Update

[ghstack-poisoned]

.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=11
+ARG DEVTOOLSET_VERSION=13
 
 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-${DEVTOOLSET_VERSION}-toolchain
+RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
 # Just add everything as a safe.directory for git since these will be used in multiple places with git
 RUN git config --global --add safe.directory '*'
 ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
@@ -41,6 +41,7 @@ RUN bash ./install_conda.sh && rm install_conda.sh
 # Install CUDA
 FROM base as cuda
 ARG CUDA_VERSION=12.6
+ARG DEVTOOLSET_VERSION=13
 RUN rm -rf /usr/local/cuda-*
 ADD ./common/install_cuda.sh install_cuda.sh
 COPY ./common/install_nccl.sh install_nccl.sh
@@ -50,7 +51,8 @@ ENV CUDA_HOME=/usr/local/cuda-${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:$PATH
+ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
+
 
 FROM cuda as cuda12.6
 RUN bash ./install_cuda.sh 12.6
@@ -68,8 +70,22 @@ FROM cuda as cuda13.0
 RUN bash ./install_cuda.sh 13.0
 ENV DESIRED_CUDA=13.0
 
-FROM ${ROCM_IMAGE} as rocm
+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
 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
@@ -88,6 +104,7 @@ 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

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

.ci/docker/build.sh

@@ -168,6 +168,18 @@ case "$tag" in
     VISION=yes
     TRITON=yes
     ;;
+  pytorch-linux-jammy-py3.11-clang12)
+    ANACONDA_PYTHON_VERSION=3.11
+    CLANG_VERSION=12
+    VISION=no
+    TRITON=no
+    ;;
+  pytorch-linux-jammy-py3.12-clang12)
+    ANACONDA_PYTHON_VERSION=3.12
+    CLANG_VERSION=12
+    VISION=no
+    TRITON=no
+    ;;
   pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
     if [[ $tag =~ "jammy" ]]; then
       ANACONDA_PYTHON_VERSION=3.10
@@ -195,9 +207,9 @@ case "$tag" in
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-jammy-xpu-n-py3 | pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks)
+  pytorch-linux-noble-xpu-n-py3 | pytorch-linux-noble-xpu-n-py3-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=13
     VISION=yes
     XPU_VERSION=2025.2
     NINJA_VERSION=1.9.0
@@ -248,6 +260,12 @@ case "$tag" in
     HALIDE=yes
     TRITON=yes
     ;;
+  pytorch-linux-jammy-cuda12.8-py3.12-pallas)
+    CUDA_VERSION=12.8.1
+    ANACONDA_PYTHON_VERSION=3.12
+    GCC_VERSION=11
+    PALLAS=yes
+    ;;
   pytorch-linux-jammy-py3.12-triton-cpu)
     CUDA_VERSION=12.6
     ANACONDA_PYTHON_VERSION=3.12
@@ -261,9 +279,9 @@ case "$tag" in
     PYTHON_VERSION=3.10
     CUDA_VERSION=12.8.1
     ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc11)
+  pytorch-linux-jammy-aarch64-py3.10-gcc13)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=13
     ACL=yes
     VISION=yes
     OPENBLAS=yes
@@ -281,9 +299,9 @@ case "$tag" in
     # from pytorch/llvm:9.0.1 is x86 specific
     SKIP_LLVM_SRC_BUILD_INSTALL=yes
     ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
+  pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=13
     ACL=yes
     VISION=yes
     OPENBLAS=yes
@@ -369,6 +387,7 @@ docker build \
        --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
        --build-arg "EXECUTORCH=${EXECUTORCH}" \
        --build-arg "HALIDE=${HALIDE}" \
+       --build-arg "PALLAS=${PALLAS}" \
        --build-arg "XPU_VERSION=${XPU_VERSION}" \
        --build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
        --build-arg "ACL=${ACL:-}" \

.ci/docker/ci_commit_pins/jax.txt

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

.ci/docker/common/install_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
+  apt-get install -y g++-$GCC_VERSION gfortran-$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

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

.ci/docker/common/install_libgomp.sh

@@ -0,0 +1,56 @@
+#!/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_xpu.sh

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

.ci/docker/libtorch/build.sh

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

.ci/docker/manywheel/Dockerfile_2_28_aarch64

@@ -50,6 +50,10 @@ 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

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

.ci/docker/requirements-docs.txt

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

.ci/docker/ubuntu/Dockerfile

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

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

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

.ci/magma-rocm/README.md

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

.ci/pytorch/build.sh

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

.ci/pytorch/common_utils.sh

@@ -96,7 +96,6 @@ function pip_build_and_install() {
     python3 -m pip wheel \
       --no-build-isolation \
       --no-deps \
-      --no-use-pep517 \
       -w "${wheel_dir}" \
       "${build_target}"
   fi

.ci/pytorch/python_doc_push_script.sh

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

.ci/pytorch/test.sh

@@ -208,6 +208,8 @@ 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
@@ -337,7 +339,7 @@ test_python() {
 
 test_python_smoke() {
   # Smoke tests for H100/B200
-  time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 inductor/test_max_autotune $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
+  time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 inductor/test_max_autotune inductor/test_cutedsl_grouped_mm $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
   assert_git_not_dirty
 }
 
@@ -824,6 +826,11 @@ test_inductor_halide() {
   assert_git_not_dirty
 }
 
+test_inductor_pallas() {
+  python test/run_test.py --include inductor/test_pallas.py --verbose
+  assert_git_not_dirty
+}
+
 test_inductor_triton_cpu() {
   python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
   assert_git_not_dirty
@@ -1724,6 +1731,8 @@ elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
   test_inductor_halide
+elif [[ "${TEST_CONFIG}" == *inductor-pallas* ]]; then
+  test_inductor_pallas
 elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then

