prints for static inputs indices

.ci/docker/almalinux/Dockerfile

@@ -7,13 +7,13 @@ ENV LC_ALL en_US.UTF-8
 ENV LANG en_US.UTF-8
 ENV LANGUAGE en_US.UTF-8
 
-ARG DEVTOOLSET_VERSION=13
+ARG DEVTOOLSET_VERSION=11
 
 RUN yum -y update
 RUN yum -y install epel-release
 # install glibc-langpack-en make sure en_US.UTF-8 locale is available
 RUN yum -y install glibc-langpack-en
-RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
+RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
 # Just add everything as a safe.directory for git since these will be used in multiple places with git
 RUN git config --global --add safe.directory '*'
 ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
@@ -41,7 +41,6 @@ RUN bash ./install_conda.sh && rm install_conda.sh
 # Install CUDA
 FROM base as cuda
 ARG CUDA_VERSION=12.6
-ARG DEVTOOLSET_VERSION=13
 RUN rm -rf /usr/local/cuda-*
 ADD ./common/install_cuda.sh install_cuda.sh
 COPY ./common/install_nccl.sh install_nccl.sh
@@ -51,8 +50,7 @@ ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
 # Preserve CUDA_VERSION for the builds
 ENV CUDA_VERSION=${CUDA_VERSION}
 # Make things in our path by default
-ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
-
+ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
 
 FROM cuda as cuda12.6
 RUN bash ./install_cuda.sh 12.6
@@ -70,22 +68,8 @@ FROM cuda as cuda13.0
 RUN bash ./install_cuda.sh 13.0
 ENV DESIRED_CUDA=13.0
 
-FROM ${ROCM_IMAGE} as rocm_base
-ARG DEVTOOLSET_VERSION=13
-ENV LC_ALL en_US.UTF-8
-ENV LANG en_US.UTF-8
-ENV LANGUAGE en_US.UTF-8
-# Install devtoolset on ROCm base image
-RUN yum -y update && \
-    yum -y install epel-release && \
-    yum -y install glibc-langpack-en && \
-    yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
-RUN git config --global --add safe.directory '*'
-ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
-
-FROM rocm_base as rocm
+FROM ${ROCM_IMAGE} as rocm
 ARG PYTORCH_ROCM_ARCH
-ARG DEVTOOLSET_VERSION=13
 ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
 ADD ./common/install_mkl.sh install_mkl.sh
 RUN bash ./install_mkl.sh && rm install_mkl.sh
@@ -104,7 +88,6 @@ COPY --from=cuda13.0  /usr/local/cuda-13.0 /usr/local/cuda-13.0
 
 # Final step
 FROM ${BASE_TARGET} as final
-ARG DEVTOOLSET_VERSION=13
 COPY --from=openssl            /opt/openssl           /opt/openssl
 COPY --from=patchelf           /patchelf              /usr/local/bin/patchelf
 COPY --from=conda              /opt/conda             /opt/conda

.ci/docker/almalinux/build.sh

@@ -36,7 +36,11 @@ case ${DOCKER_TAG_PREFIX} in
     ;;
   rocm*)
     BASE_TARGET=rocm
-    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
+    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+    # add gfx950, gfx115x conditionally starting in ROCm 7.0
+    if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
+        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+    fi
     EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;
   *)
@@ -59,7 +63,7 @@ docker build \
   --target final \
   --progress plain \
   --build-arg "BASE_TARGET=${BASE_TARGET}" \
-  --build-arg "DEVTOOLSET_VERSION=13" \
+  --build-arg "DEVTOOLSET_VERSION=11" \
   ${EXTRA_BUILD_ARGS} \
   -t ${tmp_tag} \
   $@ \

.ci/docker/build.sh

@@ -168,18 +168,6 @@ 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 [[ $tag =~ "jammy" ]]; then
       ANACONDA_PYTHON_VERSION=3.10
@@ -207,9 +195,9 @@ case "$tag" in
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-noble-xpu-n-py3 | pytorch-linux-noble-xpu-n-py3-inductor-benchmarks)
+  pytorch-linux-jammy-xpu-n-py3 | pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=13
+    GCC_VERSION=11
     VISION=yes
     XPU_VERSION=2025.2
     NINJA_VERSION=1.9.0
@@ -260,12 +248,6 @@ case "$tag" in
     HALIDE=yes
     TRITON=yes
     ;;
-  pytorch-linux-jammy-cuda12.8-py3.12-pallas)
-    CUDA_VERSION=12.8.1
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=11
-    PALLAS=yes
-    ;;
   pytorch-linux-jammy-py3.12-triton-cpu)
     CUDA_VERSION=12.6
     ANACONDA_PYTHON_VERSION=3.12
@@ -279,9 +261,9 @@ case "$tag" in
     PYTHON_VERSION=3.10
     CUDA_VERSION=12.8.1
     ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc13)
+  pytorch-linux-jammy-aarch64-py3.10-gcc11)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=13
+    GCC_VERSION=11
     ACL=yes
     VISION=yes
     OPENBLAS=yes
@@ -289,19 +271,9 @@ case "$tag" in
     # from pytorch/llvm:9.0.1 is x86 specific
     SKIP_LLVM_SRC_BUILD_INSTALL=yes
     ;;
-  pytorch-linux-jammy-aarch64-py3.10-clang21)
+  pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=21
-    ACL=yes
-    VISION=yes
-    OPENBLAS=yes
-    # snadampal: skipping llvm src build install because the current version
-    # from pytorch/llvm:9.0.1 is x86 specific
-    SKIP_LLVM_SRC_BUILD_INSTALL=yes
-    ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=13
+    GCC_VERSION=11
     ACL=yes
     VISION=yes
     OPENBLAS=yes
@@ -387,7 +359,6 @@ docker build \
        --build-arg "INDUCTOR_BENCHMARKS=${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

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

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +1 @@
-bfeb066872bc1e8b2d2bc0a3b295b99dd77206e7
+7416ffcb92cdbe98d9f97e4e6f95247e46dfc9fd

.ci/docker/common/install_clang.sh

@@ -8,8 +8,8 @@ if [ -n "$CLANG_VERSION" ]; then
     # work around ubuntu apt-get conflicts
     sudo apt-get -y -f install
     wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
-    if [[ $CLANG_VERSION -ge 18 ]]; then
-      apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-${CLANG_VERSION} main"
+    if [[ $CLANG_VERSION == 18 ]]; then
+      apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
     fi
   fi
 

.ci/docker/common/install_gcc.sh

@@ -7,11 +7,11 @@ if [ -n "$GCC_VERSION" ]; then
   # Need the official toolchain repo to get alternate packages
   add-apt-repository ppa:ubuntu-toolchain-r/test
   apt-get update
-  apt-get install -y g++-$GCC_VERSION gfortran-$GCC_VERSION
+  apt-get install -y g++-$GCC_VERSION
   update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
   update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
   update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-"$GCC_VERSION" 50
+
 
   # Cleanup package manager
   apt-get autoclean && apt-get clean

.ci/docker/common/install_jax.sh

@@ -1,40 +0,0 @@
-#!/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_libgomp.sh

@@ -1,56 +0,0 @@
-#!/bin/bash
-# Script used only in CD pipeline
-
-set -ex
-
-# install dependencies
-dnf -y install gmp-devel libmpc-devel texinfo flex bison
-
-cd /usr/local/src
-# fetch source for gcc 13
-git clone --depth 1 --single-branch -b releases/gcc-13.3.0 https://github.com/gcc-mirror/gcc.git gcc-13.3.0
-
-mkdir -p gcc-13.3.0/build-gomp
-cd gcc-13.3.0/build-gomp
-
-# configure gcc build
-# I got these flags by:
-# 1. downloading the source rpm for gcc-11 on AlmaLinux 8 container
-#    dnf install -y dnf-plugins-core rpmdevtools
-#   dnf download --source libgomp
-# 2. extracting the gcc.spec from the source.
-#    rpmdev-extract gcc-xx.src.rpm
-# 3. extracting optflags and ld_flags from gcc.spec:
-#    rpm --eval '%{optflags}'
-#    rpm --eval '%{build_ldflags}'
-#
-# I had to remove the following flags because they didn't compile for this version of libgomp:
-#   -Werror=format-security
-#   -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1
-#   -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1
-#
-# I added -march=armv8-a -mtune=generic to make them explicit. I don't think they're strictly needed.
-
-OPT_FLAGS='-O2 -march=armv8-a -mtune=generic'\
-' -fexceptions -g -grecord-gcc-switches -pipe -Wall'\
-' -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS'\
-' -fstack-protector-strong -fasynchronous-unwind-tables'\
-' -fstack-clash-protection'
-
-LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,now'
-
-CFLAGS="$OPT_FLAGS" \
-CXXFLAGS="$OPT_FLAGS" \
-LDFLAGS="$LDFLAGS" \
-../configure \
-  --prefix=/usr \
-  --libdir=/usr/lib64 \
-  --enable-languages=c,c++ \
-  --disable-multilib \
-  --disable-bootstrap \
-  --enable-libgomp
-
-# only build libgomp
-make -j$(nproc) all-target-libgomp
-
-make install-target-libgomp

.ci/docker/common/install_openblas.sh

@@ -10,7 +10,6 @@ git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION}" -
 
 OPENBLAS_CHECKOUT_DIR="OpenBLAS"
 OPENBLAS_BUILD_FLAGS="
-CC=gcc
 NUM_THREADS=128
 USE_OPENMP=1
 NO_SHARED=0

.ci/docker/common/install_xpu.sh

@@ -9,7 +9,7 @@ set -xe
 
 function install_ubuntu() {
     . /etc/os-release
-    if [[ ! " jammy noble " =~ " ${VERSION_CODENAME} " ]]; then
+    if [[ ! " jammy " =~ " ${VERSION_CODENAME} " ]]; then
         echo "Ubuntu version ${VERSION_CODENAME} not supported"
         exit
     fi
@@ -35,24 +35,25 @@ function install_ubuntu() {
     # The xpu-smi packages
     apt-get install -y flex bison xpu-smi
 
-    # Compute and Media Runtimes
-    if [[ " ${VERSION_CODENAME} " =~ " noble " ]]; then
+    if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
+        # Compute and Media Runtimes
         apt-get install -y \
-            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 \
+            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 \
             libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-            mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
-    else # jammy
+            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
         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 ${XPU_PACKAGES}
@@ -65,7 +66,7 @@ function install_ubuntu() {
 function install_rhel() {
     . /etc/os-release
     if [[ "${ID}" == "rhel" ]]; then
-        if [[ ! " 8.8 8.10 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
+        if [[ ! " 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
             echo "RHEL version ${VERSION_ID} not supported"
             exit
         fi
@@ -146,7 +147,7 @@ function install_sles() {
 XPU_DRIVER_VERSION=""
 if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
     # Use GPU driver LTS releases
-    XPU_DRIVER_VERSION="/lts/2523"
+    XPU_DRIVER_VERSION="/lts/2350"
 fi
 
 # Default use Intel® oneAPI Deep Learning Essentials 2025.1

.ci/docker/libtorch/build.sh

@@ -49,7 +49,11 @@ 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;gfx950;gfx1150;gfx1151"
+        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
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;
     *)

.ci/docker/manywheel/Dockerfile_2_28_aarch64

@@ -50,10 +50,6 @@ RUN rm install_ninja.sh
 ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
 ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
 
-# Build a newer version of libgomp than that supported in in Almalinux 8.
-COPY ./common/install_libgomp.sh install_libgomp.sh
-RUN bash ./install_libgomp.sh && rm install_libgomp.sh
-
 # git236+ would refuse to run git commands in repos owned by other users
 # Which causes version check to fail, as pytorch repo is bind-mounted into the image
 # Override this behaviour by treating every folder as safe

.ci/docker/manywheel/build.sh

@@ -87,7 +87,11 @@ 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;gfx950;gfx1150;gfx1151"
+        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
         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/requirements-docs.txt

@@ -1,11 +1,15 @@
-sphinx==7.2.6
+sphinx==5.3.0
 #Description: This is used to generate PyTorch docs
-#Pinned versions: 7.2.6
+#Pinned versions: 5.3.0
 
-pytorch_sphinx_theme2==0.2.0
-#Description: This is needed to generate PyTorch docs
-#Pinned versions: 0.2.0
+standard-imghdr==3.13.0; python_version >= "3.13"
+#Description: This is needed by Sphinx, so it needs to be added here.
+# The reasons are as follows:
+# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
+# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
+# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
 
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.
@@ -32,17 +36,17 @@ tensorboard==2.18.0 ; python_version >= "3.13"
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 2.13.0
 
-breathe==4.36.0
+breathe==4.34.0
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.36.0
+#Pinned versions: 4.34.0
 
-exhale==0.3.7
+exhale==0.2.3
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.3.7
+#Pinned versions: 0.2.3
 
-docutils==0.20
+docutils==0.16
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.20
+#Pinned versions: 0.16
 
 bs4==0.0.1
 #Description: This is used to generate PyTorch C++ docs
@@ -52,13 +56,13 @@ IPython==8.12.0
 #Description: This is used to generate PyTorch functorch docs
 #Pinned versions: 8.12.0
 
-myst-nb==1.3.0
+myst-nb==0.17.2
 #Description: This is used to generate PyTorch functorch and torch.compile docs.
-#Pinned versions: 1.3.0
+#Pinned versions: 0.17.2
 
 # The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
 python-etcd==0.4.5
 sphinx-copybutton==0.5.0
-sphinx-design==0.6.1
+sphinx-design==0.4.0
 sphinxcontrib-mermaid==1.0.0
-myst-parser==4.0.1
+myst-parser==0.18.1

.ci/docker/triton_version.txt

@@ -1 +1 @@
-3.5.1
+3.5.0

.ci/docker/ubuntu/Dockerfile

@@ -143,15 +143,6 @@ 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

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

.ci/magma-rocm/README.md

@@ -30,6 +30,7 @@ into a tarball, with the following structure:
 More specifically, `build_magma.sh` copies over the relevant files from the `package_files` directory depending on the ROCm version.
 Outputted binaries should be in the `output` folder.
 
