Update

.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/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/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
@@ -826,11 +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
@@ -1731,8 +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/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 @@
-ad5816f0eee1c873df1b7d371c69f1f811a89387
+3b0e7a6f192ca2715e7e6cbe5db007aea7165fe2

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-ccb801b88af136454798b945175c4c87e636ac33
+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/b200-distributed.yml

@@ -37,6 +37,7 @@ 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-distributed-b200
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'

.github/workflows/b200-symm-mem.yml

@@ -37,6 +37,7 @@ 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-sm100-symm
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'

.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

.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

@@ -52,6 +52,7 @@ 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-sm100
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'
@@ -72,4 +73,4 @@ jobs:
       docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.test-matrix }}
       aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit
+    secrets: inherit

.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/upload-test-stats.yml

@@ -11,16 +11,15 @@ on:
       - 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

@@ -18,8 +18,6 @@ Please report security issues using https://github.com/pytorch/pytorch/security/
 
 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.
-
 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

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/DeviceAccelerator.h

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

aten/src/ATen/Dispatch.h

@@ -6,7 +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.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/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/Linear.cpp

@@ -50,35 +50,18 @@ static inline bool parseLinearFlatten3d() {
 // `_flatten_nd_linear` flattens all but the last dimension of the input tensor
 // before passing it to linear operation
 static inline Tensor _flatten_nd_linear(const Tensor& input, const Tensor& weight, const Tensor& bias) {
-  const auto input_sizes = input.sym_sizes();
-
-  const auto result_flattened = [&]() -> Tensor {
-    const auto input_ncols = input_sizes.back();
-    const auto input_flattened_nrows = [&]() -> c10::SymInt {
-      // can't use -1 in reshape because it errors when a dimension is 0
-      auto flattened_nrows = c10::SymInt{1};
-      for (const auto& size : input_sizes.slice(0, input_sizes.size() - 1)) {
-        flattened_nrows *= size;
-      }
-      return flattened_nrows;
-    }();
-
-    const auto input_flattened = input.view_symint({input_flattened_nrows, input_ncols});
-    if (weight.layout() == c10::kStrided) {
-      return at::addmm(bias, input_flattened, weight.t());
-    } else {
-      // weight is sparse, and addmm for sparse expects matmul lhs to be sparse,
-      // so we transpose the problem.
-      // NOTE: at::matmul handles (dense @ sparse) similarly.
-      const auto bias_t = (bias.dim() >= 2) ? bias.mT() : bias.unsqueeze(-1);
-      return at::addmm(bias_t, weight, input_flattened.t()).t();
+    const auto input_sizes = input.sym_sizes();
+    // can't use -1 in reshape because it errors when a dimension is 0
+    c10::SymInt flattened_dim = 1;
+    for (int64_t i = 0, ndim = input_sizes.size(); i < ndim - 1; ++i) {
+      flattened_dim = flattened_dim * input_sizes[i];
     }
-  }();
-
-  // Unflatten flattened row dims
-  auto result_sizes = c10::SymDimVector{input_sizes.begin(), input_sizes.end()};
-  result_sizes.back() = result_flattened.sym_size(1);
-  return result_flattened.view_symint(result_sizes);
+    auto inp_reshape = input.reshape_symint({flattened_dim, input_sizes.at(input_sizes.size() -1)});
+    const auto result = at::addmm(bias, inp_reshape, weight.t());
+    auto new_size = input_sizes.slice(0, input_sizes.size() - 1);
+    c10::SymDimVector sizes_vec(new_size.begin(), new_size.end());
+    sizes_vec.push_back(result.sym_size(1));
+    return result.view_symint(sizes_vec);
 }
 
 
@@ -107,23 +90,15 @@ Tensor linear(const Tensor& input, const Tensor& weight, const std::optional<Ten
     // Fused op is marginally faster.
     return at::addmm(*bias, input, weight.t());
   }
-
-  const auto is_bias_likely_fusable = (
-      bias->defined() &&
-      // cuBLASLt: will fuse in the epilogue without copies
-      // when input/weight/bias are all strided.
-      // When weight is not strided, bias will not be fused,
-      // but we can still dispatch here to avoid at::matmul
-      // path which will probably use a very similar
-      // flattening optimization.
-      ((bias->dim() == 1 || bias->squeeze().dim() == 1) && bias->is_contiguous_or_false())
-  );
-  if (is_bias_likely_fusable && !input.is_xla()) {
-    // Also hit the fused path for contiguous nD input, if not using xla
+  if (bias->defined() && !input.is_xla()) {
+    // Also hit the fused path for contiguous 3D input, if not using xla
     // backend. Reshaping/flattening has some performance implications on xla.
-    if (input.is_contiguous_or_false()) {
+    bool is_contiguous = input.is_contiguous_or_false();
+    if (is_contiguous && input_dim == 3) {
       return _flatten_nd_linear(input, weight, *bias);
-    } else if (parseLinearFlatten3d()) {
+    } else if (is_contiguous && input.layout() == c10::kStrided && weight.layout() == c10::kStrided && bias->dim() == 1) {
+      return _flatten_nd_linear(input, weight, *bias);
+    } else if (parseLinearFlatten3d() && input_dim == 3) {
       // If user forces flattening via env var
       const Tensor input_cont = input.contiguous();
       return _flatten_nd_linear(input_cont, weight, *bias);

aten/src/ATen/native/TensorCompare.cpp

@@ -23,7 +23,6 @@
 #include <ATen/ops/_aminmax_native.h>
 #include <ATen/ops/_assert_async_native.h>
 #include <ATen/ops/_assert_scalar_native.h>
-#include <ATen/ops/_async_error_native.h>
 #include <ATen/ops/_functional_assert_async_native.h>
 #include <ATen/ops/_functional_assert_scalar_native.h>
 #include <ATen/ops/_make_per_tensor_quantized_tensor.h>
@@ -480,14 +479,6 @@ Tensor isfinite(const Tensor& self) {
   });
 }
 