.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-build-isolation
+python -m build --wheel --no-isolation
 if ($LASTEXITCODE -ne 0) { exit 1 }
 
 # Install the wheel locally

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

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

.github/actionlint.yaml

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

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-3b0e7a6f192ca2715e7e6cbe5db007aea7165fe2
+ad5816f0eee1c873df1b7d371c69f1f811a89387

.github/ci_commit_pins/vision.txt

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

.github/ci_commit_pins/xla.txt

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

.github/labeler.yml

@@ -138,7 +138,8 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -148,7 +149,8 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -158,7 +160,21 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
+- aten/src/ATen/native/cuda/*Blas.cpp
+- aten/src/ATen/cuda/CUDA*Blas.*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
 - third_party/fbgemm
+
+"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

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

.github/pytorch-probot.yml

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

.github/scripts/delete_old_branches.py

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

.github/scripts/filter_test_configs.py

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

.github/scripts/get_workflow_job_id.py

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

.github/scripts/github_utils.py

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

.github/scripts/gitutils.py

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

.github/scripts/lintrunner.sh

@@ -34,6 +34,9 @@ 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 Iterable
+from collections.abc import Callable, Iterable
 from dataclasses import dataclass
 from functools import cache
 from pathlib import Path
 from re import Pattern
-from typing import Any, Callable, cast, NamedTuple, Optional
+from typing import Any, cast, NamedTuple, Optional
 from warnings import warn
 
 import yaml

.github/workflows/b200-distributed.yml

@@ -37,7 +37,6 @@ 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

@@ -37,7 +37,6 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.12xlarge.memory
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'

.github/workflows/docker-builds.yml

@@ -56,6 +56,8 @@ jobs:
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
           pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
           pytorch-linux-jammy-py3.10-clang12,
+          pytorch-linux-jammy-py3.11-clang12,
+          pytorch-linux-jammy-py3.12-clang12,
           pytorch-linux-jammy-py3.13-clang12,
           pytorch-linux-jammy-py3.14-clang12,
           pytorch-linux-jammy-rocm-n-py3,
@@ -65,9 +67,10 @@ jobs:
           pytorch-linux-jammy-py3.10-gcc11,
           pytorch-linux-jammy-py3-gcc11-inductor-benchmarks,
           pytorch-linux-jammy-py3.12-halide,
+          pytorch-linux-jammy-cuda12.8-py3.12-pallas,
           pytorch-linux-jammy-xpu-n-1-py3,
-          pytorch-linux-jammy-xpu-n-py3,
-          pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks,
+          pytorch-linux-noble-xpu-n-py3,
+          pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,
@@ -77,11 +80,11 @@ jobs:
           pytorch-linux-noble-riscv64-py3.12-gcc14
         ]
         include:
-          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11
+          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13
             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-gcc11-inductor-benchmarks
+          - docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13-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/docker-cache-mi300.yml

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

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

@@ -0,0 +1,108 @@
+name: docker-cache-rocm
+
+on:
+  workflow_run:
+    workflows: [docker-builds]
+    # TODO: Uncomment before merging
+    #branches: [main, release]
+    types:
+      - completed
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}
+  cancel-in-progress: true
+
+permissions:
+  id-token: write
+  contents: read
+  actions: read
+
+jobs:
+  download-docker-builds-artifacts:
+    if: github.repository_owner == 'pytorch'
+    name: download-docker-builds-artifacts
+    runs-on: ubuntu-latest
+    outputs:
+      pytorch-linux-jammy-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}
+      pytorch-linux-noble-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}
+      pytorch-linux-jammy-rocm-n-py3-benchmarks: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}
+    steps:
+      - name: Download artifacts
+        uses: actions/download-artifact@v4.1.7
+        with:
+          run-id: ${{ github.event.workflow_run.id }}
+          path: ./docker-builds-artifacts
+          merge-multiple: true
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Process artifacts
+        id: process-artifacts
+        run: |
+          ls -R ./docker-builds-artifacts
+          cat ./docker-builds-artifacts/*txt >> "${GITHUB_OUTPUT}"
+          cat "${GITHUB_OUTPUT}"
+
+  docker-cache:
+    if: github.repository_owner == 'pytorch'
+    needs: download-docker-builds-artifacts
+    strategy:
+      fail-fast: false
+      matrix:
+        runner: [linux.rocm.gfx942.docker-cache]
+        docker-image: [
+          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}",
+          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}",
+          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}"
+        ]
+    runs-on: "${{ matrix.runner }}"
+    steps:
+      - name: debug
+        run: |
+          JSON_STRINGIFIED="${{ toJSON(needs.download-docker-builds-artifacts.outputs) }}"
+          echo "Outputs of download-docker-builds-artifacts job: ${JSON_STRINGIFIED}"
+
+      - name: configure aws credentials
+        id: aws_creds
+        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
+        with:
+          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
+          aws-region: us-east-1
+          role-duration-seconds: 18000
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        continue-on-error: false
+        uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
+
+      - name: Generate ghrc.io tag
+        id: ghcr-io-tag
+        run: |
+            ecr_image="${{ matrix.docker-image }}"
+            ghcr_image="ghcr.io/pytorch/ci-image:${ecr_image##*:}"
+            echo "ghcr_image=${ghcr_image}" >> "$GITHUB_OUTPUT"
+
+      - name: Pull docker image
+        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
+        with:
+          docker-image: ${{ steps.ghcr-io-tag.outputs.ghcr_image }}
+
+      - name: Save as tarball
+        run: |
+          docker_image_tag=${{ matrix.docker-image }}
+          docker_image_tag="${docker_image_tag#*:}" # Remove everything before and including first ":"
+          docker_image_tag="${docker_image_tag%-*}" # Remove everything after and including last "-"
+          ref_name=${{ github.event.workflow_run.head_branch }}
+          if [[ $ref_name =~ "release/" ]]; then
+            ref_suffix="release"
+          elif [[ $ref_name == "main" ]]; then
+            ref_suffix="main"
+          else
+            # TODO: Remove below
+            ref_suffix="main"
+            # echo "Unexpected branch in ref_name: ${ref_name}" && exit 1
+          fi
+          docker tag ${{ steps.ghcr-io-tag.outputs.ghcr_image }} ${{ matrix.docker-image }}
+          # mv is atomic operation, so we use intermediate tar.tmp file to prevent read-write contention
+          docker save -o ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ${{ matrix.docker-image }}
+          mv ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ~/pytorch-data/docker/${docker_image_tag}_${ref_suffix}.tar

