Update

[ghstack-poisoned]

.ci/docker/almalinux/build.sh

@@ -36,11 +36,7 @@ case ${DOCKER_TAG_PREFIX} in
     ;;
   rocm*)
     BASE_TARGET=rocm
-    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-    # add gfx950, gfx115x conditionally starting in ROCm 7.0
-    if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
-        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-    fi
+    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
     EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;
   *)

.ci/docker/build.sh

@@ -168,6 +168,18 @@ case "$tag" in
     VISION=yes
     TRITON=yes
     ;;
+  pytorch-linux-jammy-py3.11-clang12)
+    ANACONDA_PYTHON_VERSION=3.11
+    CLANG_VERSION=12
+    VISION=no
+    TRITON=no
+    ;;
+  pytorch-linux-jammy-py3.12-clang12)
+    ANACONDA_PYTHON_VERSION=3.12
+    CLANG_VERSION=12
+    VISION=no
+    TRITON=no
+    ;;
   pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
     if [[ $tag =~ "jammy" ]]; then
       ANACONDA_PYTHON_VERSION=3.10
@@ -195,9 +207,9 @@ case "$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-noble-xpu-n-py3 | pytorch-linux-noble-xpu-n-py3-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=13
     VISION=yes
     XPU_VERSION=2025.2
     NINJA_VERSION=1.9.0
@@ -248,6 +260,12 @@ case "$tag" in
     HALIDE=yes
     TRITON=yes
     ;;
+  pytorch-linux-jammy-cuda12.8-py3.12-pallas)
+    CUDA_VERSION=12.8.1
+    ANACONDA_PYTHON_VERSION=3.12
+    GCC_VERSION=11
+    PALLAS=yes
+    ;;
   pytorch-linux-jammy-py3.12-triton-cpu)
     CUDA_VERSION=12.6
     ANACONDA_PYTHON_VERSION=3.12
@@ -369,6 +387,7 @@ docker build \
        --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
        --build-arg "EXECUTORCH=${EXECUTORCH}" \
        --build-arg "HALIDE=${HALIDE}" \
+       --build-arg "PALLAS=${PALLAS}" \
        --build-arg "XPU_VERSION=${XPU_VERSION}" \
        --build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
        --build-arg "ACL=${ACL:-}" \

.ci/docker/ci_commit_pins/jax.txt

@@ -0,0 +1 @@
+0.8.0

.ci/docker/common/install_jax.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+set -ex
+
+source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
+
+# Get the pinned JAX version (same for all CUDA versions)
+JAX_VERSION=$(get_pinned_commit /ci_commit_pins/jax)
+
+function install_jax_12() {
+  echo "Installing JAX ${JAX_VERSION} with CUDA 12 support"
+  pip_install "jax[cuda12]==${JAX_VERSION}" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+
+  # Verify installation
+  python -c "import jax"  # check for errors
+  echo "JAX ${JAX_VERSION} installation completed successfully for CUDA 12"
+}
+
+function install_jax_13() {
+  echo "Installing JAX ${JAX_VERSION} with CUDA 13 support"
+  pip_install "jax[cuda13]==${JAX_VERSION}" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+
+  # Verify installation
+  python -c "import jax"  # check for errors
+  echo "JAX ${JAX_VERSION} installation completed successfully for CUDA 13"
+}
+
+# idiomatic parameter and option handling in sh
+while test $# -gt 0
+do
+    case "$1" in
+    12.4|12.6|12.6.*|12.8|12.8.*|12.9|12.9.*) install_jax_12;
+        ;;
+    13.0|13.0.*) install_jax_13;
+        ;;
+    *) echo "bad argument $1"; exit 1
+        ;;
+    esac
+    shift
+done

.ci/docker/common/install_xpu.sh

@@ -9,7 +9,7 @@ set -xe
 
 function install_ubuntu() {
     . /etc/os-release
-    if [[ ! " jammy " =~ " ${VERSION_CODENAME} " ]]; then
+    if [[ ! " jammy noble " =~ " ${VERSION_CODENAME} " ]]; then
         echo "Ubuntu version ${VERSION_CODENAME} not supported"
         exit
     fi
@@ -35,25 +35,24 @@ function install_ubuntu() {
     # The xpu-smi packages
     apt-get install -y flex bison xpu-smi
 
-    if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
-        # Compute and Media Runtimes
+    # Compute and Media Runtimes
+    if [[ " ${VERSION_CODENAME} " =~ " noble " ]]; then
         apt-get install -y \
-            intel-opencl-icd intel-level-zero-gpu level-zero \
-            intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
-            libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
+            intel-opencl-icd libze-intel-gpu1 libze1 \
+            intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
+            libegl-mesa0 libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
             libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-            mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
-        # Development Packages
-        apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
-    else # rolling driver
+            mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
+    else # jammy
         apt-get install -y \
             intel-opencl-icd libze-intel-gpu1 libze1 \
             intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
             libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
             libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
             mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
-        apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
     fi
+    # Development Packages
+    apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
 
     # Install Intel Support Packages
     apt-get install -y ${XPU_PACKAGES}
@@ -66,7 +65,7 @@ function install_ubuntu() {
 function install_rhel() {
     . /etc/os-release
     if [[ "${ID}" == "rhel" ]]; then
-        if [[ ! " 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
+        if [[ ! " 8.8 8.10 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
             echo "RHEL version ${VERSION_ID} not supported"
             exit
         fi
@@ -147,7 +146,7 @@ function install_sles() {
 XPU_DRIVER_VERSION=""
 if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
     # Use GPU driver LTS releases
-    XPU_DRIVER_VERSION="/lts/2350"
+    XPU_DRIVER_VERSION="/lts/2523"
 fi
 
 # Default use Intel® oneAPI Deep Learning Essentials 2025.1

.ci/docker/libtorch/build.sh

@@ -49,11 +49,7 @@ case ${DOCKER_TAG_PREFIX} in
         fi
         BASE_TARGET=rocm
         GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
-        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950, gfx115x conditionally starting in ROCm 7.0
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-        fi
+        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;
     *)

.ci/docker/manywheel/build.sh

@@ -87,11 +87,7 @@ case ${image} in
         MANY_LINUX_VERSION="2_28"
         DEVTOOLSET_VERSION="11"
         GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
-        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950, gfx115x conditionally starting in ROCm 7.0
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-        fi
+        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
         DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
         ;;
     manylinux2_28-builder:xpu)

.ci/docker/ubuntu/Dockerfile

@@ -143,6 +143,15 @@ COPY ci_commit_pins/halide.txt halide.txt
 RUN if [ -n "${HALIDE}" ]; then bash ./install_halide.sh; fi
 RUN rm install_halide.sh common_utils.sh halide.txt
 
+ARG PALLAS
+ARG CUDA_VERSION
+# Install JAX with CUDA support (for Pallas)
+COPY ./common/install_jax.sh install_jax.sh
+COPY ./common/common_utils.sh common_utils.sh
+COPY ./ci_commit_pins/jax.txt /ci_commit_pins/jax.txt
+RUN if [ -n "${PALLAS}" ]; then bash ./install_jax.sh ${CUDA_VERSION}; fi
+RUN rm -f install_jax.sh common_utils.sh /ci_commit_pins/jax.txt
+
 ARG ONNX
 # Install ONNX dependencies
 COPY ./common/install_onnx.sh ./common/common_utils.sh ./

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

@@ -8,9 +8,11 @@ from abc import ABC, abstractmethod
 
 
 try:
-    from typing import Any, Callable, Required, TypedDict  # Python 3.11+
+    from collections.abc import Callable  # Python 3.11+
+    from typing import Any, Required, TypedDict
 except ImportError:
-    from typing import Any, Callable, TypedDict
+    from collections.abc import Callable
+    from typing import Any, TypedDict
 
     from typing_extensions import Required  # Fallback for Python <3.11
 

.ci/pytorch/test.sh

@@ -824,6 +824,11 @@ test_inductor_halide() {
   assert_git_not_dirty
 }
 
+test_inductor_pallas() {
+  python test/run_test.py --include inductor/test_pallas.py --verbose
+  assert_git_not_dirty
+}
+
 test_inductor_triton_cpu() {
   python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
   assert_git_not_dirty
@@ -1724,6 +1729,8 @@ elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
   test_inductor_halide
+elif [[ "${TEST_CONFIG}" == *inductor-pallas* ]]; then
+  test_inductor_pallas
 elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-cfbc5c2f1c798991715a6b06bb3ce46478c4487c
+ccb801b88af136454798b945175c4c87e636ac33

.github/labeler.yml

@@ -138,7 +138,8 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -148,7 +149,8 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -158,7 +160,8 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
 - third_party/fbgemm

.github/nitpicks.yml

@@ -10,3 +10,4 @@
   pathFilter:
     - 'torch/csrc/inductor/aoti_torch/c/*'
     - 'torch/csrc/inductor/aoti_torch/generated/*'
+    - 'torch/csrc/stable/c/*'

.github/pytorch-probot.yml

@@ -2,8 +2,8 @@ tracking_issue: 24422
 ciflow_tracking_issue: 64124
 ciflow_push_tags:
 - ciflow/b200
-- ciflow/b200-symm-mem
 - ciflow/b200-distributed
+- ciflow/b200-symm-mem
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
@@ -22,6 +22,8 @@ ciflow_push_tags:
 - ciflow/inductor-perf-test-nightly-xpu
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm
+- ciflow/inductor-rocm-mi200
+- ciflow/inductor-rocm-mi300
 - ciflow/linux-aarch64
 - ciflow/mps
 - ciflow/nightly
@@ -33,11 +35,13 @@ ciflow_push_tags:
 - ciflow/quantization-periodic
 - ciflow/riscv64
 - ciflow/rocm
+- ciflow/rocm-mi200
 - ciflow/rocm-mi300
 - ciflow/rocm-mi355
 - ciflow/rocm-navi31
 - ciflow/s390
 - ciflow/slow
+- ciflow/slow-rocm-mi200
 - ciflow/torchbench
 - ciflow/triton_binaries
 - ciflow/trunk

.github/scripts/delete_old_branches.py

@@ -1,10 +1,11 @@
 # Delete old branches
 import os
 import re
+from collections.abc import Callable
 from datetime import datetime
 from functools import lru_cache
 from pathlib import Path
-from typing import Any, Callable
+from typing import Any
 
 from github_utils import gh_fetch_json_dict, gh_graphql
 from gitutils import GitRepo

.github/scripts/filter_test_configs.py

@@ -8,10 +8,11 @@ import re
 import subprocess
 import sys
 import warnings
+from collections.abc import Callable
 from enum import Enum
 from functools import cache
 from logging import info
-from typing import Any, Callable, Optional
+from typing import Any, Optional
 from urllib.request import Request, urlopen
 
 import yaml

.github/scripts/get_workflow_job_id.py

@@ -11,7 +11,8 @@ import sys
 import time
 import urllib
 import urllib.parse
-from typing import Any, Callable, Optional
+from collections.abc import Callable
+from typing import Any, Optional
 from urllib.request import Request, urlopen
 
 

.github/scripts/github_utils.py

@@ -3,8 +3,9 @@
 import json
 import os
 import warnings
+from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Any, Callable, cast, Optional, Union
+from typing import Any, cast, Optional, Union
 from urllib.error import HTTPError
 from urllib.parse import quote
 from urllib.request import Request, urlopen

.github/scripts/gitutils.py

@@ -4,10 +4,10 @@ import os
 import re
 import tempfile
 from collections import defaultdict
-from collections.abc import Iterator
+from collections.abc import Callable, Iterator
 from datetime import datetime
 from functools import wraps
-from typing import Any, Callable, cast, Optional, TypeVar, Union
+from typing import Any, cast, Optional, TypeVar, Union
 
 
 T = TypeVar("T")

.github/scripts/trymerge.py

@@ -17,12 +17,12 @@ import re
 import time
 import urllib.parse
 from collections import defaultdict
-from collections.abc import Iterable
+from collections.abc import Callable, Iterable
 from dataclasses import dataclass
 from functools import cache
 from pathlib import Path
 from re import Pattern
-from typing import Any, Callable, cast, NamedTuple, Optional
+from typing import Any, cast, NamedTuple, Optional
 from warnings import warn
 
 import yaml

.github/workflows/docker-builds.yml

@@ -56,6 +56,8 @@ jobs:
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
           pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
           pytorch-linux-jammy-py3.10-clang12,
+          pytorch-linux-jammy-py3.11-clang12,
+          pytorch-linux-jammy-py3.12-clang12,
           pytorch-linux-jammy-py3.13-clang12,
           pytorch-linux-jammy-py3.14-clang12,
           pytorch-linux-jammy-rocm-n-py3,
@@ -65,9 +67,10 @@ jobs:
           pytorch-linux-jammy-py3.10-gcc11,
           pytorch-linux-jammy-py3-gcc11-inductor-benchmarks,
           pytorch-linux-jammy-py3.12-halide,
+          pytorch-linux-jammy-cuda12.8-py3.12-pallas,
           pytorch-linux-jammy-xpu-n-1-py3,
-          pytorch-linux-jammy-xpu-n-py3,
-          pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks,
+          pytorch-linux-noble-xpu-n-py3,
+          pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,

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

@@ -83,8 +83,8 @@ jobs:
     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
+      build-environment: linux-noble-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3-inductor-benchmarks
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -117,7 +117,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-jammy-xpu-n-py3.10
+      build-environment: linux-noble-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: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
@@ -137,7 +137,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-jammy-xpu-n-py3.10
+      build-environment: linux-noble-xpu-n-py3.10
       dashboard-tag: training-${{ 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 }}

.github/workflows/inductor-rocm-mi200.yml

@@ -7,7 +7,7 @@ on:
     branches:
       - release/*
     tags:
-      - ciflow/inductor-rocm/*
+      - ciflow/inductor-rocm-mi200/*
   workflow_dispatch:
 
 concurrency:

.github/workflows/inductor-rocm-mi300.yml

@@ -7,6 +7,7 @@ on:
       - release/*
     tags:
       - ciflow/inductor-rocm/*
+      - ciflow/inductor-rocm-mi300/*
   workflow_dispatch:
 
 concurrency:

.github/workflows/inductor-unittest.yml

@@ -81,6 +81,32 @@ jobs:
       test-matrix: ${{ needs.inductor-halide-build.outputs.test-matrix }}
     secrets: inherit
 
+  inductor-pallas-build:
+    name: inductor-pallas-build
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      build-environment: linux-jammy-cuda12.8-py3.12-gcc11
+      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-py3.12-pallas
+      cuda-arch-list: '8.9'
+      runner: linux.8xlarge.memory
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      test-matrix: |
+        { include: [
+          { config: "inductor-pallas", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.12xlarge.nvidia.gpu" },
+        ]}
+    secrets: inherit
+
+  inductor-pallas-test:
+    name: inductor-pallas-test
+    uses: ./.github/workflows/_linux-test.yml
+    needs: inductor-pallas-build
+    with:
+      build-environment: linux-jammy-py3.12-gcc11
+      docker-image: ${{ needs.inductor-pallas-build.outputs.docker-image }}
+      test-matrix: ${{ needs.inductor-pallas-build.outputs.test-matrix }}
+    secrets: inherit
+
   inductor-triton-cpu-build:
     name: inductor-triton-cpu-build
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/periodic-rocm-mi200.yml

@@ -11,7 +11,6 @@ on:
     - cron: 29 8 * * *  # about 1:29am PDT, for mem leak check and rerun disabled tests
   push:
     tags:
-      - ciflow/periodic/*
       - ciflow/periodic-rocm-mi200/*
     branches:
       - release/*

.github/workflows/periodic-rocm-mi300.yml

@@ -11,6 +11,7 @@ on:
     - cron: 29 8 * * *  # about 1:29am PDT, for mem leak check and rerun disabled tests
   push:
     tags:
+      - ciflow/periodic/*
       - ciflow/periodic-rocm-mi300/*
     branches:
       - release/*

.github/workflows/pull.yml

@@ -342,16 +342,16 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-inductor-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-jammy-xpu-n-py3_10-build:
-    name: linux-jammy-xpu-n-py3.10
+  linux-noble-xpu-n-py3_10-build:
+    name: linux-noble-xpu-n-py3.10
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       # This should sync with the build in xpu.yml but xpu uses a larger runner
       # sync-tag: linux-xpu-n-build
       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
+      build-environment: linux-noble-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 4, runner: "linux.idc.xpu" },

.github/workflows/rocm-mi200.yml

@@ -5,7 +5,7 @@ on:
     branches:
       - release/*
     tags:
-      - ciflow/rocm/*
+      - ciflow/rocm-mi200/*
   workflow_dispatch:
   schedule:
     - cron: 29 8 * * *  # about 1:29am PDT

.github/workflows/rocm-mi300.yml

@@ -6,6 +6,7 @@ on:
       - main
       - release/*
     tags:
+      - ciflow/rocm/*
       - ciflow/rocm-mi300/*
   workflow_dispatch:
   schedule:

