Ci with mkdlnn op back

.bc-linter.yml

@@ -1,15 +0,0 @@
-version: 1
-paths:
-include:
-  - "**/*.py"
-exclude:
-  - ".*"
-  - ".*/**"
-  - "**/.*/**"
-  - "**/.*"
-  - "**/_*/**"
-  - "**/_*.py"
-  - "**/test/**"
-  - "**/benchmarks/**"
-  - "**/test_*.py"
-  - "**/*_test.py"

.ci/aarch64_linux/aarch64_ci_build.sh

@@ -3,20 +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" == *"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 "$0")" >/dev/null 2>&1 ; pwd -P )"
@@ -30,22 +18,14 @@ cd /
 # on the mounted pytorch repo
 git config --global --add safe.directory /pytorch
 pip install -r /pytorch/requirements.txt
-pip install auditwheel==6.2.0 wheel
+pip install auditwheel==6.2.0
 if [ "$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/README.md

@@ -120,8 +120,8 @@ If your new Docker image needs a library installed from a specific pinned commit
    If you're introducing a new argument to the Docker build, make sure to add it in the Docker build step in `.ci/docker/build.sh`:
    ```bash
    docker build \
-     ....
-     --build-arg "NEW_ARG_1=${NEW_ARG_1}"
+      ....
+      --build-arg "NEW_ARG_1=${NEW_ARG_1}"
    ```
 
 3. **Update Dockerfile logic**:

.ci/docker/almalinux/Dockerfile

@@ -69,8 +69,7 @@ RUN bash ./install_cuda.sh 13.0
 ENV DESIRED_CUDA=13.0
 
 FROM ${ROCM_IMAGE} as rocm
-ARG PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
+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

.ci/docker/almalinux/build.sh

@@ -36,12 +36,6 @@ case ${DOCKER_TAG_PREFIX} in
     ;;
   rocm*)
     BASE_TARGET=rocm
-    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-    # add gfx950 conditionally starting in ROCm 7.0
-    if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
-        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
-    fi
-    EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;
   *)
     echo "ERROR: Unknown docker tag ${DOCKER_TAG_PREFIX}"

.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 $2}')
@@ -114,19 +114,31 @@ case "$tag" in
     UCC_COMMIT=${_UCC_COMMIT}
     TRITON=yes
     ;;
-  pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11)
-    CUDA_VERSION=13.0.0
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks)
+    CUDA_VERSION=12.8.1
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
+    GCC_VERSION=9
     VISION=yes
     KATEX=yes
     UCX_COMMIT=${_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=${_UCC_COMMIT}
+    TRITON=yes
+    INDUCTOR_BENCHMARKS=yes
+    ;;
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.13-gcc9-inductor-benchmarks)
+    CUDA_VERSION=12.8.1
+    ANACONDA_PYTHON_VERSION=3.13
     GCC_VERSION=9
     VISION=yes
     KATEX=yes
@@ -161,8 +173,8 @@ case "$tag" in
     VISION=yes
     ONNX=yes
     ;;
-  pytorch-linux-jammy-py3.10-clang12)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-py3.9-clang12)
+    ANACONDA_PYTHON_VERSION=3.9
     CLANG_VERSION=12
     VISION=yes
     TRITON=yes
@@ -175,6 +187,20 @@ 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 [[ $tag =~ "benchmarks" ]]; then
+      INDUCTOR_BENCHMARKS=yes
+    fi
+    ;;
+  pytorch-linux-noble-rocm-alpha-py3)
+    ANACONDA_PYTHON_VERSION=3.12
+    GCC_VERSION=11
+    VISION=yes
     ROCM_VERSION=7.0
     NINJA_VERSION=1.9.0
     TRITON=yes
@@ -182,28 +208,25 @@ case "$tag" in
     UCX_COMMIT=${_UCX_COMMIT}
     UCC_COMMIT=${_UCC_COMMIT}
     PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950"
-    if [[ $tag =~ "benchmarks" ]]; then
-      INDUCTOR_BENCHMARKS=yes
-    fi
     ;;
-  pytorch-linux-jammy-xpu-n-1-py3)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-xpu-2025.0-py3)
+    ANACONDA_PYTHON_VERSION=3.9
+    GCC_VERSION=11
+    VISION=yes
+    XPU_VERSION=2025.0
+    NINJA_VERSION=1.9.0
+    TRITON=yes
+    ;;
+  pytorch-linux-jammy-xpu-2025.1-py3)
+    ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
     VISION=yes
     XPU_VERSION=2025.1
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-jammy-xpu-n-py3)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
-    VISION=yes
-    XPU_VERSION=2025.2
-    NINJA_VERSION=1.9.0
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-gcc11-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
+    ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
     VISION=yes
     KATEX=yes
@@ -211,8 +234,8 @@ case "$tag" in
     DOCS=yes
     INDUCTOR_BENCHMARKS=yes
     ;;
-  pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-clang12)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-clang12)
+    ANACONDA_PYTHON_VERSION=3.9
     CUDA_VERSION=12.8.1
     CLANG_VERSION=12
     VISION=yes
@@ -223,8 +246,8 @@ case "$tag" in
     CLANG_VERSION=18
     VISION=yes
     ;;
-  pytorch-linux-jammy-py3.10-gcc11)
-    ANACONDA_PYTHON_VERSION=3.10
+  pytorch-linux-jammy-py3.9-gcc11)
+    ANACONDA_PYTHON_VERSION=3.9
     GCC_VERSION=11
     VISION=yes
     KATEX=yes
@@ -251,10 +274,13 @@ case "$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)
@@ -441,3 +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 @@
-e0dda9059d082537cee36be6c5e4fe3b18c880c0
+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 @@
-v2.28.3-1
+v2.27.5-1