.github/workflows/h100-distributed.yml

@@ -37,7 +37,6 @@ 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-gcc11-inductor-benchmarks
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13-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-jammy-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks
+      build-environment: linux-noble-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3-inductor-benchmarks
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -117,7 +117,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-jammy-xpu-n-py3.10
+      build-environment: linux-noble-xpu-n-py3.10
       dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-false-cppwrapper-true-aotinductor-true-freezing_cudagraphs-false-cudagraphs_low_precision-false
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
@@ -137,7 +137,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-jammy-xpu-n-py3.10
+      build-environment: linux-noble-xpu-n-py3.10
       dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}

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

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

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

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

.github/workflows/inductor-unittest.yml

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

.github/workflows/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-gcc11
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
       runner: linux.arm64.m7g.4xlarge
       test-matrix: |
         { include: [

.github/workflows/nightly.yml

@@ -5,9 +5,11 @@ on:
     - cron: 0 0 * * *
   push:
     tags:
-      # 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]+
+      # 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]+
       - 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-gcc11
+      docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
       test-matrix: |
         { include: [
           { config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.arm64.m8g.4xlarge" },

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

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

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

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

.github/workflows/pull.yml

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

.github/workflows/rocm-mi200.yml

@@ -1,15 +1,16 @@
-name: rocm
+name: rocm-mi200
 
 on:
   push:
     branches:
       - release/*
     tags:
-      - ciflow/rocm/*
+      - ciflow/rocm-mi200/*
   workflow_dispatch:
   schedule:
     - cron: 29 8 * * *  # about 1:29am PDT
-    - cron: 0 * * * *
+    - cron: 0 */3 * * *
+
 
 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/rocm-mi300.yml

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

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

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

.github/workflows/slow.yml

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

.github/workflows/test-b200.yml

@@ -52,7 +52,6 @@ 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'
@@ -73,4 +72,4 @@ 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

@@ -41,7 +41,6 @@ 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

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

.github/workflows/xpu.yml

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

.gitignore

@@ -127,6 +127,7 @@ torch/test/
 torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h
 torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h
 torch/version.py
+torch/_inductor/kernel/vendored_templates/*
 minifier_launcher.py
 aten/src/ATen/native/transformers/hip/flash_attn/ck/fmha_fwd_d*
 aten/src/ATen/native/transformers/hip/flash_attn/ck/fmha_bwd_d*

.lintrunner.toml

@@ -143,7 +143,8 @@ init_command = [
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
     'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
-    'numpy==2.1.0 ; python_version >= "3.12"',
+    'numpy==2.1.0 ; python_version >= "3.12" and python_version <= "3.13"',
+    'numpy==2.3.4 ; python_version >= "3.14"',
     'expecttest==0.3.0',
     'pyrefly==0.36.2',
     'sympy==1.13.3',
@@ -185,6 +186,8 @@ 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',
@@ -1401,7 +1404,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'usort==1.0.8.post1',
     'isort==6.0.1',
-    'ruff==0.13.1',  # sync with RUFF
+    'ruff==0.14.4',  # sync with RUFF
 ]
 is_formatter = true
 
@@ -1536,7 +1539,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.13.1',  # sync with PYFMT
+    'ruff==0.14.4',  # sync with PYFMT
 ]
 is_formatter = true
 

CMakeLists.txt

@@ -736,6 +736,44 @@ 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)
@@ -1402,9 +1440,6 @@ 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 "${USE_CUSTOM_DEBINFO}")
@@ -1444,56 +1479,5 @@ if(BUILD_BUNDLE_PTXAS AND USE_CUDA)
           DESTINATION "${CMAKE_INSTALL_BINDIR}")
 endif()
 
-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()
+include(cmake/Summary.cmake)
+caffe2_print_configuration_summary()

CODEOWNERS

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

LICENSE

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

SECURITY.md

@@ -18,6 +18,8 @@ Please report security issues using https://github.com/pytorch/pytorch/security/
 
 All reports submitted through the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
 
+**Note on crashes and out of bounds access**: PyTorch is a computational framework that performs operations on behalf of the caller. Like many low-level libraries, PyTorch generally does not validate all inputs to every function—the responsibility for providing valid arguments lies with the calling code. While crashes and out of bounds memory access should be reported as bugs, they are generally not considered security vulnerabilities in PyTorch's threat model.
+
 Please refer to the following page for our responsible disclosure policy, reward guidelines, and those things that should not be reported:
 
 https://www.facebook.com/whitehat

aten/src/ATen/Context.h

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

aten/src/ATen/DeviceAccelerator.h

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

aten/src/ATen/Dispatch.h

@@ -6,6 +6,7 @@
 #include <c10/util/Half.h>
 #include <c10/util/Metaprogramming.h>
 #include <c10/util/complex.h>
+#include <torch/headeronly/core/Dispatch.h>
 
 #ifdef __CUDACC__
 #include <cuda.h> // For CUDA_VERSION
@@ -61,12 +62,9 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     }                                                 \
   } while (0)
 