+
 ## Pushing
 
 Packages can be uploaded to an S3 bucket using:

.ci/magma-rocm/build_magma.sh

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

.ci/pytorch/build.sh

@@ -168,16 +168,14 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/compiler/latest/env/vars.sh
   # shellcheck disable=SC1091
-  source /opt/intel/oneapi/umf/latest/env/vars.sh
-  # shellcheck disable=SC1091
   source /opt/intel/oneapi/ccl/latest/env/vars.sh
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/pti/latest/env/vars.sh
   # Enable XCCL build
   export USE_XCCL=1
   export USE_MPI=0
+  # XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
+  export USE_KINETO=0
   export TORCH_XPU_ARCH_LIST=pvc
 fi
 

.ci/pytorch/common_utils.sh

@@ -96,6 +96,7 @@ function pip_build_and_install() {
     python3 -m pip wheel \
       --no-build-isolation \
       --no-deps \
+      --no-use-pep517 \
       -w "${wheel_dir}" \
       "${build_target}"
   fi
@@ -307,28 +308,6 @@ function install_torchao() {
   pip_build_and_install "git+https://github.com/pytorch/ao.git@${commit}" dist/ao
 }
 
-function install_flash_attn_cute() {
-  echo "Installing FlashAttention CuTe from GitHub..."
-  # Grab latest main til we have a pinned commit
-  local flash_attn_commit
-  flash_attn_commit=$(git ls-remote https://github.com/Dao-AILab/flash-attention.git HEAD | cut -f1)
-
-  # Clone the repo to a temporary directory
-  rm -rf flash-attention-build
-  git clone --depth 1 --recursive https://github.com/Dao-AILab/flash-attention.git flash-attention-build
-
-  pushd flash-attention-build
-  git checkout "${flash_attn_commit}"
-
-  # Install only the 'cute' sub-directory
-  pip_install -e flash_attn/cute/
-  popd
-
-  # remove the local repo
-  rm -rf flash-attention-build
-  echo "FlashAttention CuTe installation complete."
-}
-
 function print_sccache_stats() {
   echo 'PyTorch Build Statistics'
   sccache --show-stats

.ci/pytorch/python_doc_push_script.sh

@@ -89,41 +89,23 @@ if [ "$is_main_doc" = true ]; then
 
   make coverage
   # Now we have the coverage report, we need to make sure it is empty.
-  # Sphinx 7.2.6+ format: python.txt contains a statistics table with a TOTAL row
-  # showing the undocumented count in the third column.
-  # Example: | TOTAL | 99.83% | 2 |
+  # Count the number of lines in the file and turn that number into a variable
+  # $lines. The `cut -f1 ...` is to only parse the number, not the filename
+  # Skip the report header by subtracting 2: the header will be output even if
+  # there are no undocumented items.
   #
   # Also: see docs/source/conf.py for "coverage_ignore*" items, which should
   # be documented then removed from there.
-
-  # Extract undocumented count from TOTAL row in Sphinx 7.2.6 statistics table
-  # The table format is: | Module | Coverage | Undocumented |
-  # Extract the third column (undocumented count) from the TOTAL row
-  undocumented=$(grep "| TOTAL" build/coverage/python.txt | awk -F'|' '{print $4}' | tr -d ' ')
-
-  if [ -z "$undocumented" ] || ! [[ "$undocumented" =~ ^[0-9]+$ ]]; then
+  lines=$(wc -l build/coverage/python.txt 2>/dev/null |cut -f1 -d' ')
+  undocumented=$((lines - 2))
+  if [ $undocumented -lt 0 ]; then
     echo coverage output not found
     exit 1
-  elif [ "$undocumented" -gt 0 ]; then
-    set +x  # Disable command echoing for cleaner output
-    echo ""
-    echo "====================="
-    echo "UNDOCUMENTED OBJECTS:"
-    echo "====================="
-    echo ""
-    # Find the line number of the TOTAL row and print only what comes after it
-    total_line=$(grep -n "| TOTAL" build/coverage/python.txt | cut -d: -f1)
-    if [ -n "$total_line" ]; then
-      # Print only the detailed list (skip the statistics table)
-      tail -n +$((total_line + 2)) build/coverage/python.txt
-    else
-      # Fallback to showing entire file if TOTAL line not found
-      cat build/coverage/python.txt
-    fi
-    echo ""
+  elif [ $undocumented -gt 0 ]; then
+    echo undocumented objects found:
+    cat build/coverage/python.txt
     echo "Make sure you've updated relevant .rsts in docs/source!"
-    echo "You can reproduce locally by running 'cd docs && make coverage && tail -n +\$((grep -n \"| TOTAL\" build/coverage/python.txt | cut -d: -f1) + 2)) build/coverage/python.txt'"
-    set -x  # Re-enable command echoing
+    echo "You can reproduce locally by running 'cd docs && make coverage && cat build/coverage/python.txt'"
     exit 1
   fi
 else

.ci/pytorch/test.sh

@@ -208,8 +208,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   source /opt/intel/oneapi/ccl/latest/env/vars.sh
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/pti/latest/env/vars.sh
   # Check XPU status before testing
   timeout 30 xpu-smi discovery || true
 fi
@@ -344,18 +342,8 @@ test_python_smoke() {
 }
 
 test_python_smoke_b200() {
-  # Targeted smoke tests for B200 including FlashAttention CuTe coverage
-  install_flash_attn_cute
-  time python test/run_test.py \
-    --include \
-      test_matmul_cuda \
-      test_scaled_matmul_cuda \
-      inductor/test_fp8 \
-      nn/attention/test_fa4 \
-      nn/attention/test_open_registry \
-      inductor/test_flex_flash \
-    $PYTHON_TEST_EXTRA_OPTION \
-    --upload-artifacts-while-running
+  # Targeted smoke tests for B200 - staged approach to avoid too many failures
+  time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
   assert_git_not_dirty
 }
 
@@ -836,11 +824,6 @@ 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
@@ -1741,8 +1724,6 @@ 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

.ci/pytorch/win-test-helpers/arm64/build_pytorch.ps1

@@ -70,7 +70,7 @@ sccache --zero-stats
 sccache --show-stats
 
 # Build the wheel
-python -m build --wheel --no-isolation
+python -m build --wheel --no-build-isolation
 if ($LASTEXITCODE -ne 0) { exit 1 }
 
 # Install the wheel locally

.github/ISSUE_TEMPLATE/release-feature-request.yml

@@ -1,11 +1,11 @@
-name: 🚀 New Feature for Release
+name: 🚀 Release highlight for proposed Feature
 description: Submit a Release highlight for proposed Feature
 labels: ["release-feature-request"]
 
 body:
 - type: textarea
   attributes:
-    label: New Feature for Release
+    label: Release highlight for proposed Feature
     description: >
       Example: “A torch.special module, analogous to SciPy's special module.”
 - type: input

.github/actionlint.yaml

@@ -63,7 +63,7 @@ self-hosted-runner:
     - linux.rocm.gpu.gfx942.1
     - linux.rocm.gpu.gfx942.2
     - linux.rocm.gpu.gfx942.4
-    - linux.rocm.gfx942.docker-cache
+    - rocm-docker
     # Org wise AWS `mac2.metal` runners (2020 Mac mini hardware powered by Apple silicon M1 processors)
     - macos-m1-stable
     - macos-m1-14

.github/actions/pytest-cache-download/action.yml

@@ -38,9 +38,9 @@ runs:
       run: |
         python3 .github/scripts/pytest_cache.py \
           --download \
-          --cache_dir "$GITHUB_WORKSPACE/$CACHE_DIR" \
-          --pr_identifier "$GITHUB_REF" \
-          --job_identifier "$JOB_IDENTIFIER" \
-          --temp_dir "$RUNNER_TEMP" \
-          --repo "$REPO" \
-          --bucket "$BUCKET" \
+          --cache_dir $GITHUB_WORKSPACE/$CACHE_DIR \
+          --pr_identifier $GITHUB_REF \
+          --job_identifier $JOB_IDENTIFIER \
+          --temp_dir $RUNNER_TEMP \
+          --repo $REPO \
+          --bucket $BUCKET \

.github/actions/pytest-cache-upload/action.yml

@@ -47,11 +47,11 @@ runs:
       run: |
         python3 .github/scripts/pytest_cache.py \
           --upload \
-          --cache_dir "$GITHUB_WORKSPACE/$CACHE_DIR" \
-          --pr_identifier "$GITHUB_REF" \
-          --job_identifier "$JOB_IDENTIFIER" \
-          --sha "$SHA" \
-          --test_config "$TEST_CONFIG" \
-          --shard "$SHARD" \
-          --repo "$REPO" \
-          --temp_dir "$RUNNER_TEMP" \
+          --cache_dir $GITHUB_WORKSPACE/$CACHE_DIR \
+          --pr_identifier $GITHUB_REF \
+          --job_identifier $JOB_IDENTIFIER \
+          --sha $SHA \
+          --test_config $TEST_CONFIG \
+          --shard $SHARD \
+          --repo $REPO \
+          --temp_dir $RUNNER_TEMP \

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-07b6cbde121417a70e4dc871adb6d27030e0ce3f
+3b0e7a6f192ca2715e7e6cbe5db007aea7165fe2

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-acccf86477759b2d3500f1ae1be065f7b1e409ec
+cfbc5c2f1c798991715a6b06bb3ce46478c4487c

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-e4d25697f9dc5eedaf8f0a5bf085c62c5455a53a
+c8b09f5f77d6bf6fb7ed7a9aa83e5d8156b3a5e9

.github/copilot-instructions.md

@@ -1,125 +0,0 @@
-# PyTorch Copilot Instructions
-
-This is the PyTorch machine learning framework codebase. These instructions help AI agents navigate and contribute effectively.
-
-## Architecture Overview
-
-### Core Components
-
-- **c10/** - Core library (C++-10 compatible) for essential, binary-size-conscious functionality
-- **aten/** - ATen tensor library (C++), PyTorch's foundation without autograd
-  - `aten/src/ATen/native/` - Modern operator implementations (CPU/CUDA/MPS/sparse)
-  - `aten/src/ATen/native/native_functions.yaml` - **Critical**: Declarative operator registry
-- **torch/** - Python bindings and public API
-  - `torch/csrc/` - C++ Python bindings (hand-written and generated)
-  - `torch/csrc/autograd/` - Reverse-mode automatic differentiation
-  - `torch/csrc/jit/` - TorchScript JIT compiler
-- **torchgen/** - Code generation tooling that reads `native_functions.yaml`
-- **tools/** - Build scripts, autograd derivatives, code generation
-
-### The Code Generation Workflow
-
-**Most operator changes require editing `native_functions.yaml`**, not direct C++ files. This YAML file:
-1. Declares operator signatures, variants (function/method), and dispatch behavior
-2. Gets processed by `torchgen/` to generate C++/Python bindings
-3. Produces headers in `build/aten/src/ATen/` during compilation
-
-Example entry structure:
-```yaml
-- func: my_op(Tensor self, Scalar alpha=1) -> Tensor
-  variants: function, method
-  dispatch:
-    CPU: my_op_cpu
-    CUDA: my_op_cuda
-```
-
-After editing `native_functions.yaml`, implement kernels in `aten/src/ATen/native/` (see `aten/src/ATen/native/README.md`).
-
-## Development Workflows
-
-### Building from Source
-
-**Never run `setup.py` directly** - use pip with editable install:
-```bash
-python -m pip install --no-build-isolation -v -e .
-```
-
-Speed up builds:
-- `DEBUG=1` - Debug symbols with `-g -O0`
-- `USE_CUDA=0` - Skip CUDA compilation
-- `BUILD_TEST=0` - Skip C++ test binaries
-- Install `ninja` (`pip install ninja`) for faster builds
-- Use `ccache` for incremental compilation caching
-
-Rebuild specific targets: `(cd build && ninja <target>)`
-
-### Testing
-
-**Critical**: DO NOT run entire test suites. Run specific tests only:
-```bash
-python test/test_torch.py TestTorch.test_specific_case
-```
-
-**Test structure**: All tests use `torch.testing._internal.common_utils`:
-```python
-from torch.testing._internal.common_utils import run_tests, TestCase
-
-class TestFeature(TestCase):
-    def test_something(self):
-        # Use self.assertEqual for tensor comparisons
-        pass
-
-if __name__ == "__main__":
-    run_tests()
-```
-
-**For bug fixes**: Create a standalone reproduction script first, verify it fails, then fix and add to appropriate test file.
-
-### Linting
-
-Run linter (not pre-commit): `lintrunner -a` (auto-applies fixes)
-
-## Project-Specific Conventions
-
-### Memory and Storage
-- **Storage is never nullptr** (but `StorageImpl.data` may be nullptr for unallocated outputs)
-- CUDA device info lives in storage objects
-
-### Python-C++ Integration (`torch/csrc/`)
-- Always include `Python.h` **first** to avoid `_XOPEN_SOURCE` redefinition errors
-- Use `pybind11::gil_scoped_acquire` before calling Python API or using `THPObjectPtr`
-- Wrap entry points with `HANDLE_TH_ERRORS` / `END_HANDLE_TH_ERRORS` for exception conversion
-
-### Dispatch System
-- PyTorch uses operator dispatch to route calls to backend-specific kernels
-- Prefer `CompositeExplicitAutograd` dispatch when writing device-agnostic compound ops
-- See `aten/src/ATen/native/README.md` for dispatch keyword guidance
-
-## Git Workflow (AI Agent Specific)
-
-When preparing PRs from this environment:
-```bash
-git stash -u
-git reset --hard $(cat /tmp/orig_work.txt)  # Reset to LOCAL branch
-git stash pop
-# Resolve conflicts if necessary
-```
-
-## Common Gotchas
-
-1. **Editing generated files** - If it's in `build/`, don't edit it. Edit the source template or `native_functions.yaml`
-2. **NVCC template compilation** - NVCC is stricter about C++ than gcc/clang; code working on Linux may fail Windows CI
-3. **Windows symbol visibility** - Use `TORCH_API` macros for exported symbols (required on Windows, optional on Linux)
-4. **No internet access** - DO NOT attempt to install dependencies during development
-
-## Key Files Reference
-
-- `AGENTS.md` - Instructions specific to AI coding agents
-- `CONTRIBUTING.md` - Comprehensive human contributor guide
-- `GLOSSARY.md` - Terminology (ATen, kernels, operations, JIT, TorchScript)
-- `aten/src/ATen/native/README.md` - Operator implementation guide
-- `tools/autograd/derivatives.yaml` - Gradient definitions for autograd
-
-## Performance Debugging
-
-Use `TORCH_SHOW_CPP_STACKTRACES=1` for C++ traces in Python errors. For profiling, prefer `py-spy` over manual instrumentation.

.github/labeler.yml

@@ -138,8 +138,7 @@
 - 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/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -149,8 +148,7 @@
 - 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/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -160,21 +158,7 @@
 - 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/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
 - third_party/fbgemm
-
-"ciflow/mps":
-- aten/src/ATen/mps/**
-- aten/src/ATen/native/mps/**
-- torch/_inductor/codegen/mps.py
-- test/test_mps.py
-- test/inductor/test_mps_basic.py
-
-"ciflow/h100-symm-mem":
-- torch/csrc/distributed/c10d/symm_mem/**
-- torch/distributed/_symmetric_memory/**
-- test/distributed/**/*mem*
-- test/distributed/**/*mem*/**

.github/nitpicks.yml

@@ -10,4 +10,3 @@
   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-distributed
 - ciflow/b200-symm-mem
+- ciflow/b200-distributed
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
@@ -22,8 +22,6 @@ 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
@@ -35,13 +33,11 @@ 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

@@ -1,11 +1,10 @@
 # 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
+from typing import Any, Callable
 
 from github_utils import gh_fetch_json_dict, gh_graphql
 from gitutils import GitRepo

.github/scripts/filter_test_configs.py

@@ -8,11 +8,10 @@ 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, Optional
+from typing import Any, Callable, Optional
 from urllib.request import Request, urlopen
 
 import yaml

.github/scripts/get_workflow_job_id.py

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

.github/scripts/github_utils.py

@@ -3,9 +3,8 @@
 import json
 import os
 import warnings
-from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Any, cast, Optional, Union
+from typing import Any, Callable, 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 Callable, Iterator
+from collections.abc import Iterator
 from datetime import datetime
 from functools import wraps
-from typing import Any, cast, Optional, TypeVar, Union
+from typing import Any, Callable, cast, Optional, TypeVar, Union
 
 
 T = TypeVar("T")

.github/scripts/lintrunner.sh

@@ -34,9 +34,6 @@ python3 torch/utils/data/datapipes/gen_pyi.py
 # Also check generated pyi files
 find torch -name '*.pyi' -exec git add --force -- "{}" +
 
-# Print current environment
-python3 -m pip freeze
-
 RC=0
 # Run lintrunner on all files
 if ! lintrunner --force-color --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS} 2> /dev/null; then

.github/scripts/trymerge.py

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

.github/workflows/_rocm-test.yml

@@ -97,8 +97,8 @@ jobs:
         shell: bash
         run: |
           ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx')
-          if [[ $ngpu -lt 2 ]]; then #We are temporarily reducing this down to 2 from 4 so that we can run tests on nodes with less gpus.
-            echo "Error: only $ngpu GPU(s) detected, at least 2 GPUs are needed for distributed jobs"
+          if [[ $ngpu -lt 4 ]]; then
+            echo "Error: only $ngpu GPU(s) detected, at least 4 GPUs are needed for distributed jobs"
             exit 1
           fi
 