.github/workflows/slow-rocm-mi200.yml

@@ -0,0 +1,81 @@
+# This workflow is dedicated to host slow jobs that are run only periodically because
+# they are too slow to run in every commit.  The list of slow tests can be found in
+# https://github.com/pytorch/test-infra/blob/generated-stats/stats/slow-tests.json
+name: slow-rocm-mi200
+
+on:
+  push:
+    branches:
+      - release/*
+    tags:
+      - ciflow/slow/*
+      - ciflow/slow-rocm-mi200/*
+  schedule:
+    - cron: 0 */3 * * *
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}-${{ github.event.schedule }}
+  cancel-in-progress: true
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  llm-td:
+    if: github.repository_owner == 'pytorch'
+    name: before-test
+    uses: ./.github/workflows/llm_td_retrieval.yml
+    permissions:
+      id-token: write
+      contents: read
+
+  target-determination:
+    name: before-test
+    uses: ./.github/workflows/target_determination.yml
+    needs: llm-td
+    permissions:
+      id-token: write
+      contents: read
+
+  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: ${{ github.ref_type }}
+
+  linux-jammy-rocm-py3_10-build:
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
+      sync-tag: rocm-build
+      test-matrix: |
+        { include: [
+          { config: "slow", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
+          { config: "slow", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
+        ]}
+    secrets: inherit
+
+  linux-jammy-rocm-py3_10-test:
+    permissions:
+      id-token: write
+      contents: read
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_rocm-test.yml
+    needs:
+      - linux-jammy-rocm-py3_10-build
+      - target-determination
+    with:
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
+    secrets: inherit

.github/workflows/slow.yml

@@ -105,36 +105,6 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-py3_10-clang12-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-jammy-rocm-py3_10-build:
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
-      test-matrix: |
-        { include: [
-          { config: "slow", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
-          { config: "slow", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
-    secrets: inherit
-
   linux-jammy-py3_10-clang18-asan-build:
     name: linux-jammy-py3.10-clang18-asan
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/upload-test-stats.yml

@@ -11,15 +11,16 @@ on:
       - inductor
       - unstable
       - slow
+      - slow-rocm-mi200
       - unstable-periodic
       - inductor-periodic
-      - rocm
+      - rocm-mi200
       - rocm-mi300
       - rocm-mi355
       - inductor-micro-benchmark
       - inductor-micro-benchmark-x86
       - inductor-cu124
-      - inductor-rocm
+      - inductor-rocm-mi200
       - inductor-rocm-mi300
       - mac-mps
       - linux-aarch64

.github/workflows/xpu.yml

@@ -47,15 +47,15 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-jammy-xpu-n-py3_10-build:
-    name: linux-jammy-xpu-n-py3.10
+  linux-noble-xpu-n-py3_10-build:
+    name: linux-noble-xpu-n-py3.10
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       sync-tag: linux-xpu-n-build
       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
+      build-environment: linux-noble-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -74,17 +74,17 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-jammy-xpu-n-py3_10-test:
-    name: linux-jammy-xpu-n-py3.10
+  linux-noble-xpu-n-py3_10-test:
+    name: linux-noble-xpu-n-py3.10
     uses: ./.github/workflows/_xpu-test.yml
-    needs: linux-jammy-xpu-n-py3_10-build
+    needs: linux-noble-xpu-n-py3_10-build
     permissions:
       id-token: write
       contents: read
     with:
-      build-environment: linux-jammy-xpu-n-py3.10
-      docker-image: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.test-matrix }}
+      build-environment: linux-noble-xpu-n-py3.10
+      docker-image: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.test-matrix }}
     secrets: inherit
 
   windows-xpu-n-1-build:

.lintrunner.toml

@@ -143,7 +143,8 @@ init_command = [
     '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"',
+    'numpy==2.1.0 ; python_version >= "3.12" and python_version <= "3.13"',
+    'numpy==2.3.4 ; python_version >= "3.14"',
     'expecttest==0.3.0',
     'pyrefly==0.36.2',
     'sympy==1.13.3',
@@ -1401,7 +1402,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'usort==1.0.8.post1',
     'isort==6.0.1',
-    'ruff==0.13.1',  # sync with RUFF
+    'ruff==0.14.4',  # sync with RUFF
 ]
 is_formatter = true
 
@@ -1536,7 +1537,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.13.1',  # sync with PYFMT
+    'ruff==0.14.4',  # sync with PYFMT
 ]
 is_formatter = true
 

CODEOWNERS

@@ -210,8 +210,12 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
 /test/inductor/test_flex_attention.py @drisspg
 /test/inductor/test_flex_decoding.py @drisspg
 
-# Low Precision GEMMs
+# Low Precision & Grouped GEMMs
 /aten/src/ATen/native/cuda/Blas.cpp @drisspg @slayton58
+/aten/src/ATen/native/cuda/GroupedBlas.cpp @drisspg @slayton58
+/aten/src/ATen/native/cuda/ScaledBlas.cpp @drisspg @slayton58
 /aten/src/ATen/cuda/CUDABlas.cpp @drisspg @slayton58
 /aten/src/ATen/cuda/CUDABlas.h @drisspg @slayton58
+/aten/src/ATen/cuda/CUDAScaledBlas.cpp @drisspg @slayton58
+/aten/src/ATen/cuda/CUDAScaledBlas.h @drisspg @slayton58
 /test/test_scaled_matmul_cuda.py @drisspg @slayton58

aten/src/ATen/Context.h

@@ -174,6 +174,12 @@ class TORCH_API Context {
   static long versionCuDNN() {
     return detail::getCUDAHooks().versionCuDNN();
   }
+  static long versionRuntimeCuDNN() {
+    return detail::getCUDAHooks().versionRuntimeCuDNN();
+  }
+  static long versionCuDNNFrontend() {
+    return detail::getCUDAHooks().versionCuDNNFrontend();
+  }
   static bool hasCuSOLVER() {
     return detail::getCUDAHooks().hasCuSOLVER();
   }

aten/src/ATen/DeviceAccelerator.h

@@ -94,6 +94,11 @@ TORCH_API inline void resetPeakStats(c10::DeviceIndex device_index) {
   at::getDeviceAllocator(device_type)->resetPeakStats(device_index);
 }
 
+TORCH_API inline std::pair<size_t, size_t> getMemoryInfo(
+    c10::DeviceIndex device_index) {
+  const auto device_type = getAccelerator(true).value();
+  return at::getDeviceAllocator(device_type)->getMemoryInfo(device_index);
+}
 } // namespace at::accelerator
 
 namespace at {

aten/src/ATen/Dispatch.h

@@ -6,6 +6,7 @@
 #include <c10/util/Half.h>
 #include <c10/util/Metaprogramming.h>
 #include <c10/util/complex.h>
+#include <torch/headeronly/core/Dispatch.h>
 
 #ifdef __CUDACC__
 #include <cuda.h> // For CUDA_VERSION
@@ -61,12 +62,9 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     }                                                 \
   } while (0)
 
-#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...)                 \
-  case enum_type: {                                                           \
-    AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type);                              \
-    using HINT [[maybe_unused]] = c10::impl::ScalarTypeToCPPTypeT<enum_type>; \
-    return __VA_ARGS__();                                                     \
-  }
+#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...) \
+  THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(                      \
+      AT_PRIVATE_CHECK_SELECTIVE_BUILD, enum_type, HINT, __VA_ARGS__)
 
 #define AT_DISPATCH_CASE(enum_type, ...) \
   AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
@@ -95,14 +93,6 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     return __VA_ARGS__();                                                   \
   }
 
-namespace detail {
-
-inline at::ScalarType scalar_type(at::ScalarType s) {
-  return s;
-}
-
-} // namespace detail
-
 // The AT_DISPATCH_* family of macros provides the ability to
 // conveniently generate specializations of a kernel over all of the
 // dtypes we care about in PyTorch.  We call it "dispatch" because
@@ -190,27 +180,13 @@ inline at::ScalarType scalar_type(at::ScalarType s) {
 // but we're just being safe (and it doesn't hurt.)  Note we must
 // use it to shut up warnings about unused store.
 
-#define AT_DISPATCH_SWITCH(TYPE, NAME, ...)                                 \
-  [&] {                                                                     \
-    const auto& the_type = TYPE;                                            \
-    constexpr const char* at_dispatch_name = NAME;                          \
-    /* 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:                                                              \
-        TORCH_CHECK_NOT_IMPLEMENTED(                                        \
-            false,                                                          \
-            '"',                                                            \
-            at_dispatch_name,                                               \
-            "\" not implemented for '",                                     \
-            toString(_st),                                                  \
-            "'");                                                           \
-    }                                                                       \
-    C10_DIAGNOSTIC_POP()                                                    \
-  }()
+#define AT_DISPATCH_SWITCH(TYPE, NAME, ...) \
+  THO_DISPATCH_SWITCH_TMPL(                 \
+      RECORD_KERNEL_FUNCTION_DTYPE,         \
+      TORCH_CHECK_NOT_IMPLEMENTED,          \
+      TYPE,                                 \
+      NAME,                                 \
+      __VA_ARGS__)
 
 #define AT_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \

aten/src/ATen/Dispatch_v2.h

@@ -1,3 +1,8 @@
+#pragma once
+
+#include <torch/headeronly/core/Dispatch_v2.h>
+
+// Get AT_DISPATCH_SWITCH and AT_DISPATCH_CASE:
 #include <ATen/Dispatch.h>
 
 // This is a new implementation of the AT_DISPATCH macro family from
@@ -74,41 +79,19 @@
 // macro expansion occurs, mediated with AT_EXPAND and AT_GUARD.  I mostly
 // relied on GPT4 to help me get it right.
 
-// Public API macros
-
 // See documentation above
 #define AT_DISPATCH_V2(TYPE, NAME, BODY, ...) \
-  AT_DISPATCH_SWITCH(TYPE, NAME, AT_AP_VAR(AT_WRAP(BODY), TYPE, __VA_ARGS__))
-
-// This macro lets you pass an arbitrary expression that may contain internal
-// commas to another macro without having the commas causing the expression
-// to be interpreted as being multiple arguments
-#define AT_WRAP(...) __VA_ARGS__
-
-#define AT_FLOAT8_TYPES                                          \
-  c10::kFloat8_e5m2, c10::kFloat8_e5m2fnuz, c10::kFloat8_e4m3fn, \
-      c10::kFloat8_e4m3fnuz, c10::kFloat8_e8m0fnu
-
-#define AT_INTEGRAL_TYPES \
-  c10::kByte, c10::kChar, c10::kInt, c10::kLong, c10::kShort
-#define AT_FLOATING_TYPES c10::kDouble, c10::kFloat
-#define AT_BAREBONES_UNSIGNED_TYPES c10::kUInt16, c10::kUInt32, c10::kUInt64
-#define AT_INTEGRAL_TYPES_V2 \
-  AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)
-#define AT_COMPLEX_TYPES c10::kComplexDouble, c10::kComplexFloat
-#define AT_QINT_TYPES c10::kQInt8, c10::kQUInt8, c10::kQInt32
-// NB: not *actually* all types
-#define AT_ALL_TYPES AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
-#define AT_ALL_TYPES_AND_COMPLEX \
-  AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES)
-
-// Helper macros
+  THO_DISPATCH_V2_TMPL(                       \
+      AT_DISPATCH_SWITCH,                     \
+      AT_DISPATCH_CASE,                       \
+      TYPE,                                   \
+      NAME,                                   \
+      AT_WRAP(BODY),                          \
+      __VA_ARGS__)
 
+// Unused helper macros, kept for BC:
 #define AT_AP_VAR(N, T, ...) \
   AT_EXPAND(AT_CONCAT(AT_AP, AT_NUM_ARGS(__VA_ARGS__))(AT_WRAP(N), __VA_ARGS__))
-#define AT_CONCAT(a, b) AT_CONCAT_AUX(a, b)
-#define AT_CONCAT_AUX(a, b) a##b
-#define AT_EXPAND(X) X
 
 // Ensure we never have too many scalar types for the expansion here to
 // support.  To bump this, you must regenerate the macros below.
@@ -119,12 +102,6 @@ static_assert(static_cast<int>(c10::ScalarType::NumOptions) < 60);
 
 num_args = 60
 
-nums = ', '.join(str(i) for i in reversed(range(num_args+1)))
-args = ', '.join(f'_{i}' for i in range(1, num_args+1))
-
-print(f'#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, {nums}))')
-print(f'#define AT_NUM_ARGS_AUX({args}, N, ...) N')
-
 for i in range(1, num_args+1):
     args = ', '.join(f'_{i}' for i in range(1, i+1))
     cases = ' '.join([f'AT_DISPATCH_CASE(_{j}, N)' for j in range(1, i+1)])
@@ -135,8 +112,6 @@ for i in range(1, num_args+1):
 // Begin generated code
 // clang-format off
 
-#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
-#define AT_NUM_ARGS_AUX(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, N, ...) N
 #define AT_AP1(N, _1) AT_DISPATCH_CASE(_1, N)
 #define AT_AP2(N, _1, _2) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N)
 #define AT_AP3(N, _1, _2, _3) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N)

aten/src/ATen/core/CachingHostAllocator.h

@@ -226,8 +226,8 @@ template <
     typename B = HostBlock<S>>
 struct CachingHostAllocatorImpl {
   virtual ~CachingHostAllocatorImpl() {
-    active_ = false;
-    if (pinned_use_background_threads()) {
+    if (active_) {
+      active_ = false;
       getBackgroundThreadPool()->waitWorkComplete();
     }
   }
@@ -260,6 +260,7 @@ struct CachingHostAllocatorImpl {
     if (pinned_use_background_threads()) {
       // Launch the background thread and process events in a loop.
       static bool background_thread_flag [[maybe_unused]] = [this] {
+        active_ = true;
         getBackgroundThreadPool()->run([&]() {
           while (active_) {
             process_events();
@@ -683,9 +684,9 @@ struct CachingHostAllocatorImpl {
   alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
   std::deque<std::pair<E, B*>> events_; // event queue paired with block
 
-  // Indicates whether the object is active.
+  // Indicates whether the event-processing thread pool is active.
   // Set to false in the destructor to signal background threads to stop.
-  std::atomic<bool> active_{true};
+  std::atomic<bool> active_{false};
 protected:
   alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
 };

aten/src/ATen/cuda/detail/CUDAHooks.cpp

@@ -21,6 +21,7 @@
 
 #if AT_CUDNN_ENABLED()
 #include <ATen/cudnn/cudnn-wrapper.h>
+#include <cudnn_frontend.h>
 #endif
 
 #if AT_MAGMA_ENABLED()
@@ -351,6 +352,26 @@ long CUDAHooks::versionCuDNN() const {
 #endif
 }
 
+long CUDAHooks::versionRuntimeCuDNN() const {
+#if AT_CUDNN_ENABLED()
+#ifndef USE_STATIC_CUDNN
+  return cudnnGetVersion();
+#else
+  return CUDNN_VERSION;
+#endif
+#else
+  TORCH_CHECK(false, "Cannot query CuDNN version if ATen_cuda is not built with CuDNN");
+#endif
+}
+
+long CUDAHooks::versionCuDNNFrontend() const {
+#if AT_CUDNN_ENABLED()
+  return CUDNN_FRONTEND_VERSION;
+#else
+  TORCH_CHECK(false, "Cannot query CuDNN Frontend version if ATen_cuda is not built with CuDNN");
+#endif
+}
+
 long CUDAHooks::versionMIOpen() const {
 #if AT_ROCM_ENABLED()
   return MIOPEN_VERSION_MAJOR * 10000 +

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

@@ -49,6 +49,8 @@ struct CUDAHooks : public at::CUDAHooksInterface {
   bool hasCUDART() const override;
   long versionCUDART() const override;
   long versionCuDNN() const override;
+  long versionRuntimeCuDNN() const override;
+  long versionCuDNNFrontend() const override;
   long versionMIOpen() const override;
   std::string showConfig() const override;
   double batchnormMinEpsilonCuDNN() const override;

aten/src/ATen/detail/CUDAHooksInterface.h

@@ -174,6 +174,14 @@ struct TORCH_API CUDAHooksInterface : AcceleratorHooksInterface {
     TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP);
   }
 
