add workflow to dispatch

ghstack-source-id: 633724d746aef541a6c82f13cbbef076f23a707c
Pull-Request: https://github.com/pytorch/pytorch/pull/161681

.bc-linter.yml

@@ -13,4 +13,3 @@ exclude:
   - "**/benchmarks/**"
   - "**/test_*.py"
   - "**/*_test.py"
-  - "tools/**"

.ci/aarch64_linux/aarch64_ci_build.sh

@@ -3,22 +3,8 @@ set -eux -o pipefail
 
 GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
 
-# Set CUDA architecture lists to match x86 build_cuda.sh
-if [[ "$GPU_ARCH_VERSION" == *"12.6"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0"
-elif [[ "$GPU_ARCH_VERSION" == *"12.8"* ]]; then
+if [[ "$GPU_ARCH_VERSION" == *"12.9"* ]]; then
     export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
-elif [[ "$GPU_ARCH_VERSION" == *"12.9"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
-elif [[ "$GPU_ARCH_VERSION" == *"13.0"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;11.0;12.0+PTX"
-fi
-
-# Compress the fatbin with -compress-mode=size for CUDA 13
-if [[ "$DESIRED_CUDA" == *"13"* ]]; then
-    export TORCH_NVCC_FLAGS="-compress-mode=size"
-    # Bundle ptxas into the cu13 wheel, see https://github.com/pytorch/pytorch/issues/163801
-    export BUILD_BUNDLE_PTXAS=1
 fi
 
 SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
@@ -32,22 +18,14 @@ cd /
 # on the mounted pytorch repo
 git config --global --add safe.directory /pytorch
 pip install -r /pytorch/requirements.txt
-pip install auditwheel==6.2.0 wheel
+pip install auditwheel==6.2.0
 if [ "$DESIRED_CUDA" = "cpu" ]; then
     echo "BASE_CUDA_VERSION is not set. Building cpu wheel."
-    python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
+    #USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
+    USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
 else
     echo "BASE_CUDA_VERSION is set to: $DESIRED_CUDA"
     export USE_SYSTEM_NCCL=1
-
-    # Check if we should use NVIDIA libs from PyPI (similar to x86 build_cuda.sh logic)
-    if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
-        echo "Bundling CUDA libraries with wheel for aarch64."
-    else
-        echo "Using nvidia libs from pypi for aarch64."
-        echo "Updated PYTORCH_EXTRA_INSTALL_REQUIREMENTS for aarch64: $PYTORCH_EXTRA_INSTALL_REQUIREMENTS"
-        export USE_NVIDIA_PYPI_LIBS=1
-    fi
-
-    python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
+    #USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
+    USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
 fi

.ci/aarch64_linux/aarch64_wheel_ci_build.py

@@ -13,6 +13,49 @@ def list_dir(path: str) -> list[str]:
     return check_output(["ls", "-1", path]).decode().split("\n")
 
 
+def build_ArmComputeLibrary() -> None:
+    """
+    Using ArmComputeLibrary for aarch64 PyTorch
+    """
+    print("Building Arm Compute Library")
+    acl_build_flags = [
+        "debug=0",
+        "neon=1",
+        "opencl=0",
+        "os=linux",
+        "openmp=1",
+        "cppthreads=0",
+        "arch=armv8a",
+        "multi_isa=1",
+        "fixed_format_kernels=1",
+        "build=native",
+    ]
+    acl_install_dir = "/acl"
+    acl_checkout_dir = os.getenv("ACL_SOURCE_DIR", "ComputeLibrary")
+    if os.path.isdir(acl_install_dir):
+        shutil.rmtree(acl_install_dir)
+    if not os.path.isdir(acl_checkout_dir) or not len(os.listdir(acl_checkout_dir)):
+        check_call(
+            [
+                "git",
+                "clone",
+                "https://github.com/ARM-software/ComputeLibrary.git",
+                "-b",
+                "v25.02",
+                "--depth",
+                "1",
+                "--shallow-submodules",
+            ]
+        )
+
+    check_call(
+        ["scons", "Werror=1", f"-j{os.cpu_count()}"] + acl_build_flags,
+        cwd=acl_checkout_dir,
+    )
+    for d in ["arm_compute", "include", "utils", "support", "src", "build"]:
+        shutil.copytree(f"{acl_checkout_dir}/{d}", f"{acl_install_dir}/{d}")
+
+
 def replace_tag(filename) -> None:
     with open(filename) as f:
         lines = f.readlines()
@@ -26,186 +69,62 @@ def replace_tag(filename) -> None:
         f.writelines(lines)
 
 
-def patch_library_rpath(
-    folder: str,
-    lib_name: str,
-    use_nvidia_pypi_libs: bool = False,
-    desired_cuda: str = "",
-) -> None:
-    """Apply patchelf to set RPATH for a library in torch/lib"""
-    lib_path = f"{folder}/tmp/torch/lib/{lib_name}"
-
-    if use_nvidia_pypi_libs:
-        # For PyPI NVIDIA libraries, construct CUDA RPATH
-        cuda_rpaths = [
-            "$ORIGIN/../../nvidia/cudnn/lib",
-            "$ORIGIN/../../nvidia/nvshmem/lib",
-            "$ORIGIN/../../nvidia/nccl/lib",
-            "$ORIGIN/../../nvidia/cusparselt/lib",
-        ]
-
-        if "130" in desired_cuda:
-            cuda_rpaths.append("$ORIGIN/../../nvidia/cu13/lib")
-        else:
-            cuda_rpaths.extend(
-                [
-                    "$ORIGIN/../../nvidia/cublas/lib",
-                    "$ORIGIN/../../nvidia/cuda_cupti/lib",
-                    "$ORIGIN/../../nvidia/cuda_nvrtc/lib",
-                    "$ORIGIN/../../nvidia/cuda_runtime/lib",
-                    "$ORIGIN/../../nvidia/cufft/lib",
-                    "$ORIGIN/../../nvidia/curand/lib",
-                    "$ORIGIN/../../nvidia/cusolver/lib",
-                    "$ORIGIN/../../nvidia/cusparse/lib",
-                    "$ORIGIN/../../nvidia/nvtx/lib",
-                    "$ORIGIN/../../nvidia/cufile/lib",
-                ]
-            )
-
-        # Add $ORIGIN for local torch libs
-        rpath = ":".join(cuda_rpaths) + ":$ORIGIN"
-    else:
-        # For bundled libraries, just use $ORIGIN
-        rpath = "$ORIGIN"
-
-    if os.path.exists(lib_path):
-        os.system(
-            f"cd {folder}/tmp/torch/lib/; "
-            f"patchelf --set-rpath '{rpath}' --force-rpath {lib_name}"
-        )
-
-
-def copy_and_patch_library(
-    src_path: str,
-    folder: str,
-    use_nvidia_pypi_libs: bool = False,
-    desired_cuda: str = "",
-) -> None:
-    """Copy a library to torch/lib and patch its RPATH"""
-    if os.path.exists(src_path):
-        lib_name = os.path.basename(src_path)
-        shutil.copy2(src_path, f"{folder}/tmp/torch/lib/{lib_name}")
-        patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
-
-
 def package_cuda_wheel(wheel_path, desired_cuda) -> None:
     """
     Package the cuda wheel libraries
     """
     folder = os.path.dirname(wheel_path)
+    wheelname = os.path.basename(wheel_path)
     os.mkdir(f"{folder}/tmp")
     os.system(f"unzip {wheel_path} -d {folder}/tmp")
-    # Delete original wheel since it will be repackaged
-    os.system(f"rm {wheel_path}")
+    libs_to_copy = [
+        "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12",
+        "/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so",
+        "/usr/local/cuda/lib64/libcudnn.so.9",
+        "/usr/local/cuda/lib64/libcublas.so.12",
+        "/usr/local/cuda/lib64/libcublasLt.so.12",
+        "/usr/local/cuda/lib64/libcudart.so.12",
+        "/usr/local/cuda/lib64/libcufft.so.11",
+        "/usr/local/cuda/lib64/libcusparse.so.12",
+        "/usr/local/cuda/lib64/libcusparseLt.so.0",
+        "/usr/local/cuda/lib64/libcusolver.so.11",
+        "/usr/local/cuda/lib64/libcurand.so.10",
+        "/usr/local/cuda/lib64/libnccl.so.2",
+        "/usr/local/cuda/lib64/libnvJitLink.so.12",
+        "/usr/local/cuda/lib64/libnvrtc.so.12",
+        "/usr/local/cuda/lib64/libnvshmem_host.so.3",
+        "/usr/local/cuda/lib64/libcudnn_adv.so.9",
+        "/usr/local/cuda/lib64/libcudnn_cnn.so.9",
+        "/usr/local/cuda/lib64/libcudnn_graph.so.9",
+        "/usr/local/cuda/lib64/libcudnn_ops.so.9",
+        "/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9",
+        "/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9",
+        "/usr/local/cuda/lib64/libcudnn_heuristic.so.9",
+        "/lib64/libgomp.so.1",
+        "/usr/lib64/libgfortran.so.5",
+        "/acl/build/libarm_compute.so",
+        "/acl/build/libarm_compute_graph.so",
+        "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
+        "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
+        "/usr/local/lib/libnvpl_lapack_core.so.0",
+        "/usr/local/lib/libnvpl_blas_core.so.0",
+    ]
 
-    # Check if we should use PyPI NVIDIA libraries or bundle system libraries
-    use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
-
-    if use_nvidia_pypi_libs:
-        print("Using nvidia libs from pypi - skipping CUDA library bundling")
-        # For PyPI approach, we don't bundle CUDA libraries - they come from PyPI packages
-        # We only need to bundle non-NVIDIA libraries
-        minimal_libs_to_copy = [
-            "/lib64/libgomp.so.1",
-            "/usr/lib64/libgfortran.so.5",
-            "/acl/build/libarm_compute.so",
-            "/acl/build/libarm_compute_graph.so",
-            "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_lapack_core.so.0",
-            "/usr/local/lib/libnvpl_blas_core.so.0",
-        ]
-
-        # Copy minimal libraries to unzipped_folder/torch/lib
-        for lib_path in minimal_libs_to_copy:
-            copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
-
-        # Patch torch libraries used for searching libraries
-        torch_libs_to_patch = [
-            "libtorch.so",
-            "libtorch_cpu.so",
-            "libtorch_cuda.so",
-            "libtorch_cuda_linalg.so",
-            "libtorch_global_deps.so",
-            "libtorch_python.so",
-            "libtorch_nvshmem.so",
-            "libc10.so",
-            "libc10_cuda.so",
-            "libcaffe2_nvrtc.so",
-            "libshm.so",
-        ]
-        for lib_name in torch_libs_to_patch:
-            patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
-    else:
-        print("Bundling CUDA libraries with wheel")
-        # Original logic for bundling system CUDA libraries
-        # Common libraries for all CUDA versions
-        common_libs = [
-            # Non-NVIDIA system libraries
-            "/lib64/libgomp.so.1",
-            "/usr/lib64/libgfortran.so.5",
-            "/acl/build/libarm_compute.so",
-            "/acl/build/libarm_compute_graph.so",
-            # Common CUDA libraries (same for all versions)
-            "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_lapack_core.so.0",
-            "/usr/local/lib/libnvpl_blas_core.so.0",
-            "/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so",
-            "/usr/local/cuda/lib64/libcudnn.so.9",
-            "/usr/local/cuda/lib64/libcusparseLt.so.0",
-            "/usr/local/cuda/lib64/libcurand.so.10",
-            "/usr/local/cuda/lib64/libnccl.so.2",
-            "/usr/local/cuda/lib64/libnvshmem_host.so.3",
-            "/usr/local/cuda/lib64/libcudnn_adv.so.9",
-            "/usr/local/cuda/lib64/libcudnn_cnn.so.9",
-            "/usr/local/cuda/lib64/libcudnn_graph.so.9",
-            "/usr/local/cuda/lib64/libcudnn_ops.so.9",
-            "/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9",
-            "/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9",
-            "/usr/local/cuda/lib64/libcudnn_heuristic.so.9",
+    if "129" in desired_cuda:
+        libs_to_copy += [
+            "/usr/local/cuda/lib64/libnvrtc-builtins.so.12.9",
             "/usr/local/cuda/lib64/libcufile.so.0",
             "/usr/local/cuda/lib64/libcufile_rdma.so.1",
-            "/usr/local/cuda/lib64/libcusparse.so.12",
         ]
 
-        # CUDA version-specific libraries
-        if "13" in desired_cuda:
-            minor_version = desired_cuda[-1]
-            version_specific_libs = [
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.13",
-                "/usr/local/cuda/lib64/libcublas.so.13",
-                "/usr/local/cuda/lib64/libcublasLt.so.13",
-                "/usr/local/cuda/lib64/libcudart.so.13",
-                "/usr/local/cuda/lib64/libcufft.so.12",
-                "/usr/local/cuda/lib64/libcusolver.so.12",
-                "/usr/local/cuda/lib64/libnvJitLink.so.13",
-                "/usr/local/cuda/lib64/libnvrtc.so.13",
-                f"/usr/local/cuda/lib64/libnvrtc-builtins.so.13.{minor_version}",
-            ]
-        elif "12" in desired_cuda:
-            # Get the last character for libnvrtc-builtins version (e.g., "129" -> "9")
-            minor_version = desired_cuda[-1]
-            version_specific_libs = [
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12",
-                "/usr/local/cuda/lib64/libcublas.so.12",
-                "/usr/local/cuda/lib64/libcublasLt.so.12",
-                "/usr/local/cuda/lib64/libcudart.so.12",
-                "/usr/local/cuda/lib64/libcufft.so.11",
-                "/usr/local/cuda/lib64/libcusolver.so.11",
-                "/usr/local/cuda/lib64/libnvJitLink.so.12",
-                "/usr/local/cuda/lib64/libnvrtc.so.12",
-                f"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.{minor_version}",
-            ]
-        else:
-            raise ValueError(f"Unsupported CUDA version: {desired_cuda}.")
-
-        # Combine all libraries
-        libs_to_copy = common_libs + version_specific_libs
-
-        # Copy libraries to unzipped_folder/torch/lib
-        for lib_path in libs_to_copy:
-            copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
+    # Copy libraries to unzipped_folder/a/lib
+    for lib_path in libs_to_copy:
+        lib_name = os.path.basename(lib_path)
+        shutil.copy2(lib_path, f"{folder}/tmp/torch/lib/{lib_name}")
+        os.system(
+            f"cd {folder}/tmp/torch/lib/; "
+            f"patchelf --set-rpath '$ORIGIN' --force-rpath {folder}/tmp/torch/lib/{lib_name}"
+        )
 
     # Make sure the wheel is tagged with manylinux_2_28
     for f in os.scandir(f"{folder}/tmp/"):
@@ -213,8 +132,14 @@ def package_cuda_wheel(wheel_path, desired_cuda) -> None:
             replace_tag(f"{f.path}/WHEEL")
             break
 
-    os.system(f"wheel pack {folder}/tmp/ -d {folder}")
-    os.system(f"rm -rf {folder}/tmp/")
+    os.mkdir(f"{folder}/cuda_wheel")
+    os.system(f"cd {folder}/tmp/; zip -r {folder}/cuda_wheel/{wheelname} *")
+    shutil.move(
+        f"{folder}/cuda_wheel/{wheelname}",
+        f"{folder}/{wheelname}",
+        copy_function=shutil.copy2,
+    )
+    os.system(f"rm -rf {folder}/tmp/ {folder}/cuda_wheel/")
 
 
 def complete_wheel(folder: str) -> str:
@@ -237,7 +162,14 @@ def complete_wheel(folder: str) -> str:
             f"/{folder}/dist/{repaired_wheel_name}",
         )
     else:
-        repaired_wheel_name = list_dir(f"/{folder}/dist")[0]
+        repaired_wheel_name = wheel_name.replace(
+            "linux_aarch64", "manylinux_2_28_aarch64"
+        )
+        print(f"Renaming {wheel_name} wheel to {repaired_wheel_name}")
+        os.rename(
+            f"/{folder}/dist/{wheel_name}",
+            f"/{folder}/dist/{repaired_wheel_name}",
+        )
 
     print(f"Copying {repaired_wheel_name} to artifacts")
     shutil.copy2(
@@ -274,21 +206,11 @@ if __name__ == "__main__":
     ).decode()
 
     print("Building PyTorch wheel")
-    build_vars = ""
+    build_vars = "CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000 "
     # MAX_JOB=5 is not required for CPU backend (see commit 465d98b)
     if enable_cuda:
         build_vars += "MAX_JOBS=5 "
 
-        # Handle PyPI NVIDIA libraries vs bundled libraries
-        use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
-        if use_nvidia_pypi_libs:
-            print("Configuring build for PyPI NVIDIA libraries")
-            # Configure for dynamic linking (matching x86 logic)
-            build_vars += "ATEN_STATIC_CUDA=0 USE_CUDA_STATIC_LINK=0 USE_CUPTI_SO=1 "
-        else:
-            print("Configuring build for bundled NVIDIA libraries")
-            # Keep existing static linking approach - already configured above
-
     override_package_version = os.getenv("OVERRIDE_PACKAGE_VERSION")
     desired_cuda = os.getenv("DESIRED_CUDA")
     if override_package_version is not None:
@@ -313,17 +235,23 @@ if __name__ == "__main__":
         build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1 "
 
     if enable_mkldnn:
+        build_ArmComputeLibrary()
         print("build pytorch with mkldnn+acl backend")
-        build_vars += "USE_MKLDNN=ON USE_MKLDNN_ACL=ON "
-        build_vars += "ACL_ROOT_DIR=/acl "
+        build_vars += (
+            "USE_MKLDNN=ON USE_MKLDNN_ACL=ON "
+            "ACL_ROOT_DIR=/acl "
+            "LD_LIBRARY_PATH=/pytorch/build/lib:/acl/build:$LD_LIBRARY_PATH "
+            "ACL_INCLUDE_DIR=/acl/build "
+            "ACL_LIBRARY=/acl/build "
+        )
         if enable_cuda:
             build_vars += "BLAS=NVPL "
         else:
-            build_vars += "BLAS=OpenBLAS OpenBLAS_HOME=/opt/OpenBLAS "
+            build_vars += "BLAS=OpenBLAS OpenBLAS_HOME=/OpenBLAS "
     else:
         print("build pytorch without mkldnn backend")
 
-    os.system(f"cd /pytorch; {build_vars} python3 -m build --wheel --no-isolation")
+    os.system(f"cd /pytorch; {build_vars} python3 setup.py bdist_wheel")
     if enable_cuda:
         print("Updating Cuda Dependency")
         filename = os.listdir("/pytorch/dist/")

.ci/aarch64_linux/build_aarch64_wheel.py

@@ -241,7 +241,7 @@ def wait_for_connection(addr, port, timeout=15, attempt_cnt=5):
         try:
             with socket.create_connection((addr, port), timeout=timeout):
                 return
-        except (ConnectionRefusedError, TimeoutError):  # noqa: PERF203
+        except (ConnectionRefusedError, socket.timeout):  # noqa: PERF203
             if i == attempt_cnt - 1:
                 raise
             time.sleep(timeout)
@@ -299,6 +299,40 @@ def install_condaforge_python(host: RemoteHost, python_version="3.8") -> None:
         )
 
 
+def build_OpenBLAS(host: RemoteHost, git_clone_flags: str = "") -> None:
+    print("Building OpenBLAS")
+    host.run_cmd(
+        f"git clone https://github.com/xianyi/OpenBLAS -b v0.3.28 {git_clone_flags}"
+    )
+    make_flags = "NUM_THREADS=64 USE_OPENMP=1 NO_SHARED=1 DYNAMIC_ARCH=1 TARGET=ARMV8"
+    host.run_cmd(
+        f"pushd OpenBLAS && make {make_flags} -j8 && sudo make {make_flags} install && popd && rm -rf OpenBLAS"
+    )
+
+
+def build_ArmComputeLibrary(host: RemoteHost, git_clone_flags: str = "") -> None:
+    print("Building Arm Compute Library")
+    acl_build_flags = " ".join(
+        [
+            "debug=0",
+            "neon=1",
+            "opencl=0",
+            "os=linux",
+            "openmp=1",
+            "cppthreads=0",
+            "arch=armv8a",
+            "multi_isa=1",
+            "fixed_format_kernels=1",
+            "build=native",
+        ]
+    )
+    host.run_cmd(
+        f"git clone https://github.com/ARM-software/ComputeLibrary.git -b v25.02 {git_clone_flags}"
+    )
+
+    host.run_cmd(f"cd ComputeLibrary && scons Werror=1 -j8 {acl_build_flags}")
+
+
 def embed_libgomp(host: RemoteHost, use_conda, wheel_name) -> None:
     host.run_cmd("pip3 install auditwheel")
     host.run_cmd(
@@ -408,7 +442,7 @@ def build_torchvision(
     if host.using_docker():
         build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
 
-    host.run_cmd(f"cd vision && {build_vars} python3 -m build --wheel --no-isolation")
+    host.run_cmd(f"cd vision && {build_vars} python3 setup.py bdist_wheel")
     vision_wheel_name = host.list_dir("vision/dist")[0]
     embed_libgomp(host, use_conda, os.path.join("vision", "dist", vision_wheel_name))
 
@@ -463,7 +497,7 @@ def build_torchdata(
     if host.using_docker():
         build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
 
-    host.run_cmd(f"cd data && {build_vars} python3 -m build --wheel --no-isolation")
+    host.run_cmd(f"cd data && {build_vars} python3 setup.py bdist_wheel")
     wheel_name = host.list_dir("data/dist")[0]
     embed_libgomp(host, use_conda, os.path.join("data", "dist", wheel_name))
 
@@ -519,7 +553,7 @@ def build_torchtext(
     if host.using_docker():
         build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
 
-    host.run_cmd(f"cd text && {build_vars} python3 -m build --wheel --no-isolation")
+    host.run_cmd(f"cd text && {build_vars} python3 setup.py bdist_wheel")
     wheel_name = host.list_dir("text/dist")[0]
     embed_libgomp(host, use_conda, os.path.join("text", "dist", wheel_name))
 
@@ -580,7 +614,7 @@ def build_torchaudio(
     host.run_cmd(
         f"cd audio && export FFMPEG_ROOT=$(pwd)/third_party/ffmpeg && export USE_FFMPEG=1 \
         && ./packaging/ffmpeg/build.sh \
-        && {build_vars} python3 -m build --wheel --no-isolation"
+        && {build_vars} python3 setup.py bdist_wheel"
     )
 
     wheel_name = host.list_dir("audio/dist")[0]
@@ -666,6 +700,7 @@ def start_build(
     configure_system(
         host, compiler=compiler, use_conda=use_conda, python_version=python_version
     )
+    build_OpenBLAS(host, git_clone_flags)
 
     if host.using_docker():
         print("Move libgfortant.a into a standard location")
@@ -688,12 +723,10 @@ def start_build(
         f"git clone --recurse-submodules -b {branch} https://github.com/pytorch/pytorch {git_clone_flags}"
     )
 