-void _async_error(std::string_view msg) {
-  TORCH_CHECK(0, msg);
-}
-
-void _async_error_meta(std::string_view msg) {
-  // Do NOT error, it's an async error!
-}
-
 void _assert_async_cpu(const Tensor& self) {
   TORCH_CHECK(
       native::is_nonzero(self),

aten/src/ATen/native/TensorShape.cpp

@@ -1,6 +1,5 @@
 #include <ATen/core/ATen_fwd.h>
 #include <c10/core/ScalarType.h>
-#include <c10/core/SymInt.h>
 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
 #include <ATen/AccumulateType.h>
 #include <ATen/Dispatch.h>
@@ -1711,37 +1710,11 @@ Tensor narrow_symint(
       "], but got ",
       start,
       ")")
-
-  auto cond1 = TORCH_GUARD_OR_FALSE(start.sym_lt(0));
-  auto cond2 = TORCH_GUARD_OR_FALSE(start.sym_ge(0));
-
-  if (cond1 || cond2) {
-    if (cond1) {
-      start = start + cur_size;
-    }
-
-    TORCH_SYM_CHECK(
-        start.sym_le(cur_size - length),
-        "start (",
-        start,
-        ") + length (",
-        length,
-        ") exceeds dimension size (",
-        cur_size,
-        ").");
-    return at::slice_symint(self, dim, start, start + length, 1);
+  if (start < 0) {
+    start = start + cur_size;
   }
-
-  // Unbacked start handling!
-
-  // Bounds check without converting start:
-  // - If start < 0: need (start + cur_size) + length <= cur_size, i.e., start +
-  // length <= 0
-  // - If start >= 0: need start + length <= cur_size
-  auto end = start + length;
   TORCH_SYM_CHECK(
-      (start.sym_lt(0).sym_and((end).sym_le(0)))
-          .sym_or(start.sym_ge(0).sym_and((end).sym_le(cur_size))),
+      start.sym_le(cur_size - length),
       "start (",
       start,
       ") + length (",
@@ -1749,28 +1722,7 @@ Tensor narrow_symint(
       ") exceeds dimension size (",
       cur_size,
       ").");
-
-  if (TORCH_GUARD_OR_FALSE(end.sym_ne(0))) {
-    return at::slice_symint(self, dim, start, end, 1);
-  } else {
-    // Cannot statically determine the condition due to unbacked.
-    // This is an interesting situation; when start is negative and
-    // start + length == 0, slice and narrow do different things.
-    // i.e., x.narrow(0, -2, 2) != x[-2:0]; in that case, we want to
-    // pass curr_size instead of 0. Otherwise, they would do the same thing.
-    // This says at runtime: if start < 0 and end == 0, then pass curr_size
-    // instead of 0.
-
-    auto use_different = start.sym_lt(0).sym_and(end.sym_eq(0)).toSymInt();
-    auto result =
-        at::slice_symint(self, dim, start, end + use_different * cur_size, 1);
-
-    // Ensure slice allocated unbacked size is specialized to length.
-    SymInt new_size = result.sym_size(dim);
-    TORCH_SYM_CHECK(new_size.sym_eq(length), "")
-
-    return result;
-  }
+  return at::slice_symint(self, dim, start, start + length, 1);
 }
 
 // This overload exists purely for XLA, because they wanted to pass in
@@ -1784,8 +1736,8 @@ Tensor narrow_tensor_symint(
       start.dim() == 0 &&
           isIntegralType(start.scalar_type(), /*includeBool=*/false),
       "start must be an 0-dim integral Tensor.");
-  c10::SymInt st = start.item().toSymInt();
-  return at::narrow_symint(self, dim, std::move(st), std::move(length));
+  int64_t st = start.item<int64_t>();
+  return at::narrow_symint(self, dim, c10::SymInt(st), std::move(length));
 }
 
 std::

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

@@ -293,7 +293,7 @@ struct ComputeLocationBase<scalar_t, /*align_corners=*/false> {
     , empty(size <= 0) {}
 
   inline Vec unnormalize(const Vec &in) const {
-    return (in + Vec(static_cast<scalar_t>(1))) * Vec(scaling_factor) - Vec(static_cast<scalar_t>(0.5));
+    return (in + Vec(1)) * Vec(scaling_factor) - Vec(0.5);
   }
 
   inline Vec clip_coordinates(const Vec &in) const {
@@ -831,7 +831,7 @@ struct ApplyGridSample<scalar_t, 2, GridSamplerInterpolation::Bicubic,
 
   // constant used in cubic convolution
   // could be -0.5 or -0.75, use the same value in UpSampleBicubic2d.h
-  const Vec A = Vec(static_cast<scalar_t>(-0.75));
+  const Vec A = Vec(-0.75);
 
   ApplyGridSample(const TensorAccessor<const scalar_t, 4>& input)
     : inp_H(input.size(2))

aten/src/ATen/native/cuda/Blas.cpp

@@ -147,24 +147,14 @@ static bool isGloballyDisabledAddmmCudaLt(const at::Device& device) {
 /*
  * Check whether for the given input we want to enable the Lt interface
  */
-static bool isInputCompliesAddmmCudaLt(
-    Tensor& result,
-    const Tensor& self,
-    const Tensor& mat1,
-    const Tensor& mat2,
-    const Scalar& beta,
-    const Scalar& alpha,
-    Activation activation
-) {
-  #ifdef USE_ROCM
+static bool isInputCompliesAddmmCudaLt(Tensor& result, const Tensor& self, const Tensor& mat1, const Tensor& mat2, const Scalar& beta, const Scalar& alpha) {
   // Implies 2D bias which we currently not send through Lt.
   // TODO: this check is done pre col-major input preparation,
   // so, this condition can be ralexed in cases when a col-major
   // copy of result is needed.
-  if (self.is_same(result) || self.dim() == 2) {
+  if (result.is_same(self)) {
     return false;
   }
-  #endif
 