+  virtual long versionRuntimeCuDNN() const {
+    TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP);
+  }
+
+  virtual long versionCuDNNFrontend() const {
+    TORCH_CHECK(false, "Cannot query cuDNN Frontend version without ATen_cuda library. ", CUDA_HELP);
+  }
+
   virtual long versionMIOpen() const {
     TORCH_CHECK(false, "Cannot query MIOpen version without ATen_cuda library. ", CUDA_HELP);
   }

aten/src/ATen/functorch/BatchedTensorImpl.h

@@ -157,6 +157,8 @@ constexpr DispatchKeySet kKeysToPropagateToWrapper({
   DispatchKey::Negative,
   DispatchKey::Conjugate,
   DispatchKey::XLA,
+  DispatchKey::XPU,
+  DispatchKey::HPU,
   DispatchKey::CUDA,
   DispatchKey::CPU,
   DispatchKey::PrivateUse1,

aten/src/ATen/native/Convolution.cpp

@@ -409,7 +409,7 @@ struct ConvParams {
     if (!detail::getCUDAHooks().compiledWithCuDNN() || !input.is_cuda() || !cudnn_enabled) {
       return false;
     }
-    static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
+    static long cudnn_version = detail::getCUDAHooks().versionRuntimeCuDNN();
     // broken on cuDNN 9.8 - 9.14
     if (cudnn_version >= 90800 && cudnn_version < 91500) {
       if (cudnn_conv_suggest_memory_format(input, weight) == at::MemoryFormat::Contiguous &&
@@ -453,7 +453,7 @@ struct ConvParams {
     }
     // native kernel doesn't support 64-bit non-splittable case
     if (!(canUse32BitIndexMath(input) && canUse32BitIndexMath(weight))) {
-      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionCuDNN() : -1;
+      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionRuntimeCuDNN() : -1;
       // TODO(eqy): remove this once cuDNN fixes 64-bit depthwise support, first broken in 9.11x
       if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous) {
         if (cudnn_version < 0 || cudnn_version > 91000) {

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

@@ -884,6 +884,69 @@ struct type_specialized_kernel_launcher {
   }
 };
 
+template <int arg_index>
+struct type_specialized_broadcast_kernel_launcher {
+  template <
+      typename func_t,
+      typename array_t,
+      typename dtypes_t,
+      typename calc_t>
+  static void apply(
+      int64_t numel,
+      func_t f,
+      array_t data,
+      dtypes_t dtypes,
+      calc_t offset_calc) {
+        using traits = function_traits<func_t>;
+        using ret_t = typename traits::result_type;
+        using arg0_t = typename traits::template arg<0>::type;
+        using arg1_t = typename traits::template arg<1>::type;
+        if (dtypes[0] == rt_binary_specializations[arg_index][0] &&
+          dtypes[1] == rt_binary_specializations[arg_index][1] &&
+          dtypes[2] == rt_binary_specializations[arg_index][2]) {
+            using ret_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][0]>;
+            using arg0_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][1]>;
+            using arg1_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][2]>;
+            constexpr int grp_sz = 128;
+            launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
+              if (unrl) {
+                auto offsets0 = offset_calc.get(idx);
+                auto offsets1 = offset_calc.get(idx + grp_sz);
+                auto offsets2 = offset_calc.get(idx + grp_sz * 2);
+                auto offsets3 = offset_calc.get(idx + grp_sz * 3);
+                void* out0 = data[0] + offsets0[0];
+                void* out1 = data[0] + offsets1[0];
+                void* out2 = data[0] + offsets2[0];
+                void* out3 = data[0] + offsets3[0];
+                auto u = c10::load<arg0_cpp_t>(data[1] + offsets0[1]);
+                auto v = c10::load<arg1_cpp_t>(data[2] + offsets0[2]);
+                ret_t result0 = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
+                auto u1 = c10::load<arg0_cpp_t>(data[1] + offsets1[1]);
+                auto v1 = c10::load<arg1_cpp_t>(data[2]+ offsets1[2]);
+                ret_t result1 = f(c10::convert<arg0_t>(u1), c10::convert<arg1_t>(v1));
+                auto u2 = c10::load<arg0_cpp_t>(data[1] + offsets2[1]);
+                auto v2 = c10::load<arg1_cpp_t>(data[2] + offsets2[2]);
+                ret_t result2 = f(c10::convert<arg0_t>(u2), c10::convert<arg1_t>(v2));
+                auto u3 = c10::load<arg0_cpp_t>(data[1] + offsets3[1]);
+                auto v3 = c10::load<arg1_cpp_t>(data[2] + offsets3[2]);
+                ret_t result3 = f(c10::convert<arg0_t>(u3), c10::convert<arg1_t>(v3));
+                *(ret_cpp_t*)out0 = c10::convert<ret_cpp_t>(result0);
+                *(ret_cpp_t*)out1 = c10::convert<ret_cpp_t>(result1);
+                *(ret_cpp_t*)out2 = c10::convert<ret_cpp_t>(result2);
+                *(ret_cpp_t*)out3 = c10::convert<ret_cpp_t>(result3);
+              } else {
+                auto offsets = offset_calc.get(idx);
+                void* out = data[0] + offsets[0];
+                auto u = c10::load<arg0_cpp_t>(data[1] + offsets[1]);
+                auto v = c10::load<arg1_cpp_t>(data[2] + offsets[2]);
+                ret_t result = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
+                *(ret_cpp_t*)out = c10::convert<ret_cpp_t>(result);
+              }
+            });
+        }
+      }
+};
+
 } // namespace
 #endif
 
@@ -1002,6 +1065,32 @@ void gpu_kernel_impl(TensorIteratorBase& iter, const func_t& f) {
     }
     auto offset_calc = ::make_offset_calculator<traits::arity + 1>(iter);
 #ifdef USE_ROCM
+    if (check_binary_rt_types_for_specialization(iter)) {
+      // constexpr to reduce the amount of kernels generated for
+      // broadcast elementwise with mexed dtypes and limit which functors are actually
+      // applied to the load and store at compile time.
+      using func_tuple = typename traits::ArgsTuple;
+      if constexpr (
+        std::is_same_v<float, arg0_t> && traits::arity == 2 &&
+        check_binary_functor_types_for_specialization<
+          func_tuple,
+          float,
+          float,
+          traits::arity,
+          /*arg_num=*/0>::check()) {
+            memory::detail::static_unroll<
+              type_specialized_broadcast_kernel_launcher,
+              rt_binary_specializations.size()>::with_args(
+                numel,
+                f,
+                data,
+                dtypes,
+                offset_calc
+            );
+            return;
+      }
+    }
+
     constexpr int grp_sz = 128;
     launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
       if (unrl) {

aten/src/ATen/native/mkldnn/xpu/detail/QConv.cpp

@@ -133,7 +133,7 @@ at::Tensor quantized_convolution(
   // supported in conv.
   mask_weight = weight_zero_points.numel() > 1 ? 1 : 0;
   if (groups > 1 && weight_zero_points.numel() > 1)
-    mask_weight = (2 ^ 0) | (2 ^ 1); // 2^0 (group) | 2^1 (output channel)
+    mask_weight = (1 << 0) | (1 << 1); // 2^0 (group) | 2^1 (output channel)
   dnnl::primitive_attr pattr;
 
   bool src_need_zp = (act_zero_point != 0);

aten/src/ATen/native/mps/operations/Blas.mm

@@ -141,6 +141,9 @@ static Tensor& addmv_out_mps_impl(const Tensor& self,
   };
 
   MPSStream* stream = at::mps::getCurrentMPSStream();
+  if (result.numel() == 0) {
+    return result;
+  }
   Tensor matMulVec = at::mm(mat, vec.unsqueeze(1)).squeeze(1);
 
   @autoreleasepool {

aten/src/ATen/native/native_functions.yaml

@@ -2803,7 +2803,7 @@
 - func: floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
   device_check: NoCheck   # TensorIterator
   dispatch:
-    CPU, CUDA, MPS: floor_divide_out
+    CPU, CUDA, MPS, MTIA: floor_divide_out
     SparseCPU, SparseCUDA, SparseMPS: floor_divide_out_sparse_zerodim
 
 - func: floor_divide.Scalar(Tensor self, Scalar other) -> Tensor
@@ -4383,7 +4383,7 @@
   variants: function, method
   dispatch:
     CompositeExplicitAutograd: mv
-    SparseCPU, SparseCUDA: mv_sparse
+    SparseCPU, SparseCUDA, SparseMPS: mv_sparse
 
 - func: mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
   dispatch:

aten/src/ATen/native/transformers/cuda/sdp_utils.cpp

@@ -478,7 +478,7 @@ bool check_cudnn_tensor_shapes(sdp_params const& params, bool debug) {
   const auto s_k = params.key.sym_size(2);
   const auto d_qk = params.query.sym_size(3);
   const auto d_v = params.value.sym_size(3);
-  long cudnn_version = at::detail::getCUDAHooks().versionCuDNN();
+  long cudnn_version = at::detail::getCUDAHooks().versionRuntimeCuDNN();
   if (cudnn_version < 8903) {
     if (debug) {
       TORCH_WARN("SDPA fprop requires cudnn 8.9.3 or higher");
@@ -709,7 +709,7 @@ bool can_use_cudnn_attention(const sdp_params& params, bool debug) {
   return false;
 #endif
 #if defined(CUDNN_VERSION)
-  static auto cudnn_version = cudnnGetVersion();
+  static auto cudnn_version = at::detail::getCUDAHooks().versionRuntimeCuDNN();
   if (params.dropout > 0.0 && cudnn_version > 91100 && cudnn_version < 91400) {
     if (debug) {
       TORCH_WARN(CUDNN_VERSION, " cuDNN version does not support droppout in SDPA (9.11 - 9.13).");

benchmarks/sparse/spmm.py

@@ -52,19 +52,18 @@ def test_sparse_coo_and_csr(m, n, k, nnz, test_count):
         start.record()
         coo.matmul(mat)
         stop.record()
-
         times.append(start.elapsed_time(stop))
 
-        coo_mean_time = sum(times) / len(times)
+    coo_mean_time = sum(times) / len(times)
 
-        times = []
-        for _ in range(test_count):
-            start.record()
-            csr.matmul(mat)
-            stop.record()
-            times.append(start.elapsed_time(stop))
+    times = []
+    for _ in range(test_count):
+        start.record()
+        csr.matmul(mat)
+        stop.record()
+        times.append(start.elapsed_time(stop))
 
-            csr_mean_time = sum(times) / len(times)
+    csr_mean_time = sum(times) / len(times)
 
     return coo_mean_time, csr_mean_time
 

c10/core/AutogradState.h

@@ -1,6 +1,8 @@
 #pragma once
 
+#include <c10/core/SafePyObject.h>
 #include <c10/macros/Export.h>
+#include <optional>
 
 namespace c10 {
 
@@ -15,7 +17,8 @@ struct C10_API AutogradState {
       bool inference_mode,
       bool fw_grad_mode,
       bool multithreading_enabled)
-      : grad_mode_(grad_mode),
+      : graph_exec_group_(std::nullopt),
+        grad_mode_(grad_mode),
         inference_mode_(inference_mode),
         fw_grad_mode_(fw_grad_mode),
         multithreading_enabled_(multithreading_enabled),
@@ -41,6 +44,10 @@ struct C10_API AutogradState {
     view_replay_enabled_ = view_replay_enabled;
   }
 
+  void set_graph_exec_group(std::optional<SafePyObject> group) {
+    graph_exec_group_ = std::move(group);
+  }
+
   bool get_grad_mode() const {
     return grad_mode_;
   }
@@ -61,7 +68,12 @@ struct C10_API AutogradState {
     return view_replay_enabled_;
   }
 
+  const std::optional<SafePyObject>& get_graph_exec_group() const {
+    return graph_exec_group_;
+  }
+
  private:
+  std::optional<SafePyObject> graph_exec_group_;
   bool grad_mode_ : 1;
   bool inference_mode_ : 1;
   bool fw_grad_mode_ : 1;

c10/core/CachingDeviceAllocator.h

@@ -96,6 +96,10 @@ struct C10_API DeviceAllocator : public c10::Allocator {
 
   // Resets peak memory usage statistics for the specified device
   virtual void resetPeakStats(c10::DeviceIndex device) = 0;
+
+  // Return the free memory size and total memory size in bytes for the
+  // specified device.
+  virtual std::pair<size_t, size_t> getMemoryInfo(c10::DeviceIndex device) = 0;
 };
 
 // This function is used to get the DeviceAllocator for a specific device type

c10/cuda/CUDACachingAllocator.h

@@ -345,6 +345,13 @@ class CUDAAllocator : public DeviceAllocator {
       c10::DeviceIndex device,
       std::shared_ptr<AllocatorState> pps) = 0;
   virtual std::string name() = 0;
+  std::pair<size_t, size_t> getMemoryInfo(c10::DeviceIndex device) override {
+    c10::DeviceGuard device_guard({at::kCUDA, device});
+    size_t free = 0;
+    size_t total = 0;
+    C10_CUDA_CHECK(cudaMemGetInfo(&free, &total));
+    return {free, total};
+  }
 };
 
 // Allocator object, statically initialized

c10/test/build.bzl

@@ -66,6 +66,15 @@ def define_targets(rules):
         ],
     )
 
+    rules.cc_test(
+        name = "util/nofatal_test",
+        srcs = ["util/nofatal_test.cpp"],
+        deps = [
+            "//c10/util:base",
+            "@com_google_googletest//:gtest_main",
+        ],
+    )
+
     rules.cc_test(
         name = "util/ssize_test",
         srcs = ["util/ssize_test.cpp"],

c10/test/util/nofatal_test.cpp

@@ -0,0 +1,53 @@
+#include <gtest/gtest.h>
+
+#include <c10/util/Exception.h>
+#include <c10/util/Logging.h>
+
+namespace {
+template <typename T>
+inline void expectThrowsEq(T&& fn, const char* expected_msg) {
+  try {
+    std::forward<T>(fn)();
+  } catch (const c10::Error& e) {
+    EXPECT_TRUE(
+        std::string(e.what_without_backtrace()).find(expected_msg) !=
+        std::string::npos);
+    return;
+  }
+  ADD_FAILURE() << "Expected to throw exception with message \"" << expected_msg
+                << "\" but didn't throw";
+}
+} // namespace
+
+TEST(NofatalTest, TorchCheckComparisons) {
+  // quick make sure that no-op works as expected
+  TORCH_CHECK_EQ(1, 1) << "i am a silly message " << 1;
+  expectThrowsEq(
+      []() { TORCH_CHECK_EQ(1, 2) << "i am a silly message " << 1; },
+      "Check failed: 1 == 2 (1 vs. 2). i am a silly message 1");
+  expectThrowsEq(
+      []() { TORCH_CHECK_NE(2, 2); }, "Check failed: 2 != 2 (2 vs. 2).");
+  expectThrowsEq(
+      []() { TORCH_CHECK_LT(2, 2); }, "Check failed: 2 < 2 (2 vs. 2).");
+  expectThrowsEq(
+      []() { TORCH_CHECK_LE(3, 2); }, "Check failed: 3 <= 2 (3 vs. 2).");
+  expectThrowsEq(
+      []() { TORCH_CHECK_GT(2, 2); }, "Check failed: 2 > 2 (2 vs. 2).");
+  expectThrowsEq(
+      []() { TORCH_CHECK_GE(2, 3); }, "Check failed: 2 >= 3 (2 vs. 3).");
+  expectThrowsEq(
+      []() {
+        void* p = nullptr;
+        TORCH_CHECK_NOTNULL(p);
+      },
+      "Check failed: 'p' must be non NULL.");
+
+#if GTEST_HAS_DEATH_TEST
+#ifndef NDEBUG
+  // if dbg build, DCHECK should result in deth
+  EXPECT_DEATH(TORCH_DCHECK_EQ(1, 2), "Check failed");
+#else
+  TORCH_DCHECK_EQ(1, 2); // no-op
+#endif
+#endif // GTEST_HAS_DEATH_TEST
+}

c10/util/Exception.h

@@ -702,6 +702,98 @@ namespace c10::detail {
 #define TORCH_CHECK_ARG(cond, argN, ...) \
   TORCH_CHECK(cond, "invalid argument ", argN, ": ", __VA_ARGS__)
 