-    host.run_cmd("pytorch/.ci/docker/common/install_openblas.sh")
-
     print("Building PyTorch wheel")
     build_opts = ""
     if pytorch_build_number is not None:
-        build_opts += f" -C--build-option=--build-number={pytorch_build_number}"
+        build_opts += f" --build-number {pytorch_build_number}"
     # Breakpad build fails on aarch64
     build_vars = "USE_BREAKPAD=0 "
     if branch == "nightly":
@@ -710,18 +743,15 @@ def start_build(
     if host.using_docker():
         build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
     if enable_mkldnn:
-        host.run_cmd("pytorch/.ci/docker/common/install_acl.sh")
+        build_ArmComputeLibrary(host, git_clone_flags)
         print("build pytorch with mkldnn+acl backend")
         build_vars += " USE_MKLDNN=ON USE_MKLDNN_ACL=ON"
-        build_vars += " BLAS=OpenBLAS"
-        build_vars += " OpenBLAS_HOME=/opt/OpenBLAS"
-        build_vars += " ACL_ROOT_DIR=/acl"
         host.run_cmd(
-            f"cd $HOME/pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
+            f"cd $HOME/pytorch && export ACL_ROOT_DIR=$HOME/ComputeLibrary && {build_vars} python3 setup.py bdist_wheel{build_opts}"
         )
         print("Repair the wheel")
         pytorch_wheel_name = host.list_dir("pytorch/dist")[0]
-        ld_library_path = "/acl/build:$HOME/pytorch/build/lib"
+        ld_library_path = "$HOME/acl/build:$HOME/pytorch/build/lib"
         host.run_cmd(
             f"export LD_LIBRARY_PATH={ld_library_path} && auditwheel repair $HOME/pytorch/dist/{pytorch_wheel_name}"
         )
@@ -733,7 +763,7 @@ def start_build(
     else:
         print("build pytorch without mkldnn backend")
         host.run_cmd(
-            f"cd pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
+            f"cd pytorch && {build_vars} python3 setup.py bdist_wheel{build_opts}"
         )
 
     print("Deleting build folder")
@@ -877,7 +907,7 @@ def terminate_instances(instance_type: str) -> None:
 def parse_arguments():
     from argparse import ArgumentParser
 
-    parser = ArgumentParser("Build and test AARCH64 wheels using EC2")
+    parser = ArgumentParser("Builid and test AARCH64 wheels using EC2")
     parser.add_argument("--key-name", type=str)
     parser.add_argument("--debug", action="store_true")
     parser.add_argument("--build-only", action="store_true")
@@ -974,7 +1004,7 @@ if __name__ == "__main__":
         install_condaforge_python(host, args.python_version)
         sys.exit(0)
 
-    python_version = args.python_version if args.python_version is not None else "3.10"
+    python_version = args.python_version if args.python_version is not None else "3.9"
 
     if args.use_torch_from_pypi:
         configure_system(host, compiler=args.compiler, python_version=python_version)

.ci/docker/almalinux/Dockerfile

@@ -7,13 +7,13 @@ ENV LC_ALL en_US.UTF-8
 ENV LANG en_US.UTF-8
 ENV LANGUAGE en_US.UTF-8
 
-ARG DEVTOOLSET_VERSION=13
+ARG DEVTOOLSET_VERSION=11
 
 RUN yum -y update
 RUN yum -y install epel-release
 # install glibc-langpack-en make sure en_US.UTF-8 locale is available
 RUN yum -y install glibc-langpack-en
-RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
+RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
 # Just add everything as a safe.directory for git since these will be used in multiple places with git
 RUN git config --global --add safe.directory '*'
 ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
@@ -41,7 +41,6 @@ RUN bash ./install_conda.sh && rm install_conda.sh
 # Install CUDA
 FROM base as cuda
 ARG CUDA_VERSION=12.6
-ARG DEVTOOLSET_VERSION=13
 RUN rm -rf /usr/local/cuda-*
 ADD ./common/install_cuda.sh install_cuda.sh
 COPY ./common/install_nccl.sh install_nccl.sh
@@ -51,8 +50,7 @@ ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
 # Preserve CUDA_VERSION for the builds
 ENV CUDA_VERSION=${CUDA_VERSION}
 # Make things in our path by default
-ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
-
+ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
 
 FROM cuda as cuda12.6
 RUN bash ./install_cuda.sh 12.6
@@ -70,23 +68,8 @@ FROM cuda as cuda13.0
 RUN bash ./install_cuda.sh 13.0
 ENV DESIRED_CUDA=13.0
 
-FROM ${ROCM_IMAGE} as rocm_base
-ARG DEVTOOLSET_VERSION=13
-ENV LC_ALL en_US.UTF-8
-ENV LANG en_US.UTF-8
-ENV LANGUAGE en_US.UTF-8
-# Install devtoolset on ROCm base image
-RUN yum -y update && \
-    yum -y install epel-release && \
-    yum -y install glibc-langpack-en && \
-    yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
-RUN git config --global --add safe.directory '*'
-ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
-
-FROM rocm_base as rocm
-ARG PYTORCH_ROCM_ARCH
-ARG DEVTOOLSET_VERSION=13
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
+FROM ${ROCM_IMAGE} as rocm
+ENV PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
 ADD ./common/install_mkl.sh install_mkl.sh
 RUN bash ./install_mkl.sh && rm install_mkl.sh
 ENV MKLROOT /opt/intel
@@ -104,7 +87,6 @@ COPY --from=cuda13.0  /usr/local/cuda-13.0 /usr/local/cuda-13.0
 
 # Final step
 FROM ${BASE_TARGET} as final
-ARG DEVTOOLSET_VERSION=13
 COPY --from=openssl            /opt/openssl           /opt/openssl
 COPY --from=patchelf           /patchelf              /usr/local/bin/patchelf
 COPY --from=conda              /opt/conda             /opt/conda

.ci/docker/almalinux/build.sh

@@ -36,12 +36,6 @@ 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="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-    fi
-    EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;
   *)
     echo "ERROR: Unknown docker tag ${DOCKER_TAG_PREFIX}"
@@ -63,7 +57,7 @@ docker build \
   --target final \
   --progress plain \
   --build-arg "BASE_TARGET=${BASE_TARGET}" \
-  --build-arg "DEVTOOLSET_VERSION=13" \
+  --build-arg "DEVTOOLSET_VERSION=11" \
   ${EXTRA_BUILD_ARGS} \
   -t ${tmp_tag} \
   $@ \

.ci/docker/build.sh

@@ -81,11 +81,11 @@ elif [[ "$image" == *riscv* ]]; then
   DOCKERFILE="ubuntu-cross-riscv/Dockerfile"
 fi
 
-_UCX_COMMIT=7836b165abdbe468a2f607e7254011c07d788152
-_UCC_COMMIT=430e241bf5d38cbc73fc7a6b89155397232e3f96
+_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
+_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
 if [[ "$image" == *rocm* ]]; then
-  _UCX_COMMIT=29831d319e6be55cb8c768ca61de335c934ca39e
-  _UCC_COMMIT=9f4b242cbbd8b1462cbc732eb29316cdfa124b77
+  _UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
+  _UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
 fi
 
 tag=$(echo $image | awk -F':' '{print $2}')
@@ -113,21 +113,32 @@ case "$tag" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     TRITON=yes
-    INSTALL_MINGW=yes
     ;;
-  pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11)
-    CUDA_VERSION=13.0.0
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks)
+    CUDA_VERSION=12.8.1
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=9
     VISION=yes
     KATEX=yes
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     TRITON=yes
+    INDUCTOR_BENCHMARKS=yes
     ;;
-  pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks)
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc9-inductor-benchmarks)
     CUDA_VERSION=12.8.1
-    ANACONDA_PYTHON_VERSION=3.10
+    ANACONDA_PYTHON_VERSION=3.12
+    GCC_VERSION=9
+    VISION=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    TRITON=yes
+    INDUCTOR_BENCHMARKS=yes
+    ;;
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.13-gcc9-inductor-benchmarks)
+    CUDA_VERSION=12.8.1
+    ANACONDA_PYTHON_VERSION=3.13
     GCC_VERSION=9
     VISION=yes
     KATEX=yes
@@ -176,38 +187,46 @@ case "$tag" in
     fi
     GCC_VERSION=11
     VISION=yes
+    ROCM_VERSION=6.4
+    NINJA_VERSION=1.9.0
+    TRITON=yes
+    KATEX=yes
+    UCX_COMMIT=${_UCX_COMMIT}
+    UCC_COMMIT=${_UCC_COMMIT}
+    if [[ $tag =~ "benchmarks" ]]; then
+      INDUCTOR_BENCHMARKS=yes
+    fi
+    ;;
+  pytorch-linux-noble-rocm-alpha-py3)
+    ANACONDA_PYTHON_VERSION=3.12
+    GCC_VERSION=11
+    VISION=yes
     ROCM_VERSION=7.0
     NINJA_VERSION=1.9.0
     TRITON=yes
     KATEX=yes
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
-    PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950;gfx1100"
-    if [[ $tag =~ "benchmarks" ]]; then
-      INDUCTOR_BENCHMARKS=yes
-    fi
+    PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950"
     ;;
-  pytorch-linux-jammy-xpu-n-1-py3)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-xpu-2025.0-py3)
+    ANACONDA_PYTHON_VERSION=3.9
+    GCC_VERSION=11
+    VISION=yes
+    XPU_VERSION=2025.0
+    NINJA_VERSION=1.9.0
+    TRITON=yes
+    ;;
+  pytorch-linux-jammy-xpu-2025.1-py3)
+    ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
     VISION=yes
     XPU_VERSION=2025.1
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-jammy-xpu-n-py3 | pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
-    VISION=yes
-    XPU_VERSION=2025.2
-    NINJA_VERSION=1.9.0
-    TRITON=yes
-    if [[ $tag =~ "benchmarks" ]]; then
-      INDUCTOR_BENCHMARKS=yes
-    fi
-    ;;
-  pytorch-linux-jammy-py3-gcc11-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
+    ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
     VISION=yes
     KATEX=yes
@@ -255,10 +274,13 @@ case "$tag" in
     TRITON_CPU=yes
     ;;
   pytorch-linux-jammy-linter)
-    PYTHON_VERSION=3.10
+    # TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
+    # We will need to update mypy version eventually, but that's for another day. The task
+    # would be to upgrade mypy to 1.0.0 with Python 3.11
+    PYTHON_VERSION=3.9
     ;;
-  pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-linter)
-    PYTHON_VERSION=3.10
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-linter)
+    PYTHON_VERSION=3.9
     CUDA_VERSION=12.8.1
     ;;
   pytorch-linux-jammy-aarch64-py3.10-gcc11)
@@ -271,16 +293,6 @@ case "$tag" in
     # from pytorch/llvm:9.0.1 is x86 specific
     SKIP_LLVM_SRC_BUILD_INSTALL=yes
     ;;
-  pytorch-linux-jammy-aarch64-py3.10-clang21)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=21
-    ACL=yes
-    VISION=yes
-    OPENBLAS=yes
-    # snadampal: skipping llvm src build install because the current version
-    # from pytorch/llvm:9.0.1 is x86 specific
-    SKIP_LLVM_SRC_BUILD_INSTALL=yes
-    ;;
   pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
     GCC_VERSION=11
@@ -358,7 +370,7 @@ docker build \
        --build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
        --build-arg "KATEX=${KATEX:-}" \
        --build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
-       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}" \
+       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a;gfx942}" \
        --build-arg "IMAGE_NAME=${IMAGE_NAME}" \
        --build-arg "UCX_COMMIT=${UCX_COMMIT}" \
        --build-arg "UCC_COMMIT=${UCC_COMMIT}" \
@@ -375,7 +387,6 @@ docker build \
        --build-arg "OPENBLAS=${OPENBLAS:-}" \
        --build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
        --build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
-       --build-arg "INSTALL_MINGW=${INSTALL_MINGW:-}" \
        -f $(dirname ${DOCKERFILE})/Dockerfile \
        -t "$tmp_tag" \
        "$@" \
@@ -456,3 +467,12 @@ elif [ "$HAS_TRITON" = "yes" ]; then
   echo "expecting triton to not be installed, but it is"
   exit 1
 fi
+
+# Sanity check cmake version.  Executorch reinstalls cmake and I'm not sure if
+# they support 4.0.0 yet, so exclude them from this check.
+CMAKE_VERSION=$(drun cmake --version)
+if [[ "$EXECUTORCH" != *yes* && "$CMAKE_VERSION" != *4.* ]]; then
+  echo "CMake version is not 4.0.0:"
+  drun cmake --version
+  exit 1
+fi

.ci/docker/centos-rocm/Dockerfile

@@ -56,13 +56,9 @@ ENV INSTALLED_VISION ${VISION}
 
 # Install rocm
 ARG ROCM_VERSION
-RUN mkdir ci_commit_pins
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./ci_commit_pins/rocm-composable-kernel.txt ci_commit_pins/rocm-composable-kernel.txt
 COPY ./common/install_rocm.sh install_rocm.sh
 RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh common_utils.sh
-RUN rm -r ci_commit_pins
+RUN rm install_rocm.sh
 COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
 RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
 RUN rm install_rocm_magma.sh

.ci/docker/ci_commit_pins/executorch.txt

@@ -1 +1 @@
-deb42f2a8e48f5032b4a98ee781a15fa87a157cf
+56392aa978594cc155fa8af48cd949f5b5f1823a

.ci/docker/ci_commit_pins/huggingface-requirements.txt

@@ -1,2 +1,2 @@
-transformers==4.56.0
+transformers==4.54.0
 soxr==0.5.0

.ci/docker/ci_commit_pins/nccl-cu12.txt

@@ -1 +1 @@
-v2.27.5-1
+v2.27.5-1

.ci/docker/ci_commit_pins/rocm-composable-kernel.txt

@@ -1 +0,0 @@
-7fe50dc3da2069d6645d9deb8c017a876472a977

.ci/docker/ci_commit_pins/torchbench.txt

@@ -1 +1 @@
-74a23feff57432129df84d8099e622773cf77925
+e03a63be43e33596f7f0a43b0f530353785e4a59

.ci/docker/ci_commit_pins/triton-xpu.txt

@@ -1 +1 @@
-1b0418a9a454b2b93ab8d71f40e59d2297157fae
+a6572fb0be5b9b0a19b0641a0ce05810fa04e44c

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +1 @@
-bfeb066872bc1e8b2d2bc0a3b295b99dd77206e7
+f7888497a1eb9e98d4c07537f0d0bcfe180d1363

.ci/docker/common/install_acl.sh

@@ -1,27 +1,16 @@
-#!/bin/bash
-# Script used only in CD pipeline
+set -euo pipefail
 
-set -eux
-
-ACL_VERSION=${ACL_VERSION:-"v52.6.0"}
-ACL_INSTALL_DIR="/acl"
+readonly version=v25.02
+readonly src_host=https://github.com/ARM-software
+readonly src_repo=ComputeLibrary
 
 # Clone ACL
-git clone https://github.com/ARM-software/ComputeLibrary.git -b "${ACL_VERSION}" --depth 1 --shallow-submodules
+[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
+cd ${src_repo}
+
+git checkout $version
 
-ACL_CHECKOUT_DIR="ComputeLibrary"
 # Build with scons
-pushd $ACL_CHECKOUT_DIR
 scons -j8  Werror=0 debug=0 neon=1 opencl=0 embed_kernels=0 \
   os=linux arch=armv8a build=native multi_isa=1 \
   fixed_format_kernels=1 openmp=1 cppthreads=0
-popd
-
-# Install ACL
-sudo mkdir -p ${ACL_INSTALL_DIR}
-for d in arm_compute include utils support src build
-do
-  sudo cp -r ${ACL_CHECKOUT_DIR}/${d} ${ACL_INSTALL_DIR}/${d}
-done
-
-rm -rf $ACL_CHECKOUT_DIR

.ci/docker/common/install_clang.sh

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

.ci/docker/common/install_conda.sh

@@ -49,20 +49,12 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
     export SYSROOT_DEP="sysroot_linux-64=2.17"
   fi
 
-# Install correct Python version
-# Also ensure sysroot is using a modern GLIBC to match system compilers
-if [ "$ANACONDA_PYTHON_VERSION" = "3.14" ]; then
-  as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
-             python="3.14.0" \
-             ${SYSROOT_DEP} \
-             -c conda-forge
-else
   # Install correct Python version
   # Also ensure sysroot is using a modern GLIBC to match system compilers
   as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
              python="$ANACONDA_PYTHON_VERSION" \
              ${SYSROOT_DEP}
-fi
+
   # libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
   # which is provided in libstdcxx 12 and up.
   conda_install libstdcxx-ng=12.3.0 --update-deps -c conda-forge

.ci/docker/common/install_cpython.sh

@@ -83,6 +83,10 @@ function build_cpython {
         py_suffix=${py_ver::-1}
         py_folder=$py_suffix
     fi
+    # Only b3 is available now
+    if [ "$py_suffix" == "3.14.0" ]; then
+        py_suffix="3.14.0b3"
+    fi
     wget -q $PYTHON_DOWNLOAD_URL/$py_folder/Python-$py_suffix.tgz -O Python-$py_ver.tgz
     do_cpython_build $py_ver Python-$py_suffix
 

.ci/docker/common/install_cuda.sh

@@ -10,7 +10,7 @@ else
   arch_path='sbsa'
 fi
 
-NVSHMEM_VERSION=3.4.5
+NVSHMEM_VERSION=3.3.24
 
 function install_cuda {
   version=$1
@@ -147,10 +147,10 @@ function install_128 {
 }
 
 function install_130 {
-  CUDNN_VERSION=9.13.0.50
+  CUDNN_VERSION=9.12.0.46
   echo "Installing CUDA 13.0 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
   # install CUDA 13.0 in the same container
-  install_cuda 13.0.2 cuda_13.0.2_580.95.05_linux
+  install_cuda 13.0.0 cuda_13.0.0_580.65.06_linux
 
   # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
   install_cudnn 13 $CUDNN_VERSION

.ci/docker/common/install_executorch.sh

@@ -42,27 +42,22 @@ install_pip_dependencies() {
   # A workaround, ExecuTorch has moved to numpy 2.0 which is not compatible with the current
   # numba and scipy version used in PyTorch CI
   conda_run pip uninstall -y numba scipy
-  # Yaspin is needed for running CI test (get_benchmark_analysis_data.py)
-  pip_install yaspin==3.1.0
 
   popd
 }
 
 setup_executorch() {
+  pushd executorch
+
   export PYTHON_EXECUTABLE=python
-  export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON -DEXECUTORCH_BUILD_TESTS=ON"
+  export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
 
   as_jenkins .ci/scripts/setup-linux.sh --build-tool cmake || true
+  popd
 }
 
-if [ $# -eq 0 ]; then
-  clone_executorch
-  install_buck2
-  install_conda_dependencies
-  install_pip_dependencies
-  pushd executorch
-  setup_executorch
-  popd
-else
-  "$@"
-fi
+clone_executorch
+install_buck2
+install_conda_dependencies
+install_pip_dependencies
+setup_executorch

.ci/docker/common/install_mingw.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Install MinGW-w64 for Windows cross-compilation
-apt-get update
-apt-get install -y g++-mingw-w64-x86-64-posix
-
-echo "MinGW-w64 installed successfully"
-x86_64-w64-mingw32-g++ --version

.ci/docker/common/install_onnx.sh

@@ -19,8 +19,8 @@ pip_install \
   transformers==4.36.2
 
 pip_install coloredlogs packaging
-pip_install onnxruntime==1.23.1
-pip_install onnxscript==0.5.4
+pip_install onnxruntime==1.22.1
+pip_install onnxscript==0.4.0
 
 # Cache the transformers model to be used later by ONNX tests. We need to run the transformers
 # package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/

.ci/docker/common/install_openblas.sh

@@ -3,14 +3,11 @@
 
 set -ex
 
-OPENBLAS_VERSION=${OPENBLAS_VERSION:-"v0.3.30"}
-
-# Clone OpenBLAS
-git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION}" --depth 1 --shallow-submodules
+cd /
+git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION:-v0.3.30}" --depth 1 --shallow-submodules
 
 OPENBLAS_CHECKOUT_DIR="OpenBLAS"
 OPENBLAS_BUILD_FLAGS="
-CC=gcc
 NUM_THREADS=128
 USE_OPENMP=1
 NO_SHARED=0
@@ -20,7 +17,5 @@ CFLAGS=-O3
 BUILD_BFLOAT16=1
 "
 
-make -j8 ${OPENBLAS_BUILD_FLAGS} -C $OPENBLAS_CHECKOUT_DIR
-sudo make install -C $OPENBLAS_CHECKOUT_DIR
-
-rm -rf $OPENBLAS_CHECKOUT_DIR
+make -j8 ${OPENBLAS_BUILD_FLAGS} -C ${OPENBLAS_CHECKOUT_DIR}
+make -j8 ${OPENBLAS_BUILD_FLAGS} install -C ${OPENBLAS_CHECKOUT_DIR}

.ci/docker/common/install_rocm.sh

@@ -2,11 +2,6 @@
 
 set -ex
 
-# for pip_install function
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-ROCM_COMPOSABLE_KERNEL_VERSION="$(cat $(dirname $0)/../ci_commit_pins/rocm-composable-kernel.txt)"
-
 ver() {
     printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
 }
@@ -40,7 +35,17 @@ EOF
 
     # Default url values
     rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
+    amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
+
+    # Special case for ROCM_VERSION == 7.0
+    if [[ $(ver "$ROCM_VERSION") -eq $(ver 7.0) ]]; then
+        rocm_baseurl="https://repo.radeon.com/rocm/apt/7.0_alpha2"
+        amdgpu_baseurl="https://repo.radeon.com/amdgpu/30.10_alpha2/ubuntu"
+    fi
+
+    # Add amdgpu repository
     UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
+    echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
 
     # Add rocm repository
     wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
@@ -108,8 +113,6 @@ EOF
         rm -rf HIP clr
     fi
 
-    pip_install "git+https://github.com/rocm/composable_kernel@$ROCM_COMPOSABLE_KERNEL_VERSION"
-
     # Cleanup
     apt-get autoclean && apt-get clean
     rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
@@ -173,8 +176,6 @@ install_centos() {
       sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
   done
 
-  pip_install "git+https://github.com/rocm/composable_kernel@$ROCM_COMPOSABLE_KERNEL_VERSION"
-
   # Cleanup
   yum clean all
   rm -rf /var/cache/yum

.ci/docker/common/install_rocm_magma.sh

@@ -12,8 +12,8 @@ function do_install() {
 
     rocm_version_nodot=${rocm_version//./}
 
-    # post merge of https://github.com/icl-utk-edu/magma/pull/65
-    MAGMA_VERSION=c0792ae825fb36872784892ea643dd6f3456bc5f
+    # Version 2.7.2 + ROCm related updates
+    MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
     magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
 
     rocm_dir="/opt/rocm"