.ci/docker/ci_commit_pins/nccl-cu13.txt

@@ -1 +1 @@
-v2.28.3-1
+v2.27.7-1

.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
+0958dc9b2bb815e428f721f9da599dab0dc1c5d7

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +1 @@
-27664085f804afc83df26f740bb46c365854f2c4
+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:-"v25.02"}
-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_cpython.sh

@@ -83,9 +83,9 @@ function build_cpython {
         py_suffix=${py_ver::-1}
         py_folder=$py_suffix
     fi
-    # Update to rc2 due to https://github.com/python/cpython/commit/c72699086fe4
+    # Only b3 is available now
     if [ "$py_suffix" == "3.14.0" ]; then
-        py_suffix="3.14.0rc2"
+        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=3.3.24
+NVSHMEM_VERSION=3.3.20
 
 function install_cuda {
   version=$1
@@ -65,7 +65,7 @@ function install_nvshmem {
   # This pattern is a lie as it is not consistent across versions, for 3.3.9 it was cuda_ver-arch-nvshhem-ver
   filename="libnvshmem-linux-${arch_path}-${nvshmem_version}_cuda${cuda_major_version}-archive"
   suffix=".tar.xz"
-  url="https://developer.download.nvidia.com/compute/nvshmem/redist/libnvshmem/linux-${arch_path}/${filename}${suffix}"
+  url="https://developer.download.nvidia.com/compute/redist/nvshmem/${nvshmem_version}/builds/cuda${cuda_major_version}/txz/agnostic/${dl_arch}/${filename}${suffix}"
 
   # download, unpack, install
   wget -q "${url}"
@@ -147,7 +147,8 @@ function install_128 {
 }
 
 function install_130 {
-  CUDNN_VERSION=9.13.0.50
+  CUDNN_VERSION=9.12.0.46
+  NVSHMEM_VERSION=3.3.20
   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 13.0.0 cuda_13.0.0_580.65.06_linux

.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_onnx.sh

@@ -19,8 +19,8 @@ pip_install \
   transformers==4.36.2
 
 pip_install coloredlogs packaging
-pip_install onnxruntime==1.22.1
-pip_install onnxscript==0.4.0
+pip_install onnxruntime==1.18.1
+pip_install onnxscript==0.3.1
 
 # 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

@@ -3,10 +3,8 @@
 
 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="
@@ -19,7 +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 "$1" | tr '.' ' ');
 }
@@ -42,6 +37,12 @@ EOF
     rocm_baseurl="http://repo.radeon.com/rocm/apt/${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
@@ -112,8 +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/*
@@ -177,8 +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//./}
 