+#ifndef FATAL_IF
+#ifdef C10_USE_GLOG
+#define FATAL_IF(condition)                                              \
+  condition ? (void)0                                                    \
+            : ::c10::LoggerVoidify() &                                   \
+          ::c10::MessageLogger(__FILE__, __LINE__, ::google::GLOG_FATAL) \
+              .stream()
+#else
+#define FATAL_IF(condition)            \
+  condition ? (void)0                  \
+            : ::c10::LoggerVoidify() & \
+          ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_FATAL).stream()
+#endif
+#endif
+
+#ifndef NON_FATAL_IF
+#ifdef C10_USE_GLOG
+#define NON_FATAL_IF(condition)                                \
+  condition ? (void)0                                          \
+            : ::c10::LoggerVoidify() &                         \
+          ::c10::MessageLogger(                                \
+              __FILE__, __LINE__, ::google::GLOG_FATAL, false) \
+              .stream()
+#else
+#define NON_FATAL_IF(condition)                                              \
+  condition ? (void)0                                                        \
+            : ::c10::LoggerVoidify() &                                       \
+          ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_FATAL, false) \
+              .stream()
+#endif
+#endif
+
+// Binary comparison check macros
+#define TORCH_CHECK_OP(val1, val2, op)                                      \
+  NON_FATAL_IF(((val1)op(val2)))                                            \
+      << "Check failed: " #val1 " " #op " " #val2 " (" << (val1) << " vs. " \
+      << (val2) << "). "
+
+#define TORCH_DCHECK_OP(val1, val2, op)                                       \
+  FATAL_IF(((val1)op(val2))) << "Check failed: " #val1 " " #op " " #val2 " (" \
+                             << (val1) << " vs. " << (val2) << "). "
+
+#define TORCH_CHECK_EQ(val1, val2) TORCH_CHECK_OP(val1, val2, ==)
+#define TORCH_CHECK_NE(val1, val2) TORCH_CHECK_OP(val1, val2, !=)
+#define TORCH_CHECK_LE(val1, val2) TORCH_CHECK_OP(val1, val2, <=)
+#define TORCH_CHECK_LT(val1, val2) TORCH_CHECK_OP(val1, val2, <)
+#define TORCH_CHECK_GE(val1, val2) TORCH_CHECK_OP(val1, val2, >=)
+#define TORCH_CHECK_GT(val1, val2) TORCH_CHECK_OP(val1, val2, >)
+
+// Debug versions of TORCH_CHECK_OP macros
+#ifndef NDEBUG
+#define TORCH_DCHECK_EQ(val1, val2) TORCH_DCHECK_OP(val1, val2, ==)
+#define TORCH_DCHECK_NE(val1, val2) TORCH_DCHECK_OP(val1, val2, !=)
+#define TORCH_DCHECK_LE(val1, val2) TORCH_DCHECK_OP(val1, val2, <=)
+#define TORCH_DCHECK_LT(val1, val2) TORCH_DCHECK_OP(val1, val2, <)
+#define TORCH_DCHECK_GE(val1, val2) TORCH_DCHECK_OP(val1, val2, >=)
+#define TORCH_DCHECK_GT(val1, val2) TORCH_DCHECK_OP(val1, val2, >)
+#else // !NDEBUG
+// Optimized versions - generate no code
+#define TORCH_DCHECK_EQ(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, ==)
+#define TORCH_DCHECK_NE(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, !=)
+#define TORCH_DCHECK_LE(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, <=)
+#define TORCH_DCHECK_LT(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, <)
+#define TORCH_DCHECK_GE(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, >=)
+#define TORCH_DCHECK_GT(val1, val2) \
+  while (false)                     \
+  TORCH_DCHECK_OP(val1, val2, >)
+#endif // NDEBUG
+
+// Null pointer check macro
+#define TORCH_CHECK_NOTNULL(val) \
+  ::c10::CheckNotNull(__FILE__, __LINE__, #val, (val), false)
+
+#ifndef NDEBUG
+#define TORCH_DCHECK_NOTNULL(val) \
+  ::c10::CheckNotNull(__FILE__, __LINE__, #val, (val), true)
+#else // !NDEBUG
+#define TORCH_DCHECK_NOTNULL(val) \
+  while (false)                   \
+  TORCH_CHECK_NOTNULL(val)
+#endif // NDEBUG
+
 // ----------------------------------------------------------------------------
 // Deprecated macros
 // ----------------------------------------------------------------------------

c10/util/Logging.cpp

@@ -291,6 +291,32 @@ namespace c10 {
 using fLB::FLAGS_logtostderr;
 using fLI::FLAGS_minloglevel;
 using fLI::FLAGS_v;
+
+MessageLogger::MessageLogger(
+    const char* file,
+    int line,
+    int severity,
+    bool exit_on_fatal)
+    : stream_(), severity_(severity), exit_on_fatal_(exit_on_fatal) {}
+
+MessageLogger::~MessageLogger() noexcept(false) {
+  if (severity_ == ::google::GLOG_FATAL) {
+    DealWithFatal();
+  }
+}
+
+std::stringstream& MessageLogger::stream() {
+  return stream_;
+}
+
+void MessageLogger::DealWithFatal() {
+  if (exit_on_fatal_) {
+    LOG(FATAL) << stream_.str();
+  } else {
+    throw c10::Error(stream_.str(), nullptr, nullptr);
+  }
+}
+
 } // namespace c10
 
 C10_DEFINE_int(
@@ -412,17 +438,16 @@ void ShowLogInfoToStderr() {
   FLAGS_caffe2_log_level = GLOG_INFO;
 }
 
-MessageLogger::MessageLogger(const char* file, int line, int severity)
-    : severity_(severity) {
+MessageLogger::MessageLogger(
+    const char* file,
+    int line,
+    int severity,
+    bool exit_on_fatal)
+    : severity_(severity), exit_on_fatal_(exit_on_fatal) {
   if (severity_ < FLAGS_caffe2_log_level) {
     // Nothing needs to be logged.
     return;
   }
-#ifdef ANDROID
-  tag_ = "native";
-#else // !ANDROID
-  tag_ = "";
-#endif // ANDROID
 
   time_t rawtime = 0;
   time(&rawtime);
@@ -458,7 +483,7 @@ MessageLogger::MessageLogger(const char* file, int line, int severity)
 }
 
 // Output the contents of the stream to the proper channel on destruction.
-MessageLogger::~MessageLogger() {
+MessageLogger::~MessageLogger() noexcept(false) {
   if (severity_ < FLAGS_caffe2_log_level) {
     // Nothing needs to be logged.
     return;
@@ -498,6 +523,18 @@ MessageLogger::~MessageLogger() {
   }
 }
 
+std::stringstream& MessageLogger::stream() {
+  return stream_;
+}
+
+void MessageLogger::DealWithFatal() {
+  if (exit_on_fatal_) {
+    abort();
+  } else {
+    throw c10::Error(stream_.str(), nullptr, nullptr);
+  }
+}
+
 } // namespace c10
 
 #endif // !C10_USE_GLOG

c10/util/logging_common.h

@@ -0,0 +1,74 @@
+#ifndef C10_UTIL_LOGGING_COMMON_H_
+#define C10_UTIL_LOGGING_COMMON_H_
+
+#include <c10/macros/Export.h>
+#include <sstream>
+
+namespace c10 {
+
+// MessageLogger that throws exceptions instead of aborting (glog version)
+// or logs and may abort (non-glog version).
+class C10_API MessageLogger {
+ public:
+  MessageLogger(
+      const char* file,
+      int line,
+      int severity,
+      bool exit_on_fatal = true);
+  ~MessageLogger() noexcept(false);
+
+  // Return the stream associated with the logger object.
+  std::stringstream& stream();
+
+ private:
+  // When there is a fatal log, and fatal == true, we abort
+  // otherwise, we throw.
+  void DealWithFatal();
+
+#if defined(ANDROID) && !defined(C10_USE_GLOG)
+  const char* tag_{"native"};
+#endif
+  std::stringstream stream_;
+  int severity_;
+  bool exit_on_fatal_;
+};
+
+// This class is used to explicitly ignore values in the conditional
+// logging macros. This avoids compiler warnings like "value computed
+// is not used" and "statement has no effect".
+class C10_API LoggerVoidify {
+ public:
+  LoggerVoidify() = default;
+  // This has to be an operator with a precedence lower than << but
+  // higher than ?:
+  void operator&(const std::ostream& s [[maybe_unused]]) {}
+};
+
+// Forward declarations for CheckNotNull functions
+template <typename T>
+T& CheckNotNullCommon(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal = true);
+
+template <typename T>
+T* CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T* t,
+    bool fatal = true);
+
+template <typename T>
+T& CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal = true);
+
+} // namespace c10
+
+#endif // C10_UTIL_LOGGING_COMMON_H_

c10/util/logging_is_google_glog.h

@@ -47,57 +47,53 @@ INSTANTIATE_FOR_CONTAINER(set)
 
 #endif
 
+#include <c10/util/logging_common.h>
 #include <glog/logging.h>
 
-// Additional macros on top of glog
-#define TORCH_CHECK_EQ(val1, val2) CHECK_EQ(val1, val2)
-#define TORCH_CHECK_NE(val1, val2) CHECK_NE(val1, val2)
-#define TORCH_CHECK_LE(val1, val2) CHECK_LE(val1, val2)
-#define TORCH_CHECK_LT(val1, val2) CHECK_LT(val1, val2)
-#define TORCH_CHECK_GE(val1, val2) CHECK_GE(val1, val2)
-#define TORCH_CHECK_GT(val1, val2) CHECK_GT(val1, val2)
+namespace c10 {
 
-#ifndef NDEBUG
-#define TORCH_DCHECK_EQ(val1, val2) DCHECK_EQ(val1, val2)
-#define TORCH_DCHECK_NE(val1, val2) DCHECK_NE(val1, val2)
-#define TORCH_DCHECK_LE(val1, val2) DCHECK_LE(val1, val2)
-#define TORCH_DCHECK_LT(val1, val2) DCHECK_LT(val1, val2)
-#define TORCH_DCHECK_GE(val1, val2) DCHECK_GE(val1, val2)
-#define TORCH_DCHECK_GT(val1, val2) DCHECK_GT(val1, val2)
-#else // !NDEBUG
-// These versions generate no code in optimized mode.
-#define TORCH_DCHECK_EQ(val1, val2) \
-  while (false)                     \
-  DCHECK_EQ(val1, val2)
-#define TORCH_DCHECK_NE(val1, val2) \
-  while (false)                     \
-  DCHECK_NE(val1, val2)
-#define TORCH_DCHECK_LE(val1, val2) \
-  while (false)                     \
-  DCHECK_LE(val1, val2)
-#define TORCH_DCHECK_LT(val1, val2) \
-  while (false)                     \
-  DCHECK_LT(val1, val2)
-#define TORCH_DCHECK_GE(val1, val2) \
-  while (false)                     \
-  DCHECK_GE(val1, val2)
-#define TORCH_DCHECK_GT(val1, val2) \
-  while (false)                     \
-  DCHECK_GT(val1, val2)
-#endif // NDEBUG
+[[noreturn]] void ThrowEnforceNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    const std::string& msg,
+    const void* caller);
 
-// Check that a pointer is not null.
-#define TORCH_CHECK_NOTNULL(val) CHECK_NOTNULL(val)
+template <typename T>
+T& CheckNotNullCommon(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal) {
+  if (t == nullptr) {
+    MessageLogger(file, line, ::google::GLOG_FATAL, fatal).stream()
+        << "Check failed: '" << names << "' must be non NULL. ";
+  }
+  return t;
+}
 
-#ifndef NDEBUG
-// Debug only version of TORCH_CHECK_NOTNULL
-#define TORCH_DCHECK_NOTNULL(val) DCHECK_NOTNULL(val)
-#else // !NDEBUG
-// Optimized version - generates no code.
-#define TORCH_DCHECK_NOTNULL(val) \
-  while (false)                   \
-  DCHECK_NOTNULL(val)
-#endif // NDEBUG
+template <typename T>
+T* CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T* t,
+    bool fatal) {
+  return CheckNotNullCommon(file, line, names, t, fatal);
+}
+
+template <typename T>
+T& CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal) {
+  return CheckNotNullCommon(file, line, names, t, fatal);
+}
+
+} // namespace c10
 
 // Log with source location information override (to be used in generic
 // warning/error handlers implemented as functions, not macros)

c10/util/logging_is_not_google_glog.h

@@ -13,6 +13,7 @@
 #include <vector>
 
 #include <c10/util/Flags.h>
+#include <c10/util/logging_common.h>
 
 const char CAFFE2_SEVERITY_PREFIX[] = "FEWIV";
 
@@ -24,61 +25,40 @@ const int GLOG_ERROR = 2;
 const int GLOG_WARNING = 1;
 const int GLOG_INFO = 0;
 
-class C10_API MessageLogger {
- public:
-  MessageLogger(const char* file, int line, int severity);
-  ~MessageLogger();
-  // Return the stream associated with the logger object.
-  std::stringstream& stream() {
-    return stream_;
-  }
-
- private:
-  // When there is a fatal log, we simply abort.
-  void DealWithFatal() {
-    abort();
-  }
-
-  const char* tag_;
-  std::stringstream stream_;
-  int severity_;
-};
-
-// This class is used to explicitly ignore values in the conditional
-// logging macros.  This avoids compiler warnings like "value computed
-// is not used" and "statement has no effect".
-class C10_API LoggerVoidify {
- public:
-  LoggerVoidify() = default;
-  // This has to be an operator with a precedence lower than << but
-  // higher than ?:
-  void operator&(const std::ostream& s [[maybe_unused]]) {}
-};
-
-// Log a message and terminate.
-template <class T>
-void LogMessageFatal(const char* file, int line, const T& message) {
-  MessageLogger(file, line, GLOG_FATAL).stream() << message;
-}
-
 // Helpers for TORCH_CHECK_NOTNULL(). Two are necessary to support both raw
 // pointers and smart pointers.
 template <typename T>
-T& CheckNotNullCommon(const char* file, int line, const char* names, T& t) {
+T& CheckNotNullCommon(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal) {
   if (t == nullptr) {
-    LogMessageFatal(file, line, std::string(names));
+    MessageLogger(file, line, GLOG_FATAL, fatal).stream()
+        << "Check failed: '" << names << "' must be non NULL. ";
   }
   return t;
 }
 
 template <typename T>
-T* CheckNotNull(const char* file, int line, const char* names, T* t) {
-  return CheckNotNullCommon(file, line, names, t);
+T* CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T* t,
+    bool fatal) {
+  return CheckNotNullCommon(file, line, names, t, fatal);
 }
 
 template <typename T>
-T& CheckNotNull(const char* file, int line, const char* names, T& t) {
-  return CheckNotNullCommon(file, line, names, t);
+T& CheckNotNull(
+    const char* file,
+    int line,
+    const char* names,
+    T& t,
+    bool fatal) {
+  return CheckNotNullCommon(file, line, names, t, fatal);
 }
 } // namespace c10
 
@@ -136,65 +116,6 @@ static_assert(
           ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_##n).stream()
 #endif // NDEBUG
 
-#define TORCH_CHECK_OP(val1, val2, op)                                        \
-  FATAL_IF(((val1)op(val2))) << "Check failed: " #val1 " " #op " " #val2 " (" \
-                             << (val1) << " vs. " << (val2) << ") "
-
-// TORCH_CHECK_OP macro definitions
-#define TORCH_CHECK_EQ(val1, val2) TORCH_CHECK_OP(val1, val2, ==)
-#define TORCH_CHECK_NE(val1, val2) TORCH_CHECK_OP(val1, val2, !=)
-#define TORCH_CHECK_LE(val1, val2) TORCH_CHECK_OP(val1, val2, <=)
-#define TORCH_CHECK_LT(val1, val2) TORCH_CHECK_OP(val1, val2, <)
-#define TORCH_CHECK_GE(val1, val2) TORCH_CHECK_OP(val1, val2, >=)
-#define TORCH_CHECK_GT(val1, val2) TORCH_CHECK_OP(val1, val2, >)
-
-#ifndef NDEBUG
-// Debug only versions of TORCH_CHECK_OP macros.
-#define TORCH_DCHECK_EQ(val1, val2) TORCH_CHECK_OP(val1, val2, ==)
-#define TORCH_DCHECK_NE(val1, val2) TORCH_CHECK_OP(val1, val2, !=)
-#define TORCH_DCHECK_LE(val1, val2) TORCH_CHECK_OP(val1, val2, <=)
-#define TORCH_DCHECK_LT(val1, val2) TORCH_CHECK_OP(val1, val2, <)
-#define TORCH_DCHECK_GE(val1, val2) TORCH_CHECK_OP(val1, val2, >=)
-#define TORCH_DCHECK_GT(val1, val2) TORCH_CHECK_OP(val1, val2, >)
-#else // !NDEBUG
-// These versions generate no code in optimized mode.
-#define TORCH_DCHECK_EQ(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, ==)
-#define TORCH_DCHECK_NE(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, !=)
-#define TORCH_DCHECK_LE(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, <=)
-#define TORCH_DCHECK_LT(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, <)
-#define TORCH_DCHECK_GE(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, >=)
-#define TORCH_DCHECK_GT(val1, val2) \
-  while (false)                     \
-  TORCH_CHECK_OP(val1, val2, >)
-#endif // NDEBUG
-
-// Check that a pointer is not null.
-#define TORCH_CHECK_NOTNULL(val) \
-  ::c10::CheckNotNull(           \
-      __FILE__, __LINE__, "Check failed: '" #val "' Must be non NULL", (val))
-
-#ifndef NDEBUG
-// Debug only version of TORCH_CHECK_NOTNULL
-#define TORCH_DCHECK_NOTNULL(val) \
-  ::c10::CheckNotNull(            \
-      __FILE__, __LINE__, "Check failed: '" #val "' Must be non NULL", (val))
-#else // !NDEBUG
-// Optimized version - generates no code.
-#define TORCH_DCHECK_NOTNULL(val) \
-  while (false)                   \
-  TORCH_CHECK_NOTNULL(val)
-#endif // NDEBUG
-
 // ---------------------- Support for std objects --------------------------
 // These are adapted from glog to support a limited set of logging capability
 // for STL objects.

c10/xpu/XPUCachingAllocator.cpp

@@ -1240,6 +1240,10 @@ class XPUAllocator : public DeviceAllocator {
         c10::xpu::get_raw_device(dev_to_access));
   }
 
