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 @@
-0add68262ab0a2e33b84524346cb27cbb2787356
+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/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/build-triton-wheel.yml

@@ -52,15 +52,11 @@ jobs:
       matrix:
         py_vers: [ "3.10", "3.11", "3.12", "3.13", "3.13t", "3.14", "3.14t" ]
         device: ["cuda", "rocm", "xpu", "aarch64"]
-        rocm_version: [""]
         docker-image: ["pytorch/manylinux2_28-builder:cpu"]
         include:
           - device: "rocm"
             rocm_version: "7.1"
             runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
-          - device: "rocm"
-            rocm_version: "7.0"
-            runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
           - device: "cuda"
             rocm_version: ""
             runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
@@ -175,7 +171,7 @@ jobs:
 
       - uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
         with:
-          name: pytorch-triton-wheel-${{ matrix.py_vers }}-${{ matrix.device }}${{ matrix.rocm_version != '' && format('-{0}', matrix.rocm_version) || '' }}-${{ env.PLATFORM }}
+          name: pytorch-triton-wheel-${{ matrix.py_vers }}-${{ matrix.device }}-${{ env.PLATFORM }}
           if-no-files-found: error
           path: ${{ runner.temp }}/artifacts/wheelhouse/*
 

.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

@@ -2,12 +2,12 @@ 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

@@ -5,12 +5,11 @@ on:
     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

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());

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

@@ -53,6 +53,21 @@
 @end
 
 namespace at::native::mps {
+
+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);
+  }
+}
+
 /**
  * Computes distance from lowest to highest element offset in given tensor.
  */