.ci/docker/common/install_triton.sh

@@ -57,7 +57,7 @@ if [ ! -f setup.py ]; then
   cd python
 fi
 
-pip_install pybind11==3.0.1
+pip_install pybind11==2.13.6
 
 # TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
 as_jenkins sed -i -e 's/https:\/\/tritonlang.blob.core.windows.net\/llvm-builds/https:\/\/oaitriton.blob.core.windows.net\/public\/llvm-builds/g' setup.py
@@ -66,15 +66,15 @@ if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}"
   # Triton needs at least gcc-9 to build
   apt-get install -y g++-9
 
-  CXX=g++-9 conda_run python -m build --wheel --no-isolation
+  CXX=g++-9 conda_run python setup.py bdist_wheel
 elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
   # Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
   add-apt-repository -y ppa:ubuntu-toolchain-r/test
   apt-get install -y g++-9
 
-  CXX=g++-9 conda_run python -m build --wheel --no-isolation
+  CXX=g++-9 conda_run python setup.py bdist_wheel
 else
-  conda_run python -m build --wheel --no-isolation
+  conda_run python setup.py bdist_wheel
 fi
 
 # Copy the wheel to /opt for multi stage docker builds

.ci/docker/common/install_ucc.sh

@@ -44,12 +44,8 @@ function install_ucc() {
 
   ./autogen.sh
 
-  if [[ -n "$CUDA_VERSION"  && $CUDA_VERSION == 13* ]]; then
-    NVCC_GENCODE="-gencode=arch=compute_86,code=compute_86"
-  else
-    # We only run distributed tests on Tesla M60 and A10G
-    NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
-  fi
+  # We only run distributed tests on Tesla M60 and A10G
+  NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
 
   if [[ -n "$ROCM_VERSION" ]]; then
     if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then

.ci/docker/common/install_xpu.sh

@@ -65,14 +65,10 @@ 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
-            echo "RHEL version ${VERSION_ID} not supported"
-            exit
-        fi
-    elif [[ "${ID}" == "almalinux" ]]; then
-        # Workaround for almalinux8 which used by quay.io/pypa/manylinux_2_28_x86_64
-        VERSION_ID="8.8"
+
+    if [[ ! " 8.8 8.10 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
+        echo "RHEL version ${VERSION_ID} not supported"
+        exit
     fi
 
     dnf install -y 'dnf-command(config-manager)'
@@ -150,11 +146,11 @@ if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
     XPU_DRIVER_VERSION="/lts/2350"
 fi
 
-# Default use Intel® oneAPI Deep Learning Essentials 2025.1
-if [[ "$XPU_VERSION" == "2025.2" ]]; then
-    XPU_PACKAGES="intel-deep-learning-essentials-2025.2"
-else
+# Default use Intel® oneAPI Deep Learning Essentials 2025.0
+if [[ "$XPU_VERSION" == "2025.1" ]]; then
     XPU_PACKAGES="intel-deep-learning-essentials-2025.1"
+else
+    XPU_PACKAGES="intel-deep-learning-essentials-2025.0"
 fi
 
 # The installation depends on the base OS

.ci/docker/common/patch_libstdc.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-set -xe
-# Script used in Linux x86 and aarch64 CD pipeline
-
-# Workaround for exposing statically linked libstdc++ CXX11 ABI symbols.
-# see: https://github.com/pytorch/pytorch/issues/133437
-LIBNONSHARED=$(gcc -print-file-name=libstdc++_nonshared.a)
-nm -g $LIBNONSHARED | grep " T " | grep recursive_directory_iterator | cut -c 20-  > weaken-symbols.txt
-objcopy --weaken-symbols weaken-symbols.txt $LIBNONSHARED $LIBNONSHARED

.ci/docker/libtorch/Dockerfile

@@ -74,14 +74,6 @@ RUN bash ./install_cuda.sh 13.0
 RUN bash ./install_magma.sh 13.0
 RUN ln -sf /usr/local/cuda-13.0 /usr/local/cuda
 
-# Install libibverbs for libtorch and copy to CUDA directory
-RUN apt-get update -y && \
-    apt-get install -y libibverbs-dev librdmacm-dev && \
-    cp /usr/lib/x86_64-linux-gnu/libmlx5.so* /usr/local/cuda/lib64/ && \
-    cp /usr/lib/x86_64-linux-gnu/librdmacm.so* /usr/local/cuda/lib64/ && \
-    cp /usr/lib/x86_64-linux-gnu/libibverbs.so* /usr/local/cuda/lib64/ && \
-    cp /usr/lib/x86_64-linux-gnu/libnl* /usr/local/cuda/lib64/
-
 FROM cpu as rocm
 ARG ROCM_VERSION
 ARG PYTORCH_ROCM_ARCH

.ci/docker/libtorch/build.sh

@@ -39,21 +39,13 @@ case ${DOCKER_TAG_PREFIX} in
         DOCKER_GPU_BUILD_ARG=""
         ;;
     rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
-        if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+        if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         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="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-        fi
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;
     *)

.ci/docker/manywheel/Dockerfile_2_28

@@ -130,8 +130,7 @@ ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/op
 RUN for cpython_version in "cp312-cp312" "cp313-cp313" "cp313-cp313t"; do \
     /opt/python/${cpython_version}/bin/python -m pip install setuptools wheel; \
     done;
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
+
 
 # cmake-3.18.4 from pip; force in case cmake3 already exists
 RUN yum install -y python3-pip && \
@@ -149,7 +148,7 @@ FROM cpu_final as rocm_final
 ARG ROCM_VERSION=6.0
 ARG PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
-ARG DEVTOOLSET_VERSION=13
+ARG DEVTOOLSET_VERSION=11
 ENV LDFLAGS="-Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64 -Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib"
 # Somewhere in ROCm stack, we still use non-existing /opt/rocm/hip path,
 # below workaround helps avoid error
@@ -176,6 +175,6 @@ ENV XPU_DRIVER_TYPE ROLLING
 RUN python3 -m pip install --upgrade pip && \
     python3 -mpip install cmake==3.28.4
 ADD ./common/install_xpu.sh install_xpu.sh
-ENV XPU_VERSION 2025.2
+ENV XPU_VERSION 2025.1
 RUN bash ./install_xpu.sh && rm install_xpu.sh
 RUN pushd /opt/_internal && tar -xJf static-libs-for-embedding-only.tar.xz && popd

.ci/docker/manywheel/Dockerfile_2_28_aarch64

@@ -62,13 +62,6 @@ ARG OPENBLAS_VERSION
 ADD ./common/install_openblas.sh install_openblas.sh
 RUN bash ./install_openblas.sh && rm install_openblas.sh
 
-# Install Arm Compute Library
-FROM base as arm_compute
-# use python3.9 to install scons
-RUN python3.9 -m pip install scons==4.7.0
-RUN ln -sf /opt/python/cp39-cp39/bin/scons /usr/local/bin
-COPY ./common/install_acl.sh install_acl.sh
-RUN bash ./install_acl.sh && rm install_acl.sh
 FROM base as final
 
 # remove unnecessary python versions
@@ -77,7 +70,4 @@ RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
 RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
 RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
 COPY --from=openblas     /opt/OpenBLAS/  /opt/OpenBLAS/
-COPY --from=arm_compute /acl /acl
-ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:/acl/build/:$LD_LIBRARY_PATH
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
+ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:$LD_LIBRARY_PATH

.ci/docker/manywheel/Dockerfile_cuda_aarch64

@@ -86,15 +86,6 @@ FROM base as nvpl
 ADD ./common/install_nvpl.sh install_nvpl.sh
 RUN bash ./install_nvpl.sh && rm install_nvpl.sh
 
-# Install Arm Compute Library
-FROM base as arm_compute
-# use python3.9 to install scons
-RUN python3.9 -m pip install scons==4.7.0
-RUN ln -sf /opt/python/cp39-cp39/bin/scons /usr/local/bin
-COPY ./common/install_acl.sh install_acl.sh
-RUN bash ./install_acl.sh && rm install_acl.sh
-FROM base as final
-
 FROM final as cuda_final
 ARG BASE_CUDA_VERSION
 RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
@@ -102,9 +93,5 @@ COPY --from=cuda     /usr/local/cuda-${BASE_CUDA_VERSION}  /usr/local/cuda-${BAS
 COPY --from=magma    /usr/local/cuda-${BASE_CUDA_VERSION}  /usr/local/cuda-${BASE_CUDA_VERSION}
 COPY --from=nvpl /opt/nvpl/lib/  /usr/local/lib/
 COPY --from=nvpl /opt/nvpl/include/  /usr/local/include/
-COPY --from=arm_compute /acl /acl
 RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
 ENV PATH=/usr/local/cuda/bin:$PATH
-ENV LD_LIBRARY_PATH=/acl/build/:$LD_LIBRARY_PATH
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh

.ci/docker/manywheel/Dockerfile_cxx11-abi

@@ -0,0 +1,71 @@
+FROM centos:8 as base
+
+ENV LC_ALL en_US.UTF-8
+ENV LANG en_US.UTF-8
+ENV LANGUAGE en_US.UTF-8
+ENV PATH /opt/rh/gcc-toolset-11/root/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+
+# change to a valid repo
+RUN sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-Linux-*.repo
+# enable to install ninja-build
+RUN sed -i 's|enabled=0|enabled=1|g' /etc/yum.repos.d/CentOS-Linux-PowerTools.repo
+
+RUN yum -y update
+RUN yum install -y wget curl perl util-linux xz bzip2 git patch which zlib-devel sudo
+RUN yum install -y autoconf automake make cmake gdb gcc-toolset-11-gcc-c++
+
+
+FROM base as openssl
+ADD ./common/install_openssl.sh install_openssl.sh
+RUN bash ./install_openssl.sh && rm install_openssl.sh
+
+# Install python
+FROM base as python
+RUN yum install -y openssl-devel zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel libpcap-devel xz-devel libffi-devel
+ADD common/install_cpython.sh install_cpython.sh
+RUN bash ./install_cpython.sh && rm install_cpython.sh
+
+FROM base as conda
+ADD ./common/install_conda_docker.sh install_conda.sh
+RUN bash ./install_conda.sh && rm install_conda.sh
+RUN /opt/conda/bin/conda install -y cmake
+
+FROM base as intel
+# Install MKL
+COPY --from=python             /opt/python                           /opt/python
+COPY --from=python             /opt/_internal                        /opt/_internal
+COPY --from=conda              /opt/conda                            /opt/conda
+ENV PATH=/opt/conda/bin:$PATH
+ADD ./common/install_mkl.sh install_mkl.sh
+RUN bash ./install_mkl.sh && rm install_mkl.sh
+
+FROM base as patchelf
+ADD ./common/install_patchelf.sh install_patchelf.sh
+RUN bash ./install_patchelf.sh && rm install_patchelf.sh
+RUN cp $(which patchelf) /patchelf
+
+FROM base as jni
+ADD ./common/install_jni.sh install_jni.sh
+ADD ./java/jni.h jni.h
+RUN bash ./install_jni.sh && rm install_jni.sh
+
+FROM base as libpng
+ADD ./common/install_libpng.sh install_libpng.sh
+RUN bash ./install_libpng.sh && rm install_libpng.sh
+
+FROM base as final
+COPY --from=openssl            /opt/openssl                          /opt/openssl
+COPY --from=python             /opt/python                           /opt/python
+COPY --from=python             /opt/_internal                        /opt/_internal
+COPY --from=intel              /opt/intel                            /opt/intel
+COPY --from=conda              /opt/conda                            /opt/conda
+COPY --from=patchelf           /usr/local/bin/patchelf               /usr/local/bin/patchelf
+COPY --from=jni                /usr/local/include/jni.h              /usr/local/include/jni.h
+COPY --from=libpng             /usr/local/bin/png*                   /usr/local/bin/
+COPY --from=libpng             /usr/local/bin/libpng*                /usr/local/bin/
+COPY --from=libpng             /usr/local/include/png*               /usr/local/include/
+COPY --from=libpng             /usr/local/include/libpng*            /usr/local/include/
+COPY --from=libpng             /usr/local/lib/libpng*                /usr/local/lib/
+COPY --from=libpng             /usr/local/lib/pkgconfig              /usr/local/lib/pkgconfig
+
+RUN yum install -y ninja-build

.ci/docker/manywheel/Dockerfile_s390x

@@ -115,9 +115,6 @@ RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio
 # cmake-3.28.0 from pip for onnxruntime
 RUN python3 -mpip install cmake==3.28.0
 
-ADD ./common/patch_libstdc.sh patch_libstdc.sh
-RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
-
 # build onnxruntime 1.21.0 from sources.
 # it is not possible to build it from sources using pip,
 # so just build it from upstream repository.

.ci/docker/manywheel/build.sh

@@ -28,7 +28,6 @@ fi
 MANY_LINUX_VERSION=${MANY_LINUX_VERSION:-}
 DOCKERFILE_SUFFIX=${DOCKERFILE_SUFFIX:-}
 OPENBLAS_VERSION=${OPENBLAS_VERSION:-}
-ACL_VERSION=${ACL_VERSION:-}
 
 case ${image} in
     manylinux2_28-builder:cpu)
@@ -42,6 +41,13 @@ case ${image} in
         GPU_IMAGE=arm64v8/almalinux:8
         DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13 --build-arg NINJA_VERSION=1.12.1"
         MANY_LINUX_VERSION="2_28_aarch64"
+        OPENBLAS_VERSION="v0.3.30"
+        ;;
+    manylinuxcxx11-abi-builder:cpu-cxx11-abi)
+        TARGET=final
+        GPU_IMAGE=""
+        DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
+        MANY_LINUX_VERSION="cxx11-abi"
         ;;
     manylinuxs390x-builder:cpu-s390x)
         TARGET=final
@@ -75,29 +81,21 @@ case ${image} in
         DOCKERFILE_SUFFIX="_cuda_aarch64"
         ;;
     manylinux2_28-builder:rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
-        if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+        if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         TARGET=rocm_final
         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="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
-        fi
         DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
         ;;
     manylinux2_28-builder:xpu)
         TARGET=xpu_final
         GPU_IMAGE=amd64/almalinux:8
-        DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13"
+        DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
         MANY_LINUX_VERSION="2_28"
         ;;
     *)
@@ -123,8 +121,7 @@ tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
 DOCKER_BUILDKIT=1 docker build  \
     ${DOCKER_GPU_BUILD_ARG} \
     --build-arg "GPU_IMAGE=${GPU_IMAGE}" \
-    --build-arg "OPENBLAS_VERSION=${OPENBLAS_VERSION:-}" \
-    --build-arg "ACL_VERSION=${ACL_VERSION:-}" \
+    --build-arg "OPENBLAS_VERSION=${OPENBLAS_VERSION}" \
     --target "${TARGET}" \
     -t "${tmp_tag}" \
     $@ \

.ci/docker/manywheel/build_scripts/ssl-check.py

@@ -10,6 +10,11 @@ BAD_SSL = "https://self-signed.badssl.com"
 
 print("Testing SSL certificate checking for Python:", sys.version)
 
+if sys.version_info[:2] < (2, 7) or sys.version_info[:2] < (3, 4):
+    print("This version never checks SSL certs; skipping tests")
+    sys.exit(0)
+
+
 EXC = OSError
 
 print(f"Connecting to {GOOD_SSL} should work")

.ci/docker/requirements-ci.txt

@@ -10,11 +10,6 @@ boto3==1.35.42
 #Pinned versions: 1.19.12, 1.16.34
 #test that import:
 
-build==1.3.0
-#Description: A simple, correct Python build frontend.
-#Pinned versions: 1.3.0
-#test that import:
-
 click
 #Description: Command Line Interface Creation Kit
 #Pinned versions:
@@ -52,10 +47,10 @@ flatbuffers==24.12.23
 #Pinned versions: 24.12.23
 #test that import:
 
-hypothesis==6.56.4
+hypothesis==5.35.1
 # Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
 #Description: advanced library for generating parametrized tests
-#Pinned versions: 6.56.4
+#Pinned versions: 5.35.1
 #test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
 
 junitparser==2.1.1
@@ -98,9 +93,8 @@ librosa==0.10.2 ; python_version == "3.12" and platform_machine != "s390x"
 #Pinned versions:
 #test that import:
 
-mypy==1.16.0 ; platform_system == "Linux"
+mypy==1.16.0
 # Pin MyPy version because new errors are likely to appear with each release
-# Skip on Windows as lots of type annotations are POSIX specific
 #Description: linter
 #Pinned versions: 1.16.0
 #test that import: test_typing.py, test_type_hints.py
@@ -111,17 +105,20 @@ networkx==2.8.8
 #Pinned versions: 2.8.8
 #test that import: functorch
 
-ninja==1.11.1.4
+ninja==1.11.1.3
 #Description: build system. Used in some tests. Used in build to generate build
 #time tracing information
-#Pinned versions: 1.11.1.4
+#Pinned versions: 1.11.1.3
 #test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
 
+numba==0.49.0 ; python_version < "3.9" and platform_machine != "s390x"
+numba==0.55.2 ; python_version == "3.9" and platform_machine != "s390x"
 numba==0.55.2 ; python_version == "3.10" and platform_machine != "s390x"
 numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
 #Description: Just-In-Time Compiler for Numerical Functions
-#Pinned versions: 0.55.2, 0.60.0
+#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
 #test that import: test_numba_integration.py
+#For numba issue see https://github.com/pytorch/pytorch/issues/51511
 #Need release > 0.61.2 for s390x due to https://github.com/numba/numba/pull/10073
 
 #numpy
@@ -136,14 +133,12 @@ numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
 #test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
 #test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
 #test_binary_ufuncs.py
-numpy==1.22.4; python_version == "3.10"
+numpy==1.22.4; python_version == "3.9" or python_version == "3.10"
 numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
-numpy==2.1.2; python_version >= "3.13" and python_version < "3.14"
-numpy==2.3.4; python_version >= "3.14"
+numpy==2.1.2; python_version >= "3.13"
 
 pandas==2.0.3; python_version < "3.13"
-pandas==2.2.3; python_version >= "3.13" and python_version < "3.14"
-pandas==2.3.3; python_version >= "3.14"
+pandas==2.2.3; python_version >= "3.13"
 
 #onnxruntime
 #Description: scoring engine for Open Neural Network Exchange (ONNX) models
@@ -155,8 +150,7 @@ opt-einsum==3.3
 #Pinned versions: 3.3
 #test that import: test_linalg.py
 
-optree==0.13.0 ; python_version < "3.14"
-optree==0.17.0 ; python_version >= "3.14"
+optree==0.13.0
 #Description: A library for tree manipulation
 #Pinned versions: 0.13.0
 #test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
@@ -171,12 +165,12 @@ optree==0.17.0 ; python_version >= "3.14"
 
 pillow==11.0.0
 #Description:  Python Imaging Library fork
-#Pinned versions: 11.0.0
+#Pinned versions: 10.3.0
 #test that import:
 
-protobuf==5.29.5
+protobuf==5.29.4
 #Description:  Google's data interchange format
-#Pinned versions: 5.29.5
+#Pinned versions: 5.29.4
 #test that import: test_tensorboard.py, test/onnx/*
 
 psutil
@@ -219,7 +213,7 @@ pytest-subtests==0.13.1
 #Pinned versions:
 #test that import:
 
-xdoctest==1.3.0
+xdoctest==1.1.0
 #Description: runs doctests in pytest
 #Pinned versions: 1.1.0
 #test that import:
@@ -244,9 +238,10 @@ pygments==2.15.0
 #Pinned versions: 14.1.0
 #test that import:
 
-scikit-image==0.22.0
+scikit-image==0.19.3 ; python_version < "3.10"
+scikit-image==0.22.0 ; python_version >= "3.10"
 #Description: image processing routines
-#Pinned versions: 0.22.0
+#Pinned versions:
 #test that import: test_nn.py
 
 #scikit-learn
@@ -255,8 +250,7 @@ scikit-image==0.22.0
 #test that import:
 
 scipy==1.10.1 ; python_version <= "3.11"
-scipy==1.14.1 ; python_version > "3.11" and python_version < "3.14"
-scipy==1.16.2 ; python_version >= "3.14"
+scipy==1.14.1 ; python_version >= "3.12"
 # Pin SciPy because of failing distribution tests (see #60347)
 #Description: scientific python
 #Pinned versions: 1.10.1
@@ -270,7 +264,7 @@ scipy==1.16.2 ; python_version >= "3.14"
 #test that import:
 
 # needed by torchgen utils
-typing-extensions==4.12.2
+typing-extensions>=4.10.0
 #Description: type hints for python
 #Pinned versions:
 #test that import:
@@ -328,10 +322,11 @@ pywavelets==1.7.0 ; python_version >= "3.12"
 #Pinned versions: 1.4.1
 #test that import:
 
-lxml==5.3.0 ; python_version < "3.14"
-lxml==6.0.2 ; python_version >= "3.14"
+lxml==5.3.0
 #Description: This is a requirement of unittest-xml-reporting
 
+# Python-3.9 binaries
+
 PyGithub==2.3.0
 
 sympy==1.13.3
@@ -339,14 +334,12 @@ sympy==1.13.3
 #Pinned versions:
 #test that import:
 
-onnx==1.19.1 ; python_version < "3.14"
-# Unpin once Python 3.14 is supported. See  onnxruntime issue 26309.
-onnx==1.18.0 ; python_version == "3.14"
+onnx==1.18.0
 #Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:
 
-onnxscript==0.5.4
+onnxscript==0.4.0
 #Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:
@@ -366,10 +359,9 @@ pwlf==2.2.1
 #test that import: test_sac_estimator.py
 
 # To build PyTorch itself
-pyyaml==6.0.3
+pyyaml
 pyzstd
-setuptools==78.1.1
-packaging==23.1
+setuptools>=70.1.0
 six
 
 scons==4.5.2 ; platform_machine == "aarch64"
@@ -384,16 +376,13 @@ dataclasses_json==0.6.7
 #Pinned versions: 0.6.7
 #test that import:
 
-cmake==3.31.6
+cmake==4.0.0
 #Description: required for building
 
-tlparse==0.4.0
+tlparse==0.3.30
 #Description: required for log parsing
 
-filelock==3.18.0
-#Description: required for inductor testing
-
-cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x" and platform_system != "Darwin"
+cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x"
 #Description: required for testing CUDAGraph::raw_cuda_graph(). See https://nvidia.github.io/cuda-python/cuda-bindings/latest/support.html for how this version was chosen. Note "Any fix in the latest bindings would be backported to the prior major version" means that only the newest version of cuda-bindings will get fixes. Depending on the latest version of 12.x is okay because all 12.y versions will be supported via "CUDA minor version compatibility". Pytorch builds against 13.z versions of cuda toolkit work with 12.x versions of cuda-bindings as well because newer drivers work with old toolkits.
 #test that import: test_cuda.py
 

.ci/docker/requirements-docs.txt

@@ -1,15 +1,8 @@
 sphinx==5.3.0
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 5.3.0
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@722b7e6f9ca512fcc526ad07d62b3d28c50bb6cd#egg=pytorch_sphinx_theme2
 
-standard-imghdr==3.13.0; python_version >= "3.13"
-#Description: This is needed by Sphinx, so it needs to be added here.
-# The reasons are as follows:
-# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
-# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
-# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
-
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.