+  std::pair<size_t, size_t> getMemoryInfo(DeviceIndex device) override {
+    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented yet.");
+  }
+
   double getMemoryFraction(DeviceIndex device) {
     assertValidDevice(device);
     return device_allocators[device]->getMemoryFraction();

caffe2/CMakeLists.txt

@@ -1941,6 +1941,7 @@ if(BUILD_TEST)
     foreach(test_src ${Caffe2_XPU_TEST_SRCS})
       get_filename_component(test_name ${test_src} NAME_WE)
       add_executable(${test_name} "${test_src}")
+      torch_compile_options(${test_name})
       target_link_libraries(${test_name} torch_library gtest_main)
       target_include_directories(${test_name} PRIVATE $<INSTALL_INTERFACE:include>)
       target_include_directories(${test_name} PRIVATE ${Caffe2_CPU_INCLUDE})

docs/source/accelerator.md

@@ -40,6 +40,7 @@
     :nosignatures:
 
      empty_cache
+     get_memory_info
      max_memory_allocated
      max_memory_reserved
      memory_allocated

pyproject.toml

@@ -172,9 +172,9 @@ ignore = [
     "SIM102", "SIM103", "SIM112", # flake8-simplify code styles
     "SIM105", # these ignores are from flake8-simplify. please fix or ignore with commented reason
     "SIM108", # SIM108 ignored because we prefer if-else-block instead of ternary expression
-    "SIM110",
+    "SIM110", # Checks for for loops that can be replaced with a builtin function, like any or all.
     "SIM114", # Combine `if` branches using logical `or` operator
-    "SIM115",
+    "SIM115", # Checks for cases where files are opened without using a context manager.
     "SIM116", # Disable Use a dictionary instead of consecutive `if` statements
     "SIM117",
     "SIM118",
@@ -184,7 +184,6 @@ ignore = [
     "TC006",
     # TODO: Remove Python-3.10 specific suppressions
     "B905",
-    "UP035",
 ]
 select = [
     "B",

setup.py

@@ -1646,8 +1646,7 @@ def main() -> None:
     mirror_files_into_torchgen()
     if RUN_BUILD_DEPS:
         build_deps()
-
-    mirror_inductor_external_kernels()
+        mirror_inductor_external_kernels()
 
     (
         ext_modules,

test/ao/sparsity/test_data_sparsifier.py

@@ -208,7 +208,7 @@ class _BaseDataSparsiferTestCase(TestCase):
         assert len(sparsifier1.data_groups) == len(sparsifier2.data_groups)
 
         state1 = state_dict1["state"]
-        for name in state1.keys():
+        for name in state1:
             # compare mask
             assert name in sparsifier2.state
             assert "mask" in sparsifier2.state[name]

test/ao/sparsity/test_sparsifier.py

@@ -119,7 +119,7 @@ class TestBaseSparsifier(TestCase):
         for idx in range(len(sparsifier0.groups)):
             mg0 = sparsifier0.groups[idx]
             mg1 = sparsifier1.groups[idx]
-            for key in mg0.keys():
+            for key in mg0:
                 assert key in mg1
                 if key == "module":
                     # We cannot compare modules as they are different

test/cpp/aoti_abi_check/CMakeLists.txt

@@ -10,6 +10,8 @@ set(AOTI_ABI_CHECK_TEST_SRCS
   ${AOTI_ABI_CHECK_TEST_ROOT}/main.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_cast.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_devicetype.cpp
+  ${AOTI_ABI_CHECK_TEST_ROOT}/test_dispatch.cpp
+  ${AOTI_ABI_CHECK_TEST_ROOT}/test_dispatch_v2.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_dtype.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_exception.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_headeronlyarrayref.cpp

test/cpp/aoti_abi_check/test_dispatch.cpp

@@ -0,0 +1,82 @@
+#include <gtest/gtest.h>
+
+#include <torch/headeronly/core/Dispatch.h>
+#include <torch/headeronly/core/Dispatch_v2.h>
+
+// MY_PRIVATE_CHECK_SELECTIVE_BUILD is a prelude to case block. For
+// testing, we do nothing:
+#define MY_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type) /* empty */
+
+#define MY_PRIVATE_CASE_TYPE_USING_HINT(...) \
+  THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(     \
+      MY_PRIVATE_CHECK_SELECTIVE_BUILD, __VA_ARGS__)
+
+#define MY_DISPATCH_CASE(...) \
+  THO_DISPATCH_CASE_TMPL(MY_PRIVATE_CASE_TYPE_USING_HINT, __VA_ARGS__)
+
+// MY_RECORD_KERNEL_FUNCTION_DTYPE is a prelude to switch
+// statement. For testing, we just avoid unused variable warning:
+#define MY_RECORD_KERNEL_FUNCTION_DTYPE(DISPATCHNAME, ENUMTYPE) \
+  (void)DISPATCHNAME
+
+// MY_CHECK_NOT_IMPLEMENTED is called in switch default block. For
+// testing, we count case mismatches:
+#define MY_CHECK_NOT_IMPLEMENTED(...) default_count++
+
+#define MY_DISPATCH_SWITCH(...) \
+  THO_DISPATCH_SWITCH_TMPL(     \
+      MY_RECORD_KERNEL_FUNCTION_DTYPE, MY_CHECK_NOT_IMPLEMENTED, __VA_ARGS__)
+
+// MY_CASE_FUNCTION is called in a case block. For testing, we count
+// case matches and ensure that scalar_t/index_t type is defined:
+#define MY_CASE_FUNCTION \
+  [&] {                  \
+    count++;             \
+    scalar_t tmp;        \
+    (void)tmp;           \
+  }
+#define MY_INDEX_CASE_FUNCTION \
+  [&] {                        \
+    count++;                   \
+    index_t tmp;               \
+    (void)tmp;                 \
+  }
+
+#define DEFINE_ITEM(TYPE, SCALARTYPE) ScalarType::SCALARTYPE,
+
+#define MY_DISPATCH_V2(TYPE, NAME, BODY, ...) \
+  THO_DISPATCH_V2_TMPL(                       \
+      MY_DISPATCH_SWITCH,                     \
+      MY_DISPATCH_CASE,                       \
+      TYPE,                                   \
+      NAME,                                   \
+      AT_WRAP(BODY),                          \
+      __VA_ARGS__)
+
+#define TEST_DISPATCH_V2(NAME, EXPECTEDCOUNT, ...)                             \
+  TEST(TestDispatchV2, NAME) {                                                 \
+    using torch::headeronly::ScalarType;                                       \
+    using torch::headeronly::impl::ScalarTypeToCPPTypeT;                       \
+    int8_t total_count = 0;                                                    \
+    int8_t count = 0;                                                          \
+    int8_t default_count = 0;                                                  \
+    for (ScalarType t :                                                        \
+         {AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ITEM)}) {       \
+      total_count++;                                                           \
+      MY_DISPATCH_V2(t, "test_my_dispatch_v2", MY_CASE_FUNCTION, __VA_ARGS__); \
+    }                                                                          \
+    EXPECT_EQ(count, EXPECTEDCOUNT);                                           \
+    EXPECT_EQ(default_count + count, total_count);                             \
+  }
+
+TEST_DISPATCH_V2(AT_FLOAT8_TYPES_, 5, AT_FLOAT8_TYPES);
+TEST_DISPATCH_V2(AT_INTEGRAL_TYPES_, 5, AT_INTEGRAL_TYPES);
+TEST_DISPATCH_V2(AT_FLOATING_TYPES_, 2, AT_FLOATING_TYPES);
+TEST_DISPATCH_V2(AT_BAREBONES_UNSIGNED_TYPES_, 3, AT_BAREBONES_UNSIGNED_TYPES);
+TEST_DISPATCH_V2(AT_INTEGRAL_TYPES_V2_, 8, AT_INTEGRAL_TYPES_V2);
+TEST_DISPATCH_V2(AT_COMPLEX_TYPES_, 2, AT_COMPLEX_TYPES);
+TEST_DISPATCH_V2(AT_QINT_TYPES_, 3, AT_QINT_TYPES);
+TEST_DISPATCH_V2(AT_ALL_TYPES_, 7, AT_ALL_TYPES);
+TEST_DISPATCH_V2(AT_ALL_TYPES_AND_COMPLEX_, 9, AT_ALL_TYPES_AND_COMPLEX);
+
+#undef DEFINE_ITEM

test/cpp/aoti_abi_check/test_dispatch_v2.cpp

@@ -0,0 +1,45 @@
+#include <gtest/gtest.h>
+#include <torch/headeronly/core/Dispatch_v2.h>
+#include <torch/headeronly/util/Exception.h>
+
+#define DEFINE_ITEM(TYPE, SCALARTYPE) ScalarType::SCALARTYPE,
+
+#define TEST_DISPATCH_V2(NAME, EXPECTEDCOUNT, ...)                       \
+  TEST(TestThoDispatchV2, NAME) {                                        \
+    using torch::headeronly::ScalarType;                                 \
+    using torch::headeronly::impl::ScalarTypeToCPPTypeT;                 \
+    int8_t total_count = 0;                                              \
+    int8_t count = 0;                                                    \
+    int8_t default_count = 0;                                            \
+    for (ScalarType t :                                                  \
+         {AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ITEM)}) { \
+      total_count++;                                                     \
+      try {                                                              \
+        THO_DISPATCH_V2(                                                 \
+            t,                                                           \
+            "test_tho_dispatch_v2",                                      \
+            [&] {                                                        \
+              count++;                                                   \
+              scalar_t tmp;                                              \
+              (void)tmp;                                                 \
+            },                                                           \
+            __VA_ARGS__);                                                \
+      } catch (...) {                                                    \
+        default_count++; /* counts mismatches */                         \
+      }                                                                  \
+    }                                                                    \
+    EXPECT_EQ(count, EXPECTEDCOUNT);                                     \
+    EXPECT_EQ(default_count + count, total_count);                       \
+  }
+
+TEST_DISPATCH_V2(AT_FLOAT8_TYPES_, 5, AT_FLOAT8_TYPES);
+TEST_DISPATCH_V2(AT_INTEGRAL_TYPES_, 5, AT_INTEGRAL_TYPES);
+TEST_DISPATCH_V2(AT_FLOATING_TYPES_, 2, AT_FLOATING_TYPES);
+TEST_DISPATCH_V2(AT_BAREBONES_UNSIGNED_TYPES_, 3, AT_BAREBONES_UNSIGNED_TYPES);
+TEST_DISPATCH_V2(AT_INTEGRAL_TYPES_V2_, 8, AT_INTEGRAL_TYPES_V2);
+TEST_DISPATCH_V2(AT_COMPLEX_TYPES_, 2, AT_COMPLEX_TYPES);
+TEST_DISPATCH_V2(AT_QINT_TYPES_, 3, AT_QINT_TYPES);
+TEST_DISPATCH_V2(AT_ALL_TYPES_, 7, AT_ALL_TYPES);
+TEST_DISPATCH_V2(AT_ALL_TYPES_AND_COMPLEX_, 9, AT_ALL_TYPES_AND_COMPLEX);
+
+#undef DEFINE_ITEM

test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.cpp

@@ -67,13 +67,13 @@ Tensor sgd_out_of_place(
 
 void boxed_sgd_out_of_place(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
   Tensor res = sgd_out_of_place(
-    to<Tensor>(stack[0]),
-    to<Tensor>(stack[1]),
-    float(to<double>(stack[2])),
-    to<double>(stack[3]),
-    to<bool>(stack[4]));
+    torch::stable::detail::to<Tensor>(stack[0]),
+    torch::stable::detail::to<Tensor>(stack[1]),
+    float(torch::stable::detail::to<double>(stack[2])),
+    torch::stable::detail::to<double>(stack[3]),
+    torch::stable::detail::to<bool>(stack[4]));
 
-  stack[0] = from(res);
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY(libtorch_agnostic, m) {
@@ -89,8 +89,8 @@ Tensor identity(Tensor t) {
 }
 
 void boxed_identity(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor res = identity(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  Tensor res = identity(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -108,14 +108,14 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CPU, m) {
 Tensor my_abs(Tensor t) {
   const auto num_args = 1;
   StableIValue stack[num_args];
-  stack[0] = from(t);
+  stack[0] = torch::stable::detail::from(t);
   aoti_torch_call_dispatcher("aten::abs", "", stack);
-  return to<Tensor>(stack[0]);
+  return torch::stable::detail::to<Tensor>(stack[0]);
 }
 
 void boxed_my_abs(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor tensor_res = my_abs(to<Tensor>(stack[0]));
-  stack[0] = from(tensor_res);
+  Tensor tensor_res = my_abs(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(tensor_res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -132,21 +132,21 @@ Tensor my_ones_like(Tensor t, StableIValue device) {
 
   auto mf = aoti_torch_memory_format_contiguous_format();
 
-  stack[0] = from(t);
-  stack[1] = from(std::optional(t.scalar_type()));    // dtype
-  stack[2] = from(std::nullopt);              // layout
-  stack[3] = from(std::optional(device));     // device
-  stack[4] = from(std::optional(false));      // pin_memory
-  stack[5] = from(std::optional(mf));         // memory_format
+  stack[0] = torch::stable::detail::from(t);
+  stack[1] = torch::stable::detail::from(std::optional(t.scalar_type()));    // dtype
+  stack[2] = torch::stable::detail::from(std::nullopt);              // layout
+  stack[3] = torch::stable::detail::from(std::optional(device));     // device
+  stack[4] = torch::stable::detail::from(std::optional(false));      // pin_memory
+  stack[5] = torch::stable::detail::from(std::optional(mf));         // memory_format
 
   aoti_torch_call_dispatcher("aten::ones_like", "", stack);
 
-  return to<Tensor>(stack[0]);
+  return torch::stable::detail::to<Tensor>(stack[0]);
 }
 
 void boxed_my_ones_like(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor res = my_ones_like(to<Tensor>(stack[0]), stack[1]);
-  stack[0] = from(res);
+  Tensor res = my_ones_like(torch::stable::detail::to<Tensor>(stack[0]), stack[1]);
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -159,28 +159,28 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
 
 std::tuple<Tensor, Tensor, bool> exp_neg_is_leaf(Tensor t1, Tensor t2, Tensor t3) {
   StableIValue stack_exp[1];
-  stack_exp[0] = from(t1);
+  stack_exp[0] = torch::stable::detail::from(t1);
   aoti_torch_call_dispatcher("aten::exp", "", stack_exp);
 
   StableIValue stack_neg[1];
-  stack_neg[0] = from(t2);
+  stack_neg[0] = torch::stable::detail::from(t2);
   aoti_torch_call_dispatcher("aten::neg", "", stack_neg);
 
   StableIValue stack_is_leaf[1];
-  stack_is_leaf[0] = from(t3);
+  stack_is_leaf[0] = torch::stable::detail::from(t3);
   aoti_torch_call_dispatcher("aten::is_leaf", "", stack_is_leaf);
 
   return std::make_tuple(
-    to<Tensor>(stack_exp[0]),
-    to<Tensor>(stack_neg[0]),
-    to<bool>(stack_is_leaf[0]));
+    torch::stable::detail::to<Tensor>(stack_exp[0]),
+    torch::stable::detail::to<Tensor>(stack_neg[0]),
+    torch::stable::detail::to<bool>(stack_is_leaf[0]));
 }
 
 void boxed_exp_neg_is_leaf(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto tuple = exp_neg_is_leaf(to<Tensor>(stack[0]), to<Tensor>(stack[1]), to<Tensor>(stack[2]));
-  stack[0] = from(std::get<0>(tuple));
-  stack[1] = from(std::get<1>(tuple));
-  stack[2] = from(std::get<2>(tuple));
+  auto tuple = exp_neg_is_leaf(torch::stable::detail::to<Tensor>(stack[0]), torch::stable::detail::to<Tensor>(stack[1]), torch::stable::detail::to<Tensor>(stack[2]));
+  stack[0] = torch::stable::detail::from(std::get<0>(tuple));
+  stack[1] = torch::stable::detail::from(std::get<1>(tuple));
+  stack[2] = torch::stable::detail::from(std::get<2>(tuple));
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -193,15 +193,15 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
 
 Tensor neg_exp(Tensor t) {
   StableIValue stack[1];
-  stack[0] = from(t);
+  stack[0] = torch::stable::detail::from(t);
   aoti_torch_call_dispatcher("aten::exp", "", stack);
   aoti_torch_call_dispatcher("aten::neg", "", stack);
-  return to<Tensor>(stack[0]);
+  return torch::stable::detail::to<Tensor>(stack[0]);
 }
 
 void boxed_neg_exp(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor res = neg_exp(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  Tensor res = neg_exp(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -214,10 +214,10 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
 
 Tensor divide_neg_exp(Tensor t) {
   StableIValue stack_neg[1];
-  stack_neg[0] = from(t);
+  stack_neg[0] = torch::stable::detail::from(t);
 
   StableIValue stack_exp[1];
-  stack_exp[0] = from(t);
+  stack_exp[0] = torch::stable::detail::from(t);
   aoti_torch_call_dispatcher("aten::exp", "", stack_exp);
   aoti_torch_call_dispatcher("aten::neg", "", stack_neg);
 