.github/workflows/_xpu-test.yml

@@ -344,21 +344,5 @@ jobs:
           if-no-files-found: ignore
           path: ./**/core.[1-9]*
 
-      - name: Authenticate with AWS
-        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
-        with:
-          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_upload-benchmark-results
-          # The max duration enforced by the server side
-          role-duration-seconds: 18000
-          aws-region: us-east-1
-
-      - name: Upload the benchmark results
-        uses: pytorch/test-infra/.github/actions/upload-benchmark-results@main
-        with:
-          benchmark-results-dir: test/test-reports
-          dry-run: false
-          schema-version: v3
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
       - name: Teardown XPU
         uses: ./.github/actions/teardown-xpu

.github/workflows/docker-builds.yml

@@ -56,8 +56,6 @@ 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,
@@ -67,10 +65,9 @@ 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-noble-xpu-n-py3,
-          pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
+          pytorch-linux-jammy-xpu-n-py3,
+          pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,
@@ -80,11 +77,9 @@ jobs:
           pytorch-linux-noble-riscv64-py3.12-gcc14
         ]
         include:
-          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13
+          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11
             runner: linux.arm64.m7g.4xlarge
-          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-clang21
-            runner: linux.arm64.m7g.4xlarge
-          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks
+          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks
             runner: linux.arm64.m7g.4xlarge
             timeout-minutes: 600
     # Docker uploads fail from LF runners, see https://github.com/pytorch/pytorch/pull/137358
@@ -119,22 +114,6 @@ jobs:
         with:
           docker-image: ${{ steps.build-docker-image.outputs.docker-image }}
 
-      - name: Generate output
-        if: contains(matrix.docker-image-name, 'rocm')
-        id: generate_output
-        run: |
-          docker_image_name="${{ matrix.docker-image-name }}"
-          docker_image_tag="${{ steps.build-docker-image.outputs.docker-image }}"
-          echo "${docker_image_name}=${docker_image_tag}" >> docker-builds-output-${docker_image_name}.txt
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4.4.0
-        if: contains(matrix.docker-image-name, 'rocm')
-        with:
-          name: docker-builds-artifacts-${{ matrix.docker-image-name }}
-          retention-days: 14
-          path: ./docker-builds-output-${{ matrix.docker-image-name }}.txt
-
       - uses: nick-fields/retry@7152eba30c6575329ac0576536151aca5a72780e # v3.0.0
         name: Push to https://ghcr.io/
         id: push-to-ghcr-io

.github/workflows/docker-cache-mi300.yml

@@ -0,0 +1,55 @@
+name: docker-cache-mi300
+
+on:
+  # run every 6 hours
+  schedule:
+    - cron: 0 0,6,12,18 * * *
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}
+  cancel-in-progress: true
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  docker-cache:
+    if: github.repository_owner == 'pytorch'
+    runs-on: rocm-docker
+    steps:
+      - name: Checkout PyTorch
+        uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
+        with:
+          no-sudo: true
+
+      - name: configure aws credentials
+        id: aws_creds
+        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
+        with:
+          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
+          aws-region: us-east-1
+          role-duration-seconds: 18000
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        continue-on-error: false
+        uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
+
+      - name: Calculate docker image
+        id: calculate-docker-image
+        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
+        with:
+          docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
+          push: false
+
+      - name: Pull docker image
+        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
+        with:
+          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
+
+      - name: Tar and upload to S3 bucket
+        run: |
+          sudo docker save -o ~/docker-data/pytorch/pytorch_docker_image.tar ${{ steps.calculate-docker-image.outputs.docker-image }}
+          sudo rclone copy -P --s3-upload-concurrency 64 --s3-chunk-size 200M --s3-upload-cutoff 300M ~/docker-data/pytorch/pytorch_docker_image.tar oci:pytorchbucket0002/pytorch_docker_image --progress

.github/workflows/docker-cache-rocm.yml

@@ -1,105 +0,0 @@
-name: docker-cache-rocm
-
-on:
-  workflow_run:
-    workflows: [docker-builds]
-    branches: [main, release]
-    types:
-      - completed
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-  actions: read
-
-jobs:
-  download-docker-builds-artifacts:
-    if: github.repository_owner == 'pytorch'
-    name: download-docker-builds-artifacts
-    runs-on: ubuntu-latest
-    outputs:
-      pytorch-linux-jammy-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}
-      pytorch-linux-noble-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}
-      pytorch-linux-jammy-rocm-n-py3-benchmarks: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}
-    steps:
-      - name: Download artifacts
-        uses: actions/download-artifact@v4.1.7
-        with:
-          run-id: ${{ github.event.workflow_run.id }}
-          path: ./docker-builds-artifacts
-          merge-multiple: true
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Process artifacts
-        id: process-artifacts
-        run: |
-          ls -R ./docker-builds-artifacts
-          cat ./docker-builds-artifacts/*txt >> "${GITHUB_OUTPUT}"
-          cat "${GITHUB_OUTPUT}"
-
-  docker-cache:
-    if: github.repository_owner == 'pytorch'
-    needs: download-docker-builds-artifacts
-    strategy:
-      fail-fast: false
-      matrix:
-        runner: [linux.rocm.gfx942.docker-cache]
-        docker-image: [
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}",
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}",
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}"
-        ]
-    runs-on: "${{ matrix.runner }}"
-    steps:
-      - name: debug
-        run: |
-          JSON_STRINGIFIED="${{ toJSON(needs.download-docker-builds-artifacts.outputs) }}"
-          echo "Outputs of download-docker-builds-artifacts job: ${JSON_STRINGIFIED}"
-
-      - name: configure aws credentials
-        id: aws_creds
-        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
-        with:
-          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-          aws-region: us-east-1
-          role-duration-seconds: 18000
-
-      - name: Login to Amazon ECR
-        id: login-ecr
-        continue-on-error: false
-        uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
-
-      - name: Generate ghrc.io tag
-        id: ghcr-io-tag
-        run: |
-            ecr_image="${{ matrix.docker-image }}"
-            ghcr_image="ghcr.io/pytorch/ci-image:${ecr_image##*:}"
-            echo "ghcr_image=${ghcr_image}" >> "$GITHUB_OUTPUT"
-
-      - name: Pull docker image
-        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
-        with:
-          docker-image: ${{ steps.ghcr-io-tag.outputs.ghcr_image }}
-
-      - name: Save as tarball
-        run: |
-          docker_image_tag=${{ matrix.docker-image }}
-          docker_image_tag="${docker_image_tag#*:}" # Remove everything before and including first ":"
-          docker_image_tag="${docker_image_tag%-*}" # Remove everything after and including last "-"
-          ref_name=${{ github.event.workflow_run.head_branch }}
-          if [[ $ref_name =~ "release/" ]]; then
-            ref_suffix="release"
-          elif [[ $ref_name == "main" ]]; then
-            ref_suffix="main"
-          else
-            echo "Unexpected branch in ref_name: ${ref_name}" && exit 1
-          fi
-          docker tag ${{ steps.ghcr-io-tag.outputs.ghcr_image }} ${{ matrix.docker-image }}
-          # mv is atomic operation, so we use intermediate tar.tmp file to prevent read-write contention
-          docker save -o ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ${{ matrix.docker-image }}
-          mv ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ~/pytorch-data/docker/${docker_image_tag}_${ref_suffix}.tar

.github/workflows/h100-distributed.yml

@@ -37,6 +37,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: "linux.c7i.12xlarge"
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm90-dist
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

.github/workflows/inductor-perf-test-nightly-aarch64.yml

@@ -72,7 +72,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       runner: linux.arm64.m7g.4xlarge
       build-environment: linux-jammy-aarch64-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks
       test-matrix: |
         { include: [
           { config: "inductor_huggingface_perf_cpu_aarch64", shard: 1, num_shards: 9, runner: "linux.arm64.m7g.metal" },

.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-noble-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3-inductor-benchmarks
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -117,7 +117,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-noble-xpu-n-py3.10
+      build-environment: linux-jammy-xpu-n-py3.10
       dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-false-cppwrapper-true-aotinductor-true-freezing_cudagraphs-false-cudagraphs_low_precision-false
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
@@ -137,7 +137,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-noble-xpu-n-py3.10
+      build-environment: linux-jammy-xpu-n-py3.10
       dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}

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

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

.github/workflows/inductor-rocm.yml

@@ -1,13 +1,13 @@
-name: inductor-rocm-mi200
+name: inductor-rocm
 
 on:
   schedule:
-    - cron: 0 */3 * * *
+    - cron: 0 * * * *
   push:
     branches:
       - release/*
     tags:
-      - ciflow/inductor-rocm-mi200/*
+      - ciflow/inductor-rocm/*
   workflow_dispatch:
 
 concurrency:

.github/workflows/inductor-unittest.yml

@@ -81,32 +81,6 @@ 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/linux-aarch64.yml

@@ -33,7 +33,7 @@ jobs:
     with:
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
       build-environment: linux-jammy-aarch64-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
       runner: linux.arm64.m7g.4xlarge
       test-matrix: |
         { include: [

.github/workflows/nightly.yml

@@ -5,11 +5,9 @@ on:
     - cron: 0 0 * * *
   push:
     tags:
-      # NOTE: Doc build pipelines should only get triggered on:
-      # Major or minor release candidates builds
-      - v[0-9]+.[0-9]+.0+-rc[0-9]+
-      # Final RC for major, minor and patch releases
-      - v[0-9]+.[0-9]+.[0-9]+
+      # NOTE: Doc build pipelines should only get triggered on release candidate builds
+      # Release candidate tags look like: v1.11.0-rc1
+      - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
       - ciflow/nightly/*
   workflow_dispatch:
 

.github/workflows/operator_benchmark.yml

@@ -60,7 +60,7 @@ jobs:
     with:
       build-environment: linux-jammy-aarch64-py3.10
       runner: linux.arm64.m7g.4xlarge
-      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
       test-matrix: |
         { include: [
           { config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.arm64.m8g.4xlarge" },

.github/workflows/periodic-rocm-mi200.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-mi200/*
     branches:
       - release/*

.github/workflows/periodic-rocm-mi300.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-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-noble-xpu-n-py3_10-build:
-    name: linux-noble-xpu-n-py3.10
+  linux-jammy-xpu-n-py3_10-build:
+    name: linux-jammy-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-noble-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 4, runner: "linux.idc.xpu" },

.github/workflows/rocm-mi300.yml

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

.github/workflows/rocm.yml

@@ -1,16 +1,15 @@
-name: rocm-mi200
+name: rocm
 
 on:
   push:
     branches:
       - release/*
     tags:
-      - ciflow/rocm-mi200/*
+      - ciflow/rocm/*
   workflow_dispatch:
   schedule:
     - cron: 29 8 * * *  # about 1:29am PDT
-    - cron: 0 */3 * * *
-
+    - cron: 0 * * * *
 
 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/workflows/slow-rocm-mi200.yml

@@ -1,81 +0,0 @@
-# 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

@@ -105,6 +105,36 @@ 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/test-b200.yml

@@ -5,9 +5,7 @@
 # Flow:
 # 1. Builds PyTorch with CUDA 12.8+ and sm100 architecture for B200
 # 2. Runs smoke tests on linux.dgx.b200 runner
-# 3. Tests executed are defined in .ci/pytorch/test.sh -> test_python_smoke_b200() function
-#    - Includes matmul, scaled_matmul, FP8, and FlashAttention CuTe tests
-#    - FlashAttention CuTe DSL is installed as part of test execution
+# 3. Tests executed are defined in .ci/pytorch/test.sh -> test_python_smoke() function
 #
 # Triggered by:
 # - Pull requests modifying this workflow file

.github/workflows/test-h100.yml

@@ -41,6 +41,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: linux.12xlarge.memory
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm90
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

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

@@ -1,83 +0,0 @@
-name: trunk-rocm-mi300
-
-on:
-  push:
-    branches:
-      - main
-      - release/*
-  workflow_dispatch:
-  schedule:
-    - cron: 29 8 * * *  # about 1:29am PDT
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  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: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-          { config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-          { config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-        ]}
-    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/upload-test-stats.yml

@@ -5,23 +5,21 @@ on:
     workflows:
       - pull
       - trunk
-      - trunk-rocm-mi300
       - periodic
       - periodic-rocm-mi200
       - periodic-rocm-mi300
       - inductor
       - unstable
       - slow
-      - slow-rocm-mi200
       - unstable-periodic
       - inductor-periodic
-      - rocm-mi200
+      - rocm
       - rocm-mi300
       - rocm-mi355
       - inductor-micro-benchmark
       - inductor-micro-benchmark-x86
       - inductor-cu124
-      - inductor-rocm-mi200
+      - inductor-rocm
       - inductor-rocm-mi300
       - mac-mps
       - linux-aarch64

.github/workflows/xpu.yml

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

.lintrunner.toml

@@ -143,8 +143,7 @@ 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" and python_version <= "3.13"',
-    'numpy==2.3.4 ; python_version >= "3.14"',
+    'numpy==2.1.0 ; python_version >= "3.12"',
     'expecttest==0.3.0',
     'pyrefly==0.36.2',
     'sympy==1.13.3',
@@ -186,8 +185,6 @@ include_patterns = [
     'aten/src/ATen/native/nested/cuda/*.h',
     'aten/src/ATen/native/nested/*.cpp',
     'aten/src/ATen/native/nested/*.h',
-    'aten/src/ATen/xpu/**/*.h',
-    'aten/src/ATen/xpu/**/*.cpp',
     'c10/**/*.cpp',
     'c10/**/*.h',
     'torch/*.h',
@@ -1404,7 +1401,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'usort==1.0.8.post1',
     'isort==6.0.1',
-    'ruff==0.14.4',  # sync with RUFF
+    'ruff==0.13.1',  # sync with RUFF
 ]
 is_formatter = true
 
@@ -1539,7 +1536,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.14.4',  # sync with PYFMT
+    'ruff==0.13.1',  # sync with PYFMT
 ]
 is_formatter = true
 

CMakeLists.txt

@@ -234,17 +234,7 @@ option(USE_COLORIZE_OUTPUT "Colorize output during compilation" ON)
 option(USE_ASAN "Use Address+Undefined Sanitizers" OFF)
 option(USE_LSAN "Use Leak Sanitizer" OFF)
 option(USE_TSAN "Use Thread Sanitizer" OFF)
-
-# Track whether USE_CUDA was explicitly set by the user (before option() is called)
-# If USE_CUDA is already defined in cache, it means user explicitly set it
-if(DEFINED CACHE{USE_CUDA})
-  set(_USE_CUDA_EXPLICITLY_SET TRUE)
-else()
-  set(_USE_CUDA_EXPLICITLY_SET FALSE)
-endif()
-
 option(USE_CUDA "Use CUDA" ON)
-
 option(USE_XPU "Use XPU" ON)
 cmake_dependent_option(
   BUILD_LAZY_CUDA_LINALG "Build cuda linalg ops as separate library" ON
@@ -736,44 +726,6 @@ if(NOT DEFINED USE_BLAS)
   set(USE_BLAS ON)
 endif()
 