-#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...)                 \
-  case enum_type: {                                                           \
-    AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type);                              \
-    using HINT [[maybe_unused]] = c10::impl::ScalarTypeToCPPTypeT<enum_type>; \
-    return __VA_ARGS__();                                                     \
-  }
+#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...) \
+  THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(                      \
+      AT_PRIVATE_CHECK_SELECTIVE_BUILD, enum_type, HINT, __VA_ARGS__)
 
 #define AT_DISPATCH_CASE(enum_type, ...) \
   AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
@@ -95,14 +93,6 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     return __VA_ARGS__();                                                   \
   }
 
-namespace detail {
-
-inline at::ScalarType scalar_type(at::ScalarType s) {
-  return s;
-}
-
-} // namespace detail
-
 // The AT_DISPATCH_* family of macros provides the ability to
 // conveniently generate specializations of a kernel over all of the
 // dtypes we care about in PyTorch.  We call it "dispatch" because
@@ -190,27 +180,13 @@ inline at::ScalarType scalar_type(at::ScalarType s) {
 // but we're just being safe (and it doesn't hurt.)  Note we must
 // use it to shut up warnings about unused store.
 
-#define AT_DISPATCH_SWITCH(TYPE, NAME, ...)                                 \
-  [&] {                                                                     \
-    const auto& the_type = TYPE;                                            \
-    constexpr const char* at_dispatch_name = NAME;                          \
-    /* don't use TYPE again in case it is an expensive or side-effect op */ \
-    at::ScalarType _st = ::detail::scalar_type(the_type);                   \
-    RECORD_KERNEL_FUNCTION_DTYPE(at_dispatch_name, _st);                    \
-    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")             \
-    switch (_st) {                                                          \
-      __VA_ARGS__                                                           \
-      default:                                                              \
-        TORCH_CHECK_NOT_IMPLEMENTED(                                        \
-            false,                                                          \
-            '"',                                                            \
-            at_dispatch_name,                                               \
-            "\" not implemented for '",                                     \
-            toString(_st),                                                  \
-            "'");                                                           \
-    }                                                                       \
-    C10_DIAGNOSTIC_POP()                                                    \
-  }()
+#define AT_DISPATCH_SWITCH(TYPE, NAME, ...) \
+  THO_DISPATCH_SWITCH_TMPL(                 \
+      RECORD_KERNEL_FUNCTION_DTYPE,         \
+      TORCH_CHECK_NOT_IMPLEMENTED,          \
+      TYPE,                                 \
+      NAME,                                 \
+      __VA_ARGS__)
 
 #define AT_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \

aten/src/ATen/Dispatch_v2.h

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

aten/src/ATen/core/CachingHostAllocator.h

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

aten/src/ATen/cuda/CUDABlas.cpp

@@ -388,6 +388,7 @@ 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
@@ -427,7 +428,7 @@ 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) {
-      uint32_t mask =
+      mask =
           fp16_reduction ==
                   at::CuBLASReductionOption::DisallowReducedPrecisionAllowSplitK
               ? (CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE |
@@ -444,7 +445,7 @@ 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) {
-      uint32_t mask =
+      mask =
           bf16_reduction ==
                   at::CuBLASReductionOption::DisallowReducedPrecisionAllowSplitK
               ? (CUBLASLT_REDUCTION_SCHEME_COMPUTE_TYPE |
@@ -511,17 +512,41 @@ 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;
-  TORCH_CUDABLAS_CHECK(cublasLtMatmulAlgoGetHeuristic(
-      ltHandle,
-      computeDesc.descriptor(),
-      Adesc.descriptor(),
-      Bdesc.descriptor(),
-      Cdesc.descriptor(),
-      Cdesc.descriptor(),
-      preference.descriptor(),
-      1,
-      &heuristicResult,
-      &returnedResult));
+  // 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));
+  }
   if (returnedResult == 0) {
     cublasStatus = CUBLAS_STATUS_NOT_SUPPORTED;
   }
@@ -1572,7 +1597,7 @@ bool gemm_and_bias(
   }
 
   using opmath_t = at::opmath_type<Dtype>;
-  opmath_t beta_val = 0; // bias is added in epilogue
+  opmath_t beta_val = bias ? 0 : 1; // bias is added in epilogue unless nullptr
 
   cudaDataType_t abType = CUDA_R_32F;
   cudaDataType_t cType = CUDA_R_32F;
@@ -1661,15 +1686,22 @@ bool gemm_and_bias(
     _syncCurrentWithCarveoutStream(stream, true);
   }
 #endif
-  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;
-  }
+  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);
 
-  if (bias != nullptr) {
-    computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_EPILOGUE, epilogue);
+  if (bias) {
     computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_BIAS_POINTER, bias);
   }
 

aten/src/ATen/cuda/NumericLimits.cuh

@@ -55,14 +55,6 @@ struct numeric_limits<int8_t> {
   static inline __host__ __device__ int8_t upper_bound() { return INT8_MAX; }
 };
 
-template <>
-struct numeric_limits<uint16_t> {
-  static inline __host__ __device__ uint16_t lowest() { return 0; }
-  static inline __host__ __device__ uint16_t max() { return UINT16_MAX; }
-  static inline __host__ __device__ uint16_t lower_bound() { return 0; }
-  static inline __host__ __device__ uint16_t upper_bound() { return UINT16_MAX; }
-};
-
 template <>
 struct numeric_limits<int16_t> {
   static inline __host__ __device__ int16_t lowest() { return INT16_MIN; }
@@ -71,14 +63,6 @@ struct numeric_limits<int16_t> {
   static inline __host__ __device__ int16_t upper_bound() { return INT16_MAX; }
 };
 