   #if defined(USE_ROCM) && ROCM_VERSION == 60400
   // hipblaslt TT fp32 regression on ROCm 6.4, cannot use
@@ -179,33 +169,13 @@ static bool isInputCompliesAddmmCudaLt(
   #if defined(CUDA_VERSION) || defined(USE_ROCM)
   const auto scalar_type = mat1.scalar_type();
   return (beta.toComplexDouble() == 1.0
-    // NOTE: row-major result is important when bias is 1D.
-    // This is because Lt broadcasts 1D bias over the columns
-    // while the aten::addmm API broadcasts it over the rows,
-    // and this is in conjuction with the data preparation
-    // procedure that does not transpose arguments with
-    // col-major result. For col-major result we need
-    // to explicitly transpose the problem so that bias is
-    // correctly applied.
-    // TODO: enable col-major result if needed.
-    // TODO: no need to check result's layout when
-    // !result.is_same(self) and self.dim() == 2, because
-    // self needs to be copied into result and the bias ptr
-    // will be ignored.
     && result.dim() == 2 && result.is_contiguous()
+    // Conditions for bias to be fusable
     && (
-      ( // Conditions for bias to be fusable -- implies direct Lt path without copies.
-        self.is_contiguous() &&
-        // NOTE: fine to have 1-len dims to the left from the right-most one
-        (self.dim() == 1 || self.squeeze().dim() == 1) &&
-        self.sizes().back() == mat2_sizes[1]
-      )
-      || ( // 2D bias restrictions. self.is_contiguous() is implicit when result.is_same(self),
-        // and we need to copy self into result otherwise, so the self's layout becomes irrelevant.
-        // See also TODO from above.
-        activation != Activation::None && // Lt is faster when activation is fused
-        (self.dim() == 2 && at::is_expandable_to(self.sizes(), {mat1_sizes[0], mat2_sizes[1]}))
-      )
+      self.is_contiguous() &&
+      // NOTE: fine to have 1-len dims to the left from the right-most one
+      (self.dim() == 1 || self.squeeze().dim() == 1) &&
+      self.sizes().back() == mat2_sizes[1]
     )
     && ( // some dtype restrictions
       #ifndef USE_ROCM
@@ -300,16 +270,7 @@ bool launchGemmAndBiasCublasLt(
     const Scalar& alpha,
     Activation activation = Activation::None
 ) {
-  // We apply bias in the epilogue only when it is 1D,
-  // or when it can be squeezed to 1D.
-  // self_ptr == nullptr implies ignore bias epilogue
-  // and use standard gemm-like API.
-  const auto* self_ptr = [&]() -> auto {
-    if (self.dim() == 1 || self.squeeze().dim() == 1) {
-      return self.const_data_ptr<scalar_t>();
-    }
-    return static_cast<const scalar_t*>(nullptr);
-  }();
+  const auto* self_ptr = self.const_data_ptr<scalar_t>();
 
   const auto tuning_ctx = at::cuda::tunable::getTuningContext();
   if (tuning_ctx->IsTunableOpEnabled()) {
@@ -395,7 +356,7 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
   disable_addmm_cuda_lt = isGloballyDisabledAddmmCudaLt(self.device()) || disable_addmm_cuda_lt;
   #endif
   // Condition on the input
-  disable_addmm_cuda_lt = !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha, activation) || disable_addmm_cuda_lt;
+  disable_addmm_cuda_lt = !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha) || disable_addmm_cuda_lt;
   // }
 
   at::ScalarType scalar_type = mat1.scalar_type();
@@ -405,20 +366,19 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
   if (!result.is_same(self)) {
     at::native::resize_output(result, {mat1.sizes()[0], mat2.sizes()[1]});
 
-    // We use bias ptr in the Lt path only when bias is 1D
-    const auto use_bias_ptr_lt = (self.dim() == 1) && !disable_addmm_cuda_lt;
     const auto self_maybe_expanded = [&]() -> c10::MaybeOwned<Tensor> {
-      if (!use_bias_ptr_lt) {
-        // We do expand self even before
+      if (disable_addmm_cuda_lt) {
+        // When in non-Lt path we do expand self even before
         // check for beta != 0.0 to make sure that
         // test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_errors_*
         // runs green.
         return expand_size(self, result.sizes(), "addmm");
       }
+      // copy next, should broadcast
       return c10::MaybeOwned<Tensor>::borrowed(self);
     }();
-    // We do not copy bias only when we need the bias ptr
-    if (beta.toComplexDouble() != 0.0 && !use_bias_ptr_lt) {
+    // We copy bias when in the non-Lt path
+    if (beta.toComplexDouble() != 0.0 && disable_addmm_cuda_lt) {
       // NOTE: self should broadcast over result
       at::native::copy_(result, *self_maybe_expanded);
     }

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

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

aten/src/ATen/native/cuda/GroupedBlas.cpp

@@ -22,9 +22,6 @@
 #include <ATen/native/cuda/RowwiseScaledMM.h>
 #include <ATen/native/cuda/ScaledGroupMM.h>
 #include <ATen/native/cuda/GroupMM.h>
-#ifdef USE_ROCM
-#include <ATen/native/hip/ck_group_gemm.h>
-#endif
 #include <ATen/ceil_div.h>
 