-# Prioritized Text Linker Optimization
-if(USE_PRIORITIZED_TEXT_FOR_LD)
-
-  set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
-  set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
-
-  execute_process(
-    COMMAND ${Python_EXECUTABLE}
-            ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py
-            --filein "${LINKER_SCRIPT_FILE_IN}"
-            --fout  "${LINKER_SCRIPT_FILE_OUT}"
-    RESULT_VARIABLE _gen_result
-    OUTPUT_VARIABLE _gen_output
-    ERROR_VARIABLE  _gen_error
-  )
-
-  if(NOT _gen_result EQUAL 0)
-    message(FATAL_ERROR
-      "Failed to generate linker script:\n${_gen_output}\n${_gen_error}")
-  endif()
-
-  append_cxx_flag_if_supported("-ffunction-sections" CMAKE_CXX_FLAGS)
-  append_cxx_flag_if_supported("-fdata-sections" CMAKE_CXX_FLAGS)
-  append_c_flag_if_supported("-ffunction-sections" CMAKE_C_FLAGS)
-  append_c_flag_if_supported("-fdata-sections" CMAKE_C_FLAGS)
-
-  set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
-  set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
-
-else()
-  if(LINUX AND CPU_AARCH64)
-    message(WARNING [[
-    It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
-    To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
-    ]])
-  endif()
-endif()
-
 # Build libtorch mobile library, which contains ATen/TH ops and native support
 # for TorchScript model, but doesn't contain not-yet-unified caffe2 ops;
 if(INTERN_BUILD_MOBILE)
@@ -1440,6 +1392,9 @@ if(BUILD_JNI)
   add_subdirectory(android/pytorch_android)
 endif()
 
+include(cmake/Summary.cmake)
+caffe2_print_configuration_summary()
+
 # Parse custom debug info
 if(DEFINED USE_CUSTOM_DEBINFO)
   string(REPLACE ";" " " SOURCE_FILES "${USE_CUSTOM_DEBINFO}")
@@ -1479,5 +1434,56 @@ if(BUILD_BUNDLE_PTXAS AND USE_CUDA)
           DESTINATION "${CMAKE_INSTALL_BINDIR}")
 endif()
 
-include(cmake/Summary.cmake)
-caffe2_print_configuration_summary()
+if(USE_PRIORITIZED_TEXT_FOR_LD)
+  add_compile_options(
+    $<$<COMPILE_LANGUAGE:C,CXX>:-ffunction-sections>
+    $<$<COMPILE_LANGUAGE:C,CXX>:-fdata-sections>
+  )
+  set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
+  set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
+
+  add_custom_command(
+    OUTPUT "${LINKER_SCRIPT_FILE_OUT}"
+    COMMAND ${Python_EXECUTABLE} ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py --filein "${LINKER_SCRIPT_FILE_IN}" --fout "${LINKER_SCRIPT_FILE_OUT}"
+    DEPENDS ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py "${LINKER_SCRIPT_FILE_IN}"
+    COMMENT "Generating prioritized text linker files"
+    VERBATIM
+  )
+
+  add_custom_target(generate_linker_script DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
+
+  if(BUILD_PYTHON)
+    set(LINKER_OPT_TARGETS torch_python)
+  endif()
+
+  if(NOT BUILD_LIBTORCHLESS)
+    list(APPEND LINKER_OPT_TARGETS torch_cpu c10)
+    if(USE_CUDA)
+      list(APPEND LINKER_OPT_TARGETS torch_cuda c10_cuda)
+    endif()
+    if(USE_XPU)
+      list(APPEND LINKER_OPT_TARGETS torch_xpu c10_xpu)
+    endif()
+    if(USE_ROCM)
+      list(APPEND LINKER_OPT_TARGETS torch_hip c10_hip)
+    endif()
+  endif()
+
+  foreach(tgt IN LISTS LINKER_OPT_TARGETS)
+    if(TARGET ${tgt})
+      add_dependencies("${tgt}" generate_linker_script)
+      target_link_options_if_supported(${tgt} "-T,${LINKER_SCRIPT_FILE_OUT}")
+      set_property(TARGET ${tgt} APPEND PROPERTY LINK_DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
+    else()
+       message(WARNING "Requested target '${tgt}' for linker script optimization was not found.")
+    endif()
+  endforeach()
+
+else()
+  if(LINUX AND CPU_AARCH64)
+    message(WARNING [[
+    It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
+    To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
+    ]])
+  endif()
+endif()

CODEOWNERS

@@ -210,12 +210,8 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
 /test/inductor/test_flex_attention.py @drisspg
 /test/inductor/test_flex_decoding.py @drisspg
 
-# Low Precision & Grouped GEMMs
+# Low Precision 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