-template <>
-struct numeric_limits<uint32_t> {
-  static inline __host__ __device__ uint32_t lowest() { return 0; }
-  static inline __host__ __device__ uint32_t max() { return UINT32_MAX; }
-  static inline __host__ __device__ uint32_t lower_bound() { return 0; }
-  static inline __host__ __device__ uint32_t upper_bound() { return UINT32_MAX; }
-};
-
 template <>
 struct numeric_limits<int32_t> {
   static inline __host__ __device__ int32_t lowest() { return INT32_MIN; }
@@ -87,21 +71,6 @@ struct numeric_limits<int32_t> {
   static inline __host__ __device__ int32_t upper_bound() { return INT32_MAX; }
 };
 
-template <>
-struct numeric_limits<uint64_t> {
-#ifdef _MSC_VER
-  static inline __host__ __device__ uint64_t lowest() { return 0; }
-  static inline __host__ __device__ uint64_t max() { return _UI64_MAX; }
-  static inline __host__ __device__ uint64_t lower_bound() { return 0; }
-  static inline __host__ __device__ uint64_t upper_bound() { return _UI64_MAX; }
-#else
-  static inline __host__ __device__ uint64_t lowest() { return 0; }
-  static inline __host__ __device__ uint64_t max() { return UINT64_MAX; }
-  static inline __host__ __device__ uint64_t lower_bound() { return 0; }
-  static inline __host__ __device__ uint64_t upper_bound() { return UINT64_MAX; }
-#endif
-};
-
 template <>
 struct numeric_limits<int64_t> {
 #ifdef _MSC_VER

aten/src/ATen/cuda/cub.h

@@ -24,7 +24,13 @@ 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

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

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

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

aten/src/ATen/detail/CUDAHooksInterface.h

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

aten/src/ATen/functorch/BatchedTensorImpl.h

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

aten/src/ATen/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(stream->queue(), ^() {
+  dispatch_sync_with_rethrow(stream->queue(), ^() {
     stream->synchronize(SyncType::COMMIT_AND_WAIT);
   });
   m_mutex.lock();

aten/src/ATen/mps/MPSStream.h

@@ -110,6 +110,9 @@ class TORCH_API MPSStream {
     return _stream;
   }
 
+  MTLBuffer_t getErrorBuffer();
+  void checkLastError();
+
  private:
   Stream _stream;
   MTLCommandQueue_t _commandQueue = nil;
@@ -121,6 +124,8 @@ 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();
@@ -155,4 +160,7 @@ 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
 //-----------------------------------------------------------------
@@ -30,6 +30,10 @@ 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() {
@@ -38,6 +42,8 @@ MPSStream::~MPSStream() {
   [_executionDescriptor release];
   [_compilationDescriptor release];
   _executionDescriptor = nil;
+  [_errorBuffer release];
+  _errorBuffer = nil;
   _compilationDescriptor = nil;
 
   assert(_commandBuffer == nil);
@@ -104,6 +110,7 @@ void MPSStream::commitAndWait() {
     [_prevCommandBuffer waitUntilCompleted];
     [_prevCommandBuffer release];
     _prevCommandBuffer = nil;
+    checkLastError();
   }
 
   if (_commandBuffer) {
@@ -111,6 +118,7 @@ void MPSStream::commitAndWait() {
     [_commandBuffer waitUntilCompleted];
     [_commandBuffer release];
     _commandBuffer = nil;
+    checkLastError();
   }
 }
 
@@ -153,7 +161,7 @@ void MPSStream::fill(id<MTLBuffer> buffer, uint8_t value, size_t length, size_t
   if (length == 0) {
     return;
   }
-  dispatch_sync(_serialQueue, ^() {
+  dispatch_sync_with_rethrow(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -183,7 +191,7 @@ void MPSStream::copy(id<MTLBuffer> srcBuffer,
                      size_t dstOffset,
                      uint64_t profileId,
                      SyncType syncType) {
-  dispatch_sync(_serialQueue, ^() {
+  dispatch_sync_with_rethrow(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -236,7 +244,7 @@ void MPSStream::executeMPSGraph(MPSGraph* mpsGraph, NSDictionary* feeds, NSDicti
   auto& profiler = getMPSProfiler();
   const bool isGraphProfilingEnabled = profiler.isOperationProfilingEnabled();
 
-  dispatch_sync(_serialQueue, ^() {
+  dispatch_sync_with_rethrow(_serialQueue, ^() {
     endKernelCoalescing();
     if (isGraphProfilingEnabled) {
       // this function call is only relevant for interval-based Signposts
@@ -266,6 +274,24 @@ 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
 //-----------------------------------------------------------------
@@ -289,4 +315,19 @@ 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

@@ -1009,12 +1009,25 @@ 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 meta_out = at::_ops::mul_Tensor::redispatch(meta_dks, self.to(device_), other.to(device_));
+  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);
   return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
 }
 
@@ -1023,7 +1036,9 @@ 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 meta_out = at::_ops::div_Tensor::redispatch(meta_dks, self.to(device_), other.to(device_));
+  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);
 
   if (self._is_zerotensor()) {
     if (other._is_zerotensor()) {
@@ -1052,8 +1067,9 @@ 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 meta_out = at::_ops::add_Tensor::redispatch(
-      meta_dks, self.to(device_), other.to(device_), alpha);
+  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 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().versionCuDNN();
+    static long cudnn_version = detail::getCUDAHooks().versionRuntimeCuDNN();
     // broken on cuDNN 9.8 - 9.14
     if (cudnn_version >= 90800 && cudnn_version < 91500) {
       if (cudnn_conv_suggest_memory_format(input, weight) == at::MemoryFormat::Contiguous &&
@@ -453,7 +453,7 @@ struct ConvParams {
     }
     // native kernel doesn't support 64-bit non-splittable case
     if (!(canUse32BitIndexMath(input) && canUse32BitIndexMath(weight))) {
-      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionCuDNN() : -1;
+      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionRuntimeCuDNN() : -1;
       // TODO(eqy): remove this once cuDNN fixes 64-bit depthwise support, first broken in 9.11x
       if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous) {
         if (cudnn_version < 0 || cudnn_version > 91000) {

aten/src/ATen/native/Linear.cpp

@@ -50,18 +50,35 @@ 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();
-    // 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];
+  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();
     }
-    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);
+  }();
+
+  // 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);
 }
 