.ci/docker/triton_version.txt

@@ -1 +1 @@
-3.5.1
+3.4.0

.ci/docker/triton_xpu_version.txt

@@ -1 +1 @@
-3.5.0
+3.4.0

.ci/docker/ubuntu-rocm/Dockerfile

@@ -52,13 +52,9 @@ ENV INSTALLED_VISION ${VISION}
 
 # Install rocm
 ARG ROCM_VERSION
-RUN mkdir ci_commit_pins
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./ci_commit_pins/rocm-composable-kernel.txt ci_commit_pins/rocm-composable-kernel.txt
 COPY ./common/install_rocm.sh install_rocm.sh
 RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh common_utils.sh
-RUN rm -r ci_commit_pins
+RUN rm install_rocm.sh
 COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
 RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
 RUN rm install_rocm_magma.sh

.ci/docker/ubuntu-xpu/Dockerfile

@@ -54,15 +54,12 @@ ENV OPENSSL_DIR /opt/openssl
 RUN rm install_openssl.sh
 
 ARG INDUCTOR_BENCHMARKS
-ARG ANACONDA_PYTHON_VERSION
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
 COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
 COPY ./common/common_utils.sh common_utils.sh
 COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
 COPY ci_commit_pins/timm.txt timm.txt
-COPY ci_commit_pins/torchbench.txt torchbench.txt
 RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
+RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt
 
 # Install XPU Dependencies
 ARG XPU_VERSION

.ci/docker/ubuntu/Dockerfile

@@ -66,7 +66,6 @@ ENV NCCL_LIB_DIR="/usr/local/cuda/lib64/"
 # (optional) Install UCC
 ARG UCX_COMMIT
 ARG UCC_COMMIT
-ARG CUDA_VERSION
 ENV UCX_COMMIT $UCX_COMMIT
 ENV UCC_COMMIT $UCC_COMMIT
 ENV UCX_HOME /usr
@@ -100,16 +99,9 @@ COPY ./common/common_utils.sh common_utils.sh
 COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
 COPY ci_commit_pins/timm.txt timm.txt
 COPY ci_commit_pins/torchbench.txt torchbench.txt
-# Only build aoti cpp tests when INDUCTOR_BENCHMARKS is set to True
-ENV BUILD_AOT_INDUCTOR_TEST ${INDUCTOR_BENCHMARKS}
 RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
 RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
 
-ARG INSTALL_MINGW
-COPY ./common/install_mingw.sh install_mingw.sh
-RUN if [ -n "${INSTALL_MINGW}" ]; then bash ./install_mingw.sh; fi
-RUN rm install_mingw.sh
-
 ARG TRITON
 ARG TRITON_CPU
 

.ci/libtorch/build.sh

@@ -7,4 +7,4 @@ set -ex
 
 SCRIPTPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
-USE_NVSHMEM=0 USE_CUSPARSELT=0 BUILD_PYTHONLESS=1 DESIRED_PYTHON="3.10" ${SCRIPTPATH}/../manywheel/build.sh
+USE_NVSHMEM=0 USE_CUSPARSELT=0 BUILD_PYTHONLESS=1 DESIRED_PYTHON="3.9" ${SCRIPTPATH}/../manywheel/build.sh

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

@@ -1,11 +1,12 @@
 from __future__ import annotations
 
 import logging
-import os
 import textwrap
+import xml.etree.ElementTree as ET
 from pathlib import Path
 from typing import TYPE_CHECKING
 
+from cli.lib.common.envs_helper import get_env
 from cli.lib.common.utils import get_wheels
 from jinja2 import Template
 
@@ -16,6 +17,23 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
+
+# ---- Template (title + per-command failures) ----
+_TPL_FAIL_BY_CMD = Template(
+    textwrap.dedent("""\
+    ## {{ title }}
+
+    {%- for section in sections if section.failures %}
+    ### Test Command: {{ section.label }}
+
+    {%- for f in section.failures %}
+    - {{ f }}
+    {%- endfor %}
+
+    {%- endfor %}
+""")
+)
+
 _TPL_CONTENT = Template(
     textwrap.dedent("""\
     ## {{ title }}
@@ -53,8 +71,17 @@ _TPL_TABLE = Template(
 
 
 def gh_summary_path() -> Path | None:
-    """Return the Path to the GitHub step summary file, or None if not set."""
-    p = os.environ.get("GITHUB_STEP_SUMMARY")
+    """Return the Path to the GitHub step summary file,
+    if TEMP_GITHUB_STEP_SUMMARY is set, use that instead,
+    this happens when run jobs in docker container
+    the github flow need to make sure to output the summary to github step summary after
+    """
+    p = get_env("GITHUB_STEP_SUMMARY")
+    overrides = get_env("TEMP_GITHUB_STEP_SUMMARY")
+    if overrides:
+        p = overrides
+    if not p or not Path(p).exists():
+        return None
     return Path(p) if p else None
 
 
@@ -141,3 +168,62 @@ def render_content(
     tpl = _TPL_CONTENT
     md = tpl.render(title=title, content=content, lang=lang)
     return md
+
+
+def summarize_failures_by_test_command(
+    xml_and_labels: Iterable[tuple[str | Path, str]],
+    *,
+    title: str = "Pytest Failures by Test Command",
+    dedupe_within_command: bool = True,
+) -> bool:
+    """
+    Render a single Markdown block summarizing failures grouped by test command.
+    Returns True if anything was written, False otherwise.
+    """
+    sections: list[dict] = []
+
+    for xml_path, label in xml_and_labels:
+        xmlp = Path(xml_path)
+        if not xmlp.exists():
+            logger.warning("XML %s not found, skipping", xmlp)
+            continue
+        failed = _parse_failed(xmlp)
+        if dedupe_within_command:
+            failed = sorted(set(failed))
+
+        # collect even if empty; we'll filter in the template render
+        sections.append({"label": label, "failures": failed})
+
+    # If *all* sections are empty or we collected nothing, skip writing.
+    if not sections or all(not s["failures"] for s in sections):
+        return False
+
+    md = _TPL_FAIL_BY_CMD.render(title=title, sections=sections).rstrip() + "\n"
+    return write_gh_step_summary(md)
+
+
+def _to_name_from_testcase(tc: ET.Element) -> str:
+    name = tc.attrib.get("name", "")
+    file_attr = tc.attrib.get("file")
+    if file_attr:
+        return f"{file_attr}:{name}"
+
+    classname = tc.attrib.get("classname", "")
+    parts = classname.split(".") if classname else []
+    if len(parts) >= 1:
+        mod_parts = parts[:-1] if len(parts) >= 2 else parts
+        mod_path = "/".join(mod_parts) + ".py" if mod_parts else "unknown.py"
+        return f"{mod_path}:{name}"
+    return f"unknown.py:{name or 'unknown_test'}"
+
+
+def _parse_failed(xml_path: Path) -> list[str]:
+    if not xml_path.exists():
+        return []
+    tree = ET.parse(xml_path)
+    root = tree.getroot()
+    failed: list[str] = []
+    for tc in root.iter("testcase"):
+        if any(x.tag in {"failure", "error"} for x in tc):
+            failed.append(_to_name_from_testcase(tc))
+    return failed

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

@@ -57,8 +57,8 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
         logger.info("Successfully cloned %s", target)
         return r, commit
 
-    except GitCommandError:
-        logger.exception("Git operation failed")
+    except GitCommandError as e:
+        logger.error("Git operation failed: %s", e)
         raise
 
 

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

@@ -17,11 +17,17 @@ def get_path(path: Union[str, Path], resolve: bool = False) -> Path:
     return result.resolve() if resolve else result
 
 
-def ensure_dir_exists(path: Union[str, Path]) -> Path:
-    """Create directory if it doesn't exist."""
-    path_obj = get_path(path)
-    path_obj.mkdir(parents=True, exist_ok=True)
-    return path_obj
+def ensure_path(path: Union[str, Path], is_file: bool = False) -> Path:
+    """Ensure directory or file exists.
+    If is_file=True, create parent dirs and touch the file.
+    """
+    p = Path(path)
+    if is_file:
+        p.parent.mkdir(parents=True, exist_ok=True)
+        p.touch(exist_ok=True)
+    else:
+        p.mkdir(parents=True, exist_ok=True)
+    return p
 
 
 def remove_dir(path: Union[str, Path, None]) -> None:
@@ -36,7 +42,7 @@ def remove_dir(path: Union[str, Path, None]) -> None:
 def force_create_dir(path: Union[str, Path]) -> Path:
     """Remove directory if exists, then create fresh empty directory."""
     remove_dir(path)
-    return ensure_dir_exists(path)
+    return ensure_path(path)
 
 
 def copy(src: Union[str, Path], dst: Union[str, Path]) -> None:

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

@@ -4,6 +4,7 @@ General Utility helpers for CLI tasks.
 
 import logging
 import os
+import secrets
 import shlex
 import subprocess
 import sys
@@ -11,6 +12,8 @@ from contextlib import contextmanager
 from pathlib import Path
 from typing import Optional
 
+from cli.lib.common.path_helper import ensure_path
+
 
 logger = logging.getLogger(__name__)
 
@@ -137,3 +140,42 @@ def get_wheels(
                 relpath = str((Path(dirpath) / fname).relative_to(root))
                 items.append({"pkg": pkg, "relpath": relpath})
     return items
+
+
+def attach_junitxml_if_pytest(
+    cmd: str,
+    dir: Optional[Path],
+    prefix: str,
+    *,
+    ensure_unique: bool = False,
+    resolve_xml: bool = False,
+) -> tuple[str, Optional[Path]]:
+    """
+    Append --junitxml=<ABS_PATH> to a pytest command string.
+    The XML filename is <prefix>_<random-hex>.xml.
+
+    - dir: target folder (will be created), if None, skip the junitxml attachment
+    - prefix: filename prefix (e.g., "junit" -> junit_ab12cd34.xml)
+    - ensure_unique: if True, regenerate a hash with 8 characters
+
+    Returns: (amended_cmd, abs_xml_path)
+    """
+    if "pytest" not in cmd:
+        return cmd, None
+    if dir is None:
+        return cmd, None
+    ensure_path(dir)
+
+    file_name_prefix = f"{prefix}"
+    if ensure_unique:
+        file_name_prefix += f"_{unique_hex(8)}"
+    xml_path = dir / f"{file_name_prefix}_junit_pytest.xml"
+    if resolve_xml:
+        xml_path = xml_path.resolve()
+
+    return f"{cmd} --junitxml={xml_path.as_posix()}", xml_path
+
+
+def unique_hex(length: int = 8) -> str:
+    """Return a random hex string of `length` characters."""
+    return secrets.token_hex((length + 1) // 2)[:length]

.ci/lumen_cli/cli/lib/core/vllm/lib.py

@@ -1,12 +1,18 @@
 import logging
 import os
 import textwrap
-from typing import Any
+from pathlib import Path
+from typing import Any, Optional
 
 from cli.lib.common.gh_summary import write_gh_step_summary
 from cli.lib.common.git_helper import clone_external_repo
 from cli.lib.common.pip_helper import pip_install_packages
-from cli.lib.common.utils import run_command, temp_environ, working_directory
+from cli.lib.common.utils import (
+    attach_junitxml_if_pytest,
+    run_command,
+    temp_environ,
+    working_directory,
+)
 from jinja2 import Template
 
 
@@ -41,6 +47,7 @@ def sample_vllm_test_library():
                 "pytest -v -s basic_correctness/test_cumem.py",
                 "pytest -v -s basic_correctness/test_basic_correctness.py",
                 "pytest -v -s basic_correctness/test_cpu_offload.py",
+                "VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py",
             ],
         },
         "vllm_basic_models_test": {
@@ -67,12 +74,16 @@ def sample_vllm_test_library():
                         "-v",
                         "-s",
                         "entrypoints/llm",
+                        "--ignore=entrypoints/llm/test_lazy_outlines.py",
                         "--ignore=entrypoints/llm/test_generate.py",
+                        "--ignore=entrypoints/llm/test_generate_multiple_loras.py",
                         "--ignore=entrypoints/llm/test_collective_rpc.py",
                     ]
                 ),
-                "pytest -v -s entrypoints/llm/test_generate.py",
-                "pytest -v -s entrypoints/offline_mode",
+                "pytest -v -s entrypoints/llm/test_lazy_outlines.py",
+                "pytest -v -s entrypoints/llm/test_generate.py ",
+                "pytest -v -s entrypoints/llm/test_generate_multiple_loras.py",
+                "VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode",
             ],
         },
         "vllm_regression_test": {
@@ -92,24 +103,14 @@ def sample_vllm_test_library():
             "num_gpus": 4,
             "steps": [
                 "pytest -v -s -x lora/test_chatglm3_tp.py",
+                "echo $VLLM_WORKER_MULTIPROC_METHOD",
                 "pytest -v -s -x lora/test_llama_tp.py",
-                "pytest -v -s -x lora/test_llm_with_multi_loras.py",
+                "pytest -v -s -x lora/test_multi_loras_with_tp.py",
             ],
         },
-        "vllm_distributed_test_28_failure_test": {
-            "title": "Distributed Tests (2 GPUs) pytorch 2.8 release failure",
-            "id": "vllm_distributed_test_28_failure_test",
-            "env_vars": {
-                "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
-            },
-            "num_gpus": 4,
-            "steps": [
-                "pytest -v -s distributed/test_sequence_parallel.py",
-            ],
-        },
-        "vllm_lora_28_failure_test": {
-            "title": "LoRA pytorch 2.8 failure test",
-            "id": "vllm_lora_28_failure_test",
+        "vllm_lora_280_failure_test": {
+            "title": "LoRA 280 failure test",
+            "id": "vllm_lora_280_failure_test",
             "steps": ["pytest -v lora/test_quant_model.py"],
         },
         "vllm_multi_model_processor_test": {
@@ -120,15 +121,6 @@ def sample_vllm_test_library():
                 "pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py",
             ],
         },
-        "vllm_multi_model_test_28_failure_test": {
-            "title": "Multi-Model Test (Failed 2.8 release)",
-            "id": "vllm_multi_model_test_28_failure_test",
-            "package_install": ["git+https://github.com/TIGER-AI-Lab/Mantis.git"],
-            "steps": [
-                "pytest -v -s models/multimodal/generation/test_voxtral.py",
-                "pytest -v -s models/multimodal/pooling",
-            ],
-        },
         "vllm_pytorch_compilation_unit_tests": {
             "title": "PyTorch Compilation Unit Tests",
             "id": "vllm_pytorch_compilation_unit_tests",
@@ -143,28 +135,6 @@ def sample_vllm_test_library():
                 "pytest -v -s compile/test_decorator.py",
             ],
         },
-        "vllm_language_model_test_extended_generation_28_failure_test": {
-            "title": "Language Models Test (Extended Generation) 2.8 release failure",
-            "id": "vllm_languagde_model_test_extended_generation_28_failure_test",
-            "package_install": [
-                "--no-build-isolation",
-                "git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8",
-            ],
-            "steps": [
-                "pytest -v -s models/language/generation/test_mistral.py",
-            ],
-        },
-        "vllm_distributed_test_2_gpu_28_failure_test": {
-            "title": "Distributed Tests (2 GPUs) pytorch 2.8 release failure",
-            "id": "vllm_distributed_test_2_gpu_28_failure_test",
-            "env_vars": {
-                "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
-            },
-            "num_gpus": 4,
-            "steps": [
-                "pytest -v -s distributed/test_sequence_parallel.py",
-            ],
-        },
         # TODO(elainewy):need to add g6 with 4 gpus to run this test
         "vllm_lora_test": {
             "title": "LoRA Test %N",
@@ -222,6 +192,9 @@ def run_test_plan(
     tests_map: dict[str, Any],
     shard_id: int = 0,
     num_shards: int = 0,
+    *,
+    test_summary_path: Optional[Path] = None,
+    test_summary_result: Optional[list[tuple[str, str]]] = None,
 ):
     """
     a method to run list of tests based on the test plan.
@@ -234,7 +207,6 @@ def run_test_plan(
     tests = tests_map[test_plan]
     pkgs = tests.get("package_install", [])
     title = tests.get("title", "unknown test")
-
     is_parallel = check_parallelism(tests, title, shard_id, num_shards)
     if is_parallel:
         title = title.replace("%N", f"{shard_id}/{num_shards}")
@@ -248,7 +220,15 @@ def run_test_plan(
         temp_environ(tests.get("env_vars", {})),
     ):
         failures = []
-        for step in tests["steps"]:
+        for idx, step in enumerate(tests["steps"]):
+            # generate xml report for each test for test summary if needed
+            step, xml_file_path = attach_junitxml_if_pytest(
+                cmd=step, dir=test_summary_path, prefix=f"{test_plan}_{idx}"
+            )
+            if xml_file_path and xml_file_path.exists() and test_summary_result:
+                test_summary_result.append((title, str(xml_file_path)))
+            else:
+                logger.info("No test report will be generate for %s", step)
             logger.info("Running step: %s", step)
             if is_parallel:
                 step = replace_buildkite_placeholders(step, shard_id, num_shards)

.ci/lumen_cli/cli/lib/core/vllm/vllm_build.py

@@ -20,7 +20,7 @@ from cli.lib.common.gh_summary import (
 )
 from cli.lib.common.path_helper import (
     copy,
-    ensure_dir_exists,
+    ensure_path,
     force_create_dir,
     get_path,
     is_path_exist,
@@ -63,12 +63,7 @@ class VllmBuildParameters:
     # DOCKERFILE_PATH: path to Dockerfile used when use_local_dockerfile is True"
     use_local_dockerfile: bool = env_bool_field("USE_LOCAL_DOCKERFILE", True)
     dockerfile_path: Path = env_path_field(
-        "DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile"
-    )
-
-    # the cleaning script to remove torch dependencies from pip
-    cleaning_script: Path = env_path_field(
-        "cleaning_script", ".github/ci_configs/vllm/use_existing_torch.py"
+        "DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile.tmp_vllm"
     )
 
     # OUTPUT_DIR: where docker buildx (local exporter) will write artifacts
@@ -165,13 +160,12 @@ class VllmBuildRunner(BaseRunner):
         logger.info("Running vllm build with inputs: %s", inputs)
         vllm_commit = clone_vllm()
 
-        self.cp_torch_cleaning_script(inputs)
         self.cp_dockerfile_if_exist(inputs)
         # cp torch wheels from root direct to vllm workspace if exist
         self.cp_torch_whls_if_exist(inputs)
 
         # make sure the output dir to store the build artifacts exist
-        ensure_dir_exists(Path(inputs.output_dir))
+        ensure_path(Path(inputs.output_dir))
 
         cmd = self._generate_docker_build_cmd(inputs)
         logger.info("Running docker build: \n %s", cmd)
@@ -211,11 +205,6 @@ class VllmBuildRunner(BaseRunner):
         copy(inputs.torch_whls_path, tmp_dir)
         return tmp_dir
 
-    def cp_torch_cleaning_script(self, inputs: VllmBuildParameters):
-        script = get_path(inputs.cleaning_script, resolve=True)
-        vllm_script = Path(f"./{self.work_directory}/use_existing_torch.py")
-        copy(script, vllm_script)
-
     def cp_dockerfile_if_exist(self, inputs: VllmBuildParameters):
         if not inputs.use_local_dockerfile:
             logger.info("using vllm default dockerfile.torch_nightly for build")

.ci/lumen_cli/cli/lib/core/vllm/vllm_test.py

@@ -11,15 +11,24 @@ from typing import Any
 
 from cli.lib.common.cli_helper import BaseRunner
 from cli.lib.common.envs_helper import env_path_field, env_str_field, get_env
-from cli.lib.common.path_helper import copy, get_path, remove_dir
+from cli.lib.common.gh_summary import (
+    gh_summary_path,
+    summarize_failures_by_test_command,
+)
+from cli.lib.common.path_helper import copy, remove_dir
 from cli.lib.common.pip_helper import (
     pip_install_first_match,
     pip_install_packages,
     pkg_exists,
     run_python,
 )
-from cli.lib.common.utils import run_command, working_directory
-from cli.lib.core.vllm.lib import clone_vllm, run_test_plan, sample_vllm_test_library
+from cli.lib.common.utils import ensure_path, run_command, working_directory
+from cli.lib.core.vllm.lib import (
+    clone_vllm,
+    run_test_plan,
+    sample_vllm_test_library,
+    summarize_build_info,
+)
 
 
 logger = logging.getLogger(__name__)
@@ -41,17 +50,20 @@ class VllmTestParameters:
         "VLLM_WHEELS_PATH", "./dist/external/vllm/wheels"
     )
 
-    torch_cuda_arch_list: str = env_str_field("TORCH_CUDA_ARCH_LIST", "8.9")
-
-    cleaning_script: Path = env_path_field(
-        "cleaning_script", ".github/ci_configs/vllm/use_existing_torch.py"
+    # generate a file to store test summary
+    test_summary_file: Path = env_path_field(
+        "TEMP_GITHUB_STEP_SUMMARY", "./generated_step_summary.md"
     )
 
+    torch_cuda_arch_list: str = env_str_field("TORCH_CUDA_ARCH_LIST", "8.9")
+
     def __post_init__(self):
         if not self.torch_whls_path.exists():
             raise ValueError("missing torch_whls_path")
         if not self.vllm_whls_path.exists():
             raise ValueError("missing vllm_whls_path")
+        if self.test_summary_file:
+            ensure_path(self.test_summary_file, is_file=True)
 
 
 class TestInpuType(Enum):
@@ -95,46 +107,54 @@ class VllmTestRunner(BaseRunner):
         logger.info("Display VllmTestParameters %s", params)
         self._set_envs(params)
 
-        clone_vllm(dst=self.work_directory)
-        self.cp_torch_cleaning_script(params)
+        vllm_commit = clone_vllm(dst=self.work_directory)
         with working_directory(self.work_directory):
             remove_dir(Path("vllm"))
             self._install_wheels(params)
             self._install_dependencies()
         # verify the torches are not overridden by test dependencies
-
         check_versions()
+        return vllm_commit
 
     def run(self):
         """
         main function to run vllm test
         """
-        self.prepare()
+        vllm_commit = self.prepare()
+
+        # prepare test summary
+        test_summary_path = Path("tmp_pytest_report").resolve()
+        ensure_path(test_summary_path)
+        test_summary_result = []
+
         try:
             with working_directory(self.work_directory):
                 if self.test_type == TestInpuType.TEST_PLAN:
-                    if self.num_shards > 1:
-                        run_test_plan(
-                            self.test_plan,
-                            "vllm",
-                            sample_vllm_test_library(),
-                            self.shard_id,
-                            self.num_shards,
-                        )
-                    else:
-                        run_test_plan(
-                            self.test_plan, "vllm", sample_vllm_test_library()
-                        )
+                    run_test_plan(
+                        self.test_plan,
+                        "vllm",
+                        sample_vllm_test_library(),
+                        self.shard_id,
+                        self.num_shards,
+                        test_summary_path=test_summary_path,
+                        test_summary_result=test_summary_result,
+                    )
                 else:
                     raise ValueError(f"Unknown test type {self.test_type}")
+        except Exception as e:
+            logger.error("Failed to run vllm test: %s", e)
+            raise e
         finally:
-            # double check the torches are not overridden by other packages
-            check_versions()
+            self.vllm_test_gh_summary(vllm_commit, test_summary_result)
 