CONTRIBUTING.md

@@ -18,7 +18,7 @@ aspects of contributing to PyTorch.
   - [Python Unit Testing](#python-unit-testing)
   - [Better local unit tests with `pytest`](#better-local-unit-tests-with-pytest)
   - [Local linting](#local-linting)
-    - [Running `pyrefly`](#running-pyrefly)
+    - [Running `mypy`](#running-mypy)
   - [C++ Unit Testing](#c-unit-testing)
   - [Run Specific CI Jobs](#run-specific-ci-jobs)
 - [Merging your Change](#merging-your-change)
@@ -281,7 +281,7 @@ dependencies as well as the nightly binaries into the repo directory.
 **Prerequisites**:
 The following packages should be installed with `pip`:
 - `expecttest` and `hypothesis` - required to run tests
-- `pyrefly` - recommended for type checking. [Pyrefly](https://pyrefly.org/)
+- `mypy` - recommended for linting
 - `pytest` - recommended to run tests more selectively
 Running
 ```
@@ -350,32 +350,15 @@ make lint
 
 Learn more about the linter on the [lintrunner wiki page](https://github.com/pytorch/pytorch/wiki/lintrunner)
 
-#### Running `pyrefly`
+#### Running `mypy`
 
-[Pyrefly](https://pyrefly.org/) is a high-performance static type checker for Python. It provides fast type checking along with IDE features like autocomplete and instant error feedback.
-
-PyTorch uses Pyrefly for type checking across the codebase. The configuration is managed in `pyrefly.toml` at the root of the repository.
-
-**Getting Started with Pyrefly:**
-
-To run type checking on the PyTorch codebase:
-```bash
-pyrefly check
-```
-
-For more detailed error information with summaries:
-```bash
-pyrefly check --summarize-errors
-```
-
-**Learn More:**
-- [Pyrefly Configuration](https://pyrefly.org/en/docs/configuration/) - Detailed configuration options
-- [Pyrefly IDE Features](https://pyrefly.org/en/docs/IDE-features/) - Set up Pyrefly in your editor for real-time type checking
-- [Python Typing Tutorial](https://pyrefly.org/en/docs/typing-for-python-developers/) - Learn about Python type annotations
+`mypy` is an optional static type checker for Python. We have multiple `mypy`
+configs for the PyTorch codebase that are automatically validated against whenever the linter is run.
 
 See [Guide for adding type annotations to
 PyTorch](https://github.com/pytorch/pytorch/wiki/Guide-for-adding-type-annotations-to-PyTorch)
-for PyTorch-specific guidance on how to set up `pyrefly` and tackle type annotation tasks in this codebase.
+for more information on how to set up `mypy` and tackle type annotation
+tasks.
 
 ### C++ Unit Testing
 

LICENSE

@@ -37,7 +37,7 @@ Copyright (c) 2024 Tri Dao.
 All rights reserved.
 
 All contributions by Arm:
-Copyright (c) 2021, 2023-2025 Arm Limited and/or its affiliates
+Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates
 
 All contributions from Caffe:
 Copyright(c) 2013, 2014, 2015, the respective contributors

SECURITY.md

@@ -1,7 +1,7 @@
 # Security Policy
 
  - [**Reporting a Vulnerability**](#reporting-a-vulnerability)
- - [**Using PyTorch Securely**](#using-pytorch-securely)
+ - [**Using Pytorch Securely**](#using-pytorch-securely)
    - [Untrusted models](#untrusted-models)
    - [TorchScript models](#torchscript-models)
    - [Untrusted inputs](#untrusted-inputs)
@@ -10,30 +10,28 @@
 - [**CI/CD security principles**](#cicd-security-principles)
 ## Reporting Security Issues
 
-Beware that none of the topics under [Using PyTorch Securely](#using-pytorch-securely) are considered vulnerabilities of PyTorch.
+Beware that none of the topics under [Using Pytorch Securely](#using-pytorch-securely) are considered vulnerabilities of Pytorch.
 
 However, if you believe you have found a security vulnerability in PyTorch, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
 
 Please report security issues using https://github.com/pytorch/pytorch/security/advisories/new
 
-All reports submitted through the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
-
-**Note on crashes and out of bounds access**: PyTorch is a computational framework that performs operations on behalf of the caller. Like many low-level libraries, PyTorch generally does not validate all inputs to every function—the responsibility for providing valid arguments lies with the calling code. While crashes and out of bounds memory access should be reported as bugs, they are generally not considered security vulnerabilities in PyTorch's threat model.
+All reports submitted thru the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
 
 Please refer to the following page for our responsible disclosure policy, reward guidelines, and those things that should not be reported:
 
 https://www.facebook.com/whitehat
 
 
-## Using PyTorch Securely
-**PyTorch models are programs**, so treat its security seriously -- running untrusted models is equivalent to running untrusted code. In general we recommend that model weights and the python code for the model are distributed independently. That said, be careful about where you get the python code from and who wrote it (preferentially check for a provenance or checksums, do not run any pip installed package).
+## Using Pytorch Securely
+**Pytorch models are programs**, so treat its security seriously -- running untrusted models is equivalent to running untrusted code. In general we recommend that model weights and the python code for the model are distributed independently. That said, be careful about where you get the python code from and who wrote it (preferentially check for a provenance or checksums, do not run any pip installed package).
 
 ### Untrusted models
 Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources[^data-poisoning-sources].
 
 **Prefer to execute untrusted models within a secure, isolated environment such as a sandbox** (e.g., containers, virtual machines). This helps protect your system from potentially malicious code. You can find further details and instructions in [this page](https://developers.google.com/code-sandboxing).
 
-**Be mindful of risky model formats**. Give preference to share and load weights with the appropriate format for your use case. [Safetensors](https://huggingface.co/docs/safetensors/en/index) gives the most safety but is the most restricted in what it supports. [`torch.load`](https://pytorch.org/docs/stable/generated/torch.load.html#torch.load) has a significantly larger surface of attack but is more flexible in what it can serialize. See the documentation for more details.
+**Be mindful of risky model formats**. Give preference to share and load weights with the appropriate format for your use case. [safetensors](https://huggingface.co/docs/safetensors/en/index) gives the most safety but is the most restricted in what it supports. [`torch.load`](https://pytorch.org/docs/stable/generated/torch.load.html#torch.load) has a significantly larger surface of attack but is more flexible in what it can serialize. See the documentation for more details.
 
 Even for more secure serialization formats, unexpected inputs to the downstream system can cause diverse security threats (e.g. denial of service, out of bound reads/writes) and thus we recommend extensive validation of any untrusted inputs.
 
@@ -45,7 +43,7 @@ Important Note: The trustworthiness of a model is not binary. You must always de
 
 ### TorchScript models
 
-TorchScript models should be treated the same way as locally executable code from an unknown source. Only run TorchScript models if you trust the provider. Please note, that tools for introspecting TorchScript models (such as `torch.utils.model_dump`) may also execute partial or full code stored in those models, therefore they should be used only if you trust the provider of the binary you are about to load.
+TorchScript models should treated the same way as locally executable code from an unknown source. Only run TorchScript models if you trust the provider. Please note, that tools for introspecting TorchScript models (such as `torch.utils.model_dump`) may also execute partial or full code stored in those models, therefore they should be used only if you trust the provider of the binary you are about to load.
 
 ### Untrusted inputs during training and prediction
 
@@ -61,9 +59,9 @@ If applicable, prepare your model against bad inputs and prompt injections. Some
 
 ### Data privacy
 
-**Take special security measures if you train your models with sensitive data**. Prioritize [sandboxing](https://developers.google.com/code-sandboxing) your models and:
-- Do not feed sensitive data to an untrusted model (even if runs in a sandboxed environment)
-- If you consider publishing a model that was partially trained with sensitive data, be aware that data can potentially be recovered from the trained weights (especially if the model overfits).
+**Take special security measures if your model if you train models with sensitive data**. Prioritize [sandboxing](https://developers.google.com/code-sandboxing) your models and:
+- Do not feed sensitive data to untrusted model (even if runs in a sandboxed environment)
+- If you consider publishing a model that was partially trained with sensitive data, be aware that data can potentially be recovered from the trained weights (especially if model overfits).
 
 ### Using distributed features
 

aten/src/ATen/Context.h

@@ -174,12 +174,6 @@ 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/Dispatch.h

@@ -6,7 +6,6 @@
 #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
@@ -62,9 +61,12 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     }                                                 \
   } while (0)
 
-#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_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_DISPATCH_CASE(enum_type, ...) \
   AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
@@ -93,6 +95,14 @@ 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
@@ -180,13 +190,27 @@ TORCH_API void record_kernel_function_dtype(std::string name);
 // 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, ...) \
-  THO_DISPATCH_SWITCH_TMPL(                 \
-      RECORD_KERNEL_FUNCTION_DTYPE,         \
-      TORCH_CHECK_NOT_IMPLEMENTED,          \
-      TYPE,                                 \
-      NAME,                                 \
-      __VA_ARGS__)
+#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_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \

aten/src/ATen/Dispatch_v2.h

@@ -1,8 +1,3 @@
-#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
@@ -79,19 +74,41 @@
 // 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, ...) \
-  THO_DISPATCH_V2_TMPL(                       \
-      AT_DISPATCH_SWITCH,                     \
-      AT_DISPATCH_CASE,                       \
-      TYPE,                                   \
-      NAME,                                   \
-      AT_WRAP(BODY),                          \
-      __VA_ARGS__)
+  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
 
-// 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.
@@ -102,6 +119,12 @@ 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)])
@@ -112,6 +135,8 @@ 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() {
-    if (active_) {
-      active_ = false;
+    active_ = false;
+    if (pinned_use_background_threads()) {
       getBackgroundThreadPool()->waitWorkComplete();
     }
   }
@@ -260,7 +260,6 @@ 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();
@@ -684,9 +683,9 @@ 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 event-processing thread pool is active.
+  // Indicates whether the object is active.
   // Set to false in the destructor to signal background threads to stop.
-  std::atomic<bool> active_{false};
+  std::atomic<bool> active_{true};
 protected:
   alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
 };

aten/src/ATen/cpu/vec/sve/vec_bfloat16.h

@@ -191,7 +191,7 @@ class Vectorized<BFloat16> {
     auto vals = svreinterpret_u16_bf16(values);
     vals = sveor_u16_x(ptrue, vals, mask);
     return svreinterpret_bf16_u16(vals);
-  }
+  };
   Vectorized<BFloat16> round() const;
   Vectorized<BFloat16> tan() const;
   Vectorized<BFloat16> tanh() const;
@@ -349,47 +349,47 @@ Vectorized<BFloat16> inline Vectorized<BFloat16>::frac() const {
     return convert_float_bfloat16(v1, v2);                     \
   }
 
-DEFINE_BF16_FUNC_VIA_FLOAT(isnan)
-DEFINE_BF16_FUNC_VIA_FLOAT(angle)
-DEFINE_BF16_FUNC_VIA_FLOAT(acos)
-DEFINE_BF16_FUNC_VIA_FLOAT(acosh)
-DEFINE_BF16_FUNC_VIA_FLOAT(asin)
-DEFINE_BF16_FUNC_VIA_FLOAT(atan)
-DEFINE_BF16_FUNC_VIA_FLOAT(atanh)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(atan2)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(copysign)
-DEFINE_BF16_FUNC_VIA_FLOAT(erf)
-DEFINE_BF16_FUNC_VIA_FLOAT(erfc)
-DEFINE_BF16_FUNC_VIA_FLOAT(exp)
-DEFINE_BF16_FUNC_VIA_FLOAT(exp2)
-DEFINE_BF16_FUNC_VIA_FLOAT(expm1)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(fmod)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(hypot)
-DEFINE_BF16_FUNC_VIA_FLOAT(i0)
-DEFINE_BF16_FUNC_VIA_FLOAT(i0e)
-DEFINE_BF16_FUNC_VIA_FLOAT(digamma)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(igamma)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(igammac)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(nextafter)
-DEFINE_BF16_FUNC_VIA_FLOAT(log)
-DEFINE_BF16_FUNC_VIA_FLOAT(log2)
-DEFINE_BF16_FUNC_VIA_FLOAT(log10)
-DEFINE_BF16_FUNC_VIA_FLOAT(log1p)
-DEFINE_BF16_FUNC_VIA_FLOAT(sin)
-DEFINE_BF16_FUNC_VIA_FLOAT(sinh)
-DEFINE_BF16_FUNC_VIA_FLOAT(cos)
-DEFINE_BF16_FUNC_VIA_FLOAT(cosh)
-DEFINE_BF16_FUNC_VIA_FLOAT(ceil)
-DEFINE_BF16_FUNC_VIA_FLOAT(floor)
-DEFINE_BF16_FUNC_VIA_FLOAT(round)
-DEFINE_BF16_FUNC_VIA_FLOAT(tan)
-DEFINE_BF16_FUNC_VIA_FLOAT(tanh)
-DEFINE_BF16_FUNC_VIA_FLOAT(trunc)
-DEFINE_BF16_FUNC_VIA_FLOAT(lgamma)
-DEFINE_BF16_FUNC_VIA_FLOAT(sqrt)
-DEFINE_BF16_FUNC_VIA_FLOAT(reciprocal)
-DEFINE_BF16_FUNC_VIA_FLOAT(rsqrt)
-DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(pow)
+DEFINE_BF16_FUNC_VIA_FLOAT(isnan);
+DEFINE_BF16_FUNC_VIA_FLOAT(angle);
+DEFINE_BF16_FUNC_VIA_FLOAT(acos);
+DEFINE_BF16_FUNC_VIA_FLOAT(acosh);
+DEFINE_BF16_FUNC_VIA_FLOAT(asin);
+DEFINE_BF16_FUNC_VIA_FLOAT(atan);
+DEFINE_BF16_FUNC_VIA_FLOAT(atanh);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(atan2);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(copysign);
+DEFINE_BF16_FUNC_VIA_FLOAT(erf);
+DEFINE_BF16_FUNC_VIA_FLOAT(erfc);
+DEFINE_BF16_FUNC_VIA_FLOAT(exp);
+DEFINE_BF16_FUNC_VIA_FLOAT(exp2);
+DEFINE_BF16_FUNC_VIA_FLOAT(expm1);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(fmod);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(hypot);
+DEFINE_BF16_FUNC_VIA_FLOAT(i0);
+DEFINE_BF16_FUNC_VIA_FLOAT(i0e);
+DEFINE_BF16_FUNC_VIA_FLOAT(digamma);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(igamma);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(igammac);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(nextafter);
+DEFINE_BF16_FUNC_VIA_FLOAT(log);
+DEFINE_BF16_FUNC_VIA_FLOAT(log2);
+DEFINE_BF16_FUNC_VIA_FLOAT(log10);
+DEFINE_BF16_FUNC_VIA_FLOAT(log1p);
+DEFINE_BF16_FUNC_VIA_FLOAT(sin);
+DEFINE_BF16_FUNC_VIA_FLOAT(sinh);
+DEFINE_BF16_FUNC_VIA_FLOAT(cos);
+DEFINE_BF16_FUNC_VIA_FLOAT(cosh);
+DEFINE_BF16_FUNC_VIA_FLOAT(ceil);
+DEFINE_BF16_FUNC_VIA_FLOAT(floor);
+DEFINE_BF16_FUNC_VIA_FLOAT(round);
+DEFINE_BF16_FUNC_VIA_FLOAT(tan);
+DEFINE_BF16_FUNC_VIA_FLOAT(tanh);
+DEFINE_BF16_FUNC_VIA_FLOAT(trunc);
+DEFINE_BF16_FUNC_VIA_FLOAT(lgamma);
+DEFINE_BF16_FUNC_VIA_FLOAT(sqrt);
+DEFINE_BF16_FUNC_VIA_FLOAT(reciprocal);
+DEFINE_BF16_FUNC_VIA_FLOAT(rsqrt);
+DEFINE_BF16_FUNC_VIA_FLOAT_W_ARG(pow);
 
 Vectorized<BFloat16> inline Vectorized<BFloat16>::operator==(
     const Vectorized<BFloat16>& other) const {

aten/src/ATen/cuda/CUDABlas.cpp

@@ -388,7 +388,6 @@ static inline bool bgemm_internal_cublaslt(CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(D
 #ifndef USE_ROCM
   at::Half halpha;
   at::Half hbeta;
-  uint32_t mask = -1;
 #endif
   void * alpha_ptr = α
   void * beta_ptr = β
@@ -428,7 +427,7 @@ static inline bool bgemm_internal_cublaslt(CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(D
     auto fp16_reduction = at::globalContext().allowFP16ReductionCuBLAS();
     if (fp16_reduction !=
         at::CuBLASReductionOption::AllowReducedPrecisionWithSplitK) {
-      mask =
+      uint32_t mask =
           fp16_reduction ==
                   at::CuBLASReductionOption::DisallowReducedPrecisionAllowSplitK
               ? (CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE |
@@ -445,7 +444,7 @@ static inline bool bgemm_internal_cublaslt(CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(D
     auto bf16_reduction = at::globalContext().allowBF16ReductionCuBLAS();
     if (bf16_reduction !=
         at::CuBLASReductionOption::AllowReducedPrecisionWithSplitK) {
-      mask =