 
@@ -90,15 +107,23 @@ Tensor linear(const Tensor& input, const Tensor& weight, const std::optional<Ten
     // Fused op is marginally faster.
     return at::addmm(*bias, input, weight.t());
   }
-  if (bias->defined() && !input.is_xla()) {
-    // Also hit the fused path for contiguous 3D input, if not using xla
+
+  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
     // backend. Reshaping/flattening has some performance implications on xla.
-    bool is_contiguous = input.is_contiguous_or_false();
-    if (is_contiguous && input_dim == 3) {
+    if (input.is_contiguous_or_false()) {
       return _flatten_nd_linear(input, weight, *bias);
-    } 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) {
+    } else if (parseLinearFlatten3d()) {
       // 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/PackedSequence.cpp

@@ -142,6 +142,7 @@ Tensor _pack_padded_sequence_backward_symint(const Tensor& grad, c10::SymIntArra
 std::tuple<Tensor, Tensor> _pad_packed_sequence(const Tensor& data, const Tensor& _batch_sizes, bool batch_first, const Scalar& padding_value, int64_t total_length) {
   auto batch_sizes_t = _batch_sizes.contiguous();
   checkLongTensor(batch_sizes_t);
+  TORCH_CHECK(batch_sizes_t.numel() > 0, "batch_sizes can not be empty");
 
   int64_t * batch_sizes = batch_sizes_t.data_ptr<int64_t>();
   int64_t max_batch_size = batch_sizes[0];

aten/src/ATen/native/TensorCompare.cpp

@@ -23,6 +23,7 @@
 #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>
@@ -479,6 +480,14 @@ 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

@@ -1,5 +1,6 @@
 #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>
@@ -1710,11 +1711,37 @@ Tensor narrow_symint(
       "], but got ",
       start,
       ")")
-  if (start < 0) {
-    start = start + cur_size;
+
+  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);
   }
+
+  // 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_le(cur_size - length),
+      (start.sym_lt(0).sym_and((end).sym_le(0)))
+          .sym_or(start.sym_ge(0).sym_and((end).sym_le(cur_size))),
       "start (",
       start,
       ") + length (",
@@ -1722,7 +1749,28 @@ Tensor narrow_symint(
       ") exceeds dimension size (",
       cur_size,
       ").");
-  return at::slice_symint(self, dim, start, start + length, 1);
+
+  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;
+  }
 }
 
 // This overload exists purely for XLA, because they wanted to pass in
@@ -1736,8 +1784,8 @@ Tensor narrow_tensor_symint(
       start.dim() == 0 &&
           isIntegralType(start.scalar_type(), /*includeBool=*/false),
       "start must be an 0-dim integral Tensor.");
-  int64_t st = start.item<int64_t>();
-  return at::narrow_symint(self, dim, c10::SymInt(st), std::move(length));
+  c10::SymInt st = start.item().toSymInt();
+  return at::narrow_symint(self, dim, std::move(st), std::move(length));
 }
 
 std::

aten/src/ATen/native/cpu/Reduce.h

@@ -247,8 +247,8 @@ void binary_kernel_reduce(TensorIteratorBase& iter, ops_t ops, init_t init) {
   });
 }
 
-template <typename func_t, typename vec_func_t, typename ident_t = double>
-void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, ident_t ident = static_cast<ident_t>(0)) {
+template <typename func_t, typename vec_func_t>
+void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, double ident = 0) {
   using traits = binary_function_traits<func_t>;
   static_assert(
     all_same<

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

@@ -5,7 +5,6 @@
 #include <ATen/native/ReduceOpsUtils.h>
 
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/Parallel.h>
 #include <ATen/TensorIterator.h>
 #include <ATen/OpMathType.h>
@@ -79,12 +78,12 @@ void min_all_kernel_impl(Tensor& result, const Tensor& input) {
     reduce_all_impl<int64_t>(result, input, upper_bound<int64_t>(),
       [=](int64_t a, int64_t b) -> int64_t { return min_impl(a, b); });
   } else {
-    AT_DISPATCH_V2(input.scalar_type(), "min_all", AT_WRAP([&] {
+    AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(), "min_all", [&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       reduce_all_impl_vec<scalar_t>(result, input, upper_bound<scalar_t>(),
         [=] (scalar_t a , scalar_t b) -> scalar_t { return min_impl(a, b); },
         [=](Vec a, Vec b) -> Vec { return minimum(a, b); });
-    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
+    });
   }
 }
 
@@ -104,12 +103,12 @@ void max_all_kernel_impl(Tensor& result, const Tensor& input) {
     reduce_all_impl<int64_t>(result, input, lower_bound<int64_t>(),
       [=](int64_t a, int64_t b) -> int64_t { return max_impl(a, b); });
   } else {
-    AT_DISPATCH_V2(input.scalar_type(), "max_all", AT_WRAP([&] {
+    AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(), "max_all", [&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       reduce_all_impl_vec<scalar_t>(result, input, lower_bound<scalar_t>(),
         [=] (scalar_t a , scalar_t b) -> scalar_t { return max_impl(a, b); },
         [=](Vec a, Vec b) -> Vec { return maximum(a, b); });
-    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
+    });
   }
 }
 