-    def cp_torch_cleaning_script(self, params: VllmTestParameters):
-        script = get_path(params.cleaning_script, resolve=True)
-        vllm_script = Path(f"./{self.work_directory}/use_existing_torch.py")
-        copy(script, vllm_script)
+    def vllm_test_gh_summary(
+        self, vllm_commit: str, test_summary_results: list[tuple[str, str]]
+    ):
+        if not gh_summary_path():
+            return logger.info("Skipping, not detect GH Summary env var....")
+        logger.info("Generate GH Summary ...")
+        summarize_build_info(vllm_commit)
+        summarize_failures_by_test_command(test_summary_results)
 
     def _install_wheels(self, params: VllmTestParameters):
         logger.info("Running vllm test with inputs: %s", params)

.ci/lumen_cli/pyproject.toml

@@ -6,7 +6,7 @@ dependencies = [
     "GitPython==3.1.45",
     "docker==7.1.0",
     "pytest==7.3.2",
-    "uv==0.9.6"
+    "uv==0.8.6"
 ]
 
 [tool.setuptools]

.ci/lumen_cli/tests/test_path_helper.py

@@ -8,7 +8,7 @@ from tempfile import TemporaryDirectory
 
 from cli.lib.common.path_helper import (
     copy,
-    ensure_dir_exists,
+    ensure_path,
     force_create_dir,
     get_path,
     is_path_exist,
@@ -53,12 +53,12 @@ class TestPathHelper(unittest.TestCase):
         with self.assertRaises(TypeError):
             get_path(123)  # type: ignore[arg-type]
 
-    # -------- ensure_dir_exists / force_create_dir / remove_dir --------
-    def test_ensure_dir_exists_creates_and_is_idempotent(self):
+    # -------- ensure_path / force_create_dir / remove_dir --------
+    def test_ensure_path_creates_and_is_idempotent(self):
         d = self.tmp_path / "made"
-        ensure_dir_exists(d)
+        ensure_path(d)
         self.assertTrue(d.exists() and d.is_dir())
-        ensure_dir_exists(d)
+        ensure_path(d)
 
     def test_force_create_dir_clears_existing(self):
         d = self.tmp_path / "fresh"

.ci/lumen_cli/tests/test_vllm.py

@@ -139,7 +139,7 @@ class TestBuildCmdAndRun(unittest.TestCase):
         self.assertIn("-t vllm-wheels", squashed)
 
     @patch(f"{_VLLM_BUILD_MODULE}.run_command")
-    @patch(f"{_VLLM_BUILD_MODULE}.ensure_dir_exists")
+    @patch(f"{_VLLM_BUILD_MODULE}.ensure_path")
     @patch(f"{_VLLM_BUILD_MODULE}.clone_vllm")
     @patch.object(
         vllm_build.VllmBuildRunner,

.ci/magma-rocm/Makefile

@@ -1,11 +1,11 @@
 SHELL=/usr/bin/env bash
 
 DOCKER_CMD ?= docker
-DESIRED_ROCM ?= 7.1
+DESIRED_ROCM ?= 6.4
 DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
 PACKAGE_NAME = magma-rocm
 # inherit this from underlying docker image, do not pass this env var to docker
-#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201
+#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201
 
 DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	-v $(shell git rev-parse --show-toplevel)/.ci:/builder \
@@ -16,26 +16,20 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	magma-rocm/build_magma.sh
 
 .PHONY: all
-all: magma-rocm71
-all: magma-rocm70
 all: magma-rocm64
+all: magma-rocm63
 
 .PHONY:
 clean:
 	$(RM) -r magma-*
 	$(RM) -r output
 
-.PHONY: magma-rocm71
-magma-rocm71: DESIRED_ROCM := 7.1
-magma-rocm71:
-	$(DOCKER_RUN)
-
-.PHONY: magma-rocm70
-magma-rocm70: DESIRED_ROCM := 7.0
-magma-rocm70:
-	$(DOCKER_RUN)
-
 .PHONY: magma-rocm64
 magma-rocm64: DESIRED_ROCM := 6.4
 magma-rocm64:
 	$(DOCKER_RUN)
+
+.PHONY: magma-rocm63
+magma-rocm63: DESIRED_ROCM := 6.3
+magma-rocm63:
+	$(DOCKER_RUN)

.ci/magma-rocm/build_magma.sh

@@ -6,8 +6,8 @@ set -eou pipefail
 # The script expects DESIRED_CUDA and PACKAGE_NAME to be set
 ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
 
-# https://github.com/icl-utk-edu/magma/pull/65
-MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
+# Version 2.7.2 + ROCm related updates
+MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
 
 # Folders for the build
 PACKAGE_FILES=${ROOT_DIR}/magma-rocm/package_files # metadata
@@ -20,7 +20,7 @@ mkdir -p ${PACKAGE_DIR} ${PACKAGE_OUTPUT}/linux-64 ${PACKAGE_BUILD} ${PACKAGE_RE
 
 # Fetch magma sources and verify checksum
 pushd ${PACKAGE_DIR}
-git clone https://github.com/jeffdaily/magma
+git clone https://bitbucket.org/icl/magma.git
 pushd magma
 git checkout ${MAGMA_VERSION}
 popd

.ci/manywheel/build_common.sh

@@ -142,7 +142,7 @@ time CMAKE_ARGS=${CMAKE_ARGS[@]} \
     EXTRA_CAFFE2_CMAKE_FLAGS=${EXTRA_CAFFE2_CMAKE_FLAGS[@]} \
     BUILD_LIBTORCH_CPU_WITH_DEBUG=$BUILD_DEBUG_INFO \
     USE_NCCL=${USE_NCCL} USE_RCCL=${USE_RCCL} USE_KINETO=${USE_KINETO} \
-    python -m build --wheel --no-isolation --outdir /tmp/$WHEELHOUSE_DIR
+    python setup.py bdist_wheel -d /tmp/$WHEELHOUSE_DIR
 echo "Finished setup.py bdist at $(date)"
 
 # Build libtorch packages

.ci/manywheel/build_cuda.sh

@@ -124,7 +124,6 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
     fi
     if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
         echo "Bundling with cudnn and cublas."
-
         DEPS_LIST+=(
             "/usr/local/cuda/lib64/libcudnn_adv.so.9"
             "/usr/local/cuda/lib64/libcudnn_cnn.so.9"
@@ -134,11 +133,16 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
             "/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9"
             "/usr/local/cuda/lib64/libcudnn_heuristic.so.9"
             "/usr/local/cuda/lib64/libcudnn.so.9"
+            "/usr/local/cuda/lib64/libcublas.so.12"
+            "/usr/local/cuda/lib64/libcublasLt.so.12"
             "/usr/local/cuda/lib64/libcusparseLt.so.0"
+            "/usr/local/cuda/lib64/libcudart.so.12"
+            "/usr/local/cuda/lib64/libnvrtc.so.12"
             "/usr/local/cuda/lib64/libnvrtc-builtins.so"
             "/usr/local/cuda/lib64/libcufile.so.0"
             "/usr/local/cuda/lib64/libcufile_rdma.so.1"
             "/usr/local/cuda/lib64/libnvshmem_host.so.3"
+            "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12"
             "/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so"
         )
         DEPS_SONAME+=(
@@ -150,59 +154,22 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
             "libcudnn_engines_precompiled.so.9"
             "libcudnn_heuristic.so.9"
             "libcudnn.so.9"
+            "libcublas.so.12"
+            "libcublasLt.so.12"
             "libcusparseLt.so.0"
+            "libcudart.so.12"
+            "libnvrtc.so.12"
             "libnvrtc-builtins.so"
             "libnvshmem_host.so.3"
             "libcufile.so.0"
             "libcufile_rdma.so.1"
+            "libcupti.so.12"
             "libnvperf_host.so"
         )
         # Add libnvToolsExt only if CUDA version is not 12.9
-        if [[ $CUDA_VERSION == 13* ]]; then
-            DEPS_LIST+=(
-                "/usr/local/cuda/lib64/libcublas.so.13"
-                "/usr/local/cuda/lib64/libcublasLt.so.13"
-                "/usr/local/cuda/lib64/libcudart.so.13"
-                "/usr/local/cuda/lib64/libnvrtc.so.13"
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.13"
-                "/usr/local/cuda/lib64/libibverbs.so.1"
-                "/usr/local/cuda/lib64/librdmacm.so.1"
-                "/usr/local/cuda/lib64/libmlx5.so.1"
-                "/usr/local/cuda/lib64/libnl-3.so.200"
-                "/usr/local/cuda/lib64/libnl-route-3.so.200")
-            DEPS_SONAME+=(
-                "libcublas.so.13"
-                "libcublasLt.so.13"
-                "libcudart.so.13"
-                "libnvrtc.so.13"
-                "libcupti.so.13"
-                "libibverbs.so.1"
-                "librdmacm.so.1"
-                "libmlx5.so.1"
-                "libnl-3.so.200"
-                "libnl-route-3.so.200")
-            export USE_CUPTI_SO=1
-            export ATEN_STATIC_CUDA=0
-            export USE_CUDA_STATIC_LINK=0
-            export USE_CUFILE=0
-        else
-            DEPS_LIST+=(
-                "/usr/local/cuda/lib64/libcublas.so.12"
-                "/usr/local/cuda/lib64/libcublasLt.so.12"
-                "/usr/local/cuda/lib64/libcudart.so.12"
-                "/usr/local/cuda/lib64/libnvrtc.so.12"
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12")
-            DEPS_SONAME+=(
-                "libcublas.so.12"
-                "libcublasLt.so.12"
-                "libcudart.so.12"
-                "libnvrtc.so.12"
-                "libcupti.so.12")
-
-            if [[ $CUDA_VERSION != 12.9* ]]; then
-                DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
-                DEPS_SONAME+=("libnvToolsExt.so.1")
-            fi
+        if [[ $CUDA_VERSION != 12.9* ]]; then
+            DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
+            DEPS_SONAME+=("libnvToolsExt.so.1")
         fi
     else
         echo "Using nvidia libs from pypi."

.ci/manywheel/build_libtorch.sh

@@ -104,7 +104,7 @@ if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
     export ROCclr_DIR=/opt/rocm/rocclr/lib/cmake/rocclr
 fi
 
-echo "Calling -m pip install . -v --no-build-isolation at $(date)"
+echo "Calling 'python -m pip install .' at $(date)"
 
 if [[ $LIBTORCH_VARIANT = *"static"* ]]; then
     STATIC_CMAKE_FLAG="-DTORCH_STATIC=1"

.ci/manywheel/build_rocm.sh

@@ -107,10 +107,6 @@ if [[ $ROCM_INT -ge 60200 ]]; then
     ROCM_SO_FILES+=("librocm-core.so")
 fi
 
-if [[ $ROCM_INT -ge 70000 ]]; then
-    ROCM_SO_FILES+=("librocroller.so")
-fi
-
 OS_NAME=`awk -F= '/^NAME/{print $2}' /etc/os-release`
 if [[ "$OS_NAME" == *"CentOS Linux"* || "$OS_NAME" == *"AlmaLinux"* ]]; then
     LIBGOMP_PATH="/usr/lib64/libgomp.so.1"

.ci/pytorch/build.sh

@@ -89,7 +89,7 @@ fi
 if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
   export USE_MKLDNN=1
   export USE_MKLDNN_ACL=1
-  export ACL_ROOT_DIR=/acl
+  export ACL_ROOT_DIR=/ComputeLibrary
 fi
 
 if [[ "$BUILD_ENVIRONMENT" == *riscv64* ]]; then
@@ -233,9 +233,7 @@ if [[ "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
   export BUILD_STATIC_RUNTIME_BENCHMARK=ON
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *-full-debug* ]]; then
-  export CMAKE_BUILD_TYPE=Debug
-elif [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
+if [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
   export CMAKE_BUILD_TYPE=RelWithAssert
 fi
 
@@ -292,20 +290,15 @@ else
 
       WERROR=1 python setup.py clean
 
-      WERROR=1 python -m build --wheel --no-isolation
+      WERROR=1 python setup.py bdist_wheel
     else
       python setup.py clean
       if [[ "$BUILD_ENVIRONMENT" == *xla* ]]; then
         source .ci/pytorch/install_cache_xla.sh
       fi
-      python -m build --wheel --no-isolation
+      python setup.py bdist_wheel
     fi
     pip_install_whl "$(echo dist/*.whl)"
-    if [[ "$BUILD_ENVIRONMENT" == *full-debug* ]]; then
-      # Regression test for https://github.com/pytorch/pytorch/issues/164297
-      # Torch should be importable and that's about it
-      pushd /; python -c "import torch;print(torch.__config__.show(), torch.randn(5) + 1.7)"; popd
-    fi
 
     if [[ "${BUILD_ADDITIONAL_PACKAGES:-}" == *vision* ]]; then
       install_torchvision
@@ -426,7 +419,7 @@ fi
 if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
   # export test times so that potential sharded tests that'll branch off this build will use consistent data
   # don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
-  PYTHONPATH=. python tools/stats/export_test_times.py
+  python tools/stats/export_test_times.py
 fi
 # don't do this for bazel or s390x or riscv64 as they don't use sccache
 if [[ "$BUILD_ENVIRONMENT" != *s390x* && "$BUILD_ENVIRONMENT" != *riscv64* && "$BUILD_ENVIRONMENT" != *-bazel-* ]]; then

.ci/pytorch/check_binary.sh

@@ -300,3 +300,24 @@ except RuntimeError as e:
     exit 1
   fi
 fi
+
+###############################################################################
+# Check for C++ ABI compatibility to GCC-11 - GCC 13
+###############################################################################
+if [[ "$(uname)" == 'Linux' &&  "$PACKAGE_TYPE" == 'manywheel' ]]; then
+  pushd /tmp
+  # Per https://gcc.gnu.org/onlinedocs/gcc/C_002b_002b-Dialect-Options.html
+  # gcc-11 is ABI16, gcc-13 is ABI18, gcc-14 is ABI19
+  # gcc 11 - CUDA 11.8, xpu, rocm
+  # gcc 13 - CUDA 12.6, 12.8 and cpu
+  # Please see issue for reference: https://github.com/pytorch/pytorch/issues/152426
+  if [[ "$(uname -m)" == "s390x" ]]; then
+    cxx_abi="19"
+  elif [[ "$DESIRED_CUDA" != 'xpu' && "$DESIRED_CUDA" != 'rocm'* ]]; then
+    cxx_abi="18"
+  else
+    cxx_abi="16"
+  fi
+  python -c "import torch; exit(0 if torch._C._PYBIND11_BUILD_ABI == '_cxxabi10${cxx_abi}' else 1)"
+  popd
+fi

.ci/pytorch/common_utils.sh

@@ -258,19 +258,11 @@ function install_torchrec_and_fbgemm() {
       git clone --recursive https://github.com/pytorch/fbgemm
       pushd fbgemm/fbgemm_gpu
       git checkout "${fbgemm_commit}" --recurse-submodules
-      # until the fbgemm_commit includes the tbb patch
-      patch <<'EOF'
---- a/FbgemmGpu.cmake
-+++ b/FbgemmGpu.cmake
-@@ -184,5 +184,6 @@ gpu_cpp_library(
-     fbgemm_gpu_tbe_cache
-     fbgemm_gpu_tbe_optimizers
-     fbgemm_gpu_tbe_utils
-+    tbb
-   DESTINATION
-     fbgemm_gpu)
-EOF
-      python setup.py bdist_wheel --build-variant=rocm
+      python setup.py bdist_wheel \
+        --build-variant=rocm \
+        -DHIP_ROOT_DIR="${ROCM_PATH}" \
+        -DCMAKE_C_FLAGS="-DTORCH_USE_HIP_DSA" \
+        -DCMAKE_CXX_FLAGS="-DTORCH_USE_HIP_DSA"
       popd
 
       # Save the wheel before cleaning up

.ci/pytorch/cpp_doc_push_script.sh

@@ -58,7 +58,7 @@ time python tools/setup_helpers/generate_code.py \
 
 # Build the docs
 pushd docs/cpp
-time make VERBOSE=1 html
+time make VERBOSE=1 html -j
 
 popd
 popd

.ci/pytorch/functorch_doc_push_script.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+# This is where the local pytorch install in the docker image is located
+pt_checkout="/var/lib/jenkins/workspace"
+source "$pt_checkout/.ci/pytorch/common_utils.sh"
+echo "functorch_doc_push_script.sh: Invoked with $*"
+
+set -ex -o pipefail
+
+version=${DOCS_VERSION:-nightly}
+echo "version: $version"
+
+# Build functorch docs
+pushd $pt_checkout/functorch/docs
+make html
+popd
+
+git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
+pushd functorch_ghpages
+
+if [ "$version" == "main" ]; then
+  version=nightly
+fi
+
+git rm -rf "$version" || true
+mv "$pt_checkout/functorch/docs/build/html" "$version"
+
+git add "$version" || true
+git status
+git config user.email "soumith+bot@pytorch.org"
+git config user.name "pytorchbot"
+# If there aren't changes, don't make a commit; push is no-op
+git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
+git status
+
+if [[ "${WITH_PUSH:-}" == true ]]; then
+  git push -u origin gh-pages
+fi
+
+popd

.ci/pytorch/macos-build.sh

@@ -36,11 +36,11 @@ fi
 print_cmake_info
 if [[ ${BUILD_ENVIRONMENT} == *"distributed"* ]]; then
   # Needed for inductor benchmarks, as lots of HF networks make `torch.distribtued` calls
-  USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python -m build --wheel --no-isolation
+  USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
 else
   # Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
   # that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
-  USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python -m build --wheel --no-isolation -C--build-option=--plat-name=macosx_11_0_arm64
+  USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel --plat-name macosx_11_0_arm64
 fi
 if which sccache > /dev/null; then
   print_sccache_stats

.ci/pytorch/macos-test.sh

@@ -55,7 +55,7 @@ test_python_shard() {
 
   setup_test_python
 
-  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "$1" "$NUM_TEST_SHARDS"
+  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
 
   assert_git_not_dirty
 }
@@ -195,7 +195,7 @@ torchbench_setup_macos() {
   git checkout "$(cat ../.github/ci_commit_pins/vision.txt)"
   git submodule update --init --recursive
   python setup.py clean
-  python -m pip install -e . -v --no-build-isolation
+  python setup.py develop
   popd
 
   pushd torchaudio
@@ -204,7 +204,7 @@ torchbench_setup_macos() {
   git submodule update --init --recursive
   python setup.py clean
   #TODO: Remove me, when figure out how to make TorchAudio find brew installed openmp
-  USE_OPENMP=0 python -m pip install -e . -v --no-build-isolation
+  USE_OPENMP=0 python setup.py develop
   popd
 
   checkout_install_torchbench
@@ -256,7 +256,7 @@ test_torchbench_smoketest() {
   local device=mps
   local dtypes=(undefined float16 bfloat16 notset)
   local dtype=${dtypes[$1]}
-  local models=(llama BERT_pytorch dcgan yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor vgg16)
+  local models=(hf_T5 llama BERT_pytorch dcgan hf_GPT2 yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor timm_resnet timm_vovnet vgg16)
 
   for backend in eager inductor; do
 
@@ -302,47 +302,6 @@ test_torchbench_smoketest() {
     fi
 
   done
-  echo "Pytorch benchmark on mps device completed"
-}
-
-test_aoti_torchbench_smoketest() {
-  print_cmake_info
-
-  echo "Launching AOTInductor torchbench setup"
-  pip_benchmark_deps
-  # shellcheck disable=SC2119,SC2120
-  torchbench_setup_macos
-
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-
-  local device=mps
-  local dtypes=(undefined float16 bfloat16 notset)
-  local dtype=${dtypes[$1]}
-  local models=(llama BERT_pytorch dcgan yolov3 resnet152 sam sam_fast pytorch_unet stable_diffusion_text_encoder speech_transformer Super_SloMo doctr_det_predictor doctr_reco_predictor vgg16)
-
-  echo "Launching torchbench inference performance run for AOT Inductor and dtype ${dtype}"
-  local dtype_arg="--${dtype}"
-  if [ "$dtype" == notset ]; then
-      dtype_arg="--float32"
-  fi
-  touch "$TEST_REPORTS_DIR/aot_inductor_torchbench_${dtype}_inference_${device}_performance.csv"
-  for model in "${models[@]}"; do
-    PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/torchbench.py \
-      --performance --only "$model" --export-aot-inductor --inference --devices "$device" "$dtype_arg" \
-      --output "$TEST_REPORTS_DIR/aot_inductor_torchbench_${dtype}_inference_${device}_performance.csv" || true
-    PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/torchbench.py \
-      --accuracy --only "$model" --export-aot-inductor --inference --devices "$device" "$dtype_arg" \
-      --output "$TEST_REPORTS_DIR/aot_inductor_torchbench_${dtype}_inference_${device}_accuracy.csv" || true
-  done
-
-  echo "Launching HuggingFace inference performance run for AOT Inductor and dtype ${dtype}"
-  PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/huggingface.py \
-    --performance --export-aot-inductor --inference --devices "$device" "$dtype_arg" \
-    --output "$TEST_REPORTS_DIR/aot_inductor_huggingface_${dtype}_inference_${device}_performance.csv" || true
-  PYTHONPATH="$(pwd)"/torchbench python benchmarks/dynamo/huggingface.py \
-    --accuracy --export-aot-inductor --inference --devices "$device" "$dtype_arg" \
-    --output "$TEST_REPORTS_DIR/aot_inductor_huggingface_${dtype}_inference_${device}_accuracy.csv" || true
 
   echo "Pytorch benchmark on mps device completed"
 }
@@ -391,8 +350,6 @@ elif [[ $TEST_CONFIG == *"perf_timm"* ]]; then
   test_timm_perf
 elif [[ $TEST_CONFIG == *"perf_smoketest"* ]]; then
   test_torchbench_smoketest "${SHARD_NUMBER}"
-elif [[ $TEST_CONFIG == *"aot_inductor_perf_smoketest"* ]]; then
-  test_aoti_torchbench_smoketest "${SHARD_NUMBER}"
 elif [[ $TEST_CONFIG == *"mps"* ]]; then
   test_python_mps
 elif [[ $NUM_TEST_SHARDS -gt 1 ]]; then