+      uint32_t mask =
           bf16_reduction ==
                   at::CuBLASReductionOption::DisallowReducedPrecisionAllowSplitK
               ? (CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE |
@@ -512,41 +511,17 @@ static inline bool bgemm_internal_cublaslt(CUDABLAS_BGEMM_ARGTYPES_AND_C_DTYPE(D
   cublasStatus_t cublasStatus = CUBLAS_STATUS_SUCCESS;
   cublasLtMatmulHeuristicResult_t heuristicResult = {};
   int returnedResult = 0;
-  // on Blackwell+, we fake a n > 1 matmul when querying heuristics
-  // to prevent cuBLASLt from dispatching to a GEMV kernel for batch-invariance
-#ifndef USE_ROCM
-  const bool lie_to_cublaslt = mask == CUBLASLT_REDUCTION_SCHEME_NONE && n == 1 && at::cuda::getCurrentDeviceProperties()->major >= 10;
-#else
-  const bool lie_to_cublaslt = false;
-#endif
-  if (lie_to_cublaslt) {
-     CuBlasLtMatrixLayout FakeBdesc(abType, k, 2, ldb, opb == CUBLAS_OP_T);
-     CuBlasLtMatrixLayout FakeCdesc(cType, m, 2, ldc);
-
-     TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
-        ltHandle,
-        computeDesc.descriptor(),
-        Adesc.descriptor(),
-        FakeBdesc.descriptor(),
-        FakeCdesc.descriptor(),
-        FakeCdesc.descriptor(),
-        preference.descriptor(),
-        1,
-        &heuristicResult,
-        &returnedResult));
-  } else {
-    TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
-        ltHandle,
-        computeDesc.descriptor(),
-        Adesc.descriptor(),
-        Bdesc.descriptor(),
-        Cdesc.descriptor(),
-        Cdesc.descriptor(),
-        preference.descriptor(),
-        1,
-        &heuristicResult,
-        &returnedResult));
-  }
+  TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
+      ltHandle,
+      computeDesc.descriptor(),
+      Adesc.descriptor(),
+      Bdesc.descriptor(),
+      Cdesc.descriptor(),
+      Cdesc.descriptor(),
+      preference.descriptor(),
+      1,
+      &heuristicResult,
+      &returnedResult));
   if (returnedResult == 0) {
     cublasStatus = CUBLAS_STATUS_NOT_SUPPORTED;
   }
@@ -1597,7 +1572,7 @@ bool gemm_and_bias(
   }
 
   using opmath_t = at::opmath_type<Dtype>;
-  opmath_t beta_val = bias ? 0 : 1; // bias is added in epilogue unless nullptr
+  opmath_t beta_val = 0; // bias is added in epilogue
 
   cudaDataType_t abType = CUDA_R_32F;
   cudaDataType_t cType = CUDA_R_32F;
@@ -1686,22 +1661,15 @@ bool gemm_and_bias(
     _syncCurrentWithCarveoutStream(stream, true);
   }
 #endif
-  const auto epilogue = [&]() -> cublasLtEpilogue_t {
-    // The cuBLAS documentation indicates that
-    // *_<ACTIVATION>_BIAS = *_<ACTIVATION>,
-    // but we keep it verbose here for clarity.
-    switch (activation) {
-      case GEMMAndBiasActivationEpilogue::RELU:
-        return bias ? CUBLASLT_EPILOGUE_RELU_BIAS : CUBLASLT_EPILOGUE_RELU;
-      case GEMMAndBiasActivationEpilogue::GELU:
-        return bias ? CUBLASLT_EPILOGUE_GELU_BIAS : CUBLASLT_EPILOGUE_GELU;
-      default:
-        return bias ? CUBLASLT_EPILOGUE_BIAS : CUBLASLT_EPILOGUE_DEFAULT;
-    }
-  }();
-  computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_EPILOGUE, epilogue);
+  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_BIAS;
+  if (activation == GEMMAndBiasActivationEpilogue::RELU) {
+    epilogue = CUBLASLT_EPILOGUE_RELU_BIAS;
+  } else if (activation == GEMMAndBiasActivationEpilogue::GELU) {
+    epilogue = CUBLASLT_EPILOGUE_GELU_BIAS;
+  }
 
-  if (bias) {
+  if (bias != nullptr) {
+    computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_EPILOGUE, epilogue);
     computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_BIAS_POINTER, bias);
   }
 

aten/src/ATen/cuda/cub.cuh

@@ -213,7 +213,7 @@ inline void inclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
       scan_op,
       num_items,
       at::cuda::getCurrentCUDAStream());
-  C10_CUDA_KERNEL_LAUNCH_CHECK();
+  C10_HIP_KERNEL_LAUNCH_CHECK();
 #else
   // non synchronizing cub call
   // even though cub is supposed to support tensors with int_max elements, in reality it doesn't,
@@ -471,7 +471,7 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
       init_value,
       num_items,
       at::cuda::getCurrentCUDAStream());
-  C10_CUDA_KERNEL_LAUNCH_CHECK();
+  C10_HIP_KERNEL_LAUNCH_CHECK();
 #else
   // non synchronizing cub call
   // even though cub is supposed to support tensors with int_max elements, in reality it doesn't,

aten/src/ATen/cuda/cub.h

@@ -24,13 +24,7 @@ namespace detail {
 // radix_sort_pairs doesn't interact with value_t other than to copy
 // the data, so we can save template instantiations by reinterpreting
 // it as an opaque type.
-// We use native integer types for 1/2/4/8-byte values to reduce
-// register usage in CUDA kernels. For sizes > 8 fall back to char array.
 template <int N> struct alignas(N) OpaqueType { char data[N]; };
-template <> struct alignas(1) OpaqueType<1> { uint8_t data; };
-template <> struct alignas(2) OpaqueType<2> { uint16_t data; };
-template <> struct alignas(4) OpaqueType<4> { uint32_t data; };
-template <> struct alignas(8) OpaqueType<8> { uint64_t data; };
 
 template<typename key_t, int value_size>
 void radix_sort_pairs_impl(

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

@@ -21,7 +21,6 @@
 
 #if AT_CUDNN_ENABLED()
 #include <ATen/cudnn/cudnn-wrapper.h>
-#include <cudnn_frontend.h>
 #endif
 
 #if AT_MAGMA_ENABLED()
@@ -352,26 +351,6 @@ 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

@@ -49,8 +49,6 @@ 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

@@ -174,14 +174,6 @@ 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

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

aten/src/ATen/hip/impl/HIPAllocatorMasqueradingAsCUDA.h

@@ -0,0 +1,239 @@
+#pragma once
+
+#include <c10/hip/HIPCachingAllocator.h>
+
+// Use of c10::hip namespace here makes hipification easier, because
+// I don't have to also fix namespaces.  Sorry!
+namespace c10::hip {
+
+// Takes a valid HIPAllocator (of any sort) and turns it into
+// an allocator pretending to be a CUDA allocator.  See
+// Note [Masquerading as CUDA]
+class HIPAllocatorMasqueradingAsCUDA final : public HIPCachingAllocator::HIPAllocator {
+  HIPCachingAllocator::HIPAllocator* allocator_;
+public:
+  explicit HIPAllocatorMasqueradingAsCUDA(HIPCachingAllocator::HIPAllocator* allocator)
+    : allocator_(allocator) {}
+
+  virtual ~HIPAllocatorMasqueradingAsCUDA() = default;
+
+  // From c10::Allocator
+
+  DataPtr allocate(size_t size) override {
+    DataPtr r = allocator_->allocate(size);
+    r.unsafe_set_device(Device(c10::DeviceType::CUDA, r.device().index()));
+    return r;
+  }
+
+  bool is_simple_data_ptr(const DataPtr& data_ptr) const override {
+    return allocator_->is_simple_data_ptr(data_ptr);
+  }
+
+  DeleterFnPtr raw_deleter() const override {
+    return allocator_->raw_deleter();
+  }
+
+  void copy_data(void* dest, const void* src, std::size_t count) const final {
+    allocator_->copy_data(dest, src, count);
+  }
+
+  // From DeviceAllocator
+
+  bool initialized() override {
+    return allocator_->initialized();
+  }
+
+  void emptyCache(MempoolId_t mempool_id = {0, 0}) override {
+    allocator_->emptyCache(mempool_id);
+  }
+
+  void recordStream(const DataPtr& ptr, c10::Stream stream) override {
+    HIPStream hip_stream = HIPStream(stream);
+    recordStream(ptr, hip_stream);
+  }
+
+  CachingDeviceAllocator::DeviceStats getDeviceStats(c10::DeviceIndex device) override {
+    return allocator_->getDeviceStats(device);
+  }
+
+  void resetAccumulatedStats(c10::DeviceIndex device) override {
+    allocator_->resetAccumulatedStats(device);
+  }
+
+  void resetPeakStats(c10::DeviceIndex device) override {
+    allocator_->resetPeakStats(device);
+  }
+
+  // From CUDAAllocator
+
+  void* raw_alloc(size_t nbytes) override {
+    return allocator_->raw_alloc(nbytes);
+  }
+
+  void* raw_alloc_with_stream(size_t nbytes, hipStream_t stream) override {
+    return allocator_->raw_alloc_with_stream(nbytes, stream);
+  }
+
+  void raw_delete(void* ptr) override {
+    allocator_->raw_delete(ptr);
+  }
+
+  void init(int device_count) override {
+    allocator_->init(device_count);
+  }
+
+  double getMemoryFraction(c10::DeviceIndex device) override {
+    return allocator_->getMemoryFraction(device);
+  }
+
+  void setMemoryFraction(double fraction, c10::DeviceIndex device) override {
+    allocator_->setMemoryFraction(fraction, device);
+  }
+
+  std::vector<HIPCachingAllocator::StreamSegmentSize> getExpandableSegmentSizes(c10::DeviceIndex device) override {
+    return allocator_->getExpandableSegmentSizes(device);
+  }
+
+  void enable(bool value) override {
+    allocator_->enable(value);
+  }
+
+  bool isEnabled() const override {
+    return allocator_->isEnabled();
+  }
+
+  void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) override {
+    allocator_->cacheInfo(device, largestBlock);
+  }
+
+  void* getBaseAllocation(void* ptr, size_t* size) override {
+    return allocator_->getBaseAllocation(ptr, size);
+  }
+
+  void recordStream(const DataPtr& ptr, HIPStream stream) override {
+    allocator_->recordStream(ptr, stream);
+  }
+
+  HIPCachingAllocator::SnapshotInfo snapshot(MempoolId_t mempool_id = {0, 0}) override {
+    return allocator_->snapshot(mempool_id);
+  }
+
+  void beginAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      std::function<bool(hipStream_t)> filter) override {
+    allocator_->beginAllocateToPool(device, mempool_id, filter);
+  }
+
+  void endAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id) override {
+    allocator_->endAllocateToPool(device, mempool_id);
+  }
+
+  void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) override {
+    allocator_->releasePool(device, mempool_id);
+  }
+
+  int getPoolUseCount(c10::DeviceIndex device, MempoolId_t mempool_id) override {
+    return allocator_->getPoolUseCount(device, mempool_id);
+  }
+
+  void createOrIncrefPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      HIPAllocator* allocator = nullptr) override {
+    allocator_->createOrIncrefPool(device, mempool_id, allocator);
+  }
+
+  void setUseOnOOM(c10::DeviceIndex device, MempoolId_t mempool_id) override {
+    allocator_->setUseOnOOM(device, mempool_id);
+  }
+
+  bool checkPoolLiveAllocations(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      const std::unordered_set<void*>& expected_live_allocations) override {
+    return allocator_->checkPoolLiveAllocations(device, mempool_id, expected_live_allocations);
+  }
+
+  HIPCachingAllocator::ShareableHandle shareIpcHandle(void* ptr) override {
+    return allocator_->shareIpcHandle(ptr);
+  }
+
+  std::shared_ptr<void> getIpcDevPtr(std::string handle) override {
+    return allocator_->getIpcDevPtr(handle);
+  }
+
+  bool isHistoryEnabled() override {
+    return allocator_->isHistoryEnabled();
+  }
+
+  void recordHistory(
+      bool enabled,
+      HIPCachingAllocator::CreateContextFn context_recorder,
+      size_t alloc_trace_max_entries,
+      HIPCachingAllocator::RecordContext when,
+      bool clearHistory) override {
+    allocator_->recordHistory(enabled, context_recorder, alloc_trace_max_entries, when, clearHistory);
+  }
+
+  void recordAnnotation(
+      const std::vector<std::pair<std::string, std::string>>& md) override {
+    allocator_->recordAnnotation(md);
+  }
+
+  void pushCompileContext(std::string& md) override {
+    allocator_->pushCompileContext(md);
+  }
+
+  void popCompileContext() override {
+    allocator_->popCompileContext();
+  }
+
+  void attachOutOfMemoryObserver(HIPCachingAllocator::OutOfMemoryObserver observer) override {
+    allocator_->attachOutOfMemoryObserver(observer);
+  }
+
+  void attachAllocatorTraceTracker(HIPCachingAllocator::AllocatorTraceTracker tracker) override {
+    allocator_->attachAllocatorTraceTracker(tracker);
+  }
+
+  void enablePeerAccess(c10::DeviceIndex dev, c10::DeviceIndex dev_to_access) override {
+    allocator_->enablePeerAccess(dev, dev_to_access);
+  }
+
+  hipError_t memcpyAsync(
+      void* dst,
+      int dstDevice,
+      const void* src,
+      int srcDevice,
+      size_t count,
+      hipStream_t stream,
+      bool p2p_enabled) override {
+    return allocator_->memcpyAsync(dst, dstDevice, src, srcDevice, count, stream, p2p_enabled);
+  }
+
+  std::shared_ptr<HIPCachingAllocator::AllocatorState> getCheckpointState(
+      c10::DeviceIndex device,
+      MempoolId_t id) override {
+    return allocator_->getCheckpointState(device, id);
+  }
+
+  HIPCachingAllocator::CheckpointDelta setCheckpointPoolState(
+      c10::DeviceIndex device,
+      std::shared_ptr<HIPCachingAllocator::AllocatorState> pps) override {
+    auto cpd = allocator_->setCheckpointPoolState(device, pps);
+    for (auto& ptr : cpd.dataptrs_allocd) {
+      ptr.unsafe_set_device(Device(c10::DeviceType::CUDA, ptr.device().index()));
+    }
+    return cpd;
+  }
+
+  std::string name() override {
+    return allocator_->name();
+  }
+
+};
+
+} // namespace c10::hip

aten/src/ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.cpp

@@ -0,0 +1,18 @@
+#include <c10/hip/HIPCachingAllocator.h>
+#include <ATen/hip/impl/HIPAllocatorMasqueradingAsCUDA.h>
+#include <ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.h>
+
+namespace c10 { namespace hip {
+namespace HIPCachingAllocatorMasqueradingAsCUDA {
+
+HIPCachingAllocator::HIPAllocator* get() {
+  static HIPAllocatorMasqueradingAsCUDA allocator(HIPCachingAllocator::get());
+  return &allocator;
+}
+
+void recordStreamMasqueradingAsCUDA(const DataPtr& ptr, HIPStreamMasqueradingAsCUDA stream) {
+  HIPCachingAllocator::recordStream(ptr, stream.hip_stream());
+}
+
+} // namespace HIPCachingAllocatorMasqueradingAsCUDA
+}} // namespace c10::hip

aten/src/ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.h

@@ -0,0 +1,194 @@
+#pragma once
+
+#include <c10/hip/HIPCachingAllocator.h>
+#include <ATen/hip/impl/HIPAllocatorMasqueradingAsCUDA.h>
+#include <ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h>
+
+namespace c10 {
+// forward declaration
+class DataPtr;
+namespace hip {
+namespace HIPCachingAllocatorMasqueradingAsCUDA {
+
+C10_HIP_API HIPCachingAllocator::HIPAllocator* get();
+C10_HIP_API void recordStreamMasqueradingAsCUDA(const DataPtr& ptr, HIPStreamMasqueradingAsCUDA stream);
+
+inline void* raw_alloc(size_t nbytes) {
+  return get()->raw_alloc(nbytes);
+}
+
+inline void* raw_alloc_with_stream(size_t nbytes, hipStream_t stream) {
+  return get()->raw_alloc_with_stream(nbytes, stream);
+}
+
+inline void raw_delete(void* ptr) {
+  return get()->raw_delete(ptr);
+}
+
+inline void init(int device_count) {
+  return get()->init(device_count);
+}
+
+inline double getMemoryFraction(c10::DeviceIndex device) {
+  return get()->getMemoryFraction(device);
+}
+
+inline void setMemoryFraction(double fraction, c10::DeviceIndex device) {
+  return get()->setMemoryFraction(fraction, device);
+}
+
+inline void emptyCache(MempoolId_t mempool_id = {0, 0}) {
+  return get()->emptyCache(mempool_id);
+}
+
+inline void enable(bool value) {
+  return get()->enable(value);
+}
+
+inline bool isEnabled() {
+  return get()->isEnabled();
+}
+
+inline void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) {
+  return get()->cacheInfo(device, largestBlock);
+}
+
+inline void* getBaseAllocation(void* ptr, size_t* size) {
+  return get()->getBaseAllocation(ptr, size);
+}
+
+inline c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+    c10::DeviceIndex device) {
+  return get()->getDeviceStats(device);
+}
+
+inline void resetAccumulatedStats(c10::DeviceIndex device) {
+  return get()->resetAccumulatedStats(device);
+}
+
+inline void resetPeakStats(c10::DeviceIndex device) {
+  return get()->resetPeakStats(device);
+}
+
+inline HIPCachingAllocator::SnapshotInfo snapshot(MempoolId_t mempool_id = {0, 0}) {
+  return get()->snapshot(mempool_id);
+}
+
+inline std::shared_ptr<HIPCachingAllocator::AllocatorState> getCheckpointState(
+    c10::DeviceIndex device,
+    MempoolId_t id) {
+  return get()->getCheckpointState(device, id);
+}
+
+inline HIPCachingAllocator::CheckpointDelta setCheckpointPoolState(
+    c10::DeviceIndex device,
+    std::shared_ptr<HIPCachingAllocator::AllocatorState> pps) {
+  return get()->setCheckpointPoolState(device, std::move(pps));
+}
+
+inline void beginAllocateToPool(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    std::function<bool(hipStream_t)> filter) {
+  get()->beginAllocateToPool(device, mempool_id, std::move(filter));
+}
+
+inline void endAllocateToPool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  get()->endAllocateToPool(device, mempool_id);
+}
+
+inline void recordHistory(
+    bool enabled,
+    HIPCachingAllocator::CreateContextFn context_recorder,