 #ifdef USE_FBGEMM_GENAI
@@ -669,19 +666,12 @@ std::optional<c10::ScalarType> out_dtype) {
   // _scaled_mm_allowed_device is used here within _grouped_mm_cuda which seems incorrect since scale is not used.
   // the _grouped_mm_fallback should be safe for any ROCm GPU since it's just calling typical mm/bmm
   bool use_fast_path = false;
-  if (at::detail::getCUDAHooks().isGPUArch({"gfx942", "gfx950"})) {
-    use_fast_path = true;
-  }
 #endif
   const auto out_dtype_ = _resolve_grouped_mm_out_dtype(mat_a, mat_b, out_dtype);
   Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
   if (use_fast_path) {
     // fast path, no d2h sync needed
-#ifndef USE_ROCM
     at::cuda::detail::bf16bf16_grouped_mm(mat_a, mat_b, offs, bias, out);
-#else
-    at::hip::detail::group_gemm_ck(mat_a, mat_b, offs, bias, out);
-#endif
   } else {
     _grouped_mm_fallback(mat_a, mat_b, offs, bias, out_dtype, out);
   }

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

@@ -5,6 +5,7 @@
 #include <array>
 #include <type_traits>
 #include <ATen/core/TensorBase.h>
+#include <ATen/ceil_div.h>
 #include <ATen/Dispatch.h>
 #include <ATen/Dispatch_v2.h>
 #include <ATen/cuda/CUDAContext.h>
@@ -73,6 +74,7 @@ void gpu_index_kernel(TensorIteratorBase& iter, const IntArrayRef index_size, co
 
   char* const out_ptr = static_cast<char*>(iter.data_ptr(0));
   char* const in_ptr = static_cast<char*>(iter.data_ptr(1));
+
   if (is_gather_like && num_indices==1) {
       const size_t element_size = iter.element_size(0);
       constexpr size_t alignment = 16;
@@ -82,9 +84,16 @@ void gpu_index_kernel(TensorIteratorBase& iter, const IntArrayRef index_size, co
         auto ind_dim_size = index_size[0];
         auto inp_stride_bytes = index_stride[0];
         auto out_stride_bytes = iter.strides(0)[1];
-        at::native::vectorized_gather_kernel_launch<alignment, int64_t>(out_ptr, in_ptr, (int64_t*)iter.data_ptr(2), num_ind,
-        slice_size, ind_dim_size, inp_stride_bytes, out_stride_bytes, /*allow_neg_indices*/true);
-        return;
+        // avoid grid overflow in the fast kernel
+        const int64_t vec_chunks = ceil_div(slice_size, alignment);
+        const int64_t blocks_per_slice_upper = ceil_div(vec_chunks, (int64_t)launch_size_nd);
+        const int max_grid_y = at::cuda::getCurrentDeviceProperties()->maxGridSize[1];
+        // if it's an eligible grid we use the fast path, otherwise default to slower path
+        if (blocks_per_slice_upper <= max_grid_y) {
+          at::native::vectorized_gather_kernel_launch<alignment, int64_t>(out_ptr, in_ptr, (int64_t*)iter.data_ptr(2), num_ind,
+          slice_size, ind_dim_size, inp_stride_bytes, out_stride_bytes, /*allow_neg_indices*/true);
+          return;
+        }
     }
   }
 

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

@@ -13,12 +13,11 @@ __global__ void vectorized_gather_kernel(char * out, char * inp, index_t * idx,
     if (allow_neg_indices) {
         ind = (ind < 0) ? ind + ind_dim_size : ind;
     }
-    CUDA_KERNEL_ASSERT_VERBOSE(ind >=0 && ind < ind_dim_size && "vectorized gather kernel index out of bounds");
-    // off is guaranteed to be within int32 limits
-    for (int32_t off = (blockDim.x * blockIdx.y + threadIdx.x) * Alignment; off < slice_size; off += blockDim.x * gridDim.y * Alignment) {
-      auto vec = at::native::memory::ld_vec<Alignment>(inp + ind * inp_stride + off);
-      at::native::memory::st_vec<Alignment>(out + blockIdx.x * (int32_t)out_stride + off, vec);  // out offset is guaranteed to be within int32 limits
-    }
+    CUDA_KERNEL_ASSERT_VERBOSE(ind >=0 && ind < ind_dim_size && "vectorized gather kernel index out of bounds", "Expected 0 <= index < ind_dim_size(%ld), but got index = %ld", ind_dim_size, ind);
+    int32_t off = (blockDim.x * blockIdx.y + threadIdx.x) * Alignment; // off is guaranteed to be within int32 limits
+    if (off >= slice_size) return;
+    auto vec = at::native::memory::ld_vec<Alignment>(inp + ind * inp_stride + off);
+    at::native::memory::st_vec<Alignment>(out + blockIdx.x * (int32_t)out_stride + off, vec);  // out offset is guaranteed to be within int32 limits
 }
 
 
@@ -31,9 +30,7 @@ void vectorized_gather_kernel_launch(char * out, char * inp, index_t * idx, int
   auto num_threads = at::round_up(
       at::ceil_div(slice_size_in_bytes, Alignment),
       static_cast<int64_t>(C10_WARP_SIZE));
-  uint32_t grid_y = at::cuda::getCurrentDeviceProperties()->maxGridSize[1];
-  grid_y = std::min(static_cast<uint32_t>(at::ceil_div(slice_size_in_bytes, max_num_threads * Alignment)), grid_y);
-  dim3 grid = {static_cast<uint32_t>(num_ind), grid_y, 1};
+  dim3 grid = {static_cast<uint32_t>(num_ind), static_cast<uint32_t>(at::ceil_div(slice_size_in_bytes, max_num_threads * Alignment)), 1};
   auto block = std::min(max_num_threads, num_threads);
   vectorized_gather_kernel<Alignment, index_t><<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(out, inp, idx, num_ind, slice_size_in_bytes,
   ind_dim_size, inp_stride_bytes, out_stride_bytes, allow_neg_indices);