.ci/pytorch/multigpu-test.sh

@@ -26,7 +26,6 @@ if [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
     time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
-    time python test/run_test.py --verbose -i distributed/test_aten_comm_compute_reordering
     time python test/run_test.py --verbose -i distributed/test_store
     time python test/run_test.py --verbose -i distributed/test_symmetric_memory
     time python test/run_test.py --verbose -i distributed/test_pg_wrapper

.ci/pytorch/numba-cuda-13.patch

@@ -1,25 +0,0 @@
-From 6e08c9d08e9de59c7af28b720289debbbd384764 Mon Sep 17 00:00:00 2001
-From: Michael Wang <13521008+isVoid@users.noreply.github.com>
-Date: Tue, 1 Apr 2025 17:28:05 -0700
-Subject: [PATCH] Avoid bumping certain driver API to avoid future breakage
- (#185)
-
-Co-authored-by: isVoid <isVoid@users.noreply.github.com>
----
- numba_cuda/numba/cuda/cudadrv/driver.py | 3 +++
- 1 file changed, 3 insertions(+)
-
-diff --git a/numba_cuda/numba/cuda/cudadrv/driver.py b/numba_cuda/numba/cuda/cudadrv/driver.py
-index 1641bf77..233e9ed7 100644
---- a/numba_cuda/numba/cuda/cudadrv/driver.py
-+++ b/numba_cuda/numba/cuda/cudadrv/driver.py
-@@ -365,6 +365,9 @@ def _find_api(self, fname):
-         else:
-             variants = ('_v2', '')
- 
-+        if fname in ("cuCtxGetDevice", "cuCtxSynchronize"):
-+            return getattr(self.lib, fname)
-+
-         for variant in variants:
-             try:
-                 return getattr(self.lib, f'{fname}{variant}')

.ci/pytorch/smoke_test/check_binary_symbols.py

@@ -32,9 +32,6 @@ LIBTORCH_NAMESPACE_LIST = (
     "torch::",
 )
 
-# Patterns for detecting statically linked libstdc++ symbols
-STATICALLY_LINKED_CXX11_ABI = [re.compile(r".*recursive_directory_iterator.*")]
-
 
 def _apply_libtorch_symbols(symbols):
     return [
@@ -56,17 +53,12 @@ def get_symbols(lib: str) -> list[tuple[str, str, str]]:
     return [x.split(" ", 2) for x in lines.decode("latin1").split("\n")[:-1]]
 
 
-def grep_symbols(
-    lib: str, patterns: list[Any], symbol_type: str | None = None
-) -> list[str]:
+def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
     def _grep_symbols(
         symbols: list[tuple[str, str, str]], patterns: list[Any]
     ) -> list[str]:
         rc = []
         for _s_addr, _s_type, s_name in symbols:
-            # Filter by symbol type if specified
-            if symbol_type and _s_type != symbol_type:
-                continue
             for pattern in patterns:
                 if pattern.match(s_name):
                     rc.append(s_name)
@@ -88,18 +80,6 @@ def grep_symbols(
         return functools.reduce(list.__add__, (x.result() for x in tasks), [])
 
 
-def check_lib_statically_linked_libstdc_cxx_abi_symbols(lib: str) -> None:
-    cxx11_statically_linked_symbols = grep_symbols(
-        lib, STATICALLY_LINKED_CXX11_ABI, symbol_type="T"
-    )
-    num_statically_linked_symbols = len(cxx11_statically_linked_symbols)
-    print(f"num_statically_linked_symbols (T): {num_statically_linked_symbols}")
-    if num_statically_linked_symbols > 0:
-        raise RuntimeError(
-            f"Found statically linked libstdc++ symbols (recursive_directory_iterator): {cxx11_statically_linked_symbols[:100]}"
-        )
-
-
 def check_lib_symbols_for_abi_correctness(lib: str) -> None:
     print(f"lib: {lib}")
     cxx11_symbols = grep_symbols(lib, LIBTORCH_CXX11_PATTERNS)
@@ -127,7 +107,6 @@ def main() -> None:
 
     libtorch_cpu_path = str(install_root / "lib" / "libtorch_cpu.so")
     check_lib_symbols_for_abi_correctness(libtorch_cpu_path)
-    check_lib_statically_linked_libstdc_cxx_abi_symbols(libtorch_cpu_path)
 
 
 if __name__ == "__main__":

.ci/pytorch/smoke_test/smoke_test.py

@@ -386,8 +386,8 @@ def smoke_test_compile(device: str = "cpu") -> None:
 
 
 def smoke_test_nvshmem() -> None:
-    if not torch.cuda.is_available() or target_os == "windows":
-        print("Windows platform or CUDA is not available, skipping NVSHMEM test")
+    if not torch.cuda.is_available():
+        print("CUDA is not available, skipping NVSHMEM test")
         return
 
     # Check if NVSHMEM is compiled in current build
@@ -396,9 +396,7 @@ def smoke_test_nvshmem() -> None:
     except ImportError:
         # Not built with NVSHMEM support.
         # torch is not compiled with NVSHMEM prior to 2.9
-        from torch.torch_version import TorchVersion
-
-        if TorchVersion(torch.__version__) < (2, 9):
+        if torch.__version__ < "2.9":
             return
         else:
             # After 2.9: NVSHMEM is expected to be compiled in current build

.ci/pytorch/test.sh

@@ -32,18 +32,6 @@ if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* && -d /v
   git config --global --add safe.directory /var/lib/jenkins/workspace
 fi
 
-
-# Patch numba to avoid CUDA-13 crash, see https://github.com/pytorch/pytorch/issues/162878
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  NUMBA_CUDA_DIR=$(python -c "import os;import numba.cuda; print(os.path.dirname(numba.cuda.__file__))" 2>/dev/null || true)
-  if [ -n "$NUMBA_CUDA_DIR" ]; then
-    NUMBA_PATCH="$(dirname "$(realpath "${BASH_SOURCE[0]}")")/numba-cuda-13.patch"
-    pushd "$NUMBA_CUDA_DIR"
-    patch -p4 <"$NUMBA_PATCH"
-    popd
-  fi
-fi
-
 echo "Environment variables:"
 env
 
@@ -324,26 +312,20 @@ test_python_shard() {
 
   # modify LD_LIBRARY_PATH to ensure it has the conda env.
   # This set of tests has been shown to be buggy without it for the split-build
-  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
+  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
 
   assert_git_not_dirty
 }
 
 test_python() {
   # shellcheck disable=SC2086
-  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
+  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
   assert_git_not_dirty
 }
 
 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
-  assert_git_not_dirty
-}
-
-test_python_smoke_b200() {
-  # Targeted smoke tests for B200 - staged approach to avoid too many failures
-  time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
+  # Smoke tests for H100
+  time python test/run_test.py --include test_matmul_cuda inductor/test_fp8 inductor/test_max_autotune $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
   assert_git_not_dirty
 }
 
@@ -392,7 +374,6 @@ test_dynamo_wrapped_shard() {
     --exclude-distributed-tests \
     --exclude-torch-export-tests \
     --exclude-aot-dispatch-tests \
-    --exclude-quantization-tests \
     --shard "$1" "$NUM_TEST_SHARDS" \
     --verbose \
     --upload-artifacts-while-running
@@ -437,7 +418,7 @@ test_inductor_distributed() {
 
   # this runs on both single-gpu and multi-gpu instance. It should be smart about skipping tests that aren't supported
   # with if required # gpus aren't available
-  python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_aten_comm_compute_reordering distributed/test_compute_comm_reordering --verbose
+  python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_compute_comm_reordering --verbose
   assert_git_not_dirty
 }
 
@@ -460,37 +441,31 @@ test_inductor_shard() {
     --verbose
 }
 
-test_inductor_aoti_cpp() {
+test_inductor_aoti() {
+  # docker build uses bdist_wheel which does not work with test_aot_inductor
+  # TODO: need a faster way to build
   if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
     # We need to hipify before building again
     python3 tools/amd_build/build_amd.py
   fi
   if [[ "$BUILD_ENVIRONMENT" == *sm86* ]]; then
+    BUILD_COMMAND=(TORCH_CUDA_ARCH_LIST=8.6 USE_FLASH_ATTENTION=OFF python -m pip install --no-build-isolation -v -e .)
     # TODO: Replace me completely, as one should not use conda libstdc++, nor need special path to TORCH_LIB
     TEST_ENVS=(CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="/opt/conda/envs/py_3.10/lib:${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}")
   else
+    BUILD_COMMAND=(python -m pip install --no-build-isolation -v -e .)
     TEST_ENVS=(CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}")
   fi
 
+  # aoti cmake custom command requires `torch` to be installed
+  # initialize the cmake build cache and install torch
+  /usr/bin/env "${BUILD_COMMAND[@]}"
+  # rebuild with the build cache with `BUILD_AOT_INDUCTOR_TEST` enabled
+  /usr/bin/env CMAKE_FRESH=1 BUILD_AOT_INDUCTOR_TEST=1 "${BUILD_COMMAND[@]}"
+
   /usr/bin/env "${TEST_ENVS[@]}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference cpp/test_vec_half_AVX2 -dist=loadfile
 }
 
-test_inductor_aoti_cross_compile_for_windows() {
-
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-
-  # Set WINDOWS_CUDA_HOME environment variable
-  WINDOWS_CUDA_HOME="$(pwd)/win-torch-wheel-extracted"
-  export WINDOWS_CUDA_HOME
-
-  echo "WINDOWS_CUDA_HOME is set to: $WINDOWS_CUDA_HOME"
-  echo "Contents:"
-  ls -lah "$(pwd)/win-torch-wheel-extracted/lib/x64/" || true
-
-  python test/inductor/test_aoti_cross_compile_windows.py -k compile --package-dir "$TEST_REPORTS_DIR" --win-torch-lib-dir "$(pwd)/win-torch-wheel-extracted/torch/lib"
-}
-
 test_inductor_cpp_wrapper_shard() {
   if [[ -z "$NUM_TEST_SHARDS" ]]; then
     echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
@@ -521,14 +496,6 @@ test_inductor_cpp_wrapper_shard() {
     -k 'take' \
     --shard "$1" "$NUM_TEST_SHARDS" \
     --verbose
-
-  if [[ "${BUILD_ENVIRONMENT}" == *xpu* ]]; then
-    python test/run_test.py \
-      --include inductor/test_mkldnn_pattern_matcher \
-      -k 'xpu' \
-      --shard "$1" "$NUM_TEST_SHARDS" \
-      --verbose
-  fi
 }
 
 # "Global" flags for inductor benchmarking controlled by TEST_CONFIG
@@ -572,8 +539,6 @@ fi
 
 if [[ "${TEST_CONFIG}" == *cpu* ]]; then
   DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
-elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
-  DYNAMO_BENCHMARK_FLAGS+=(--device xpu)
 else
   DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
 fi
@@ -667,8 +632,6 @@ test_perf_for_dashboard() {
     device=cuda_b200
   elif [[ "${TEST_CONFIG}" == *rocm* ]]; then
     device=rocm
-  elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
-    device=xpu
   fi
 
   for mode in "${modes[@]}"; do
@@ -848,7 +811,7 @@ test_dynamo_benchmark() {
       elif [[ "${suite}" == "timm_models" ]]; then
         export TORCHBENCH_ONLY_MODELS="inception_v3"
       elif [[ "${suite}" == "torchbench" ]]; then
-        export TORCHBENCH_ONLY_MODELS="BERT_pytorch"
+        export TORCHBENCH_ONLY_MODELS="hf_Bert"
       fi
     fi
     test_single_dynamo_benchmark "dashboard" "$suite" "$shard_id" "$@"
@@ -879,13 +842,13 @@ test_inductor_torchbench_smoketest_perf() {
   mkdir -p "$TEST_REPORTS_DIR"
 
   python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --float16 --training \
-    --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only BERT_pytorch \
+    --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only hf_Bert \
     --output "$TEST_REPORTS_DIR/inductor_training_smoketest.csv"
   # The threshold value needs to be actively maintained to make this check useful
   python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_training_smoketest.csv" -t 1.4
 
   # Check memory compression ratio for a few models
-  for test in BERT_pytorch yolov3; do
+  for test in hf_Albert timm_vision_transformer; do
     python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --amp --training \
       --disable-cudagraphs --batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" \
       --only $test --output "$TEST_REPORTS_DIR/inductor_training_smoketest_$test.csv"
@@ -896,7 +859,7 @@ test_inductor_torchbench_smoketest_perf() {
   done
 
   # Perform some "warm-start" runs for a few huggingface models.
-  for test in AllenaiLongformerBase DistilBertForMaskedLM DistillGPT2 GoogleFnet YituTechConvBert; do
+  for test in AlbertForQuestionAnswering AllenaiLongformerBase DistilBertForMaskedLM DistillGPT2 GoogleFnet YituTechConvBert; do
     python benchmarks/dynamo/huggingface.py --accuracy --training --amp --inductor --device cuda --warm-start-latency \
       --only $test --output "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv"
     python benchmarks/dynamo/check_accuracy.py \
@@ -910,7 +873,7 @@ test_inductor_set_cpu_affinity(){
   export LD_PRELOAD="$JEMALLOC_LIB":"$LD_PRELOAD"
   export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"
 
-  if [[ "$(uname -m)" != "aarch64" ]]; then
+  if [[ "${TEST_CONFIG}" != *aarch64* ]]; then
     # Use Intel OpenMP for x86
     IOMP_LIB="$(dirname "$(which python)")/../lib/libiomp5.so"
     export LD_PRELOAD="$IOMP_LIB":"$LD_PRELOAD"
@@ -924,7 +887,7 @@ test_inductor_set_cpu_affinity(){
   cores=$((cpus / thread_per_core))
 
   # Set number of cores to 16 on aarch64 for performance runs
-  if [[ "$(uname -m)" == "aarch64" && $cores -gt 16 ]]; then
+  if [[ "${TEST_CONFIG}" == *aarch64* && $cores -gt 16 ]]; then
     cores=16
   fi
   export OMP_NUM_THREADS=$cores
@@ -1175,12 +1138,6 @@ test_distributed() {
   fi
 }
 
-test_quantization() {
-  echo "Testing quantization"
-
-  python test/test_quantization.py
-}
-
 test_rpc() {
   echo "Testing RPC C++ tests"
   # NB: the ending test_rpc must match the current function name for the current
@@ -1427,7 +1384,7 @@ EOF
   pip3 install -r requirements.txt
   # shellcheck source=./common-build.sh
   source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-  python -m build --wheel --no-isolation -C--build-option=--bdist-dir="base_bdist_tmp" --outdir "base_dist"
+  python setup.py bdist_wheel --bdist-dir="base_bdist_tmp" --dist-dir="base_dist"
   python -mpip install base_dist/*.whl
   echo "::endgroup::"
 
@@ -1575,10 +1532,14 @@ test_executorch() {
   install_torchvision
   install_torchaudio
 
-  INSTALL_SCRIPT="$(pwd)/.ci/docker/common/install_executorch.sh"
-
   pushd /executorch
-  "${INSTALL_SCRIPT}" setup_executorch
+
+  export PYTHON_EXECUTABLE=python
+  export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
+
+  # NB: We need to rebuild ExecuTorch runner here because it depends on PyTorch
+  # from the PR
+  bash .ci/scripts/setup-linux.sh --build-tool cmake
 
   echo "Run ExecuTorch unit tests"
   pytest -v -n auto
@@ -1592,14 +1553,17 @@ test_executorch() {
 
   popd
 
+  # Test torchgen generated code for Executorch.
+  echo "Testing ExecuTorch op registration"
+  "$BUILD_BIN_DIR"/test_edge_op_registration
+
   assert_git_not_dirty
 }
 
 test_linux_aarch64() {
   python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
         test_transformers test_multiprocessing test_numpy_interop test_autograd test_binary_ufuncs test_complex test_spectral_ops \
-        test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops profiler/test_memory_profiler \
-        distributed/elastic/timer/api_test distributed/elastic/timer/local_timer_example distributed/elastic/timer/local_timer_test \
+        test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops \
         --shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
 
   # Dynamo tests
@@ -1625,12 +1589,11 @@ test_operator_benchmark() {
   TEST_REPORTS_DIR=$(pwd)/test/test-reports
   mkdir -p "$TEST_REPORTS_DIR"
   TEST_DIR=$(pwd)
-  ARCH=$(uname -m)
 
   test_inductor_set_cpu_affinity
 
   cd benchmarks/operator_benchmark/pt_extension
-  python -m pip install . -v --no-build-isolation
+  python -m pip install .
 
   cd "${TEST_DIR}"/benchmarks/operator_benchmark
   $TASKSET python -m benchmark_all_test --device "$1" --tag-filter "$2" \
@@ -1640,28 +1603,9 @@ test_operator_benchmark() {
   pip_install pandas
   python check_perf_csv.py \
       --actual "${TEST_REPORTS_DIR}/operator_benchmark_eager_float32_cpu.csv" \
-      --expected "${ARCH}_expected_ci_operator_benchmark_eager_float32_cpu.csv"
+      --expected "expected_ci_operator_benchmark_eager_float32_cpu.csv"
 }
 
-test_operator_microbenchmark() {
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-  TEST_DIR=$(pwd)
-
-  cd benchmarks/operator_benchmark/pt_extension
-  python -m pip install .
-
-  cd "${TEST_DIR}"/benchmarks/operator_benchmark
-
-  for OP_BENCHMARK_TESTS in matmul mm addmm bmm conv; do
-    $TASKSET python -m pt.${OP_BENCHMARK_TESTS}_test --tag-filter long \
-      --output-json-for-dashboard "${TEST_REPORTS_DIR}/operator_microbenchmark_${OP_BENCHMARK_TESTS}_compile.json" \
-      --benchmark-name "PyTorch operator microbenchmark" --use-compile
-    $TASKSET python -m pt.${OP_BENCHMARK_TESTS}_test --tag-filter long \
-      --output-json-for-dashboard "${TEST_REPORTS_DIR}/operator_microbenchmark_${OP_BENCHMARK_TESTS}.json" \
-      --benchmark-name "PyTorch operator microbenchmark"
-  done
-}
 
 if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
   (cd test && python -c "import torch; print(torch.__config__.show())")
@@ -1677,7 +1621,7 @@ if [[ "${TEST_CONFIG}" == *numpy_2* ]]; then
     python -m pip install --pre numpy==2.0.2 scipy==1.13.1 numba==0.60.0
   fi
   python test/run_test.py --include dynamo/test_functions.py dynamo/test_unspec.py test_binary_ufuncs.py test_fake_tensor.py test_linalg.py test_numpy_interop.py test_tensor_creation_ops.py test_torch.py torch_np/test_basic.py
-elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" == 'default' ]]; then
+elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" != *perf_cpu_aarch64* ]]; then
   test_linux_aarch64
 elif [[ "${TEST_CONFIG}" == *backward* ]]; then
   test_forward_backward_compatibility
@@ -1694,8 +1638,6 @@ elif [[ "${TEST_CONFIG}" == *executorch* ]]; then
   test_executorch
 elif [[ "$TEST_CONFIG" == 'jit_legacy' ]]; then
   test_python_legacy_jit
-elif [[ "$TEST_CONFIG" == 'quantization' ]]; then
-  test_quantization
 elif [[ "${BUILD_ENVIRONMENT}" == *libtorch* ]]; then
   # TODO: run some C++ tests
   echo "no-op at the moment"
@@ -1718,8 +1660,6 @@ elif [[ "${TEST_CONFIG}" == *operator_benchmark* ]]; then
     test_operator_benchmark cpu ${TEST_MODE}
 
   fi
-elif [[ "${TEST_CONFIG}" == *operator_microbenchmark* ]]; then
-  test_operator_microbenchmark
 elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
@@ -1728,8 +1668,6 @@ elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
   test_inductor_micro_benchmark
-elif [[ "${TEST_CONFIG}" == *aoti_cross_compile_for_windows* ]]; then
-  test_inductor_aoti_cross_compile_for_windows
 elif [[ "${TEST_CONFIG}" == *huggingface* ]]; then
   install_torchvision
   id=$((SHARD_NUMBER-1))
@@ -1761,7 +1699,7 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
   else
     # Do this after checkout_install_torchbench to ensure we clobber any
     # nightlies that torchbench may pull in
-    if [[ "${TEST_CONFIG}" != *cpu* && "${TEST_CONFIG}" != *xpu* ]]; then
+    if [[ "${TEST_CONFIG}" != *cpu* ]]; then
       install_torchrec_and_fbgemm
     fi
     PYTHONPATH=/torchbench test_dynamo_benchmark torchbench "$id"
@@ -1770,11 +1708,16 @@ elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper* ]]; then
   install_torchvision
   PYTHONPATH=/torchbench test_inductor_cpp_wrapper_shard "$SHARD_NUMBER"
   if [[ "$SHARD_NUMBER" -eq "1" ]]; then
-    test_inductor_aoti_cpp
+    test_inductor_aoti
   fi
 elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
   install_torchvision
   test_inductor_shard "${SHARD_NUMBER}"
+  if [[ "${SHARD_NUMBER}" == 1 ]]; then
+    if [[ "${BUILD_ENVIRONMENT}" != linux-jammy-py3.9-gcc11-build ]]; then
+      test_inductor_distributed
+    fi
+  fi
 elif [[ "${TEST_CONFIG}" == *einops* ]]; then
   test_einops
 elif [[ "${TEST_CONFIG}" == *dynamo_wrapped* ]]; then
@@ -1824,14 +1767,10 @@ elif [[ "${BUILD_ENVIRONMENT}" == *xpu* ]]; then
   test_xpu_bin
 elif [[ "${TEST_CONFIG}" == smoke ]]; then
   test_python_smoke
-elif [[ "${TEST_CONFIG}" == smoke_b200 ]]; then
-  test_python_smoke_b200
 elif [[ "${TEST_CONFIG}" == h100_distributed ]]; then
   test_h100_distributed
 elif [[ "${TEST_CONFIG}" == "h100-symm-mem" ]]; then
   test_h100_symm_mem
-elif [[ "${TEST_CONFIG}" == "b200-symm-mem" ]]; then
-  test_h100_symm_mem
 elif [[ "${TEST_CONFIG}" == h100_cutlass_backend ]]; then
   test_h100_cutlass_backend
 else