+    size_t alloc_trace_max_entries,
+    HIPCachingAllocator::RecordContext when,
+    bool clearHistory) {
+  return get()->recordHistory(
+      enabled, context_recorder, alloc_trace_max_entries, when, clearHistory);
+}
+
+inline void recordAnnotation(
+    const std::vector<std::pair<std::string, std::string>>& md) {
+  return get()->recordAnnotation(md);
+}
+
+inline void pushCompileContext(std::string& md) {
+  return get()->pushCompileContext(md);
+}
+
+inline void popCompileContext() {
+  return get()->popCompileContext();
+}
+
+inline bool isHistoryEnabled() {
+  return get()->isHistoryEnabled();
+}
+
+inline bool checkPoolLiveAllocations(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    const std::unordered_set<void*>& expected_live_allocations) {
+  return get()->checkPoolLiveAllocations(
+      device, mempool_id, expected_live_allocations);
+}
+
+inline void attachOutOfMemoryObserver(HIPCachingAllocator::OutOfMemoryObserver observer) {
+  return get()->attachOutOfMemoryObserver(std::move(observer));
+}
+
+inline void attachAllocatorTraceTracker(HIPCachingAllocator::AllocatorTraceTracker tracker) {
+  return get()->attachAllocatorTraceTracker(std::move(tracker));
+}
+
+inline void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  return get()->releasePool(device, mempool_id);
+}
+
+inline void createOrIncrefPool(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    HIPCachingAllocator::HIPAllocator* allocator_ptr = nullptr) {
+  get()->createOrIncrefPool(device, mempool_id, allocator_ptr);
+}
+
+inline void setUseOnOOM(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  get()->setUseOnOOM(device, mempool_id);
+}
+
+inline int getPoolUseCount(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  return get()->getPoolUseCount(device, mempool_id);
+}
+
+inline std::shared_ptr<void> getIpcDevPtr(std::string handle) {
+  return get()->getIpcDevPtr(std::move(handle));
+}
+
+inline HIPCachingAllocator::ShareableHandle shareIpcHandle(void* ptr) {
+  return get()->shareIpcHandle(ptr);
+}
+
+inline std::string name() {
+  return get()->name();
+}
+
+inline hipError_t memcpyAsync(
+    void* dst,
+    int dstDevice,
+    const void* src,
+    int srcDevice,
+    size_t count,
+    hipStream_t stream,
+    bool p2p_enabled) {
+  return get()->memcpyAsync(
+      dst, dstDevice, src, srcDevice, count, stream, p2p_enabled);
+}
+
+inline void enablePeerAccess(
+    c10::DeviceIndex dev,
+    c10::DeviceIndex dev_to_access) {
+  return get()->enablePeerAccess(dev, dev_to_access);
+}
+
+} // namespace HIPCachingAllocatorMasqueradingAsCUDA
+} // namespace hip
+} // namespace c10

aten/src/ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.cpp

@@ -0,0 +1,14 @@
+#include <ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h>
+
+// THIS IS A MASSIVE HACK.  This will BREAK you Caffe2 CUDA code if you
+// load ATen_hip, even if you don't ever actually use ATen_hip at runtime.
+//
+// If you ever link ATen_hip statically into the full library along
+// with ATen_cuda (libomnibus), the loading order of this versus the regular
+// ATen_cuda will be nondeterministic, and you'll nondeterministically get
+// one or the other.  (This will be obvious because all of your code
+// will fail.)
+//
+// This hack can be removed once PyTorch is out-of-place HIPified, and
+// doesn't pretend CUDA is HIP.
+C10_REGISTER_GUARD_IMPL(CUDA, at::cuda::HIPGuardImplMasqueradingAsCUDA)

aten/src/ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h

@@ -0,0 +1,383 @@
+#pragma once
+
+#include <ATen/hip/HIPConfig.h>
+
+// The includes of HIPGuard.h
+#include <c10/hip/impl/HIPGuardImpl.h>
+#include <c10/hip/HIPMacros.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/impl/InlineDeviceGuard.h>
+#include <c10/core/impl/InlineStreamGuard.h>
+#include <c10/util/Exception.h>
+
+#include <c10/hip/impl/HIPGuardImpl.h>
+
+#include <ATen/hip/impl/HIPCachingAllocatorMasqueradingAsCUDA.h>
+#include <ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h>
+
+// Use of c10::hip namespace here makes hipification easier, because
+// I don't have to also fix namespaces.  Sorry!
+namespace c10 { namespace hip {
+
+// Note [Masquerading as CUDA]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// c10_hip is very easy to understand: it is HIPified from c10_cuda,
+// and anywhere you said CUDA, the source code now says HIP.  HIPified
+// PyTorch is much harder to understand: it is HIPified from regular
+// PyTorch, yes, but NO source-to-source translation from CUDA to
+// HIP occurs; instead, anywhere we see "CUDA", it actually means "HIP".
+// For example, when you use HIPified PyTorch, you say x.cuda() to
+// move a tensor onto ROCm device.  We call this situation "HIP
+// masquerading as CUDA".
+//
+// This leads to a very awkward situation when we want to call c10_hip
+// code from PyTorch, since c10_hip is expecting things to be called
+// HIP, but PyTorch is calling them CUDA (masquerading as HIP).  To
+// fix this impedance mismatch, we have MasqueradingAsCUDA variants
+// for all c10_hip classes.  These translate between the "HIP" and "CUDA
+// masquerading as HIP" worlds.  For example,
+// HIPGuardImplMasqueradingAsCUDA (this file) provides something like a
+// HIPGuardImpl, but it reports its DeviceType as CUDA (e.g., type()
+// returns CUDA, getDevice() reports the current HIP device as a CUDA
+// device.)
+//
+// We should be able to delete all of these classes entirely once
+// we switch PyTorch to calling a HIP a HIP.
+//
+// When you add a new MasqueradingAsCUDA class/function, you need to
+// also update the rewrite rules in torch/utils/hipify/cuda_to_hip_mappings.py
+//
+//
+//
+// By the way, note that the cpp file associated with this also
+// *overwrites* the entry in the DeviceGuardImpl registry for CUDA with
+// this HIP implementation.
+
+struct HIPGuardImplMasqueradingAsCUDA final : public c10::impl::DeviceGuardImplInterface {
+  static constexpr c10::DeviceType static_type = c10::DeviceType::CUDA;
+  HIPGuardImplMasqueradingAsCUDA() {}
+  HIPGuardImplMasqueradingAsCUDA(c10::DeviceType t) {
+    TORCH_INTERNAL_ASSERT(t == c10::DeviceType::CUDA);
+  }
+  c10::DeviceType type() const override {
+    return c10::DeviceType::CUDA;
+  }
+  Device exchangeDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    Device old_device = getDevice();
+    if (old_device.index() != d.index()) {
+      C10_HIP_CHECK(hipSetDevice(d.index()));
+    }
+    return old_device;
+  }
+  Device getDevice() const override {
+    int device;
+    C10_HIP_CHECK(hipGetDevice(&device));
+    return Device(c10::DeviceType::CUDA, device);
+  }
+  void setDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    C10_HIP_CHECK(hipSetDevice(d.index()));
+  }
+  void uncheckedSetDevice(Device d) const noexcept override {
+    C10_HIP_CHECK_WARN(hipSetDevice(d.index()));
+  }
+  Stream getStream(Device d) const override {
+    return getCurrentHIPStreamMasqueradingAsCUDA(d.index()).unwrap();
+  }
+  Stream getDefaultStream(Device d) const override {
+    return getDefaultHIPStreamMasqueradingAsCUDA(d.index());
+  }
+  Stream getNewStream(Device d, int priority = 0) const override {
+    return getStreamFromPoolMasqueradingAsCUDA(priority, d.index());
+  }
+  Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false) const override {
+    return getStreamFromPoolMasqueradingAsCUDA(isHighPriority, d.index());
+  }
+  Stream exchangeStream(Stream s) const override {
+    HIPStreamMasqueradingAsCUDA cs(s);
+    auto old_stream = getCurrentHIPStreamMasqueradingAsCUDA(s.device().index());
+    setCurrentHIPStreamMasqueradingAsCUDA(cs);
+    return old_stream.unwrap();
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    int deviceCnt;
+    hipError_t _err;
+    _err = hipGetDeviceCount(&deviceCnt);
+    if(_err != hipErrorNoDevice && _err != hipSuccess)
+        C10_HIP_CHECK(_err);
+    return deviceCnt;
+  }
+
+  // Event-related functions
+  // Note: hipEventCreateWithFlags should be called on the same device as
+  //  the recording stream's device.
+  void createEvent(
+    hipEvent_t* hip_event,
+    const EventFlag flag) const {
+    // Maps PyTorch's Event::Flag to HIP flag
+    auto hip_flag = hipEventDefault;
+    switch (flag) {
+      case EventFlag::PYTORCH_DEFAULT:
+        hip_flag = hipEventDisableTiming;
+        break;
+      case EventFlag::BACKEND_DEFAULT:
+        hip_flag = hipEventDefault;
+        break;
+      default:
+        TORCH_CHECK(false, "HIP event received unknown flag");
+    }
+
+    C10_HIP_CHECK(hipEventCreateWithFlags(hip_event, hip_flag));
+  }
+
+  void destroyEvent(
+    void* event,
+    const DeviceIndex device_index) const noexcept override {
+    if (!event) return;
+    auto hip_event = static_cast<hipEvent_t>(event);
+    int orig_device;
+    C10_HIP_CHECK_WARN(hipGetDevice(&orig_device));
+    C10_HIP_CHECK_WARN(hipSetDevice(device_index));
+    C10_HIP_CHECK_WARN(hipEventDestroy(hip_event));
+    C10_HIP_CHECK_WARN(hipSetDevice(orig_device));
+  }
+
+  void record(void** event,
+    const Stream& stream,
+    const DeviceIndex device_index,
+    const EventFlag flag) const override {
+    TORCH_CHECK(device_index == -1 || device_index == stream.device_index(),
+      "Event device index ",
+      device_index,
+      " does not match recording stream's device index ",
+      stream.device_index(),
+      ".");
+
+    hipEvent_t hip_event = static_cast<hipEvent_t>(*event);
+    HIPStreamMasqueradingAsCUDA hip_stream{stream};
+
+    // Moves to stream's device to record
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+
+    // Creates the event (lazily)
+    if (!hip_event) createEvent(&hip_event, flag);
+    C10_HIP_CHECK(hipEventRecord(hip_event, hip_stream));
+    // Makes the void* point to the (possibly just allocated) HIP event
+    *event = hip_event;
+
+    // Resets device
+    setDevice(orig_device);
+  }
+
+  void block(
+    void* event,
+    const Stream& stream) const override {
+    if (!event) return;
+    hipEvent_t hip_event = static_cast<hipEvent_t>(event);
+    HIPStreamMasqueradingAsCUDA hip_stream{stream};
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+    C10_HIP_CHECK(hipStreamWaitEvent(
+      hip_stream,
+      hip_event,
+      /*flags (must be zero)=*/ 0));
+    setDevice(orig_device);
+  }
+
+  bool queryEvent(void* event) const override {
+    if (!event) return true;
+    hipEvent_t hip_event = static_cast<hipEvent_t>(event);
+    const hipError_t err = hipEventQuery(hip_event);
+    if (err != hipErrorNotReady) C10_HIP_CHECK(err);
+    else {
+      // ignore and clear the error if not ready
+      (void)hipGetLastError();
+    }
+    return (err == hipSuccess);
+  }
+
+  // Stream-related functions
+  bool queryStream(const Stream& stream) const override {
+    HIPStreamMasqueradingAsCUDA hip_stream{stream};
+    return hip_stream.query();
+  }
+
+  void synchronizeStream(const Stream& stream) const override {
+    HIPStreamMasqueradingAsCUDA hip_stream{stream};
+    hip_stream.synchronize();
+  }
+
+  void synchronizeEvent(void* event) const override {
+    if (!event)
+      return;
+    hipEvent_t hip_event = static_cast<hipEvent_t>(event);
+    C10_HIP_CHECK(hipEventSynchronize(hip_event));
+  }
+
+  // Note: synchronizeDevice can be safely called from any device
+  void synchronizeDevice(const c10::DeviceIndex device_index) const override {
+    int orig_device{-1};
+    C10_HIP_CHECK(hipGetDevice(&orig_device));
+    C10_HIP_CHECK(hipSetDevice(device_index));
+    C10_HIP_CHECK(hipDeviceSynchronize());
+    C10_HIP_CHECK(hipSetDevice(orig_device));
+  }
+
+  void recordDataPtrOnStream(
+    const c10::DataPtr& data_ptr,
+    const Stream& stream) const override {
+    HIPStreamMasqueradingAsCUDA hip_stream{stream};
+    HIPCachingAllocatorMasqueradingAsCUDA::recordStreamMasqueradingAsCUDA(data_ptr, hip_stream);
+  }
+
+  double elapsedTime(void* event1, void* event2, const DeviceIndex device_index)
+      const override {
+    TORCH_CHECK(
+        event1 && event2,
+        "Both events must be recorded before calculating elapsed time.");
+    int orig_device;
+    C10_HIP_CHECK(hipGetDevice(&orig_device));
+    C10_HIP_CHECK(hipSetDevice(device_index));
+    hipEvent_t hip_event1 = static_cast<hipEvent_t>(event1);
+    hipEvent_t hip_event2 = static_cast<hipEvent_t>(event2);
+    float time_ms = 0;
+    // raise hipErrorNotReady if either event is recorded but not yet completed
+    C10_HIP_CHECK(hipEventElapsedTime(&time_ms, hip_event1, hip_event2));
+    C10_HIP_CHECK(hipSetDevice(orig_device));
+    return static_cast<double>(time_ms);
+  }
+};
+
+// All of the guards which have HIPGuardImpl burned in need to also have
+// variants using HIPGuardImplMasqueradingAsCUDA.
+
+/// This code is all a direct copy from c10/cuda/HIPGuardMasqueradingAsCUDA.h, but with
+/// the correct InlineDeviceGuard burned in.  Sorry about the
+/// copy-pasting.
+
+struct HIPGuardMasqueradingAsCUDA {
+  explicit HIPGuardMasqueradingAsCUDA() = delete;
+  explicit HIPGuardMasqueradingAsCUDA(DeviceIndex device_index) : guard_(device_index) {}
+  explicit HIPGuardMasqueradingAsCUDA(Device device) : guard_(device) {}
+
+  HIPGuardMasqueradingAsCUDA(const HIPGuardMasqueradingAsCUDA&) = delete;
+  HIPGuardMasqueradingAsCUDA& operator=(const HIPGuardMasqueradingAsCUDA&) = delete;
+  HIPGuardMasqueradingAsCUDA(HIPGuardMasqueradingAsCUDA&& other) = delete;
+  HIPGuardMasqueradingAsCUDA& operator=(HIPGuardMasqueradingAsCUDA&& other) = delete;
+
+  void set_device(Device device) { guard_.set_device(device); }
+  void reset_device(Device device) { guard_.reset_device(device); }
+  void set_index(DeviceIndex device_index) { guard_.set_index(device_index); }
+  Device original_device() const { return guard_.original_device(); }
+  Device current_device() const { return guard_.current_device(); }
+
+ private:
+  c10::impl::InlineDeviceGuard<HIPGuardImplMasqueradingAsCUDA> guard_;
+};
+
+struct OptionalHIPGuardMasqueradingAsCUDA {
+  explicit OptionalHIPGuardMasqueradingAsCUDA() : guard_() {}
+  explicit OptionalHIPGuardMasqueradingAsCUDA(std::optional<Device> device_opt) : guard_(device_opt) {}
+  explicit OptionalHIPGuardMasqueradingAsCUDA(std::optional<DeviceIndex> device_index_opt) : guard_(device_index_opt) {}
+
+  OptionalHIPGuardMasqueradingAsCUDA(const OptionalHIPGuardMasqueradingAsCUDA&) = delete;
+  OptionalHIPGuardMasqueradingAsCUDA& operator=(const OptionalHIPGuardMasqueradingAsCUDA&) = delete;
+  OptionalHIPGuardMasqueradingAsCUDA(OptionalHIPGuardMasqueradingAsCUDA&& other) = delete;
+  OptionalHIPGuardMasqueradingAsCUDA& operator=(OptionalHIPGuardMasqueradingAsCUDA&& other) = delete;
+
+  void set_device(Device device) { guard_.set_device(device); }
+  void reset_device(Device device) { guard_.reset_device(device); }
+  void set_index(DeviceIndex device_index) { guard_.set_index(device_index); }
+  std::optional<Device> original_device() const { return guard_.original_device(); }
+  std::optional<Device> current_device() const { return guard_.current_device(); }
+  void reset() { guard_.reset(); }
+
+private:
+  c10::impl::InlineOptionalDeviceGuard<HIPGuardImplMasqueradingAsCUDA> guard_;
+};
+
+struct HIPStreamGuardMasqueradingAsCUDA {
+  explicit HIPStreamGuardMasqueradingAsCUDA() = delete;
+  explicit HIPStreamGuardMasqueradingAsCUDA(Stream stream) : guard_(stream) {}
+  HIPStreamGuardMasqueradingAsCUDA(const HIPStreamGuardMasqueradingAsCUDA&) = delete;
+  HIPStreamGuardMasqueradingAsCUDA& operator=(const HIPStreamGuardMasqueradingAsCUDA&) = delete;
+  HIPStreamGuardMasqueradingAsCUDA(HIPStreamGuardMasqueradingAsCUDA&& other) = delete;
+  HIPStreamGuardMasqueradingAsCUDA& operator=(HIPStreamGuardMasqueradingAsCUDA&& other) = delete;
+
+  void reset_stream(Stream stream) { guard_.reset_stream(stream); }
+
+  HIPStreamMasqueradingAsCUDA original_stream() const {
+    return HIPStreamMasqueradingAsCUDA(HIPStreamMasqueradingAsCUDA::UNCHECKED, guard_.original_stream());
+  }
+  HIPStreamMasqueradingAsCUDA current_stream() const {
+    return HIPStreamMasqueradingAsCUDA(HIPStreamMasqueradingAsCUDA::UNCHECKED, guard_.current_stream());
+  }
+
+  Device current_device() const { return guard_.current_device(); }
+  Device original_device() const { return guard_.original_device(); }
+
+private:
+  c10::impl::InlineStreamGuard<HIPGuardImplMasqueradingAsCUDA> guard_;
+};
+
+struct OptionalHIPStreamGuardMasqueradingAsCUDA {
+  explicit OptionalHIPStreamGuardMasqueradingAsCUDA() : guard_() {}
+  explicit OptionalHIPStreamGuardMasqueradingAsCUDA(Stream stream) : guard_(stream) {}
+  explicit OptionalHIPStreamGuardMasqueradingAsCUDA(std::optional<Stream> stream_opt) : guard_(stream_opt) {}
+
+  OptionalHIPStreamGuardMasqueradingAsCUDA(const OptionalHIPStreamGuardMasqueradingAsCUDA&) = delete;