@@ -200,7 +199,7 @@ void aminmax_allreduce_kernel(
       }
     );
   } else {
-    AT_DISPATCH_V2(input.scalar_type(), "aminmax_cpu", AT_WRAP([&] {
+    AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, input.scalar_type(), "aminmax_cpu", [&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       using scalar_t_pair = std::pair<scalar_t, scalar_t>;
       reduce_all_impl_vec_two_outputs<scalar_t>(
@@ -215,7 +214,7 @@ void aminmax_allreduce_kernel(
         [=](Vec a, Vec b) -> Vec { return minimum(a, b); },
         [=](Vec a, Vec b) -> Vec { return maximum(a, b); }
       );
-    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
+    });
   }
 }
 

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

@@ -3,7 +3,6 @@
 
 #include <ATen/core/Tensor.h>
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/OpMathType.h>
 #include <ATen/cpu/vec/vec.h>
 #include <ATen/cpu/vec/functional.h>
@@ -339,24 +338,43 @@ void or_kernel_impl(TensorIterator& iter) {
   }
 }
 
+template<typename scalar_t>
+struct MinValuesOps: public at::native::MinOps<scalar_t> {
+  using arg_t = typename MinOps<scalar_t>::arg_t;
+  static scalar_t project(arg_t arg) {
+    return arg.first;
+  }
+};
+
 void min_values_kernel_impl(TensorIterator& iter) {
-  AT_DISPATCH_V2(iter.dtype(), "min_values_cpu", AT_WRAP([&iter] {
+  if (iter.dtype() == kLong) {
+    // This case is special because of Vectorized<int64_t> does not
+    // handle upper_bound<int64_t>().
+    // See: https://github.com/pytorch/pytorch/issues/43254
+    using scalar_t = int64_t;
+    binary_kernel_reduce(
+      iter,
+      MinValuesOps<scalar_t>{},
+      std::pair<scalar_t, int64_t>(upper_bound<scalar_t>(), -1));
+    return;
+  }
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "min_values_cpu", [&iter] {
     binary_kernel_reduce_vec(
       iter,
       [](scalar_t a, scalar_t b) -> scalar_t { return min_impl(a, b); },
       [](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return minimum(a, b); },
-      upper_bound<scalar_t>());
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+      static_cast<double>(upper_bound<scalar_t>()));
+  });
 }
 
 void max_values_kernel_impl(TensorIterator& iter) {
-  AT_DISPATCH_V2(iter.dtype(), "max_values_cpu", AT_WRAP([&iter] {
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "max_values_cpu", [&iter] {
     binary_kernel_reduce_vec(
       iter,
       [](scalar_t a, scalar_t b) -> scalar_t { return max_impl(a, b); },
       [](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return maximum(a, b); },
       lower_bound<scalar_t>());
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+  });
 }
 
 void argmax_kernel_impl(TensorIterator &iter) {

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

@@ -11,7 +11,6 @@
 #include <vector>
 
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/Parallel.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/TensorIterator.h>
@@ -107,7 +106,7 @@ void min_kernel_impl(
     bool keepdim) {
   int64_t self_dim_size = ensure_nonempty_size(self, dim);
 
-  AT_DISPATCH_V2(self.scalar_type(), "min_cpu", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool, self.scalar_type(), "min_cpu", [&] {
     compare_base_kernel<scalar_t>(result, indice, self, dim, keepdim, [&] (
       scalar_t* result_data, int64_t* indice_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -129,7 +128,7 @@ void min_kernel_impl(
         *indice_data = index;
       }
     );
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool);
+  });
 }
 
 void max_kernel_impl(
@@ -140,7 +139,7 @@ void max_kernel_impl(
     bool keepdim) {
   int64_t self_dim_size = ensure_nonempty_size(self, dim);
 
-  AT_DISPATCH_V2(self.scalar_type(), "max_cpu", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool, self.scalar_type(), "max_cpu", [&] {
     compare_base_kernel<scalar_t>(result, indice, self, dim, keepdim, [&] (
       scalar_t* result_data, int64_t* indice_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -162,7 +161,7 @@ void max_kernel_impl(
         *indice_data = index;
       }
     );
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool);
+  });
 }
 
 void aminmax_kernel(
@@ -187,7 +186,7 @@ void aminmax_kernel(
     return;
   }
 
-  AT_DISPATCH_V2(self.scalar_type(), "aminmax_cpu", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Bool, ScalarType::BFloat16, ScalarType::Half, self.scalar_type(), "aminmax_cpu", [&] {
     compare_base_kernel<scalar_t, scalar_t>(min_result, max_result, self, wrap_dim, keepdim, [&] (
       scalar_t* min_result_data, scalar_t* max_result_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -210,7 +209,7 @@ void aminmax_kernel(
         *max_result_data = max_number;
       }
     );
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Bool, ScalarType::BFloat16, ScalarType::Half);
+  });
 }
 
 void where_kernel_impl(TensorIterator &iter) {

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

@@ -147,14 +147,24 @@ 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) {
+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
   // 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 (result.is_same(self)) {
+  if (self.is_same(result) || self.dim() == 2) {
     return false;
   }
+  #endif
 