.ci/pytorch/test_fa3_abi_stable.sh

@@ -1,32 +0,0 @@
-#!/bin/bash
-
-set -ex -o pipefail
-
-# Suppress ANSI color escape sequences
-export TERM=vt100
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-# shellcheck source=./common-build.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-
-echo "Environment variables"
-env
-
-echo "Testing FA3 stable wheel still works with currently built torch"
-
-echo "Installing ABI Stable FA3 wheel"
-# The wheel was built on https://github.com/Dao-AILab/flash-attention/commit/b3846b059bf6b143d1cd56879933be30a9f78c81
-# on torch nightly torch==2.9.0.dev20250830+cu129
-$MAYBE_SUDO pip -q install https://s3.amazonaws.com/ossci-linux/wheels/flash_attn_3-3.0.0b1-cp39-abi3-linux_x86_64.whl
-
-pushd flash-attention/hopper
-export PYTHONPATH=$PWD
-pytest -v -s \
-  "test_flash_attn.py::test_flash_attn_output[1-1-192-False-False-False-0.0-False-False-mha-dtype0]" \
-  "test_flash_attn.py::test_flash_attn_varlen_output[511-1-64-True-False-False-0.0-False-False-gqa-dtype2]" \
-  "test_flash_attn.py::test_flash_attn_kvcache[1-128-128-False-False-True-None-0.0-False-False-True-False-True-False-gqa-dtype0]" \
-  "test_flash_attn.py::test_flash_attn_race_condition[97-97-192-True-dtype0]" \
-  "test_flash_attn.py::test_flash_attn_combine[2-3-64-dtype1]" \
-  "test_flash_attn.py::test_flash3_bw_compatibility"
-popd

.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 setup.py bdist_wheel
 if ($LASTEXITCODE -ne 0) { exit 1 }
 
 # Install the wheel locally

.ci/pytorch/win-test-helpers/build_pytorch.bat

@@ -38,12 +38,10 @@ if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
 
 :: Update CMake
-:: TODO: Investigate why this helps MKL detection, even when CMake from choco is not used
 call choco upgrade -y cmake --no-progress --installargs 'ADD_CMAKE_TO_PATH=System' --apply-install-arguments-to-dependencies --version=3.27.9
 if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
 
-:: TODO: Move to .ci/docker/requirements-ci.txt
 call pip install mkl==2024.2.0 mkl-static==2024.2.0 mkl-include==2024.2.0
 if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
@@ -132,14 +130,14 @@ if "%USE_CUDA%"=="1" (
 :: Print all existing environment variable for debugging
 set
 
-python -m build --wheel --no-isolation
+python setup.py bdist_wheel
 if errorlevel 1 goto fail
 if not errorlevel 0 goto fail
 sccache --show-stats
 python -c "import os, glob; os.system('python -mpip install --no-index --no-deps ' + glob.glob('dist/*.whl')[0])"
 (
   if "%BUILD_ENVIRONMENT%"=="" (
-    echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_ROOT_DIR%\Scripts\activate.bat %CONDA_ROOT_DIR%\envs\py_tmp` in Command Prompt before running Git Bash.
+    echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3` in Command Prompt before running Git Bash.
   ) else (
     copy /Y "dist\*.whl" "%PYTORCH_FINAL_PACKAGE_DIR%"
 

.ci/pytorch/win-test-helpers/installation-helpers/activate_miniconda3.bat

@@ -3,12 +3,12 @@ if "%BUILD_ENVIRONMENT%"=="" (
 ) else (
   set CONDA_PARENT_DIR=C:\Jenkins
 )
-set CONDA_ROOT_DIR=%CONDA_PARENT_DIR%\Miniconda3
+
 
 :: Be conservative here when rolling out the new AMI with conda. This will try
 :: to install conda as before if it couldn't find the conda installation. This
 :: can be removed eventually after we gain enough confidence in the AMI
-if not exist %CONDA_ROOT_DIR% (
+if not exist %CONDA_PARENT_DIR%\Miniconda3 (
   set INSTALL_FRESH_CONDA=1
 )
 
@@ -17,14 +17,10 @@ if "%INSTALL_FRESH_CONDA%"=="1" (
   if errorlevel 1 exit /b
   if not errorlevel 0 exit /b
 
-  %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_ROOT_DIR%
+  %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
   if errorlevel 1 exit /b
   if not errorlevel 0 exit /b
 )
 
 :: Activate conda so that we can use its commands, i.e. conda, python, pip
-call %CONDA_ROOT_DIR%\Scripts\activate.bat %CONDA_ROOT_DIR%
-:: Activate conda so that we can use its commands, i.e. conda, python, pip
-call conda activate py_tmp
-
-call pip install -r .ci/docker/requirements-ci.txt
+call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

.ci/pytorch/win-test-helpers/setup_pytorch_env.bat

@@ -14,7 +14,7 @@ if not errorlevel 0 exit /b
 :: build\torch. Rather than changing all these references, making a copy of torch folder
 :: from conda to the current workspace is easier. The workspace will be cleaned up after
 :: the job anyway
-xcopy /s %CONDA_ROOT_DIR%\envs\py_tmp\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
+xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
 
 pushd .
 if "%VC_VERSION%" == "" (

.ci/pytorch/win-test-helpers/test_libtorch.bat

@@ -15,35 +15,37 @@ if errorlevel 1 exit /b 1
 if not errorlevel 0 exit /b 1
 
 cd %TMP_DIR_WIN%\build\torch\test
-
-:: Enable delayed variable expansion to make the list
-setlocal enabledelayedexpansion
-set EXE_LIST=
 for /r "." %%a in (*.exe) do (
-  if "%%~na" == "c10_intrusive_ptr_benchmark" (
-    @REM NB: This is not a gtest executable file, thus couldn't be handled by
-    @REM pytest-cpp and is excluded from test discovery by run_test
-    call "%%~fa"
+    call :libtorch_check "%%~na" "%%~fa"
     if errorlevel 1 goto fail
-    if not errorlevel 0 goto fail
-  ) else (
-    if "%%~na" == "verify_api_visibility" (
-      @REM Skip verify_api_visibility as it is a compile-level test
-    ) else (
-      set EXE_LIST=!EXE_LIST! cpp/%%~na
-    )
-  )
 )
 
+goto :eof
+
+:libtorch_check
+
 cd %CWD%
 set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\test
 
-:: Run python test\run_test.py on the list
-set NO_TD=True && python test\run_test.py --cpp --verbose -i !EXE_LIST!
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
+:: Skip verify_api_visibility as it a compile level test
+if "%~1" == "verify_api_visibility" goto :eof
 
-goto :eof
+echo Running "%~2"
+if "%~1" == "c10_intrusive_ptr_benchmark" (
+  :: NB: This is not a gtest executable file, thus couldn't be handled by pytest-cpp
+  call "%~2"
+  goto :eof
+)
+
+python test\run_test.py --cpp --verbose -i "cpp/%~1"
+if errorlevel 1 (
+  echo %1 failed with exit code %errorlevel%
+  goto fail
+)
+if not errorlevel 0 (
+  echo %1 failed with exit code %errorlevel%
+  goto fail
+)
 
 :eof
 exit /b 0

.ci/pytorch/win-test-helpers/test_python_shard.bat

@@ -25,7 +25,7 @@ echo Copying over test times file
 robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
 
 echo Run nn tests
-python run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
+python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
 if ERRORLEVEL 1 goto fail
 
 popd

.ci/pytorch/win-test.sh

@@ -37,8 +37,23 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
   export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
 fi
 
-# TODO: Move this to .ci/docker/requirements-ci.txt
-python -m pip install "psutil==5.9.1" nvidia-ml-py "pytest-shard==0.1.2"
+# TODO: Move both of them to Windows AMI
+python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0 protobuf==5.29.4 pytest-subtests==0.13.1
+
+# Install Z3 optional dependency for Windows builds.
+python -m pip install z3-solver==4.15.1.0
+
+# Install tlparse for test\dynamo\test_structured_trace.py UTs.
+python -m pip install tlparse==0.3.30
+
+# Install parameterized
+python -m pip install parameterized==0.8.1
+
+# Install pulp for testing ilps under torch\distributed\_tools
+python -m pip install pulp==2.9.0
+
+# Install expecttest to merge https://github.com/pytorch/pytorch/pull/155308
+python -m pip install expecttest==0.3.0
 
 run_tests() {
     # Run nvidia-smi if available

.ci/pytorch/windows/arm64/build_pytorch.bat

@@ -48,7 +48,7 @@ sccache --zero-stats
 sccache --show-stats
 
 :: Call PyTorch build script
-python -m build --wheel --no-isolation --outdir "%PYTORCH_FINAL_PACKAGE_DIR%"
+python setup.py bdist_wheel -d "%PYTORCH_FINAL_PACKAGE_DIR%"
 
 :: show sccache stats
 sccache --show-stats

.ci/pytorch/windows/cuda128.bat

@@ -37,10 +37,10 @@ IF "%CUDA_PATH_V128%"=="" (
 )
 
 IF "%BUILD_VISION%" == "" (
-    set TORCH_CUDA_ARCH_LIST=7.0;7.5;8.0;8.6;9.0;10.0;12.0
+    set TORCH_CUDA_ARCH_LIST=6.1;7.0;7.5;8.0;8.6;9.0;10.0;12.0
     set TORCH_NVCC_FLAGS=-Xfatbin -compress-all
 ) ELSE (
-    set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
+    set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
 )
 
 set "CUDA_PATH=%CUDA_PATH_V128%"

.ci/pytorch/windows/internal/copy.bat

@@ -1,20 +1,12 @@
-
-if %CUDA_VERSION% geq 130 (
-    set "dll_path=bin\x64"
-) else (
-    set "dll_path=bin"
-)
-
-copy "%CUDA_PATH%\%dll_path%\cusparse*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\cublas*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\cudart*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\curand*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\cufft*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\cusolver*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\nvrtc*64_*.dll*" pytorch\torch\lib
-copy "%CUDA_PATH%\%dll_path%\nvJitLink_*.dll*"  pytorch\torch\lib
+copy "%CUDA_PATH%\bin\cusparse*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\cublas*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\cudart*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\curand*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\cufft*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\cusolver*64_*.dll*" pytorch\torch\lib
 
 copy "%CUDA_PATH%\bin\cudnn*64_*.dll*" pytorch\torch\lib
+copy "%CUDA_PATH%\bin\nvrtc*64_*.dll*" pytorch\torch\lib
 copy "%CUDA_PATH%\extras\CUPTI\lib64\cupti64_*.dll*" pytorch\torch\lib
 copy "%CUDA_PATH%\extras\CUPTI\lib64\nvperf_host*.dll*" pytorch\torch\lib
 
@@ -28,3 +20,8 @@ copy "%libuv_ROOT%\bin\uv.dll" pytorch\torch\lib
 if exist "C:\Windows\System32\zlibwapi.dll" (
     copy "C:\Windows\System32\zlibwapi.dll"  pytorch\torch\lib
 )
+
+::copy nvJitLink dll is requires for cuda 12+
+if exist "%CUDA_PATH%\bin\nvJitLink_*.dll*" (
+    copy "%CUDA_PATH%\bin\nvJitLink_*.dll*"  pytorch\torch\lib
+)

.ci/pytorch/windows/internal/driver_update.bat

@@ -1,9 +1,9 @@
-set WIN_DRIVER_VN=580.88
-set "DRIVER_DOWNLOAD_LINK=https://ossci-windows.s3.amazonaws.com/%WIN_DRIVER_VN%-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe" & REM @lint-ignore
-curl --retry 3 -kL %DRIVER_DOWNLOAD_LINK% --output %WIN_DRIVER_VN%-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe
+set WIN_DRIVER_VN=528.89
+set "DRIVER_DOWNLOAD_LINK=https://ossci-windows.s3.amazonaws.com/%WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe" & REM @lint-ignore
+curl --retry 3 -kL %DRIVER_DOWNLOAD_LINK% --output %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe
 if errorlevel 1 exit /b 1
 
-start /wait %WIN_DRIVER_VN%-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe -s -noreboot
+start /wait %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe -s -noreboot
 if errorlevel 1 exit /b 1
 
-del %WIN_DRIVER_VN%-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe || ver > NUL
+del %WIN_DRIVER_VN%-data-center-tesla-desktop-winserver-2016-2019-2022-dch-international.exe || ver > NUL

.ci/pytorch/windows/internal/install_python.bat

@@ -7,9 +7,12 @@ if "%DESIRED_PYTHON%" == "3.13t" (
     set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.13.0/python-3.13.0-amd64.exe"
     set ADDITIONAL_OPTIONS="Include_freethreaded=1"
     set PYTHON_EXEC="python3.13t"
+) else if "%DESIRED_PYTHON%"=="3.14" (
+    echo Python version is set to 3.14 or 3.14t
+    set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0rc1-amd64.exe"
 ) else if "%DESIRED_PYTHON%"=="3.14t" (
     echo Python version is set to 3.14 or 3.14t
-    set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0-amd64.exe"
+    set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0rc1-amd64.exe"
     set ADDITIONAL_OPTIONS="Include_freethreaded=1"
     set PYTHON_EXEC="python3.14t"
 ) else (
@@ -25,5 +28,5 @@ start /wait "" python-amd64.exe /quiet InstallAllUsers=1 PrependPath=0 Include_t
 if errorlevel 1 exit /b 1
 
 set "PATH=%CD%\Python\Scripts;%CD%\Python;%PATH%"
-%PYTHON_EXEC% -m pip install --upgrade pip setuptools packaging wheel build
+%PYTHON_EXEC% -m pip install --upgrade pip setuptools packaging wheel
 if errorlevel 1 exit /b 1

.ci/pytorch/windows/internal/setup.bat

@@ -86,7 +86,7 @@ copy /Y "%LIBTORCH_PREFIX%-%PYTORCH_BUILD_VERSION%.zip" "%PYTORCH_FINAL_PACKAGE_
 goto build_end
 
 :pytorch
-%PYTHON_EXEC% -m build --wheel --no-isolation --outdir "%PYTORCH_FINAL_PACKAGE_DIR%"
+%PYTHON_EXEC% setup.py bdist_wheel -d "%PYTORCH_FINAL_PACKAGE_DIR%"
 
 :build_end
 IF ERRORLEVEL 1 exit /b 1

.ci/pytorch/windows/internal/static_lib_test.bat

@@ -63,7 +63,7 @@ if errorlevel 1 exit /b 1
 call %CONDA_HOME%\condabin\activate.bat testenv
 if errorlevel 1 exit /b 1
 
-call conda install  -y -q -c conda-forge libuv=1.51
+call conda install  -y -q -c conda-forge libuv=1.39
 call conda install -y -q intel-openmp
 
 echo "install and test libtorch"

.ci/pytorch/windows/internal/xpu_install.bat

@@ -13,9 +13,9 @@ if not exist "%SRC_DIR%\temp_build" mkdir "%SRC_DIR%\temp_build"
 :xpu_bundle_install_start
 
 set XPU_BUNDLE_PARENT_DIR=C:\Program Files (x86)\Intel\oneAPI
-set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/75d4eb97-914a-4a95-852c-7b9733d80f74/intel-deep-learning-essentials-2025.1.3.8_offline.exe
+set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d6d6c17-ca2d-4735-9331-99447e4a1280/intel-deep-learning-essentials-2025.0.1.28_offline.exe
 set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.deep-learning-essentials.product
-set XPU_BUNDLE_VERSION=2025.1.3+5
+set XPU_BUNDLE_VERSION=2025.0.1+20
 set XPU_BUNDLE_INSTALLED=0
 set XPU_BUNDLE_UNINSTALL=0
 set XPU_EXTRA_URL=NULL
@@ -24,9 +24,9 @@ set XPU_EXTRA_VERSION=2025.0.1+1226
 set XPU_EXTRA_INSTALLED=0
 set XPU_EXTRA_UNINSTALL=0
 
-if not [%XPU_VERSION%]==[] if [%XPU_VERSION%]==[2025.2] (
-    set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/24751ead-ddc5-4479-b9e6-f9fe2ff8b9f2/intel-deep-learning-essentials-2025.2.1.25_offline.exe
-    set XPU_BUNDLE_VERSION=2025.2.1+20
+if not [%XPU_VERSION%]==[] if [%XPU_VERSION%]==[2025.1] (
+    set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/75d4eb97-914a-4a95-852c-7b9733d80f74/intel-deep-learning-essentials-2025.1.3.8_offline.exe
+    set XPU_BUNDLE_VERSION=2025.1.3+5
 )
 
 :: Check if XPU bundle is target version or already installed
@@ -90,3 +90,14 @@ if errorlevel 1 exit /b 1
 del xpu_extra.exe
 
 :xpu_install_end
+
+if not "%XPU_ENABLE_KINETO%"=="1" goto install_end
+:: Install Level Zero SDK
+set XPU_EXTRA_LZ_URL=https://github.com/oneapi-src/level-zero/releases/download/v1.14.0/level-zero-sdk_1.14.0.zip
+curl -k -L %XPU_EXTRA_LZ_URL% --output "%SRC_DIR%\temp_build\level_zero_sdk.zip"
+echo "Installing level zero SDK..."
+7z x "%SRC_DIR%\temp_build\level_zero_sdk.zip" -o"%SRC_DIR%\temp_build\level_zero"
+set "INCLUDE=%SRC_DIR%\temp_build\level_zero\include;%INCLUDE%"
+del "%SRC_DIR%\temp_build\level_zero_sdk.zip"
+
+:install_end

.ci/pytorch/windows/setup_build.bat

@@ -18,7 +18,7 @@ if "%DESIRED_PYTHON%" == "3.9" %PYTHON_EXEC% -m pip install numpy==2.0.2 cmake
 
 %PYTHON_EXEC% -m pip install pyyaml
 %PYTHON_EXEC% -m pip install mkl-include mkl-static
-%PYTHON_EXEC% -m pip install boto3 requests ninja typing_extensions setuptools==72.1.0
+%PYTHON_EXEC% -m pip install boto3 ninja typing_extensions setuptools==72.1.0
 
 where cmake.exe
 

.ci/wheel/build_wheel.sh

@@ -85,7 +85,7 @@ mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
 # Create an isolated directory to store this builds pytorch checkout and conda
 # installation
 if [[ -z "$MAC_PACKAGE_WORK_DIR" ]]; then
-    MAC_PACKAGE_WORK_DIR="$(pwd)/tmp_wheel_${DESIRED_PYTHON}_$(date +%H%M%S)"
+    MAC_PACKAGE_WORK_DIR="$(pwd)/tmp_wheel_conda_${DESIRED_PYTHON}_$(date +%H%M%S)"
 fi
 mkdir -p "$MAC_PACKAGE_WORK_DIR" || true
 if [[ -n ${GITHUB_ACTIONS} ]]; then
@@ -96,11 +96,11 @@ fi
 whl_tmp_dir="${MAC_PACKAGE_WORK_DIR}/dist"
 mkdir -p "$whl_tmp_dir"
 
-mac_version='macosx-11_0-arm64'
+mac_version='macosx_11_0_arm64'
 libtorch_arch='arm64'
 
 # Create a consistent wheel package name to rename the wheel to
-wheel_filename_new="${TORCH_PACKAGE_NAME}-${build_version}${build_number_prefix}-cp${python_nodot}-none-${mac_version//[-,]/_}.whl"
+wheel_filename_new="${TORCH_PACKAGE_NAME}-${build_version}${build_number_prefix}-cp${python_nodot}-none-${mac_version}.whl"
 
 ###########################################################
 
@@ -124,58 +124,93 @@ popd
 
 export TH_BINARY_BUILD=1
 export INSTALL_TEST=0 # dont install test binaries into site-packages
-export MACOSX_DEPLOYMENT_TARGET=11.0
+export MACOSX_DEPLOYMENT_TARGET=10.15
+export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
 
+SETUPTOOLS_PINNED_VERSION="==70.1.0"
+PYYAML_PINNED_VERSION="==5.3"
 EXTRA_CONDA_INSTALL_FLAGS=""
 CONDA_ENV_CREATE_FLAGS=""
 RENAME_WHEEL=true
 case $desired_python in
     3.14t)
         echo "Using 3.14 deps"
-        mac_version='macosx-11.0-arm64'
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=6.0.1"
         NUMPY_PINNED_VERSION="==2.1.0"
+        CONDA_ENV_CREATE_FLAGS="python-freethreading"
+        EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
+        desired_python="3.14.0rc1"
         RENAME_WHEEL=false
         ;;
     3.14)
         echo "Using 3.14t deps"
-        mac_version='macosx-11.0-arm64'
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=6.0.1"
         NUMPY_PINNED_VERSION="==2.1.0"
+        EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
+        desired_python="3.14.0rc1"
         RENAME_WHEEL=false
         ;;
     3.13t)
-        echo "Using 3.13t deps"
-        mac_version='macosx-11.0-arm64'
+        echo "Using 3.13 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=6.0.1"
         NUMPY_PINNED_VERSION="==2.1.0"
+        CONDA_ENV_CREATE_FLAGS="python-freethreading"
+        EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge"
+        desired_python="3.13"
         RENAME_WHEEL=false
         ;;
     3.13)
         echo "Using 3.13 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=6.0.1"
         NUMPY_PINNED_VERSION="==2.1.0"
         ;;
     3.12)
         echo "Using 3.12 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=6.0.1"
         NUMPY_PINNED_VERSION="==2.0.2"
         ;;
     3.11)
         echo "Using 3.11 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=5.3"
         NUMPY_PINNED_VERSION="==2.0.2"
         ;;
     3.10)
         echo "Using 3.10 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=5.3"
+        NUMPY_PINNED_VERSION="==2.0.2"
+        ;;
+    3.9)
+        echo "Using 3.9 deps"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=5.3"
         NUMPY_PINNED_VERSION="==2.0.2"
         ;;
     *)
-        echo "Unsupported version $desired_python"
-        exit 1
+        echo "Using default deps"
+        NUMPY_PINNED_VERSION="==1.11.3"
         ;;
 esac
 
+# Install into a fresh env
+tmp_env_name="wheel_py$python_nodot"
+conda create ${EXTRA_CONDA_INSTALL_FLAGS} -yn "$tmp_env_name" python="$desired_python" ${CONDA_ENV_CREATE_FLAGS}
+source activate "$tmp_env_name"
+
 PINNED_PACKAGES=(
+    "setuptools${SETUPTOOLS_PINNED_VERSION}"
+    "pyyaml${PYYAML_PINNED_VERSION}"
     "numpy${NUMPY_PINNED_VERSION}"
 )
-python -mvenv ~/${desired_python}-build
-source ~/${desired_python}-build/bin/activate
-retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements.txt"
+retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements-build.txt"
+pip install requests ninja typing-extensions
+retry pip install -r "${pytorch_rootdir}/requirements.txt" || true
 retry brew install libomp
 
 # For USE_DISTRIBUTED=1 on macOS, need libuv, which is build as part of tensorpipe submodule
@@ -186,11 +221,11 @@ export USE_QNNPACK=OFF
 export BUILD_TEST=OFF
 
 pushd "$pytorch_rootdir"
-echo "Calling -m build --wheel --no-isolation at $(date)"
+echo "Calling setup.py bdist_wheel at $(date)"
 
-_PYTHON_HOST_PLATFORM=${mac_version} ARCHFLAGS="-arch arm64" python -m build --wheel --no-isolation --outdir "$whl_tmp_dir" -C--plat-name="${mac_version//[-.]/_}"
+python setup.py bdist_wheel -d "$whl_tmp_dir"
 