aten/src/ATen/native/hip/ck_group_gemm.h

@@ -1,19 +0,0 @@
-#pragma once
-
-#include <ATen/Tensor.h>
-#include <c10/core/ScalarType.h>
-#include <optional>
-
-namespace at {
-namespace hip {
-namespace detail {
-void group_gemm_ck(
-    const at::Tensor& mat_a,
-    const at::Tensor& mat_b,
-    const std::optional<at::Tensor>& offs,
-    const std::optional<at::Tensor>& bias,
-    at::Tensor& out);
-
-} // namespace detail
-} // namespace hip
-} // namespace at

aten/src/ATen/native/hip/ck_group_gemm.hip

@@ -1,462 +0,0 @@
-#undef __HIP_NO_HALF_CONVERSIONS__
-#include <ATen/hip/HIPContext.h>
-#include <ATen/Tensor.h>
-#include <ATen/TensorAccessor.h>
-#include <c10/hip/HIPStream.h>
-#include <iostream>
-#include <vector>
-#include <optional>
-#include <type_traits>
-
-#include <ck/ck.hpp>
-#include <ck/tensor_operation/gpu/device/tensor_layout.hpp>
-#include <ck/tensor_operation/gpu/device/gemm_specialization.hpp>
-#include <ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_splitk_xdl_cshuffle_two_stage.hpp>
-#include <ck/tensor_operation/gpu/element/element_wise_operation.hpp>
-#include <ck/utility/tuple.hpp>
-
-template <ck::index_t... Is>
-using S = ck::Sequence<Is...>;
-
-namespace at {
-namespace hip {
-namespace detail {
-
-namespace CkTypes {
-    using BF16 = ck::bhalf_t;
-    using F16 = ck::half_t;
-    using F32 = float;
-    using PassThrough = ck::tensor_operation::element_wise::PassThrough;
-}
-
-template <typename ALayout, typename BLayout, typename DataType>
-using GroupedGemmKernel = ck::tensor_operation::device::DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage<
-    ALayout, BLayout, ck::Tuple<>, ck::tensor_layout::gemm::RowMajor,
-    DataType, DataType, CkTypes::F32, DataType, ck::Tuple<>, DataType,
-    CkTypes::PassThrough, CkTypes::PassThrough, CkTypes::PassThrough,
-    ck::tensor_operation::device::GemmSpecialization::MNKPadding,
-    1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2,
-    S<1,4,64,1>,  S<0,2,1,3>, S<0,2,1,3>,
-    3, 8, 8, 1,
-    S<1,4,64,1>,  S<0,2,1,3>, S<0,2,1,3>,
-    3, 8, 8, 1,
-    1, 1,
-    S<1,32,1,8>, 4
->;
-
-template <typename ALayout, typename BLayout, typename DataType>
-void launch_grouped_bgemm_ck_impl_dispatch(
-    const at::Tensor& mat_a,
-    const at::Tensor& mat_b,
-    const std::optional<at::Tensor>& offs,
-    at::Tensor& out)
-{
-    using DeviceOp = GroupedGemmKernel<ALayout, BLayout, DataType>;
-    using PassThrough = CkTypes::PassThrough;
-
-    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
-    std::vector<const void*> p_a_ptrs, p_b_ptrs;
-    std::vector<void*> p_e_ptrs;
-    // Note: d_ptrs will be resized after we populate the other vectors
-
-    const int mat_a_dim = mat_a.dim();
-    const int mat_b_dim = mat_b.dim();
-
-    const char* a_ptr_base = reinterpret_cast<const char*>(mat_a.data_ptr());
-    const char* b_ptr_base = reinterpret_cast<const char*>(mat_b.data_ptr());
-    char* out_ptr_base = reinterpret_cast<char*>(out.data_ptr());
-    const size_t a_element_size = mat_a.element_size();
-    const size_t b_element_size = mat_b.element_size();
-    const size_t out_element_size = out.element_size();
-
-    // for each group, calculate m,n,k,lda,ldb,ldc and A,B,out pointer base addresses.
-    if (mat_a_dim == 2 && mat_b_dim == 2) {
-        // 2D*2D case requires offset tensor
-        auto offs_accessor = offs->accessor<int, 1>();
-        int num_groups = offs_accessor.size(0);
-        const int M = mat_a.size(0); // number of rows in A
-        const int N = mat_b.size(1); // number of columns in B
-        const int K = mat_a.size(1); // columns in A == rows in B
-        // for 2d*2d input, output is 3d.
-        // for each group, A columns (K) are sliced. M and N dimensions are not sliced.
-        for (int i = 0; i < num_groups; ++i) {
-            int start_k = (i == 0) ? 0 : offs_accessor[i-1];
-            int end_k = offs_accessor[i];
-            int k = end_k - start_k;
-
-            //K dimension are sliced, hence select stride(1) always.
-            //K dimension is always dimension 1, regardless of memory layout (row/column major)
-            const void* group_a_ptr = a_ptr_base + start_k * mat_a.stride(1) * a_element_size;
-            const void* group_b_ptr;
-            int ldb;
-
-            if (std::is_same<BLayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major B [K,N]: K values are horizontally adjacent, use stride(1) for K offset
-                group_b_ptr = b_ptr_base + start_k * mat_b.stride(1) * b_element_size;
-                // Leading dimension = distance between rows = stride(0)
-                ldb = mat_b.stride(0);
-            } else {
-                // Column-major B [K,N]: K values are vertically adjacent, use stride(0) for K offset
-                group_b_ptr = b_ptr_base + start_k * mat_b.stride(0) * b_element_size;
-                // Leading dimension = distance between columns = stride(1)
-                ldb = mat_b.stride(1);
-            }
-
-            // Calculate output pointer for group i in 3D tensor [num_groups, M, N]
-            // stride(0) = M*N elements between groups, so skip i*stride(0) elements to reach group i
-            void* group_e_ptr = out_ptr_base + i * out.stride(0) * out_element_size;
-            int lda, ldc;
-            if (std::is_same<ALayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major A [M,K]: leading dimension = distance between rows = stride(0)
-                lda = mat_a.stride(0);
-            } else {