@@ -225,12 +225,12 @@ Tensor divide_neg_exp(Tensor t) {
   stack_div[0] = stack_neg[0];
   stack_div[1] = stack_exp[0];
   aoti_torch_call_dispatcher("aten::divide", "Tensor", stack_div);
-  return to<Tensor>(stack_div[0]);
+  return torch::stable::detail::to<Tensor>(stack_div[0]);
 }
 
 void boxed_divide_neg_exp(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor res = divide_neg_exp(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  Tensor res = divide_neg_exp(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -246,8 +246,8 @@ bool is_contiguous(Tensor t) {
 }
 
 void boxed_is_contiguous(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  bool res = is_contiguous(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  bool res = is_contiguous(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -263,9 +263,9 @@ Tensor my_transpose(Tensor t, int64_t dim0, int64_t dim1) {
 }
 
 void boxed_my_transpose(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_transpose(to<Tensor>(stack[0]), to<int64_t>(stack[1]), to<int64_t>(stack[2]));
+  auto res = my_transpose(torch::stable::detail::to<Tensor>(stack[0]), torch::stable::detail::to<int64_t>(stack[1]), torch::stable::detail::to<int64_t>(stack[2]));
 
-  stack[0] = from(res);
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_empty_like(Tensor t) {
@@ -273,8 +273,8 @@ Tensor my_empty_like(Tensor t) {
 }
 
 void boxed_empty_like(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_empty_like(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_empty_like(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 bool my_is_cpu(Tensor t) {
@@ -283,8 +283,8 @@ bool my_is_cpu(Tensor t) {
 
 
 void boxed_my_is_cpu(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_is_cpu(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_is_cpu(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor fill_infinity(Tensor t) {
@@ -296,8 +296,8 @@ void boxed_fill_infinity(
     StableIValue* stack,
     uint64_t num_args,
     uint64_t num_outputs) {
-  auto res = fill_infinity(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = fill_infinity(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_pad(Tensor t) {
@@ -310,8 +310,8 @@ void boxed_my_pad(
     StableIValue* stack,
     uint64_t num_args,
     uint64_t num_outputs) {
-  auto res = my_pad(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_pad(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_narrow(Tensor t, int64_t dim, int64_t start, int64_t length) {
@@ -323,11 +323,11 @@ void boxed_my_narrow(
     uint64_t num_args,
     uint64_t num_outputs) {
   auto res = my_narrow(
-      to<Tensor>(stack[0]),
-      to<int64_t>(stack[1]),
-      to<int64_t>(stack[2]),
-      to<int64_t>(stack[3]));
-  stack[0] = from(res);
+      torch::stable::detail::to<Tensor>(stack[0]),
+      torch::stable::detail::to<int64_t>(stack[1]),
+      torch::stable::detail::to<int64_t>(stack[2]),
+      torch::stable::detail::to<int64_t>(stack[3]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_new_empty_dtype_variant(Tensor t) {
@@ -342,8 +342,8 @@ Tensor my_new_empty_dtype_variant(Tensor t) {
 }
 
 void boxed_my_new_empty_dtype_variant(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_new_empty_dtype_variant(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_new_empty_dtype_variant(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_new_zeros_dtype_variant(Tensor t) {
@@ -352,8 +352,8 @@ Tensor my_new_zeros_dtype_variant(Tensor t) {
 }
 
 void boxed_my_new_zeros_dtype_variant(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_new_zeros_dtype_variant(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_new_zeros_dtype_variant(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_copy_(Tensor dst, Tensor src, bool non_blocking) {
@@ -361,8 +361,8 @@ Tensor my_copy_(Tensor dst, Tensor src, bool non_blocking) {
 }
 
 void boxed_my_copy_(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor tensor_res = my_copy_(to<Tensor>(stack[0]), to<Tensor>(stack[1]), to<bool>(stack[2]));
-  stack[0] = from(tensor_res);
+  Tensor tensor_res = my_copy_(torch::stable::detail::to<Tensor>(stack[0]), torch::stable::detail::to<Tensor>(stack[1]), torch::stable::detail::to<bool>(stack[2]));
+  stack[0] = torch::stable::detail::from(tensor_res);
 }
 
 Tensor my_clone(Tensor t) {
@@ -370,8 +370,8 @@ Tensor my_clone(Tensor t) {
 }
 
 void boxed_my_clone(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  Tensor tensor_res = my_clone(to<Tensor>(stack[0]));
-  stack[0] = from(tensor_res);
+  Tensor tensor_res = my_clone(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(tensor_res);
 }
 
 
@@ -408,8 +408,8 @@ Tensor my_zero_(Tensor t) {
 }
 
 void boxed_my_zero_(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_zero_(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_zero_(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_amax(Tensor t) {
@@ -417,8 +417,8 @@ Tensor my_amax(Tensor t) {
 }
 
 void boxed_my_amax(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_amax(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_amax(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 Tensor my_amax_vec(Tensor t) {
@@ -426,8 +426,8 @@ Tensor my_amax_vec(Tensor t) {
 }
 
 void boxed_my_amax_vec(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
-  auto res = my_amax_vec(to<Tensor>(stack[0]));
-  stack[0] = from(res);
+  auto res = my_amax_vec(torch::stable::detail::to<Tensor>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -464,8 +464,8 @@ void boxed_test_default_constructor(
     StableIValue* stack,
     uint64_t num_args,
     uint64_t num_outputs) {
-  bool res = test_default_constructor(to<bool>(stack[0]));
-  stack[0] = from(res);
+  bool res = test_default_constructor(torch::stable::detail::to<bool>(stack[0]));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -478,6 +478,56 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
   m.impl("my_amax_vec", &boxed_my_amax_vec);
 }
 
+std::vector<Tensor> my__foreach_mul(torch::headeronly::HeaderOnlyArrayRef<Tensor> self, torch::headeronly::HeaderOnlyArrayRef<Tensor> other) {
+  std::array<StableIValue, 2> stack = {torch::stable::detail::from(self), torch::stable::detail::from(other)};
+  aoti_torch_call_dispatcher("aten::_foreach_mul", "List", stack.data());
+  return torch::stable::detail::to<std::vector<Tensor>>(stack[0]);
+}
+
+void boxed_my__foreach_mul(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
+  // Why is the following NOT torch::stable::detail::to<HeaderOnlyArrayRef<Tensor>>(stack[0])? Because calling `to`
+  // on a StableIValue means that the result is owning its underlying data now! HeaderOnlyArrayRef
+  // is not owning, so it cannot safely steward the result of the torch::stable::detail::to<>.
+  auto res = my__foreach_mul(torch::stable::detail::to<std::vector<Tensor>>(stack[0]), torch::stable::detail::to<std::vector<Tensor>>(stack[1]));
+  stack[0] = torch::stable::detail::from(res);
+}
+
+void my__foreach_mul_(torch::headeronly::HeaderOnlyArrayRef<Tensor> self, torch::headeronly::HeaderOnlyArrayRef<Tensor> other) {
+  std::array<StableIValue, 2> stack = {torch::stable::detail::from(self), torch::stable::detail::from(other)};
+  aoti_torch_call_dispatcher("aten::_foreach_mul_", "List", stack.data());
+}
+
+void boxed_my__foreach_mul_(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
+  my__foreach_mul_(torch::stable::detail::to<std::vector<Tensor>>(stack[0]), torch::stable::detail::to<std::vector<Tensor>>(stack[1]));
+}
+
+std::vector<Tensor> make_tensor_clones_and_call_foreach(Tensor t1, Tensor t2) {
+  // This function tests that my__foreach_mul can take in std::initializer_lists
+  // in addition to std::vectors.
+  Tensor t1_1 = my_clone(t1);
+  Tensor t1_2 = my_clone(t1);
+  Tensor t2_1 = my_clone(t2);
+  Tensor t2_2 = my_clone(t2);
+  return my__foreach_mul({t1_1, t2_1}, {t1_2, t2_2});
+}
+
+void boxed_make_tensor_clones_and_call_foreach(StableIValue* stack, uint64_t num_args, uint64_t num_outputs) {
+  auto res = make_tensor_clones_and_call_foreach(torch::stable::detail::to<Tensor>(stack[0]), torch::stable::detail::to<Tensor>(stack[1]));
+  stack[0] = torch::stable::detail::from(res);
+}
+
+STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
+  m.def("my__foreach_mul(Tensor[] self, Tensor[] other) -> Tensor[]");
+  m.def("my__foreach_mul_(Tensor(a!)[] self, Tensor[] other) -> ()");
+  m.def("make_tensor_clones_and_call_foreach(Tensor t1, Tensor t2) -> Tensor[]");
+}
+
+STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
+  m.impl("my__foreach_mul", &boxed_my__foreach_mul);
+  m.impl("my__foreach_mul_", &boxed_my__foreach_mul_);
+  m.impl("make_tensor_clones_and_call_foreach", &boxed_make_tensor_clones_and_call_foreach);
+}
+
 // Test functions for torch::stable::accelerator APIs
 
 #ifdef LAE_USE_CUDA
@@ -500,8 +550,8 @@ void boxed_test_device_guard(
     StableIValue* stack,
     uint64_t num_args,
     uint64_t num_outputs) {
-  int res = test_device_guard(static_cast<int64_t>(to<int64_t>(stack[0])));
-  stack[0] = from(res);
+  int res = test_device_guard(static_cast<int64_t>(torch::stable::detail::to<int64_t>(stack[0])));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 int64_t test_device_guard_set_index() {
@@ -520,7 +570,7 @@ void boxed_test_device_guard_set_index(
     uint64_t num_args,
     uint64_t num_outputs) {
   int64_t res = test_device_guard_set_index();
-  stack[0] = from(res);
+  stack[0] = torch::stable::detail::from(res);
 }
 
 int64_t test_stream(int32_t device_index) {
@@ -536,8 +586,8 @@ void boxed_test_stream(
     StableIValue* stack,
     uint64_t num_args,
     uint64_t num_outputs) {
-  int64_t res = test_stream(static_cast<int64_t>(to<int64_t>(stack[0])));
-  stack[0] = from(res);
+  int64_t res = test_stream(static_cast<int64_t>(torch::stable::detail::to<int64_t>(stack[0])));
+  stack[0] = torch::stable::detail::from(res);
 }
 
 int64_t test_get_current_device_index() {
@@ -549,7 +599,7 @@ void boxed_test_get_current_device_index(
     uint64_t num_args,
     uint64_t num_outputs) {
   int64_t res = test_get_current_device_index();
-  stack[0] = from(res);
+  stack[0] = torch::stable::detail::from(res);
 }
 
 STABLE_TORCH_LIBRARY_FRAGMENT(libtorch_agnostic, m) {
@@ -565,4 +615,5 @@ STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CompositeExplicitAutograd, m) {
   m.impl("test_stream", &boxed_test_stream);
   m.impl("test_get_current_device_index", &boxed_test_get_current_device_index);
 }
+
 #endif // LAE_USE_CUDA

test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/ops.py

@@ -333,3 +333,45 @@ def my_new_zeros_dtype_variant(t) -> Tensor:
     Returns: New zeros tensor
     """
     return torch.ops.libtorch_agnostic.my_new_zeros_dtype_variant.default(t)
+
+
+def my__foreach_mul_(tensors, others) -> ():
+    """
+    Updates tensors to be the result of pointwise multiplying with others.
+
+    Args:
+        tensors: list of tensors
+        others: list of tensors (with the same corresponding shapes as tensors)
+
+    Returns: nothing, tensors is updated in place.
+    """
+    torch.ops.libtorch_agnostic.my__foreach_mul_.default(tensors, others)
+
+
+def my__foreach_mul(tensors, others) -> list[Tensor]:
+    """
+    Returns a list of tensors that are the results of pointwise multiplying
+    tensors and others.
+
+    Args:
+        tensors: list of tensors
+        others: list of tensors (with the same corresponding shapes as tensors)
+
+    Returns: list of multiplied tensors
+    """
+    return torch.ops.libtorch_agnostic.my__foreach_mul.default(tensors, others)
+
+
+def make_tensor_clones_and_call_foreach(t1, t2) -> list[Tensor]:
+    """
+    Returns a list of 2 tensors corresponding to the square of the inputs.
+
+    Args:
+        t1: Tensor
+        t2: Tensor
+
+    Returns: list of [t1^2, t2^2]
+    """
+    return torch.ops.libtorch_agnostic.make_tensor_clones_and_call_foreach.default(
+        t1, t2
+    )

test/cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic.py

@@ -367,6 +367,57 @@ if not IS_WINDOWS:
             self.assertNotEqual(result.data_ptr(), expected.data_ptr())
             self.assertEqual(result.stride(), expected.stride())
 
+        def test_my__foreach_mul_(self, device):
+            import libtorch_agnostic
+
+            N = 5
+            tensors = [torch.rand(32, 16, device=device) for _ in range(N)]
+            tensors_c = [t.clone() for t in tensors]
+            others = [torch.rand(32, 16, device=device) for _ in range(N)]
+
+            libtorch_agnostic.ops.my__foreach_mul_(tensors, others)
+            expected_values = torch._foreach_mul(tensors_c, others)
+
+            for tensor_t, expected_t in zip(tensors, expected_values):
+                self.assertEqual(tensor_t, expected_t)
+
+        def test_my__foreach_mul(self, device):
+            import libtorch_agnostic
+
+            N = 5
+            tensors = [torch.rand(32, 16, device=device) for _ in range(N)]
+            others = [torch.rand(32, 16, device=device) for _ in range(N)]
+
+            result = libtorch_agnostic.ops.my__foreach_mul(tensors, others)
+            expected = torch._foreach_mul(tensors, others)
+
+            for result_t, expected_t in zip(result, expected):
+                self.assertEqual(result_t, expected_t)
+
+            def _make_cuda_tensors(prior_mem):
+                cuda_res = libtorch_agnostic.ops.my__foreach_mul(tensors, others)
+                self.assertGreater(torch.cuda.memory_allocated(device), prior_mem)
+
+                expected = torch._foreach_mul(tensors, others)
+                for result_t, expected_t in zip(cuda_res, expected):
+                    self.assertEqual(result_t, expected_t)
+
+            if tensors[0].is_cuda:
+                init_mem = torch.cuda.memory_allocated(device)
+                for _ in range(3):
+                    _make_cuda_tensors(init_mem)
+                    curr_mem = torch.cuda.memory_allocated(device)
+                    self.assertEqual(curr_mem, init_mem)
+
+        def test_make_tensor_clones_and_call_foreach(self, device):
+            import libtorch_agnostic
+
+            t1 = torch.rand(2, 5, device=device)
+            t2 = torch.rand(3, 4, device=device)
+            result = libtorch_agnostic.ops.make_tensor_clones_and_call_foreach(t1, t2)
+            self.assertEqual(result[0], t1 * t1)
+            self.assertEqual(result[1], t2 * t2)
+
     instantiate_device_type_tests(TestLibtorchAgnostic, globals(), except_for=None)
 
 if __name__ == "__main__":

test/custom_backend/test_custom_backend.py

@@ -1,6 +1,5 @@
 # Owner(s): ["module: unknown"]
 
-import os
 import tempfile
 
 from backend import get_custom_backend_library_path, Model, to_custom_backend
@@ -41,14 +40,11 @@ class TestCustomBackend(TestCase):
         self.test_execute()
 
         # Save and load.
-        f = tempfile.NamedTemporaryFile(delete=False)
-        try:
+        with tempfile.NamedTemporaryFile() as f:
             f.close()
             torch.jit.save(self.model, f.name)
             loaded = torch.jit.load(f.name)
-        finally:
-            os.unlink(f.name)
-        self.model = loaded
+            self.model = loaded
 
         # Test execution again.
         self.test_execute()

test/custom_operator/test_custom_ops.py

@@ -1,6 +1,5 @@
 # Owner(s): ["module: unknown"]
 
-import os.path
 import sys
 import tempfile
 import unittest
@@ -144,16 +143,13 @@ def forward(self, arg0_1):
         # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile
         # opens the file, and it cannot be opened multiple times in Windows. To support Windows,
         # close the file after creation and try to remove it manually.
-        file = tempfile.NamedTemporaryFile(delete=False)
-        try:
+        with tempfile.NamedTemporaryFile() as file:
             file.close()
             model.save(file.name)
             loaded = torch.jit.load(file.name)
-        finally:
-            os.unlink(file.name)
 
-        output = loaded.forward(torch.ones(5))
-        self.assertTrue(output.allclose(torch.ones(5) + 1))
+            output = loaded.forward(torch.ones(5))
+            self.assertTrue(output.allclose(torch.ones(5) + 1))
 
 
 if __name__ == "__main__":

test/distributed/_composable/fsdp/test_fully_shard_logging.py

@@ -1,7 +1,7 @@
 # Owner(s): ["module: fsdp"]
 import functools
 import os
-import unittest.mock
+import unittest
 
 import torch.distributed as dist
 from torch._dynamo.test_case import run_tests
@@ -37,9 +37,9 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 from torch.distributed.fsdp import fully_shard
-logger = logging.getLogger("torch.distributed._composable.fsdp")
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
 logger.setLevel(logging.DEBUG)
-device = {device_type.type}
+device = '{device_type.type}'
 torch.manual_seed(0)
 model = nn.Sequential(*[nn.Linear(4, 4, device=device, bias=False) for _ in range(2)])
 for layer in model:

test/distributed/_composable/test_replicate_with_fsdp.py

@@ -76,7 +76,7 @@ class ReplicateTest(MultiProcessTestCase):
             store=dist.FileStore(self.file_name, self.world_size),
         )
 
