_input_plane = (int)weight.size(0);
+  const int n_output_plane = (int)weight.size(1);
 
   bool is_batch = false;
   if (input.dim() == 4) {

aten/src/ATen/native/NamedTensor.cpp

@@ -84,8 +84,8 @@ static std::vector<int64_t> aligned_size(
     DimnameList aligned_names,
     bool is_aligning_two_tensors) {
   std::vector<int64_t> expanded_sizes(aligned_names.size(), 1);
-  ptrdiff_t dim = static_cast<ptrdiff_t>(tensor_sizes.size()) - 1;
-  ptrdiff_t idx = static_cast<ptrdiff_t>(aligned_names.size()) - 1;
+  ptrdiff_t dim = (ptrdiff_t)tensor_sizes.size() - 1;
+  ptrdiff_t idx = (ptrdiff_t)aligned_names.size() - 1;
   for (; idx >= 0 && dim >= 0; --idx) {
     if (tensor_names[dim] != aligned_names[idx]) {
       continue;

aten/src/ATen/native/RowwisePrune.cpp

@@ -25,7 +25,7 @@ std::tuple<Tensor, Tensor> _rowwise_prune_helper(
   auto mask_contig = mask.contiguous();
   auto mask_data = mask_contig.data_ptr<bool>();
   for (const auto i : c10::irange(mask.numel())) {
-    num_non_masked_rows += ((mask_data[i] == true) ? 1 : 0);
+    num_non_masked_rows += (((mask_data[i] == true)) ? 1 : 0);
   }
   int num_cols = weights.size(1);
   auto pruned_2d_tensor = at::empty({num_non_masked_rows, num_cols},

aten/src/ATen/native/SoftMax.cpp

@@ -176,7 +176,7 @@ void host_softmax(
   scalar_t* input_data_base = input.data_ptr<scalar_t>();
   scalar_t* output_data_base = output.data_ptr<scalar_t>();
   bool* mask_data_base = mask;
-  int64_t grain_size = std::min(internal::GRAIN_SIZE / dim_size, static_cast<int64_t>(1));
+  int64_t grain_size = std::min(internal::GRAIN_SIZE / dim_size, (int64_t)1);
   parallel_for(
       0, outer_size * inner_size, grain_size,
       [&](int64_t begin, int64_t end) {
@@ -265,7 +265,7 @@ void host_softmax_backward(
   scalar_t* output_data_base = output.data_ptr<scalar_t>();
   scalar_t* gradOutput_data_base = grad.data_ptr<scalar_t>();
   bool* mask_data_base = mask;
-  int64_t grain_size = std::min(internal::GRAIN_SIZE / dim_size, static_cast<int64_t>(1));
+  int64_t grain_size = std::min(internal::GRAIN_SIZE / dim_size, (int64_t)1);
   parallel_for(
       0, outer_size * inner_size, grain_size, [&](int64_t begin, int64_t end) {
         for (const auto i : c10::irange(begin, end)) {

aten/src/ATen/native/TensorAdvancedIndexing.cpp

@@ -1701,13 +1701,13 @@ Tensor& index_select_out_cpu_(
                   TORCH_CHECK_INDEX(
                       (self_i >= 0) && (self_i < self_dim_size),
                       "index out of range in self");
-                  auto self_data = const_cast<char*>(static_cast<const char*>(
-                                       selfSlice_data)) +
+                  auto self_data = static_cast<const char*>(selfSlice_data) +
                       self_i * self_stride_bytes;
                   auto result_data = static_cast<char*>(resultSlice_data) +
                       i * result_stride_bytes;
                   sub_iter.unsafe_replace_operand(0, result_data);
-                  sub_iter.unsafe_replace_operand(1, self_data);
+                  sub_iter.unsafe_replace_operand(
+                      1, const_cast<char*>(self_data));
                   copy_stub(sub_iter.device_type(), sub_iter, false);
                 };
               });

aten/src/ATen/native/TensorFactories.cpp

@@ -1382,7 +1382,7 @@ void randperm_cpu(Tensor& result, int64_t n, CPUGeneratorImpl* generator) {
   // use no-initialization Fischer-Yates variant
   // https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_.22inside-out.22_algorithm
   for (int64_t i = 0; i < n; i++) {
-    int64_t z = static_cast<int64_t>(generator->random64() % (i + 1));
+    int64_t z = (int64_t)(generator->random64() % (i + 1));
     r__data[i * r__stride_0] = i;
     r__data[i * r__stride_0] = r__data[z * r__stride_0];
     r__data[z * r__stride_0] = i;