-                // Column-major A [M,K]: leading dimension = distance between columns = stride(1)
-                lda = mat_a.stride(1);
-            }
-            // Output is always row-major in 3D tensor [num_groups, M, N]
-            // Leading dimension for each group's [M,N] slice = stride(1) = N
-            ldc = out.stride(1);
-            size_t output_group_bytes = M * N * out_element_size;
-            void* group_e_ptr_end = (char*)group_e_ptr + output_group_bytes;
-
-            gemm_descs.push_back({
-                static_cast<ck::index_t>(M),
-                static_cast<ck::index_t>(N),
-                static_cast<ck::index_t>(k),
-                static_cast<ck::index_t>(lda),
-                static_cast<ck::index_t>(ldb),
-                static_cast<ck::index_t>(ldc),
-                {} // --> stride_Ds_
-            });
-            p_a_ptrs.push_back(group_a_ptr);
-            p_b_ptrs.push_back(group_b_ptr);
-            p_e_ptrs.push_back(group_e_ptr);
-        }
-    } else if (mat_a_dim == 2 && mat_b_dim == 3) {
-        // 2D*3D case requires offset tensor
-        auto offs_accessor = offs->accessor<int, 1>();
-        int num_groups = offs_accessor.size(0);
-
-        // 2d*3d input, output is 2d.
-        // A: [m * n_groups, k], B: [n_groups, n, k] or [n_groups, k, n], Output: [m * n_groups, n]
-        // Offset divides M dimension (rows of A), each group gets different rows of A and different batch of B
-        const int K = mat_a.size(1); // columns in A
-        // For 2D-3D case: The output determines N (result width)
-        const int N = out.size(1); // N is the width of the output tensor
-
-        for (int i = 0; i < num_groups; ++i) {
-            int start_m = (i == 0) ? 0 : offs_accessor[i - 1];
-            int end_m = offs_accessor[i];
-            int m = end_m - start_m;
-
-            // Skip zero-sized groups but continue processing subsequent groups
-            if (m <= 0) {
-                continue;
-            }
-
-            // Select A rows for group i: skip start_m rows
-            const void* group_a_ptr;
-            int lda;
-            if (std::is_same<ALayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major A [total_m, K]: skip start_m rows, each row is stride(0) elements apart
-                group_a_ptr = a_ptr_base + start_m * mat_a.stride(0) * a_element_size;
-                lda = mat_a.stride(0); // distance between rows
-            } else {
-                // Column-major A [total_m, K]: skip start_m elements in the first dimension (stride(0) is between rows)
-                group_a_ptr = a_ptr_base + start_m * mat_a.stride(0) * a_element_size;
-
-                // Detect stride pattern for A tensor to determine appropriate lda calculation
-                bool a_is_strided_tensor = (mat_a.stride(0) > mat_a.size(0));
-
-                if (a_is_strided_tensor) {
-                    // For strided A tensors: stride(0) gives the actual leading dimension
-                    lda = mat_a.stride(0);
-                } else {
-                    // For non-strided A tensors: use the M dimension (total rows)
-                    lda = mat_a.size(0); // Total M dimension for column-major layout
-                }
-            }
-
-            // Select B batch for group i: B[i, :, :]
-            const void* group_b_ptr = b_ptr_base + i * mat_b.stride(0) * b_element_size;
-            int ldb;
-
-            if (std::is_same<BLayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major GEMM: expecting B as [K, N] but we have [N, K], so transpose needed
-                ldb = mat_b.stride(2); // Leading dimension for accessing as [K, N]
-            } else {
-                // Detect stride pattern to determine appropriate ldb calculation
-                bool is_strided_tensor = (mat_b.stride(2) > mat_b.size(2));
-
-                if (is_strided_tensor) {
-                    // For strided tensors: stride(2) gives the actual leading dimension
-                    ldb = mat_b.stride(2);
-                } else {
-                    // For non-strided tensors: use the N dimension
-                    ldb = mat_b.size(1);
-                }
-            }
-
-            // Output for this group: rows [start_m:end_m, :] in 2D output [total_m, N]
-            void* group_e_ptr = out_ptr_base + start_m * out.stride(0) * out_element_size;
-            int ldc = out.stride(0); // distance between rows in output (should be N for 2D case)
-
-            gemm_descs.push_back({
-                static_cast<ck::index_t>(m),
-                static_cast<ck::index_t>(N),
-                static_cast<ck::index_t>(K),
-                static_cast<ck::index_t>(lda),
-                static_cast<ck::index_t>(ldb),
-                static_cast<ck::index_t>(ldc),
-                {} // --> stride_Ds_
-            });
-            p_a_ptrs.push_back(group_a_ptr);
-            p_b_ptrs.push_back(group_b_ptr);
-            p_e_ptrs.push_back(group_e_ptr);
-        }
-    } else if (mat_a_dim == 3 && mat_b_dim == 3) {
-        // 3d*3d input, output is 3d - batched matrix multiplication
-        // A: [batch, m, k], B: [batch, k, n] or [batch, n, k] (depending on transpose), Output: [batch, m, n]
-        // Each batch is processed as a separate GEMM operation
-        const int batch_size = mat_a.size(0);
-        const int M = mat_a.size(1); // rows in each A matrix
-        const int K = mat_a.size(2); // columns in A == rows in B (or columns if B is transposed)
-
-        // Determine N from B tensor - it could be B.size(1) or B.size(2) depending on layout