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_replicate_transformer(self):
         """
         This tests that replicate works on a transformer model with fully_shard and replicate layers
@@ -126,7 +126,7 @@ class ReplicateTest(MultiProcessTestCase):
                 for parameter in layer.parameters():
                     self.assertEqual(parameter.placements, (Shard(dim=0),))
 
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_replicate_transformer_managed_modules(self):
         """
         This tests that replicate managed modules works properly. In this test we use a Transformer Module with 3 layers,
@@ -178,7 +178,7 @@ class ReplicateTest(MultiProcessTestCase):
         replicate_model = replicate(replicate_model)
         self.assertEqual(len(_get_managed_modules((replicate_model,))), 21)
 
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_replicate_tp_device_mesh(self):
         """
         This tests that a user can pass in a device mesh to replicate a module
@@ -206,7 +206,7 @@ class ReplicateTest(MultiProcessTestCase):
                 self.assertEqual(parameter.device_mesh.shape, (2,))
                 self.assertEqual(parameter.placements, (Replicate(),))
 
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_train_replicate_fsdp(self):
         """
         Tests that replicate_model has the same behavior as original model when training
@@ -253,7 +253,7 @@ class ReplicateTest(MultiProcessTestCase):
             self.assertEqual(replicate_loss, loss)
             check_sharded_parity(self, model, replicate_model)
 
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_train_parity_2d_mlp(self):
         """
         Verifies when a device mesh is passed in, the model has the same behavior as the original model when training

test/distributed/_tools/test_sac_ilp.py

@@ -80,7 +80,7 @@ class TestSACILP(TestCase):
             # postprocessing due to the fact that for ModTracker, the post backward hook
             # is not being called for modules whose inputs don't require gradients
             # TODO: fix this in ModTracker and ensure it does not lead to any perf regression
-            if _ModState.POST_BW not in mod_stats.snapshots.keys():
+            if _ModState.POST_BW not in mod_stats.snapshots:
                 mod_stats.snapshots.setdefault(_ModState.POST_BW, []).append(
                     copy.deepcopy(last_snapshot)
                 )

test/distributed/argparse_util_test.py

@@ -16,7 +16,7 @@ from torch.distributed.argparse_util import check_env, env
 class ArgParseUtilTest(unittest.TestCase):
     def setUp(self):
         # remove any lingering environment variables
-        for e in os.environ.keys():
+        for e in os.environ.keys():  # noqa: SIM118
             if e.startswith("PET_"):
                 del os.environ[e]
 

test/distributed/checkpoint/_experimental/test_staging.py

@@ -207,7 +207,7 @@ class TestDefaultStager(TestCase):
         for i, result in enumerate(staged_results):
             self.assertIsInstance(result, dict)
             # Verify the result contains the expected keys
-            for key in state_dicts[i].keys():
+            for key in state_dicts[i]:
                 self.assertIn(key, result)
 
         stager.close()

test/distributed/checkpoint/test_dtensor_resharding.py

@@ -299,7 +299,7 @@ class TestDTensorReshardMeshChange(DTensorTestBase):
 
     @with_comms
     @with_temp_dir
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_dtensor_checkpoint_with_uneven_shards(self) -> None:
         """
         Saving a dtensor with uneven shards.
@@ -436,6 +436,7 @@ class TestCheckpointableReshard(DTensorTestBase):
 
     @with_comms
     @with_temp_dir
+    @skip_if_lt_x_gpu(4)
     def test_uneven_reshard_with_checkpointable_api(self) -> None:
         """
         Saves a 1d distributed tensor that has shards with uneven sizes using Checkpointable API.
@@ -498,6 +499,7 @@ class TestCheckpointableReshard(DTensorTestBase):
 
     @with_comms
     @with_temp_dir
+    @skip_if_lt_x_gpu(4)
     def test_uneven_reshard_with_dtensor_shards_wrapper_api(self) -> None:
         """
         Saves a 1d distributed tensor that has shards with uneven sizes using Checkpointable API.

test/distributed/checkpoint/test_hf_safetensor_e2e.py

@@ -60,7 +60,7 @@ class TestSingleRankSaveLoad(TestCase):
         self.assertEqual(
             sorted(state_dict_to_save.keys()), sorted(state_dict_loaded.keys())
         )
-        for key in state_dict_to_save.keys():
+        for key in state_dict_to_save:
             self.assertTrue(
                 torch.equal(state_dict_to_save[key], state_dict_loaded[key])
             )
@@ -89,7 +89,7 @@ class TestSingleRankSaveLoad(TestCase):
         self.assertEqual(
             sorted(state_dict_to_save.keys()), sorted(state_dict_to_load.keys())
         )
-        for key in state_dict_to_save.keys():
+        for key in state_dict_to_save:
             self.assertTrue(
                 torch.equal(state_dict_to_save[key], state_dict_to_load[key])
             )
@@ -116,7 +116,7 @@ class TestSingleRankSaveLoad(TestCase):
         self.assertEqual(
             sorted(state_dict_to_save.keys()), sorted(state_dict_loaded.keys())
         )
-        for key in state_dict_to_save.keys():
+        for key in state_dict_to_save:
             self.assertTrue(
                 torch.equal(state_dict_to_save[key], state_dict_loaded[key])
             )
@@ -156,7 +156,7 @@ class TestSingleRankSaveLoad(TestCase):
         self.assertEqual(
             sorted(state_dict_to_save.keys()), sorted(state_dict_to_load.keys())
         )
-        for key in state_dict_to_save.keys():
+        for key in state_dict_to_save:
             self.assertTrue(
                 torch.equal(state_dict_to_save[key], state_dict_to_load[key])
             )

test/distributed/checkpoint/test_planner.py

@@ -18,6 +18,7 @@ from torch.distributed.checkpoint._dedup_save_plans import dedup_save_plans
 from torch.distributed.checkpoint.api import CheckpointException
 from torch.distributed.checkpoint.default_planner import (
     _create_default_local_metadata,
+    _validate_global_plan,
     create_default_global_save_plan,
     create_default_local_load_plan,
     create_default_local_save_plan,
@@ -28,6 +29,7 @@ from torch.distributed.checkpoint.filesystem import CURRENT_DCP_VERSION
 from torch.distributed.checkpoint.metadata import (
     BytesStorageMetadata,
     ChunkStorageMetadata,
+    Metadata,
     MetadataIndex,
     TensorProperties,
     TensorStorageMetadata,
@@ -560,6 +562,32 @@ class TestPlannerHelpers(TestCase):
         self.assertTrue(_compare_save_plans(plan2, plan2))
 
 
+class TestValidateGlobalPlan(TestCase):
+    def _make_metadata(self, chunks, size):
+        storage = TensorStorageMetadata(
+            properties=TensorProperties(dtype=torch.float32),
+            size=torch.Size(size),
+            chunks=chunks,
+        )
+        return Metadata(state_dict_metadata={"param": storage})
+
+    def test_non_overlapping_chunks(self):
+        chunks = [
+            ChunkStorageMetadata(offsets=torch.Size([i]), sizes=torch.Size([1]))
+            for i in range(4)
+        ]
+        metadata = self._make_metadata(chunks, [4])
+        self.assertTrue(_validate_global_plan([SavePlan([])], metadata))
+
+    def test_detect_overlapping_chunks(self):
+        chunks = [
+            ChunkStorageMetadata(offsets=torch.Size([0]), sizes=torch.Size([2])),
+            ChunkStorageMetadata(offsets=torch.Size([1]), sizes=torch.Size([2])),
+        ]
+        metadata = self._make_metadata(chunks, [4])
+        self.assertFalse(_validate_global_plan([SavePlan([])], metadata))
+
+
 class TestLoadPlanner(TestCase):
     @with_temp_dir
     def test_strict(self):

test/distributed/checkpoint/test_state_dict.py

@@ -769,7 +769,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
         model_state_dict3 = copy.deepcopy(model_state_dict3)
         self.assertEqual(len(model_state_dict2), 2)
         self.assertEqual(len(model_state_dict3), 2)
-        for key in model_state_dict3.keys():
+        for key in model_state_dict3:
             full_fqn = f"l.{key}"
             value1 = model_state_dict1[full_fqn]
             value2 = model_state_dict2[full_fqn]
@@ -886,7 +886,7 @@ class TestStateDict(DTensorTestBase, VerifyStateDictMixin):
             self.assertEqual(cpu_model_value, meta_model_value)
 
     @with_comms
-    @skip_if_lt_x_gpu(2)
+    @skip_if_lt_x_gpu(4)
     def test_setting_meta_device_model_broadcasting_and_memory(self) -> None:
         # This test verifies that we can set model state dict by a meta device model
         # With the correlated changes in state_dict, meta device model should be accepted

test/distributed/fsdp/test_fsdp_misc.py

@@ -479,6 +479,7 @@ class TestFSDPMiscMultiProcess(FSDPTest):
                     for (n, p), (n_prev, p_prev) in zip(
                         fsdp_overlap.named_parameters(), fsdp_overlap_prev_params
                     ):
+                        self.assertEqual(n, n_prev)
                         self.assertNotEqual(
                             p,
                             p_prev,

test/distributed/fsdp/test_fsdp_state_dict.py

@@ -587,9 +587,7 @@ class TestFSDPStateDict(FSDPTest):
                     model, cpu_offload.offload_params, fp16
                 )
 
-            ignore_keys = [
-                k for k in fsdp_state_dict.keys() if NON_ROOT_FSDP_PREFIX in k
-            ]
+            ignore_keys = [k for k in fsdp_state_dict if NON_ROOT_FSDP_PREFIX in k]
 
             self._validate_state_dict_contents(
                 model,
@@ -910,7 +908,7 @@ class TestFSDPStateDict(FSDPTest):
         with sd_mgr:
             fsdp_state_dict = model.state_dict()
 
-        ignore_keys = [k for k in fsdp_state_dict.keys() if NON_ROOT_FSDP_PREFIX in k]
+        ignore_keys = [k for k in fsdp_state_dict if NON_ROOT_FSDP_PREFIX in k]
         self._validate_state_dict_contents(
             model,
             fsdp_state_dict,
@@ -959,9 +957,7 @@ class TestFSDPStateDict(FSDPTest):
                 # Full name of linear_skip param tensors in SkipModel, as would be
                 # stored in checkpoint.
                 linear_skip_tensor_names = [
-                    k
-                    for k in dict(module.named_parameters()).keys()
-                    if LINEAR_SKIP in k
+                    k for k in dict(module.named_parameters()) if LINEAR_SKIP in k
                 ]
                 # skip SkipModule
                 linear_skip = getattr(module, LINEAR_SKIP)

test/distributed/launcher/api_test.py

@@ -137,7 +137,7 @@ class ElasticLaunchTest(unittest.TestCase):
         self.test_dir = tempfile.mkdtemp()
 
         # remove any lingering environment variables.
-        for env in os.environ.keys():
+        for env in os.environ.keys():  # noqa: SIM118
             if env.startswith("PET_"):
                 del os.environ[env]
 

test/distributed/launcher/test_run.py

@@ -69,7 +69,7 @@ class ElasticLaunchTest(TestCase):
         self.test_dir = tempfile.mkdtemp()
 
         # remove any lingering environment variables
-        for env in os.environ.keys():
+        for env in os.environ.keys():  # noqa: SIM118
             if env.startswith("PET_"):
                 del os.environ[env]
 

test/distributed/pipelining/test_schedule_multiproc.py

@@ -39,6 +39,7 @@ from torch.nn.modules.loss import MSELoss
 from torch.testing._internal.common_distributed import (
     MultiProcContinuousTest,
     requires_accelerator_dist_backend,
+    skip_if_lt_x_gpu,
 )
 from torch.testing._internal.common_utils import (
     check_leaked_tensors,
@@ -46,6 +47,7 @@ from torch.testing._internal.common_utils import (
     parametrize,
     run_tests,
     skip_but_pass_in_sandcastle_if,
+    TEST_MULTIACCELERATOR,
 )
 
 
@@ -56,7 +58,6 @@ batch_size = 64
 torch.manual_seed(0)
 device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu"
 backend = dist.get_default_backend_for_device(device_type)
-TEST_MULTIACCELERATOR = torch.accelerator.device_count() >= 2
 
 
 @dataclass
@@ -231,6 +232,7 @@ class ScheduleTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [_ScheduleForwardOnly])
+    @skip_if_lt_x_gpu(4)
     def test_forward_only(self, ScheduleClass):
         mod, mod_ref, x, _, _ = setup_models_and_data(self.config)
         x_clone = x.clone()
@@ -274,6 +276,7 @@ class ScheduleTest(MultiProcContinuousTest):
             ScheduleInterleavedZeroBubble,
         ],
     )
+    @skip_if_lt_x_gpu(4)
     def test_eval_inference_mode(self, ScheduleClass):
         num_microbatches = 4
         if ScheduleClass in [
@@ -351,6 +354,7 @@ class ScheduleTest(MultiProcContinuousTest):
             ScheduleInterleavedZeroBubble,
         ],
     )
+    @skip_if_lt_x_gpu(4)
     def test_return_output(self, ScheduleClass):
         num_microbatches = 4
         if ScheduleClass in [
@@ -406,6 +410,7 @@ class ScheduleTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleGPipe, Schedule1F1B])
+    @skip_if_lt_x_gpu(4)
     def test_multi_iter(self, ScheduleClass):
         mod, _, x, target, loss_fn = setup_models_and_data(self.config)
         chunks = 4
@@ -429,6 +434,7 @@ class ScheduleTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleGPipe, Schedule1F1B])
+    @skip_if_lt_x_gpu(4)
     def test_kwargs_with_tracer(self, ScheduleClass):
         mod = ModelWithKwargs(d_hid, splits=self.world_size)
         mod.to(self.device)
@@ -481,6 +487,7 @@ class ScheduleTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleGPipe, Schedule1F1B])
+    @skip_if_lt_x_gpu(4)
     def test_grad_with_tracer(self, ScheduleClass):
         mod, ref_mod, x, target, loss_fn = setup_models_and_data(self.config)
 
@@ -523,6 +530,7 @@ class ScheduleTest(MultiProcContinuousTest):
     )
     @parametrize("ScheduleClass", [ScheduleGPipe, Schedule1F1B])
     @parametrize("shape_inference", [True, False])
+    @skip_if_lt_x_gpu(4)
     def test_grad_with_manual(self, ScheduleClass, shape_inference):
         mod, ref_mod, x, target, loss_fn = setup_models_and_data(self.config)
 
@@ -586,6 +594,7 @@ class ScheduleTest(MultiProcContinuousTest):
             ScheduleInterleavedZeroBubble,
         ],
     )
+    @skip_if_lt_x_gpu(4)
     def test_grad_with_manual_interleaved(self, ScheduleClass):
         stages_per_rank = 2
         n_stages = stages_per_rank * self.world_size
@@ -650,6 +659,7 @@ class ScheduleTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleInterleavedZeroBubble])
+    @skip_if_lt_x_gpu(4)
     def test_schedule_with_weight_update_mlp_e2e(self, ScheduleClass):
         stages_per_rank = 2
         n_stages = stages_per_rank * self.world_size
@@ -736,6 +746,7 @@ class ScheduleTest(MultiProcContinuousTest):
         "schedule_class",
         [ScheduleZBVZeroBubble, ScheduleDualPipeV],
     )
+    @skip_if_lt_x_gpu(4)
     def test_v_shape_schedules(self, schedule_class):
         n_stages = 8
         rank_stages = {0: [0, 7], 1: [1, 6], 2: [2, 5], 3: [3, 4]}
@@ -780,6 +791,7 @@ class ScheduleTest(MultiProcContinuousTest):
     @skip_but_pass_in_sandcastle_if(
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
+    @skip_if_lt_x_gpu(4)
     def test_custom_function_callback(self):
         """Test the custom function callback functionality with _PipelineScheduleRuntime."""
         n_stages = 8
@@ -979,6 +991,7 @@ class ScheduleTest(MultiProcContinuousTest):
         "ScheduleClass",
         [ScheduleInterleavedZeroBubble, ScheduleInterleaved1F1B],
     )
+    @skip_if_lt_x_gpu(4)
     def test_zero_bubble_with_model_kwargs(self, ScheduleClass):
         stages_per_rank = 2
         n_stages = stages_per_rank * self.world_size
@@ -1072,6 +1085,7 @@ class CustomSchedulesTest(MultiProcContinuousTest):
         "schedule_class",
         [ScheduleVShaped, ScheduleUnbalanced],
     )
+    @skip_if_lt_x_gpu(4)
     def test_non_symmetric_stage_ids(self, schedule_class):
         n_stages = schedule_class.n_stages
         rank_stages = schedule_class.rank_stages
@@ -1121,6 +1135,7 @@ class CustomSchedulesTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleWithReorderedB])
+    @skip_if_lt_x_gpu(4)
     def test_pipeline_schedule_runtime_custom_sched(self, ScheduleClass):
         n_stages = 2
         stages_per_rank = 1
@@ -1181,6 +1196,7 @@ class CustomSchedulesTest(MultiProcContinuousTest):
         not TEST_MULTIACCELERATOR, f"{backend} test requires 2+ GPUs"
     )
     @parametrize("ScheduleClass", [ScheduleWithW])
+    @skip_if_lt_x_gpu(4)
     def test_schedule_with_native_zero_bubble(self, ScheduleClass):
         n_stages = ScheduleClass.n_stages
         num_microbatches = ScheduleClass.num_microbatches

test/distributed/pipelining/test_stage.py

@@ -24,6 +24,7 @@ from torch.testing._internal.common_utils import (
     parametrize,
     run_tests,
     skip_but_pass_in_sandcastle_if,
+    TEST_MULTIACCELERATOR,
 )
 from torch.utils._pytree import tree_map_only
 