   #if defined(USE_ROCM) && ROCM_VERSION == 60400
   // hipblaslt TT fp32 regression on ROCm 6.4, cannot use
@@ -169,13 +179,33 @@ static bool isInputCompliesAddmmCudaLt(Tensor& result, const Tensor& self, const
   #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
     && (
-      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]
+      ( // 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]}))
+      )
     )
     && ( // some dtype restrictions
       #ifndef USE_ROCM
@@ -270,7 +300,16 @@ bool launchGemmAndBiasCublasLt(
     const Scalar& alpha,
     Activation activation = Activation::None
 ) {
-  const auto* self_ptr = self.const_data_ptr<scalar_t>();
+  // 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 tuning_ctx = at::cuda::tunable::getTuningContext();
   if (tuning_ctx->IsTunableOpEnabled()) {
@@ -356,7 +395,7 @@ 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) || disable_addmm_cuda_lt;
+  disable_addmm_cuda_lt = !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha, activation) || disable_addmm_cuda_lt;
   // }
 
   at::ScalarType scalar_type = mat1.scalar_type();
@@ -366,19 +405,20 @@ 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 (disable_addmm_cuda_lt) {
-        // When in non-Lt path we do expand self even before
+      if (!use_bias_ptr_lt) {
+        // 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 copy bias when in the non-Lt path
-    if (beta.toComplexDouble() != 0.0 && disable_addmm_cuda_lt) {
+    // We do not copy bias only when we need the bias ptr
+    if (beta.toComplexDouble() != 0.0 && !use_bias_ptr_lt) {
       // NOTE: self should broadcast over result
       at::native::copy_(result, *self_maybe_expanded);
     }

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

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

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

@@ -669,9 +669,12 @@ 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;
+  // On non CK system(w/ ROCm), make sure use_fast_path is false
+#if defined(USE_ROCM_CK_GEMM)
   if (at::detail::getCUDAHooks().isGPUArch({"gfx942", "gfx950"})) {
     use_fast_path = true;
   }
+#endif //USE_ROCM_CK_GEMM
 #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_);
@@ -680,7 +683,11 @@ std::optional<c10::ScalarType> out_dtype) {
 #ifndef USE_ROCM
     at::cuda::detail::bf16bf16_grouped_mm(mat_a, mat_b, offs, bias, out);
 #else
+#if defined(USE_ROCM_CK_GEMM)
     at::hip::detail::group_gemm_ck(mat_a, mat_b, offs, bias, out);
+#else
+    TORCH_WARN("ROCm: Group Gemm through CK not selected.");
+#endif //USE_ROCM_CK_GEMM
 #endif
   } else {
     _grouped_mm_fallback(mat_a, mat_b, offs, bias, out_dtype, out);

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

@@ -1,6 +1,5 @@
 #define TORCH_ASSERT_NO_OPERATORS
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/native/DispatchStub.h>
 #include <ATen/native/ReduceAllOps.h>
@@ -29,22 +28,22 @@ void _min_max_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void aminmax_allreduce_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_V2(
-      iter.input_dtype(), "aminmax_all_cuda", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(
+      kBFloat16, kHalf, kBool, iter.input_dtype(), "aminmax_all_cuda", [&] {
         _min_max_values_kernel_cuda_impl<scalar_t>(iter);
-      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+      });
 }
 
 void aminmax_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_V2(
-      iter.input_dtype(), "aminmax_cuda", AT_WRAP([&]() {
+  AT_DISPATCH_ALL_TYPES_AND3(
+      kBFloat16, kHalf, kBool, iter.input_dtype(), "aminmax_cuda", [&]() {
         gpu_reduce_kernel<scalar_t, scalar_t>(
             iter,
             MinMaxOps<scalar_t, scalar_t, int32_t>{},
             thrust::pair<scalar_t, scalar_t>(
                 at::numeric_limits<scalar_t>::upper_bound(),
                 at::numeric_limits<scalar_t>::lower_bound()));
-      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+      });
 }
 
 } // namespace at::native

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

@@ -1,6 +1,5 @@
 #define TORCH_ASSERT_NO_OPERATORS
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/native/DispatchStub.h>
 #include <ATen/native/ReduceAllOps.h>
@@ -34,27 +33,27 @@ void max_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void max_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_V2(
-      iter.dtype(), "max_values_cuda", AT_WRAP([&]() {
+  AT_DISPATCH_ALL_TYPES_AND3(
+      kBFloat16, kHalf, kBool, iter.dtype(), "max_values_cuda", [&]() {
         max_values_kernel_cuda_impl<scalar_t>(iter);
-      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+      });
 }
 
 void max_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_V2(
-      iter.input_dtype(), "max_cuda", AT_WRAP([&]() {
+  AT_DISPATCH_ALL_TYPES_AND3(
+      kBFloat16, kHalf, kBool, iter.input_dtype(), "max_cuda", [&]() {
         gpu_reduce_kernel<scalar_t, scalar_t>(
             iter,
             MaxOps<scalar_t>{},
             thrust::pair<scalar_t, int64_t>(
                 at::numeric_limits<scalar_t>::lower_bound(), 0));
-      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+      });
 }
 
 void max_all_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_V2(iter.input_dtype(), "max_all_cuda", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "max_all_cuda", [&] {
     max_values_kernel_cuda_impl<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+  });
 }
 
 REGISTER_DISPATCH(max_values_stub, &max_values_kernel_cuda)

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

@@ -12,7 +12,6 @@
 #include <ATen/NumericUtils.h>
 
 #include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/cuda/NumericLimits.cuh>
 
@@ -34,24 +33,24 @@ void min_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void min_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "min_values_cuda", [&]() {
     min_values_kernel_cuda_impl<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+  });
 }
 
 void min_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "min_cuda", [&]() {
     gpu_reduce_kernel<scalar_t, scalar_t>(
       iter,
       MinOps<scalar_t>{},
       thrust::pair<scalar_t, int64_t>(at::numeric_limits<scalar_t>::upper_bound(), 0));
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+  });
 }
 
 void min_all_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_V2(iter.input_dtype(), "min_all_cuda", AT_WRAP([&] {
+  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "min_all_cuda", [&] {
     min_values_kernel_cuda_impl<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
+  });
 }
 
 REGISTER_DISPATCH(min_values_stub, &min_values_kernel_cuda)

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

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

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

@@ -40,8 +40,6 @@ 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

@@ -53,21 +53,6 @@
 @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.
  */