-        int N;
-        if (mat_b.size(1) == K) {
-            // B is [batch, k, n] - normal layout
-            N = mat_b.size(2);
-        } else if (mat_b.size(2) == K) {
-            // B is [batch, n, k] - transposed layout
-            N = mat_b.size(1);
-        } else {
-            TORCH_CHECK(false, "CK Group GEMM 3D-3D: B tensor dimensions incompatible with A. A=[",
-                       batch_size, ",", M, ",", K, "], B=[", mat_b.size(0), ",", mat_b.size(1), ",", mat_b.size(2), "]");
-        }
-
-        for (int i = 0; i < batch_size; ++i) {
-            // Select A batch for group i: A[i, :, :]
-            const void* group_a_ptr = a_ptr_base + i * mat_a.stride(0) * a_element_size;
-
-            // Select B batch for group i: B[i, :, :]
-            const void* group_b_ptr = b_ptr_base + i * mat_b.stride(0) * b_element_size;
-
-            // Select output batch for group i: Output[i, :, :]
-            void* group_e_ptr = out_ptr_base + i * out.stride(0) * out_element_size;
-
-            int lda, ldb, ldc;
-
-            if (std::is_same<ALayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major A: leading dimension = distance between rows = stride(1)
-                lda = mat_a.stride(1);
-            } else {
-                // Column-major A: leading dimension = distance between columns = stride(2)
-                lda = mat_a.stride(2);
-            }
-
-            if (std::is_same<BLayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major B: leading dimension = distance between rows
-                if (mat_b.size(1) == K) {
-                    // B is [batch, k, n] - normal layout
-                    ldb = mat_b.stride(1); // stride between K rows
-                } else {
-                    // B is [batch, n, k] - transposed layout, treat as [k, n] for GEMM
-                    ldb = mat_b.stride(2); // stride between N rows (since we're accessing as [k,n])
-                }
-            } else {
-                // Column-major B: leading dimension = distance between columns
-                if (mat_b.size(1) == K) {
-                    // B is [batch, k, n] - normal layout
-                    ldb = mat_b.stride(2); // stride between N columns
-                } else {
-                    // B is [batch, n, k] - transposed layout
-                    ldb = mat_b.stride(1); // stride between K columns (since we're accessing as [n,k]→[k,n])
-                }
-            }
-
-            // Output is typically row-major: leading dimension = distance between rows = stride(1)
-            ldc = out.stride(1);
-
-            gemm_descs.push_back({
-                static_cast<ck::index_t>(M),
-                static_cast<ck::index_t>(N),
-                static_cast<ck::index_t>(K),
-                static_cast<ck::index_t>(lda),
-                static_cast<ck::index_t>(ldb),
-                static_cast<ck::index_t>(ldc),
-                {} // --> stride_Ds_
-            });
-            p_a_ptrs.push_back(group_a_ptr);
-            p_b_ptrs.push_back(group_b_ptr);
-            p_e_ptrs.push_back(group_e_ptr);
-        }
-    } else if (mat_a_dim == 3 && mat_b_dim == 2) {
-        // 3D*2D case requires offset tensor
-        auto offs_accessor = offs->accessor<int, 1>();
-        int num_groups = offs_accessor.size(0);
-        // 3d*2d input, output is 3d.
-        // A: [n_groups, m, k], B: [k, total_n] (assuming row-major for both)
-        // Offset divides N dimension of B, each group gets different slice of B and different batch of A
-        const int batch_size = mat_a.size(0); // n_groups
-        const int M = mat_a.size(1); // rows in each A matrix
-        const int K = mat_a.size(2); // columns in A
-
-        // For row-major A and B case: B should be [K, total_N]
-        const int total_N = mat_b.size(1); // B is [K, total_N] for row-major
-
-        for (int i = 0; i < num_groups; ++i) {
-            int start_n = (i == 0) ? 0 : offs_accessor[i - 1];
-            int end_n = offs_accessor[i];
-            int n = end_n - start_n;
-
-            // Skip zero-sized groups but continue processing subsequent groups
-            if (n <= 0) {
-                continue;
-            }
-
-            // Select A batch for group i: A[i, :, :]
-            const void* group_a_ptr = a_ptr_base + i * mat_a.stride(0) * a_element_size;
-
-            // Select B slice for group i: B[:, start_n:end_n] (B[K, total_N])
-            const void* group_b_ptr;
-            int ldb;
-
-            // Check if B is row-major or column-major
-            if (std::is_same<BLayout, ck::tensor_layout::gemm::RowMajor>::value) {
-                // Row-major B [K, total_N]: slice columns [start_n:end_n]
-                group_b_ptr = b_ptr_base + start_n * mat_b.stride(1) * b_element_size;
-                ldb = mat_b.stride(0); // distance between rows (should be total_N)
-            } else {
-                // Column-major B [K, total_N]: slice columns [start_n:end_n]
-                group_b_ptr = b_ptr_base + start_n * mat_b.stride(1) * b_element_size;
-                ldb = mat_b.stride(1); // distance between columns (should be K)
-            }
-
-            // Select output slice for group i: Output[:, start_n:end_n]
-            void* group_e_ptr = out_ptr_base + start_n * out.stride(1) * out_element_size;
-
-            int lda, ldc;
-
-            // Row-major A: leading dimension = distance between rows = stride(1)
-            lda = mat_a.stride(1);