-echo "Finished -m build --wheel --no-isolation at $(date)"
+echo "Finished setup.py bdist_wheel at $(date)"
 
 if [[ $package_type != 'libtorch' ]]; then
     echo "delocating wheel dependencies"

.circleci/scripts/binary_populate_env.sh

@@ -71,7 +71,14 @@ export PYTORCH_BUILD_NUMBER=1
 
 # Set triton version as part of PYTORCH_EXTRA_INSTALL_REQUIREMENTS
 TRITON_VERSION=$(cat $PYTORCH_ROOT/.ci/docker/triton_version.txt)
-TRITON_CONSTRAINT="platform_system == 'Linux'"
+
+# Here PYTORCH_EXTRA_INSTALL_REQUIREMENTS is already set for the all the wheel builds hence append TRITON_CONSTRAINT
+TRITON_CONSTRAINT="platform_system == 'Linux' and platform_machine == 'x86_64'"
+
+# CUDA 12.9 builds have triton for Linux and Linux aarch64 binaries.
+if [[ "$DESIRED_CUDA" == "cu129" ]]; then
+  TRITON_CONSTRAINT="platform_system == 'Linux'"
+fi
 
 if [[ "$PACKAGE_TYPE" =~ .*wheel.* &&  -n "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" && ! "$PYTORCH_BUILD_VERSION" =~ .*xpu.* ]]; then
   TRITON_REQUIREMENT="triton==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"
@@ -163,13 +170,8 @@ if [[ "$(uname)" != Darwin ]]; then
   MEMORY_LIMIT_MAX_JOBS=12
   NUM_CPUS=$(( $(nproc) - 2 ))
 
-  if [[ "$(uname)" == Linux ]]; then
-    # Defaults here for **binary** linux builds so they can be changed in one place
-    export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
-  else
-    # For other builds
-    export MAX_JOBS=${NUM_CPUS}
-  fi
+  # Defaults here for **binary** linux builds so they can be changed in one place
+  export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
 
   cat >>"$envfile" <<EOL
   export MAX_JOBS="${MAX_JOBS}"

.circleci/scripts/binary_windows_build.sh

@@ -15,7 +15,8 @@ fi
 if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
     export VC_YEAR=2022
     export USE_SCCACHE=0
-    export XPU_VERSION=2025.2
+    export XPU_VERSION=2025.1
+    export XPU_ENABLE_KINETO=1
 fi
 
 echo "Free space on filesystem before build:"

.circleci/scripts/binary_windows_test.sh

@@ -8,7 +8,7 @@ export VC_YEAR=2022
 
 if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
     export VC_YEAR=2022
-    export XPU_VERSION=2025.2
+    export XPU_VERSION=2025.1
 fi
 
 pushd "$PYTORCH_ROOT/.ci/pytorch/"

.circleci/scripts/functorch_doc_push_script.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+# =================== The following code **should** be executed inside Docker container ===================
+
+# Install dependencies
+sudo apt-get -y update
+sudo apt-get -y install expect-dev
+
+# This is where the local pytorch install in the docker image is located
+pt_checkout="/var/lib/jenkins/workspace"
+source "$pt_checkout/.ci/pytorch/common_utils.sh"
+echo "functorch_doc_push_script.sh: Invoked with $*"
+
+set -ex
+
+version=${DOCS_VERSION:-nightly}
+echo "version: $version"
+
+# Build functorch docs
+pushd $pt_checkout/functorch/docs
+pip -q install -r requirements.txt
+make html
+popd
+
+git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
+pushd functorch_ghpages
+
+if [ $version == "main" ]; then
+  version=nightly
+fi
+
+git rm -rf "$version" || true
+mv "$pt_checkout/functorch/docs/build/html" "$version"
+
+git add "$version" || true
+git status
+git config user.email "soumith+bot@pytorch.org"
+git config user.name "pytorchbot"
+# If there aren't changes, don't make a commit; push is no-op
+git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
+git status
+
+if [[ "${WITH_PUSH:-}" == true ]]; then
+  git push -u origin gh-pages
+fi
+
+popd
+# =================== The above code **should** be executed inside Docker container ===================

.clang-tidy

@@ -59,21 +59,16 @@ performance-*,
 -performance-enum-size,
 readability-container-size-empty,
 readability-delete-null-pointer,
-readability-duplicate-include,
-readability-named-parameter,
+readability-duplicate-include
 readability-misplaced-array-index,
-readability-redundant*,
+readability-redundant*
 readability-simplify-subscript-expr,
-readability-static-definition-in-anonymous-namespace
 readability-string-compare,
 -readability-redundant-access-specifiers,
 -readability-redundant-control-flow,
--readability-redundant-inline-specifier,
 '
 HeaderFilterRegex: '^(aten/|c10/|torch/).*$'
 WarningsAsErrors: '*'
-LineFilter:
-  - name: '/usr/include/.*'
 CheckOptions:
   cppcoreguidelines-special-member-functions.AllowSoleDefaultDtor: true
   cppcoreguidelines-special-member-functions.AllowImplicitlyDeletedCopyOrMove: true

.claude/skills/add-uint-support/SKILL.md

@@ -1,319 +0,0 @@
----
-name: add-uint-support
-description: Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
----
-
-# Add Unsigned Integer (uint) Support to Operators
-
-This skill helps add support for unsigned integer types (uint16, uint32, uint64) to PyTorch operators by updating their AT_DISPATCH macros.
-
-## When to use this skill
-
-Use this skill when:
-- Adding uint16, uint32, or uint64 support to an operator
-- User mentions "unsigned types", "uint support", "barebones unsigned types"
-- Enabling support for kUInt16, kUInt32, kUInt64 in kernels
-- Working with operator implementations that need expanded type coverage
-
-## Quick reference
-
-**Add unsigned types to existing dispatch:**
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES));
-
-// After (method 1: add unsigned types explicitly)
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES));
-
-// After (method 2: use V2 integral types if AT_INTEGRAL_TYPES present)
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
-```
-
-## Type group reference
-
-**Unsigned type groups:**
-- `AT_BAREBONES_UNSIGNED_TYPES`: kUInt16, kUInt32, kUInt64
-- `AT_INTEGRAL_TYPES_V2`: AT_INTEGRAL_TYPES + AT_BAREBONES_UNSIGNED_TYPES
-
-**Relationship:**
-```cpp
-AT_INTEGRAL_TYPES          // kByte, kChar, kInt, kLong, kShort
-AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
-AT_INTEGRAL_TYPES_V2       // INTEGRAL_TYPES + BAREBONES_UNSIGNED_TYPES
-```
-
-## Instructions
-
-### Step 1: Determine if conversion to V2 is needed
-
-Check if the file uses AT_DISPATCH_V2:
-
-**If using old AT_DISPATCH:**
-- First convert to AT_DISPATCH_V2 using the at-dispatch-v2 skill
-- Then proceed with adding uint support
-
-**If already using AT_DISPATCH_V2:**
-- Proceed directly to Step 2
-
-### Step 2: Analyze the current dispatch macro
-
-Identify what type groups are currently in use:
-
-```cpp
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  // body
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-    ^^^^^^^^^^^^^^^^^^^^^^^^^
-    Current type coverage
-```
-
-Common patterns:
-- `AT_EXPAND(AT_ALL_TYPES)` → includes AT_INTEGRAL_TYPES + AT_FLOATING_TYPES
-- `AT_EXPAND(AT_INTEGRAL_TYPES)` → signed integers only
-- `AT_EXPAND(AT_FLOATING_TYPES)` → floating point types
-
-### Step 3: Choose the uint addition method
-
-Two approaches:
-
-**Method 1: Add AT_BAREBONES_UNSIGNED_TYPES explicitly**
-- Use when: You want to be explicit about adding uint support
-- Add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the type list
-
-**Method 2: Substitute AT_INTEGRAL_TYPES with AT_INTEGRAL_TYPES_V2**
-- Use when: The dispatch already uses `AT_EXPAND(AT_INTEGRAL_TYPES)`
-- More concise: replaces one type group with its superset
-- Only applicable if AT_INTEGRAL_TYPES is present
-
-### Step 4: Apply the transformation
-
-**Method 1 example:**
-```cpp
-// Before
-AT_DISPATCH_V2(
-    dtype,
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      kernel_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    kBFloat16, kHalf, kBool
-);
-
-// After (add unsigned types)
-AT_DISPATCH_V2(
-    dtype,
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      kernel_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
-    kBFloat16, kHalf, kBool
-);
-```
-
-**Method 2 example:**
-```cpp
-// Before
-AT_DISPATCH_V2(
-    dtype,
-    "integral_op",
-    AT_WRAP([&]() {
-      kernel<scalar_t>();
-    }),
-    AT_EXPAND(AT_INTEGRAL_TYPES)
-);
-
-// After (substitute with V2)
-AT_DISPATCH_V2(
-    dtype,
-    "integral_op",
-    AT_WRAP([&]() {
-      kernel<scalar_t>();
-    }),
-    AT_EXPAND(AT_INTEGRAL_TYPES_V2)
-);
-```
-
-### Step 5: Handle AT_ALL_TYPES vs individual type groups
-
-If the dispatch uses `AT_EXPAND(AT_ALL_TYPES)`:
-- `AT_ALL_TYPES` = `AT_INTEGRAL_TYPES` + `AT_FLOATING_TYPES`
-- To add uint: add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the list
-
-If the dispatch separately lists INTEGRAL and FLOATING:
-```cpp
-// Before
-AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
-
-// After (Method 2 preferred)
-AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES)
-```
-
-### Step 6: Verify all dispatch sites
-
-Check the file for ALL dispatch macros that need uint support:
-- Some operators have multiple dispatch sites (CPU, CUDA, different functions)
-- Apply the transformation consistently across all sites
-- Ensure each gets the same type coverage updates
-
-### Step 7: Validate the changes
-
-Check that:
-- [ ] AT_DISPATCH_V2 format is used (not old AT_DISPATCH)
-- [ ] Unsigned types are added via one of the two methods
-- [ ] All relevant dispatch sites in the file are updated
-- [ ] Type groups use `AT_EXPAND()`
-- [ ] Arguments are properly formatted and comma-separated
-
-## Common patterns
-
-### Pattern 1: AT_ALL_TYPES + extras
-
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
-```
-
-### Pattern 2: Separate INTEGRAL + FLOATING
-
-```cpp
-// Before
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
-```
-
-### Pattern 3: Old dispatch needs conversion first
-
-```cpp
-// Before (needs v2 conversion first)
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
-  kernel<scalar_t>();
-});
-
-// After v2 conversion
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-
-// After adding uint support
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
-```
-
-## Multiple dispatch sites example
-
-For a file with multiple functions:
-
-```cpp
-void min_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
-    impl<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
-  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  //                           Added uint support
-}
-
-void min_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
-    gpu_reduce_kernel<scalar_t>(iter);
-  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
-  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  //                           Added uint support here too
-}
-```
-
-## Decision tree
-
-Use this decision tree to determine the approach:
-
-```
-Is the file using AT_DISPATCH_V2?
-├─ No → Use at-dispatch-v2 skill first, then continue
-└─ Yes
-   └─ Does it use AT_EXPAND(AT_INTEGRAL_TYPES)?
-      ├─ Yes → Replace with AT_EXPAND(AT_INTEGRAL_TYPES_V2)
-      └─ No → Add AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to type list
-```
-
-## Edge cases
-
-### Case 1: Dispatch with only floating types
-
-If the operator only supports floating point types, don't add uint support:
-
-```cpp
-// Leave as-is - floating point only operator
-AT_DISPATCH_V2(dtype, "float_op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf);
-```
-
-### Case 2: Complex types present
-
-Unsigned types work alongside complex types:
-
-```cpp
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
-    AT_EXPAND(AT_COMPLEX_TYPES),
-    kHalf, kBFloat16);
-```
-
-### Case 3: Already has uint support
-
-Check if uint types are already present:
-- If `AT_INTEGRAL_TYPES_V2` is used → already has uint support
-- If `AT_BAREBONES_UNSIGNED_TYPES` is already in list → already has uint support
-- Skip the file if uint support is already present
-
-## Workflow
-
-When asked to add uint support:
-
-1. Read the target file
-2. Check if using AT_DISPATCH_V2:
-   - If not → use at-dispatch-v2 skill first
-3. Identify all dispatch macro sites
-4. For each dispatch:
-   - Analyze current type groups
-   - Choose method (add BAREBONES_UNSIGNED or upgrade to V2)
-   - Apply transformation with Edit tool
-5. Show the user the changes
-6. Explain what was modified
-
-## Important notes
-
-- Always check if v2 conversion is needed first
-- Apply changes consistently across all dispatch sites in the file
-- Method 2 (AT_INTEGRAL_TYPES_V2) is cleaner when applicable
-- Method 1 (explicit AT_BAREBONES_UNSIGNED_TYPES) is more explicit
-- Unsigned types are: kUInt16, kUInt32, kUInt64 (not kByte which is uint8)
-- Some operators may not semantically support unsigned types - use judgment
-
-## Testing
-
-After adding uint support, the operator should accept uint16, uint32, and uint64 tensors. The user is responsible for functional testing.

.claude/skills/at-dispatch-v2/SKILL.md

@@ -1,305 +0,0 @@
----
-name: at-dispatch-v2
-description: Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
----
-
-# AT_DISPATCH to AT_DISPATCH_V2 Converter
-
-This skill helps convert PyTorch's legacy AT_DISPATCH macros to the new AT_DISPATCH_V2 format, as defined in `aten/src/ATen/Dispatch_v2.h`.
-
-## When to use this skill
-
-Use this skill when:
-- Converting AT_DISPATCH_* macros to AT_DISPATCH_V2
-- Porting ATen kernels to use the new dispatch API
-- Working with files in `aten/src/ATen/native/` that use dispatch macros
-- User mentions "AT_DISPATCH", "dispatch v2", "Dispatch_v2.h", or macro conversion
-
-## Quick reference
-
-**Old format:**
-```cpp
-AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, dtype, "kernel_name", [&]() {
-  // lambda body
-});
-```
-
-**New format:**
-```cpp
-AT_DISPATCH_V2(dtype, "kernel_name", AT_WRAP([&]() {
-  // lambda body
-}), AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool);
-```
-
-## Key transformations
-
-1. **Reorder arguments**: `scalar_type` and `name` come first, then lambda, then types
-2. **Wrap the lambda**: Use `AT_WRAP(lambda)` to handle internal commas
-3. **Expand type groups**: Use `AT_EXPAND(AT_ALL_TYPES)` instead of implicit expansion
-4. **List individual types**: Add extra types (kHalf, kBFloat16, etc.) after expanded groups
-5. **Add include**: `#include <ATen/Dispatch_v2.h>` near other Dispatch includes
-
-## Instructions
-
-### Step 1: Add the Dispatch_v2.h include
-
-Add the v2 header near the existing `#include <ATen/Dispatch.h>`:
-
-```cpp
-#include <ATen/Dispatch.h>
-#include <ATen/Dispatch_v2.h>
-```
-
-Keep the old Dispatch.h include for now (other code may still need it).
-
-### Step 2: Identify the old dispatch pattern
-
-Common patterns to convert:
-
-- `AT_DISPATCH_ALL_TYPES_AND{2,3,4}(type1, type2, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_FLOATING_TYPES_AND{2,3}(type1, type2, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND{2,3}(type1, ..., scalar_type, name, lambda)`
-- `AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND{2,3}(type1, ..., scalar_type, name, lambda)`
-
-### Step 3: Map the old macro to type groups
-
-Identify which type group macro corresponds to the base types:
-
-| Old macro base | AT_DISPATCH_V2 type group |
-|----------------|---------------------------|
-| `ALL_TYPES` | `AT_EXPAND(AT_ALL_TYPES)` |
-| `FLOATING_TYPES` | `AT_EXPAND(AT_FLOATING_TYPES)` |
-| `INTEGRAL_TYPES` | `AT_EXPAND(AT_INTEGRAL_TYPES)` |
-| `COMPLEX_TYPES` | `AT_EXPAND(AT_COMPLEX_TYPES)` |
-| `ALL_TYPES_AND_COMPLEX` | `AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX)` |
-
-For combined patterns, use multiple `AT_EXPAND()` entries:
-```cpp
-// Old: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(...)
-// New: AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES), type1, type2
-```
-
-### Step 4: Extract the individual types
-
-From `AT_DISPATCH_*_AND2(type1, type2, ...)` or `AT_DISPATCH_*_AND3(type1, type2, type3, ...)`, extract the individual types (type1, type2, etc.).
-
-These become the trailing arguments after the type group:
-```cpp
-AT_DISPATCH_V2(..., AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool)
-                                             ^^^^^^^^^^^^^^^^^^^^^^^^
-                                             Individual types from AND3
-```
-
-### Step 5: Transform to AT_DISPATCH_V2
-
-Apply the transformation:
-
-**Pattern:**
-```cpp
-AT_DISPATCH_V2(
-  scalar_type,           // 1st: The dtype expression
-  "name",                // 2nd: The debug string
-  AT_WRAP(lambda),       // 3rd: The lambda wrapped in AT_WRAP
-  type_groups,           // 4th+: Type groups with AT_EXPAND()
-  individual_types       // Last: Individual types
-)
-```
-
-**Example transformation:**
-```cpp
-// BEFORE
-AT_DISPATCH_ALL_TYPES_AND3(
-    kBFloat16, kHalf, kBool,
-    iter.dtype(),
-    "min_values_cuda",
-    [&]() {
-      min_values_kernel_cuda_impl<scalar_t>(iter);
-    }
-);
-
-// AFTER
-AT_DISPATCH_V2(
-    iter.dtype(),
-    "min_values_cuda",
-    AT_WRAP([&]() {
-      min_values_kernel_cuda_impl<scalar_t>(iter);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    kBFloat16, kHalf, kBool
-);
-```
-
-### Step 6: Handle multi-line lambdas
-
-For lambdas with internal commas or complex expressions, AT_WRAP is essential:
-
-```cpp
-AT_DISPATCH_V2(
-    dtype,
-    "complex_kernel",
-    AT_WRAP([&]() {
-      gpu_reduce_kernel<scalar_t, scalar_t>(
-        iter,
-        MinOps<scalar_t>{},
-        thrust::pair<scalar_t, int64_t>(upper_bound(), 0)  // Commas inside!
-      );
-    }),
-    AT_EXPAND(AT_ALL_TYPES)
-);
-```
-
-### Step 7: Verify the conversion
-
-Check that:
-- [ ] `AT_WRAP()` wraps the entire lambda
-- [ ] Type groups use `AT_EXPAND()`
-- [ ] Individual types don't have `AT_EXPAND()` (just `kBFloat16`, not `AT_EXPAND(kBFloat16)`)
-- [ ] Argument order is: scalar_type, name, lambda, types
-- [ ] Include added: `#include <ATen/Dispatch_v2.h>`
-
-## Type group reference
-
-Available type group macros (use with `AT_EXPAND()`):
-
-```cpp
-AT_INTEGRAL_TYPES      // kByte, kChar, kInt, kLong, kShort
-AT_FLOATING_TYPES      // kDouble, kFloat
-AT_COMPLEX_TYPES       // kComplexDouble, kComplexFloat
-AT_QINT_TYPES         // kQInt8, kQUInt8, kQInt32
-AT_ALL_TYPES          // INTEGRAL_TYPES + FLOATING_TYPES
-AT_ALL_TYPES_AND_COMPLEX  // ALL_TYPES + COMPLEX_TYPES
-AT_INTEGRAL_TYPES_V2  // INTEGRAL_TYPES + unsigned types
-AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
-AT_FLOAT8_TYPES       // Float8 variants
-```
-
-## Common patterns
-
-### Pattern: AT_DISPATCH_ALL_TYPES_AND2
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
-  kernel<scalar_t>(data);
-});
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>(data);
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
-```
-
-### Pattern: AT_DISPATCH_FLOATING_TYPES_AND3
-
-```cpp
-// Before
-AT_DISPATCH_FLOATING_TYPES_AND3(kHalf, kBFloat16, kFloat8_e4m3fn,
-    tensor.scalar_type(), "float_op", [&] {
-  process<scalar_t>(tensor);
-});
-
-// After
-AT_DISPATCH_V2(tensor.scalar_type(), "float_op", AT_WRAP([&] {
-  process<scalar_t>(tensor);
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn);
-```
-
-### Pattern: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
-    kComplexHalf, kHalf,
-    self.scalar_type(),
-    "complex_op",
-    [&] {
-      result = compute<scalar_t>(self);
-    }
-);
-
-// After
-AT_DISPATCH_V2(
-    self.scalar_type(),
-    "complex_op",
-    AT_WRAP([&] {
-      result = compute<scalar_t>(self);
-    }),
-    AT_EXPAND(AT_ALL_TYPES),
-    AT_EXPAND(AT_COMPLEX_TYPES),
-    kComplexHalf,
-    kHalf
-);
-```
-
-## Edge cases
-
-### Case 1: No extra types (rare)
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES(dtype, "op", [&]() { kernel<scalar_t>(); });
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES));
-```
-
-### Case 2: Many individual types (AND4, AND5, etc.)
-
-```cpp
-// Before
-AT_DISPATCH_FLOATING_TYPES_AND4(kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2,
-    dtype, "float8_op", [&]() { kernel<scalar_t>(); });
-
-// After
-AT_DISPATCH_V2(dtype, "float8_op", AT_WRAP([&]() {
-  kernel<scalar_t>();
-}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2);
-```
-
-### Case 3: Lambda with no captures
-
-```cpp
-// Before
-AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBool, dtype, "op", []() {
-  static_kernel<scalar_t>();
-});
-
-// After
-AT_DISPATCH_V2(dtype, "op", AT_WRAP([]() {
-  static_kernel<scalar_t>();
-}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBool);
-```
-
-## Benefits of AT_DISPATCH_V2
-
-1. **No arity in macro name**: Don't need different macros for AND2, AND3, AND4
-2. **Composable type sets**: Mix and match type groups with `AT_EXPAND()`
-3. **Extensible**: Easy to add more types without hitting macro limits
-4. **Clearer**: Type groups are explicit, not implicit in macro name
-
-## Important notes
-
-- Keep `#include <ATen/Dispatch.h>` - other code may need it
-- The `AT_WRAP()` is mandatory - prevents comma parsing issues in the lambda
-- Type groups need `AT_EXPAND()`, individual types don't
-- The v2 API is in `aten/src/ATen/Dispatch_v2.h` - refer to it for full docs
-- See the header file for the Python script to regenerate the macro implementation
-
-## Workflow
-
-When asked to convert AT_DISPATCH macros:
-
-1. Read the file to identify all AT_DISPATCH uses
-2. Add `#include <ATen/Dispatch_v2.h>` if not present
-3. For each dispatch macro:
-   - Identify the pattern and extract components
-   - Map the base type group
-   - Extract individual types
-   - Construct the AT_DISPATCH_V2 call
-   - Apply with Edit tool
-4. Show the user the complete converted file
-5. Explain what was changed
-
-Do NOT compile or test the code - focus on accurate conversion only.