@@ -34,7 +35,6 @@ chunks = 8
 
 device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu"
 backend = dist.get_default_backend_for_device(device_type)
-TEST_MULTIACCELERATOR = torch.accelerator.device_count() >= 2
 
 torch.manual_seed(0)
 

test/distributed/tensor/test_dtensor_compile.py

@@ -464,6 +464,25 @@ def forward(self, b_parametrizations_buffer_original0, x):
         run(g, 64, 8)
         self.assertEqual(cnt.frame_count, 2)
 
+    def test_dtensor_requires_grad_recompile(self):
+        cnt = torch._dynamo.testing.CompileCounterWithBackend("aot_eager")
+        mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
+
+        @torch.compile(backend=cnt, fullgraph=True)
+        def f(x):
+            y = x * x
+            return y.to_local()
+
+        full_x = torch.randn(8, 8, requires_grad=False)
+        x = distribute_tensor(full_x, mesh, [Shard(0)])
+        f(x)
+
+        full_x = torch.randn(8, 8, requires_grad=True)
+        x = distribute_tensor(full_x, mesh, [Shard(0)])
+        f(x)
+
+        self.assertEqual(cnt.frame_count, 2)
+
     def test_dtensor_attribute_access_on_intermediate(self):
         mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
 

test/distributed/tensor/test_dtensor_export.py

@@ -535,6 +535,19 @@ class DTensorExportTest(TestCase):
 
         self.assertEqual(fn(z), gm(z)[0])
 
+    def test_dtensor_data_dependent_index(self):
+        device_mesh = init_device_mesh(self.device_type, mesh_shape=(self.world_size,))
+
+        class Foo(torch.nn.Module):
+            def forward(self, x, y):
+                return x[y]
+
+        x = torch.randn(10)
+        y = torch.randint(1, (10,)).bool()
+        x_dt = distribute_tensor(x, device_mesh, placements=[Replicate()])
+        y_dt = distribute_tensor(y, device_mesh, placements=[Replicate()])
+        _dynamo_graph_capture_for_export(Foo())(x_dt, y_dt)
+
 
 instantiate_parametrized_tests(DTensorExportTest)
 

test/distributed/tensor/test_math_ops.py

@@ -26,6 +26,7 @@ from torch.distributed.tensor.parallel import (
     RowwiseParallel,
     SequenceParallel,
 )
+from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
 from torch.testing._internal.common_utils import run_tests
 from torch.testing._internal.distributed._tensor.common_dtensor import (
     create_local_tensor_test_class,
@@ -764,6 +765,7 @@ class DistMathOpsTest(DTensorTestBase):
         self.assertEqual(grad1_norm.device_mesh, mesh_y)
 
     @with_comms
+    @skip_if_lt_x_gpu(4)
     def test_foreach_add_different_mesh(self):
         mesh_shape = (2, self.world_size // 2)
         mesh_2d = init_device_mesh(

test/distributed/tensor/test_op_strategy.py

@@ -577,7 +577,7 @@ class DistTensorReplicateStrategyRegistrationTest(DTensorTestBase):
                 self.assertEqual(
                     comm_mode.get_comm_counts(),
                     {
-                        torch.ops.c10d_functional.all_gather_into_tensor: 4,
+                        torch.ops.c10d_functional.all_gather_into_tensor: self.world_size,
                     },
                 )
                 expected_cost = [

test/distributed/test_aten_comm_compute_reordering.py

@@ -54,6 +54,7 @@ def apply_reordering_and_get_graph(graph, out_li) -> None:
         "max_compute_pre_fetch",
         "custom_runtime_estimation",
         "insert_overlap_deps",
+        "collective_estimator",
     )
     for key in config_keys:
         if (val := getattr(dist_opts, key)) is not None:
@@ -943,6 +944,50 @@ class TestComputeCommReorderingBucketing(TestComputeCommReorderingMultiProc):
             correct = func(inputs_a, inputs_b, ranks=ranks)
             self.assertTrue(same(out, correct))
 
+    @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
+    def test_collective_benchmarking_with_real_pg(self):
+        """Test collective benchmarking with real process group (falls back on fake)."""
+
+        def func(a):
+            # Test all three collective types with 8x8 (power of 2 size = 256 elements = 1024 bytes for fp32)
+            ar = _functional_collectives.all_reduce(a, "sum", "0")
+            ag = _functional_collectives.all_gather_tensor(
+                a, 0, list(range(self.world_size))
+            )
+            rs = _functional_collectives.reduce_scatter_tensor(a, "sum", 0, "0")
+
+            b = torch.matmul(a, a)
+            c = torch.matmul(ar, b)
+            return c.sum() + ag.sum() + rs.sum()
+
+        patches = {
+            **get_patches(),
+            "aten_distributed_optimizations.collective_estimator": "benchmark",
+            "aten_distributed_optimizations.custom_runtime_estimation": None,  # Remove custom estimation so benchmarking happens
+        }
+
+        with _dynamo_dist_per_rank_init(
+            self.rank,
+            self.world_size,
+            self.backend(device_type),
+            fake_pg=not at_least_x_gpu(2),
+        ):
+            inputs = torch.ones(8, 8, dtype=torch.float, device=device_type) + self.rank
+
+            with torch._inductor.config.patch(patches):
+                compiled = torch.compile(func)
+                out, aten_graph_str = run_and_get_aten_graph(compiled, inputs)
+
+                # Verify all three collective types are present
+                FileCheck().check("all_reduce").check("all_gather").check(
+                    "reduce_scatter"
+                ).run(aten_graph_str)
+
+                # Test passes if compilation succeeded with benchmarking enabled
+                # Cache verification is tricky due to multiprocess test setup
+                correct = func(inputs)
+                self.assertTrue(same(out, correct))
+
     @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
     @torch._inductor.config.patch(get_bucket_patches())
     def test_multidtype_bucketing(self):

test/distributed/test_functional_api.py

@@ -485,7 +485,7 @@ elif TEST_XPU:
 def exit_if_lt_x_accelerators(x):
     if torch.accelerator.is_available():
         if torch.accelerator.device_count() < x:
-            sys.exit(TEST_SKIPS[f"multi-accelerator-{x}"].exit_code)
+            sys.exit(TEST_SKIPS[f"multi-gpu-{x}"].exit_code)
 
 
 def with_comms(func=None):

test/dynamo/test_activation_checkpointing.py

@@ -1,4 +1,6 @@
 # Owner(s): ["module: dynamo"]
+# flake8: noqa: B950
+# flake8: noqa: E731
 import contextlib
 import copy
 import functools
@@ -15,7 +17,11 @@ import torch.nn as nn
 import torch.utils.checkpoint
 from functorch.compile import min_cut_rematerialization_partition
 from torch._dynamo.backends.common import aot_autograd
-from torch._dynamo.testing import CompileCounterWithBackend
+from torch._dynamo.testing import (
+    AotEagerAndRecordGraphs,
+    CompileCounterWithBackend,
+    normalize_gm,
+)
 from torch._higher_order_ops.wrap import tag_activation_checkpoint
 from torch.testing._internal.common_device_type import instantiate_device_type_tests
 from torch.testing._internal.common_utils import IS_WINDOWS, skipIfHpu
@@ -1649,6 +1655,43 @@ Non-primal fwd outputs from model w/o backward hook: {mod_no_hook_fwd_outputs_no
 
         self.assertEqual(opt_fn(x), fn(x))
 
+    def test_return_same_element_twice(self):
+        def gn(x):
+            y = torch.sin(x)
+            return y, y
+
+        def fn(x):
+            return torch.utils.checkpoint.checkpoint(gn, x, use_reentrant=True)
+
+        x = torch.randn(4, 4, requires_grad=True)
+        ref = fn(x)
+
+        backend = AotEagerAndRecordGraphs()
+        opt_fn = torch.compile(fn, backend=backend, fullgraph=True)
+        res = opt_fn(x)
+        self.assertEqual(ref[0], res[0])
+        self.assertEqual(ref[1], res[1])
+
+        self.assertExpectedInline(
+            normalize_gm(backend.graphs[0].print_readable(print_output=False)),
+            """\
+class GraphModule(torch.nn.Module):
+    def forward(self, L_x_: "f32[4, 4]"):
+        l_x_ = L_x_
+
+        wrap_body_0 = self.wrap_body_0
+        tag_activation_checkpoint = torch.ops.higher_order.tag_activation_checkpoint(wrap_body_0, l_x_, use_reentrant = True);  wrap_body_0 = l_x_ = None
+        getitem: "f32[4, 4]" = tag_activation_checkpoint[0]
+        getitem_1: "f32[4, 4]" = tag_activation_checkpoint[1];  tag_activation_checkpoint = None
+        return (getitem, getitem_1)
+
+    class wrap_body_0(torch.nn.Module):
+        def forward(self, l_x_: "f32[4, 4]"):
+            y: "f32[4, 4]" = torch.sin(l_x_);  l_x_ = None
+            return (y, y)
+""",
+        )
+
     @torch._dynamo.config.patch(skip_fwd_side_effects_in_bwd_under_checkpoint=True)
     def test_nonlocal_mutation(self):
         counter = 0
@@ -1672,6 +1715,114 @@ Non-primal fwd outputs from model w/o backward hook: {mod_no_hook_fwd_outputs_no
         # The mutation is not reapplied in the backward because the flag was on.
         self.assertEqual(counter, 1)
 
+    @torch._dynamo.config.patch(skip_fwd_side_effects_in_bwd_under_checkpoint=True)
+    def test_nonlocal_list_mutation(self):
+        def gn(x, z):
+            out = x.sin()
+            z.append(out)
+            return torch.cos(torch.sin(torch.matmul(x, x) @ x)), out
+
+        def fn(x):
+            z = []
+
+            out1, out2 = torch.utils.checkpoint.checkpoint(
+                gn,
+                x,
+                z,
+                use_reentrant=False,
+            )
+
+            return out1, z[0]
+
+        x = torch.randn(4, 4, requires_grad=True)
+        ref = fn(x)
+
+        opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
+        res = opt_fn(x)
+        self.assertEqual(ref[0], res[0])
+        self.assertEqual(ref[1], res[1])
+
+    @torch._dynamo.config.patch(skip_fwd_side_effects_in_bwd_under_checkpoint=True)
+    def test_nonlocal_list_mutation_hidden(self):
+        def gn(x, z):
+            o = torch.matmul(x, x) @ x
+            out = x.sin()
+            z.append(out)
+            return torch.cos(torch.sin(o)), torch.sin(x)
+
+        def fn(x):
+            z = []
+
+            outs = torch.utils.checkpoint.checkpoint(
+                gn,
+                x,
+                z,
+                use_reentrant=False,
+            )
+            out1 = outs[0]
+            # Check that the extra output pytree handling is done properly
+            out2 = outs[-1]
+
+            return out1 + out2, z[0]
+
+        x = torch.randn(4, 4, requires_grad=True)
+        ref = fn(x)
+
+        backend = AotEagerAndRecordGraphs()
+        opt_fn = torch.compile(fn, backend=backend, fullgraph=True)
+        res = opt_fn(x)
+        self.assertEqual(ref[0], res[0])
+        self.assertEqual(ref[1], res[1])
+
+        self.assertExpectedInline(
+            normalize_gm(backend.graphs[0].print_readable(print_output=False)),
+            """\
+class GraphModule(torch.nn.Module):
+    def forward(self, L_x_: "f32[4, 4]"):
+        l_x_ = L_x_
+
+        wrap_body_0 = self.wrap_body_0
+        tag_activation_checkpoint = torch.ops.higher_order.tag_activation_checkpoint(wrap_body_0, l_x_, use_reentrant = False);  wrap_body_0 = l_x_ = None
+        out1: "f32[4, 4]" = tag_activation_checkpoint[0]
+        out2: "f32[4, 4]" = tag_activation_checkpoint[1]
+        getitem_4: "f32[4, 4]" = tag_activation_checkpoint[4];  tag_activation_checkpoint = None
+
+        add: "f32[4, 4]" = out1 + out2;  out1 = out2 = None
+        return (add, getitem_4)
+
+    class wrap_body_0(torch.nn.Module):
+        def forward(self, l_x_: "f32[4, 4]"):
+            matmul: "f32[4, 4]" = torch.matmul(l_x_, l_x_)
+            o: "f32[4, 4]" = matmul @ l_x_
+
+            out: "f32[4, 4]" = l_x_.sin()
+
+            sin_1: "f32[4, 4]" = torch.sin(o)
+            child: "f32[4, 4]" = torch.cos(sin_1)
+            child_1: "f32[4, 4]" = torch.sin(l_x_);  l_x_ = None
+            return (child, child_1, matmul, o, out, sin_1)
+""",
+        )
+
+        self.assertExpectedInline(
+            normalize_gm(backend.fw_graphs[0].print_readable(print_output=False)),
+            """\
+class GraphModule(torch.nn.Module):
+    def forward(self, primals_1: "f32[4, 4]"):
+        mm: "f32[4, 4]" = torch.ops.aten.mm.default(primals_1, primals_1)
+        mm_1: "f32[4, 4]" = torch.ops.aten.mm.default(mm, primals_1);  mm = None
+
+        sin: "f32[4, 4]" = torch.ops.aten.sin.default(primals_1)
+
+        sin_1: "f32[4, 4]" = torch.ops.aten.sin.default(mm_1);  mm_1 = None
+        cos: "f32[4, 4]" = torch.ops.aten.cos.default(sin_1);  sin_1 = None
+        sin_2: "f32[4, 4]" = torch.ops.aten.sin.default(primals_1)
+
+        add: "f32[4, 4]" = torch.ops.aten.add.Tensor(cos, sin_2);  cos = sin_2 = None
+        return (add, sin, primals_1)
+""",
+        )
+
 
 devices = ["cuda", "hpu"]
 instantiate_device_type_tests(

test/dynamo/test_ctx_manager.py

@@ -408,6 +408,9 @@ class CtxManagerTests(torch._dynamo.test_case.TestCaseWithNestedGraphBreaks):
         self.assertEqual(ref0, res0)
 
     @unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
+    @unittest.skip(
+        "Will not support external events for now: https://github.com/pytorch/pytorch/issues/167257"
+    )
     def test_cuda_event_reconstruct(self):
         def fn(x):
             e = torch.cuda.Event()
@@ -425,6 +428,9 @@ class CtxManagerTests(torch._dynamo.test_case.TestCaseWithNestedGraphBreaks):
         self.assertEqual(cnts.op_count, 3)
 
     @unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
+    @unittest.skip(
+        "Will not support external events for now: https://github.com/pytorch/pytorch/issues/167257"
+    )
     def test_cuda_event_across_graph_break(self):
         def fn(x):
             e = torch.cuda.Event()
@@ -446,9 +452,12 @@ class CtxManagerTests(torch._dynamo.test_case.TestCaseWithNestedGraphBreaks):
         res = opt_fn(x)
         self.assertEqual(ref[0], res[0])
         self.assertEqual(cnts.frame_count, 2)
-        self.assertEqual(cnts.op_count, 9)
+        self.assertEqual(cnts.op_count, 10)
 
     @unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
+    @unittest.skip(
+        "Will not support external events for now: https://github.com/pytorch/pytorch/issues/167257"
+    )
     def test_cuda_event_created_outside_of_graph(self):
         user_stream = torch.cuda.Stream()
         event = torch.cuda.Event()
@@ -478,9 +487,12 @@ class CtxManagerTests(torch._dynamo.test_case.TestCaseWithNestedGraphBreaks):
         res = run_iters(func, compile=True)
         self.assertEqual(ref, res)
         self.assertEqual(cnts.frame_count, 1)
-        self.assertEqual(cnts.op_count, 3)
+        self.assertEqual(cnts.op_count, 4)
 
     @unittest.skipIf(not torch.cuda.is_available(), "requires cuda")
+    @unittest.skip(
+        "Will not support external events for now: https://github.com/pytorch/pytorch/issues/167257"
+    )
     def test_cuda_event_method_create_stream_outside_of_compile(self):
         def fn(x, cur_stream, new_stream):
             x = torch.mul(x, 1)