-            // Output is row-major: leading dimension = distance between rows = stride(0)
-            ldc = out.stride(0);
-
-            gemm_descs.push_back({
-                static_cast<ck::index_t>(M),
-                static_cast<ck::index_t>(n),
-                static_cast<ck::index_t>(K),
-                static_cast<ck::index_t>(lda),
-                static_cast<ck::index_t>(ldb),
-                static_cast<ck::index_t>(ldc),
-                {} // --> stride_Ds_
-            });
-            p_a_ptrs.push_back(group_a_ptr);
-            p_b_ptrs.push_back(group_b_ptr);
-            p_e_ptrs.push_back(group_e_ptr);
-        }
-    } else {
-        TORCH_CHECK(false, "CK Group GEMM: Unsupported dimensions, mat A dim is ", mat_a_dim, ", mat B dim is ", mat_b_dim);
-    }
-
-    TORCH_INTERNAL_ASSERT(p_a_ptrs.size() > 0, "CK Group GEMM: No valid groups");
-
-    // Initialize d_ptrs with the correct size
-    std::vector<std::array<const void*, 0>> d_ptrs(p_a_ptrs.size());
-
-    static DeviceOp gemm_instance;
-    auto argument = gemm_instance.MakeArgument(
-        p_a_ptrs, p_b_ptrs, d_ptrs, p_e_ptrs,
-        gemm_descs, PassThrough{}, PassThrough{}, PassThrough{}
-    );
-    TORCH_INTERNAL_ASSERT(gemm_instance.IsSupportedArgument(argument),
-        "CK Group GEMM: argument unsupported (shape/strides/type config)");
-    size_t arg_buf_size = gemm_instance.GetDeviceKernelArgSize(&argument);
-    size_t ws_size = gemm_instance.GetWorkSpaceSize(&argument);
-
-    void* gemm_arg_buf = nullptr;
-    void* ws_buf = nullptr;
-
-    hipMalloc(&gemm_arg_buf, arg_buf_size);
-    hipMalloc(&ws_buf, ws_size);
-
-    gemm_instance.SetDeviceKernelArgs(&argument, gemm_arg_buf);
-    gemm_instance.SetWorkSpacePointer(&argument, ws_buf);
-
-    auto invoker = gemm_instance.MakeInvoker();
-    hipStream_t stream = c10::hip::getCurrentHIPStream();
-    invoker.Run(argument, {stream});
-    hipFree(gemm_arg_buf);
-    hipFree(ws_buf);
-}
-
-void group_gemm_ck(
-    const at::Tensor& input_a,
-    const at::Tensor& input_b_colmajor,
-    const std::optional<at::Tensor>& offs,
-    const std::optional<at::Tensor>& /*bias*/,
-    at::Tensor& out)
-{
-    // Detect if input_a is row-major based on stride pattern
-    bool a_row_major = (input_a.dim() == 3) ? (input_a.stride(2) == 1) : (input_a.stride(1) == 1);
-    bool b_col_major = (input_b_colmajor.dim() == 3) ? (input_b_colmajor.stride(1) == 1) : (input_b_colmajor.stride(0) == 1);
-    // Ensure tensor A is row-major and contiguous if not already
-    at::Tensor mat_a = input_a;
-    if (!a_row_major) {
-        // If A is not row-major, make it contiguous (row-major)
-        mat_a = input_a.contiguous();
-    }
-    // Force tensor B to be column-major using double transpose trick
-    // This guarantees stride(0) == 1 and stride(1) == K for [K, N] shape
-    at::Tensor mat_b = input_b_colmajor;
-    if (!b_col_major) {
-        mat_b = input_b_colmajor.transpose(-2, -1).contiguous().transpose(-2, -1);
-    }
-
-    // For 3D tensors, check the last dimension stride for row-major detection
-    a_row_major = (mat_a.dim() == 3) ? (mat_a.stride(2) == 1) : (mat_a.stride(1) == 1);
-    bool b_row_major = (mat_b.dim() == 3) ? (mat_b.stride(2) == 1) : (mat_b.stride(1) == 1);
-
-    if (mat_a.dtype() == at::kBFloat16) {
-        // bf16 path
-        if (a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::BF16>(mat_a, mat_b, offs, out);
-        } else if (a_row_major && !b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::BF16>(mat_a, mat_b, offs, out);
-        } else if (!a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::BF16>(mat_a, mat_b, offs, out);
-        } else {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::BF16>(mat_a, mat_b, offs, out);
-        }
-    } else if (mat_a.dtype() == at::kHalf) {
-        // fp16 path
-        if (a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::F16>(mat_a, mat_b, offs, out);
-        } else if (a_row_major && !b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::F16>(mat_a, mat_b, offs, out);
-        } else if (!a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::F16>(mat_a, mat_b, offs, out);
-        } else {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::F16>(mat_a, mat_b, offs, out);
-        }
-    } else if (mat_a.dtype() == at::kFloat) {
-        // fp32 path
-        if (a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::F32>(mat_a, mat_b, offs, out);
-        } else if (a_row_major && !b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::RowMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::F32>(mat_a, mat_b, offs, out);
-        } else if (!a_row_major && b_row_major) {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::RowMajor, CkTypes::F32>(mat_a, mat_b, offs, out);
-        } else {
-            launch_grouped_bgemm_ck_impl_dispatch<ck::tensor_layout::gemm::ColumnMajor, ck::tensor_layout::gemm::ColumnMajor, CkTypes::F32>(mat_a, mat_b, offs, out);
-        }
-    } else {
-        TORCH_CHECK(false, "CK Group GEMM: Unsupported mat_a dtype");
-    }
-
-}
-
-} // namespace detail
-} // namespace hip
-} // namespace at

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

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

aten/src/ATen/native/mps/OperationUtils.h

@@ -40,6 +40,8 @@ using namespace at::mps;
 
 namespace at::native::mps {
 
+void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)());
+
 struct MPSScalar {
   id<MTLBuffer> getMTLBuffer() const {
     return __builtin_bit_cast(id<MTLBuffer>, buffer.get());