+  OptionalHIPStreamGuardMasqueradingAsCUDA& operator=(const OptionalHIPStreamGuardMasqueradingAsCUDA&) = delete;
+  OptionalHIPStreamGuardMasqueradingAsCUDA(OptionalHIPStreamGuardMasqueradingAsCUDA&& other) = delete;
+  OptionalHIPStreamGuardMasqueradingAsCUDA& operator=(OptionalHIPStreamGuardMasqueradingAsCUDA&& other) = delete;
+
+  void reset_stream(Stream stream) { guard_.reset_stream(stream); }
+
+  std::optional<HIPStreamMasqueradingAsCUDA> original_stream() const {
+    auto r = guard_.original_stream();
+    if (r.has_value()) {
+      return HIPStreamMasqueradingAsCUDA(HIPStreamMasqueradingAsCUDA::UNCHECKED, r.value());
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  std::optional<HIPStreamMasqueradingAsCUDA> current_stream() const {
+    auto r = guard_.current_stream();
+    if (r.has_value()) {
+      return HIPStreamMasqueradingAsCUDA(HIPStreamMasqueradingAsCUDA::UNCHECKED, r.value());
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  void reset() { guard_.reset(); }
+
+private:
+  c10::impl::InlineOptionalStreamGuard<HIPGuardImplMasqueradingAsCUDA> guard_;
+};
+
+struct HIPMultiStreamGuardMasqueradingAsCUDA {
+  explicit HIPMultiStreamGuardMasqueradingAsCUDA(ArrayRef<HIPStreamMasqueradingAsCUDA> streams)
+    : guard_(unwrapStreams(streams)) {}
+
+  HIPMultiStreamGuardMasqueradingAsCUDA(const HIPMultiStreamGuardMasqueradingAsCUDA&) = delete;
+  HIPMultiStreamGuardMasqueradingAsCUDA& operator=(const HIPMultiStreamGuardMasqueradingAsCUDA&) = delete;
+  HIPMultiStreamGuardMasqueradingAsCUDA(HIPMultiStreamGuardMasqueradingAsCUDA&& other) = delete;
+  HIPMultiStreamGuardMasqueradingAsCUDA& operator=(HIPMultiStreamGuardMasqueradingAsCUDA&& other) = delete;
+
+private:
+  c10::impl::InlineMultiStreamGuard<HIPGuardImplMasqueradingAsCUDA> guard_;
+
+  static std::vector<Stream> unwrapStreams(ArrayRef<HIPStreamMasqueradingAsCUDA> hipStreams) {
+    std::vector<Stream> streams;
+    streams.reserve(hipStreams.size());
+    for (const HIPStreamMasqueradingAsCUDA& hipStream : hipStreams) {
+      streams.push_back(hipStream);
+    }
+    return streams;
+  }
+};
+
+}} // namespace c10::hip

aten/src/ATen/hip/impl/HIPStreamMasqueradingAsCUDA.h

@@ -0,0 +1,135 @@
+#pragma once
+
+#include <c10/hip/HIPStream.h>
+
+// Use of c10::hip namespace here makes hipification easier, because
+// I don't have to also fix namespaces.  Sorry!
+namespace c10 { namespace hip {
+
+// See Note [Masquerading as CUDA] for motivation
+
+class HIPStreamMasqueradingAsCUDA {
+public:
+
+  enum Unchecked { UNCHECKED };
+
+  explicit HIPStreamMasqueradingAsCUDA(Stream stream)
+    : HIPStreamMasqueradingAsCUDA(UNCHECKED, stream) {
+    // We did the coercion unchecked; check that it was right.
+    TORCH_CHECK(stream.device().is_cuda() /* !!! */);
+  }
+
+  explicit HIPStreamMasqueradingAsCUDA(Unchecked, Stream stream)
+    // Unsafely coerce the "CUDA" stream into a HIP stream
+    : stream_(
+        HIPStream(
+          Stream(
+            Stream::UNSAFE,
+            Device(c10::DeviceType::HIP, stream.device_index()),
+            stream.id())
+        )
+      ) {}
+
+  // New constructor, just for this.  Does NOT coerce.
+  explicit HIPStreamMasqueradingAsCUDA(HIPStream stream) : stream_(stream) {}
+
+  bool operator==(const HIPStreamMasqueradingAsCUDA& other) const noexcept {
+    return stream_ == other.stream_;
+  }
+
+  bool operator!=(const HIPStreamMasqueradingAsCUDA& other) const noexcept {
+    return stream_ != other.stream_;
+  }
+
+  operator hipStream_t() const { return stream_.stream(); }
+
+  operator Stream() const {
+    // Unsafely coerce HIP stream into a "CUDA" stream
+    return Stream(Stream::UNSAFE, device(), id());
+  }
+
+  DeviceIndex device_index() const { return stream_.device_index(); }
+
+  // Unsafely coerce HIP device into CUDA device
+  c10::DeviceType device_type() const { return c10::DeviceType::CUDA; }
+
+  Device device() const {
+    // Unsafely coerce HIP device into CUDA device
+    return Device(c10::DeviceType::CUDA, stream_.device_index());
+  }
+
+  StreamId id() const        { return stream_.id(); }
+  bool query() const         { return stream_.query(); }
+  void synchronize() const   { stream_.synchronize(); }
+  int priority() const       { return stream_.priority(); }
+  hipStream_t stream() const { return stream_.stream(); }
+
+  Stream unwrap() const {
+    // Unsafely coerce HIP stream into "CUDA" stream
+    return Stream(Stream::UNSAFE, device(), id());
+  }
+
+  c10::StreamData3 pack3() const noexcept {
+    // Unsafely coerce HIP stream into "CUDA" stream before packing
+    return unwrap().pack3();
+  }
+
+  static HIPStreamMasqueradingAsCUDA unpack3(StreamId stream_id,
+                                             DeviceIndex device_index,
+                                             c10::DeviceType device_type) {
+    // NB: constructor manages CUDA->HIP translation for us
+    return HIPStreamMasqueradingAsCUDA(Stream::unpack3(
+        stream_id, device_index, device_type));
+  }
+
+  static std::tuple<int, int> priority_range() { return HIPStream::priority_range(); }
+
+  // New method, gets the underlying HIPStream
+  HIPStream hip_stream() const { return stream_; }
+
+private:
+  HIPStream stream_;
+};
+
+HIPStreamMasqueradingAsCUDA
+inline getStreamFromPoolMasqueradingAsCUDA(const bool isHighPriority = false, DeviceIndex device = -1) {
+  return HIPStreamMasqueradingAsCUDA(getStreamFromPool(isHighPriority, device));
+}
+
+HIPStreamMasqueradingAsCUDA
+inline getStreamFromPoolMasqueradingAsCUDA(const int priority, DeviceIndex device = -1) {
+  return HIPStreamMasqueradingAsCUDA(getStreamFromPool(priority, device));
+}
+
+HIPStreamMasqueradingAsCUDA
+inline getStreamFromExternalMasqueradingAsCUDA(hipStream_t ext_stream, DeviceIndex device) {
+  return HIPStreamMasqueradingAsCUDA(getStreamFromExternal(ext_stream, device));
+}
+
+inline HIPStreamMasqueradingAsCUDA getDefaultHIPStreamMasqueradingAsCUDA(DeviceIndex device_index = -1) {
+  return HIPStreamMasqueradingAsCUDA(getDefaultHIPStream(device_index));
+}
+
+inline HIPStreamMasqueradingAsCUDA getCurrentHIPStreamMasqueradingAsCUDA(DeviceIndex device_index = -1) {
+  return HIPStreamMasqueradingAsCUDA(getCurrentHIPStream(device_index));
+}
+
+inline void setCurrentHIPStreamMasqueradingAsCUDA(HIPStreamMasqueradingAsCUDA stream) {
+  setCurrentHIPStream(stream.hip_stream());
+}
+
+inline std::ostream& operator<<(std::ostream& stream, const HIPStreamMasqueradingAsCUDA& s) {
+  stream << s.hip_stream() << " (masquerading as CUDA)";
+  return stream;
+}
+
+}} // namespace c10::hip
+
+namespace std {
+  template <>
+  struct hash<c10::hip::HIPStreamMasqueradingAsCUDA> {
+    size_t operator()(c10::hip::HIPStreamMasqueradingAsCUDA s) const noexcept {
+      return std::hash<c10::Stream>{}(s.unwrap());
+    }
+  };
+} // namespace std

aten/src/ATen/miopen/Handle.cpp

@@ -39,7 +39,7 @@ using MIOpenPoolType = at::cuda::DeviceThreadHandlePool<
 
 miopenHandle_t getMiopenHandle() {
   c10::DeviceIndex device = 0;
-  AT_CUDA_CHECK(at::cuda::GetDevice(&device));
+  AT_CUDA_CHECK(c10::hip::GetDevice(&device));
 
   // Thread local PoolWindows are lazily-initialized
   // to avoid initialization issues that caused hangs on Windows.
@@ -51,7 +51,7 @@ miopenHandle_t getMiopenHandle() {
       pool->newPoolWindow());
 
   auto handle = myPoolWindow->reserve(device);
-  MIOPEN_CHECK(miopenSetStream(handle, at::cuda::getCurrentCUDAStream()));
+  MIOPEN_CHECK(miopenSetStream(handle, c10::hip::getCurrentHIPStream()));
   return handle;
 }
 

aten/src/ATen/mps/MPSAllocator.mm

@@ -440,7 +440,7 @@ bool MPSHeapAllocatorImpl::release_cached_buffers() {
   // we need to release the lock temporarily as synchronizing may cause deadlock with completion handlers.
   m_mutex.unlock();
   auto stream = getDefaultMPSStream();
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  dispatch_sync(stream->queue(), ^() {
     stream->synchronize(SyncType::COMMIT_AND_WAIT);
   });
   m_mutex.lock();

aten/src/ATen/mps/MPSStream.h

@@ -110,9 +110,6 @@ class TORCH_API MPSStream {
     return _stream;
   }
 
-  MTLBuffer_t getErrorBuffer();
-  void checkLastError();
-
  private:
   Stream _stream;
   MTLCommandQueue_t _commandQueue = nil;
@@ -124,8 +121,6 @@ class TORCH_API MPSStream {
   dispatch_queue_t _serialQueue = nullptr;
   // CommitAndContinue is enabled by default
   bool _enableCommitAndContinue = true;
-  // Buffer that contains last raised error
-  MTLBuffer_t _errorBuffer = nil;
 
   // use synchronize() to access any of these commit functions outside MPSStream
   void commit();
@@ -160,7 +155,4 @@ class TORCH_API MPSStreamImpl {
   MPSStreamImpl();
 };
 
-#ifdef __OBJC__
-void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)());
-#endif
 } // namespace at::mps

aten/src/ATen/mps/MPSStream.mm

@@ -3,13 +3,13 @@
 #include <ATen/mps/MPSAllocatorInterface.h>
 #include <ATen/mps/MPSProfiler.h>
 #include <ATen/mps/MPSStream.h>
-#include <c10/metal/error.h>
 
 @interface MPSGraphExecutionDescriptor ()
 @property(readwrite, atomic) BOOL enableCommitAndContinue;
 @end
 
 namespace at::mps {
+
 //-----------------------------------------------------------------
 //  MPSStream
 //-----------------------------------------------------------------
@@ -30,10 +30,6 @@ MPSStream::MPSStream(Stream stream) : _stream(stream) {
   // Choose level which optimizes for GPU
   _compilationDescriptor.optimizationLevel = MPSGraphOptimizationLevel0;
   _executionDescriptor.compilationDescriptor = _compilationDescriptor;
-
-  _errorBuffer = [MPSDevice::getInstance()->device() newBufferWithLength:sizeof(c10::metal::ErrorMessages)
-                                                                 options:MTLResourceStorageModeShared];
-  std::memset([_errorBuffer contents], 0, 1024);
 }
 
 MPSStream::~MPSStream() {
@@ -42,8 +38,6 @@ MPSStream::~MPSStream() {
   [_executionDescriptor release];
   [_compilationDescriptor release];
   _executionDescriptor = nil;
-  [_errorBuffer release];
-  _errorBuffer = nil;
   _compilationDescriptor = nil;
 
   assert(_commandBuffer == nil);
@@ -110,7 +104,6 @@ void MPSStream::commitAndWait() {
     [_prevCommandBuffer waitUntilCompleted];
     [_prevCommandBuffer release];
     _prevCommandBuffer = nil;
-    checkLastError();
   }
 
   if (_commandBuffer) {
@@ -118,7 +111,6 @@ void MPSStream::commitAndWait() {
     [_commandBuffer waitUntilCompleted];
     [_commandBuffer release];
     _commandBuffer = nil;
-    checkLastError();
   }
 }
 
@@ -161,7 +153,7 @@ void MPSStream::fill(id<MTLBuffer> buffer, uint8_t value, size_t length, size_t
   if (length == 0) {
     return;
   }
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -191,7 +183,7 @@ void MPSStream::copy(id<MTLBuffer> srcBuffer,
                      size_t dstOffset,
                      uint64_t profileId,
                      SyncType syncType) {
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -244,7 +236,7 @@ void MPSStream::executeMPSGraph(MPSGraph* mpsGraph, NSDictionary* feeds, NSDicti
   auto& profiler = getMPSProfiler();
   const bool isGraphProfilingEnabled = profiler.isOperationProfilingEnabled();
 
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     endKernelCoalescing();
     if (isGraphProfilingEnabled) {
       // this function call is only relevant for interval-based Signposts
@@ -274,24 +266,6 @@ void MPSStream::executeMPSGraph(MPSGraph* mpsGraph, NSDictionary* feeds, NSDicti
   });
 }
 
-id<MTLBuffer> MPSStream::getErrorBuffer() {
-  return _errorBuffer;
-}
-
-void MPSStream::checkLastError() {
-  auto msgs = reinterpret_cast<c10::metal::ErrorMessages*>([_errorBuffer contents]);
-  const auto& msg = msgs->msg[0];
-  if (!msgs) {
-    return;
-  }
-  unsigned int count = 0;
-  std::swap(count, msgs->count);
-  if (!count) {
-    return;
-  }
-  throw c10::AcceleratorError({msg.func, msg.file, msg.line}, 1, msg.message);
-}
-
 //-----------------------------------------------------------------
 //  MPSStreamImpl
 //-----------------------------------------------------------------
@@ -315,19 +289,4 @@ MPSStream* getDefaultMPSStream() {
   return MPSStreamImpl::getInstance();
 }
 
-// Helper methods
-void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)()) {
-  __block std::optional<std::exception_ptr> block_exception;
-  dispatch_sync(queue, ^() {
-    try {
-      block();
-    } catch (...) {
-      block_exception = std::current_exception();
-    }
-  });
-  if (block_exception) {
-    std::rethrow_exception(*block_exception);
-  }
-}
-
 } // namespace at::mps

aten/src/ATen/native/BinaryOps.cpp

@@ -1009,25 +1009,12 @@ static Device correct_out_device(const Tensor& self, const Tensor& other) {
   }
 }
 
-static Tensor send_to_meta(const Tensor& self, const Device& device) {
-  Tensor out_meta;
-  if (self._is_zerotensor() && self.unsafeGetTensorImpl()->is_wrapped_number()) {
-    out_meta = at::_efficientzerotensor(self.sizes(), self.options().device(device));
-    out_meta.unsafeGetTensorImpl()->set_wrapped_number(true);
-  } else {
-    out_meta = self.to(device);
-  }
-  return out_meta;
-}
-
 Tensor mul_zerotensor(const Tensor& self, const Tensor& other) {
   auto out_device = correct_out_device(self, other);
   // hack to use the TensorIterator to get the correct broadcasting and type promotion logic
   auto device_ = Device(DeviceType::Meta);
   constexpr c10::DispatchKeySet meta_dks(at::DispatchKey::Meta);
-  auto self_meta = send_to_meta(self, device_);
-  auto other_meta = send_to_meta(other, device_);
-  auto meta_out = at::_ops::mul_Tensor::redispatch(meta_dks, self_meta, other_meta);
+  auto meta_out = at::_ops::mul_Tensor::redispatch(meta_dks, self.to(device_), other.to(device_));
   return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
 }
 
@@ -1036,9 +1023,7 @@ Tensor div_zerotensor(const Tensor& self, const Tensor& other) {
   // hack to use the TensorIterator to get the correct broadcasting and type promotion logic
   auto device_ = Device(DeviceType::Meta);
   constexpr c10::DispatchKeySet meta_dks(at::DispatchKey::Meta);
-  auto self_meta = send_to_meta(self, device_);
-  auto other_meta = send_to_meta(other, device_);
-  auto meta_out = at::_ops::div_Tensor::redispatch(meta_dks, self_meta, other_meta);
+  auto meta_out = at::_ops::div_Tensor::redispatch(meta_dks, self.to(device_), other.to(device_));
 
   if (self._is_zerotensor()) {
     if (other._is_zerotensor()) {
@@ -1067,9 +1052,8 @@ static Tensor maybe_add_maybe_sub(const Tensor& self, const Tensor& other, const
   // hack to use the TensorIterator to get the correct broadcasting and type promotion logic
   auto device_ = Device(DeviceType::Meta);
   constexpr c10::DispatchKeySet meta_dks(at::DispatchKey::Meta);
-  auto self_meta = send_to_meta(self, device_);
-  auto other_meta = send_to_meta(other, device_);
-  auto meta_out = at::_ops::add_Tensor::redispatch(meta_dks, self_meta, other_meta, alpha);
+  auto meta_out = at::_ops::add_Tensor::redispatch(
+      meta_dks, self.to(device_), other.to(device_), alpha);
 
   auto get_out_like = [&] (const Tensor& tensor)
   {

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().versionRuntimeCuDNN();
+    static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
     // 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().versionRuntimeCuDNN() : -1;
+      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionCuDNN() : -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/Distributions.h

@@ -5,13 +5,9 @@
 #include <c10/macros/Macros.h>
 #include <c10/util/MathConstants.h>
 
-// ROCm hip compiler doesn't work well with using std:: in kernel functions
-#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
+// ROCM hcc doesn't work well with using std:: in kernel functions
 #if defined(__CUDA_ARCH__)
 #include <c10/cuda/CUDAMathCompat.h>
-#elif defined(__HIPCC__)
-#include <c10/hip/HIPMathCompat.h>
-#endif
 #define compat_exp c10::cuda::compat::exp
 #define compat_ceil c10::cuda::compat::ceil
 #define compat_floor c10::cuda::compat::floor
@@ -21,6 +17,17 @@
 #define compat_tan c10::cuda::compat::tan
 #define compat_abs c10::cuda::compat::abs
 #define compat_log1p c10::cuda::compat::log1p
+#elif defined(__HIPCC__)
+#include <c10/hip/HIPMathCompat.h>
+#define compat_exp c10::hip::compat::exp
+#define compat_ceil c10::hip::compat::ceil
+#define compat_floor c10::hip::compat::floor
+#define compat_log c10::hip::compat::log
+#define compat_pow c10::hip::compat::pow
+#define compat_sqrt c10::hip::compat::sqrt
+#define compat_tan c10::hip::compat::tan
+#define compat_abs c10::hip::compat::abs
+#define compat_log1p c10::hip::compat::log1p
 #else
 #define compat_exp std::exp
 #define compat_ceil std::ceil