-    # https://github.com/icl-utk-edu/magma/pull/65
-    MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
+    # 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/libtorch/Dockerfile

@@ -69,19 +69,6 @@ RUN bash ./install_cuda.sh 12.9
 RUN bash ./install_magma.sh 12.9
 RUN ln -sf /usr/local/cuda-12.9 /usr/local/cuda
 
-FROM cuda as cuda13.0
-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

@@ -40,16 +40,12 @@ case ${DOCKER_TAG_PREFIX} in
         ;;
     rocm*)
         # we want the patch version of 6.4 instead
-        if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
+        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:${GPU_ARCH_VERSION}-complete
         PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950 conditionally starting in ROCm 7.0
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
-        fi
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;
     *)

.ci/docker/manywheel/Dockerfile_2_28

@@ -175,6 +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
@@ -77,5 +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/:$LD_LIBRARY_PATH
+ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:$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-${BASE_CUDA_VERSION}
@@ -102,7 +93,5 @@ COPY --from=cuda     /usr/local/cuda-${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

.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/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
@@ -61,12 +67,6 @@ case ${image} in
         DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
         MANY_LINUX_VERSION="2_28"
         ;;
-    manylinux2_28-builder:cuda13*)
-        TARGET=cuda_final
-        GPU_IMAGE=amd64/almalinux:8
-        DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
-        MANY_LINUX_VERSION="2_28"
-        ;;
     manylinuxaarch64-builder:cuda*)
         TARGET=cuda_final
         GPU_IMAGE=amd64/almalinux:8
@@ -76,7 +76,7 @@ case ${image} in
         ;;
     manylinux2_28-builder:rocm*)
         # we want the patch version of 6.4 instead
-        if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
+        if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
             GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         TARGET=rocm_final
@@ -84,10 +84,6 @@ case ${image} in
         DEVTOOLSET_VERSION="11"
         GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
         PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
-        # add gfx950 conditionally starting in ROCm 7.0
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
-        fi
         DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
         ;;
     manylinux2_28-builder:xpu)
@@ -119,8 +115,7 @@ tmp_tag=$(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/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
@@ -111,12 +105,14 @@ 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
@@ -137,7 +133,7 @@ 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"
 
@@ -169,12 +165,12 @@ optree==0.13.0
 
 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
@@ -217,7 +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:
@@ -267,8 +263,13 @@ scipy==1.14.1 ; python_version >= "3.12"
 #Pinned versions:
 #test that import:
 
+tb-nightly==2.13.0a20230426
+#Description: TensorBoard
+#Pinned versions:
+#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:
@@ -329,6 +330,8 @@ pywavelets==1.7.0 ; python_version >= "3.12"
 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
@@ -341,7 +344,7 @@ onnx==1.18.0
 #Pinned versions:
 #test that import:
 
-onnxscript==0.4.0
+onnxscript==0.3.1
 #Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:
@@ -361,10 +364,9 @@ pwlf==2.2.1
 #test that import: test_sac_estimator.py
 
 # To build PyTorch itself
-pyyaml==6.0.2
+pyyaml
 pyzstd
-setuptools==78.1.1
-packaging==23.1
+setuptools>=70.1.0
 six
 
 scons==4.5.2 ; platform_machine == "aarch64"
@@ -379,16 +381,13 @@ 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 @@
-3.5.0
+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/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 $UCC_COMMIT
 ENV UCX_HOME /usr
@@ -182,6 +181,7 @@ COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
 RUN if [ -n "${SKIP_LLVM_SRC_BUILD_INSTALL}" ]; then set -eu; rm -rf /opt/llvm; fi
 
 # AWS specific CUDA build guidance
+ENV TORCH_CUDA_ARCH_LIST Maxwell
 ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
 ENV CUDA_PATH /usr/local/cuda
 

.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_CUSPARSELT=0 BUILD_PYTHONLESS=1 DESIRED_PYTHON="3.9" ${SCRIPTPATH}/../manywheel/build.sh

.ci/lumen_cli/cli/build_cli/register_build.py

@@ -2,7 +2,7 @@ import argparse
 import logging
 
 from cli.lib.common.cli_helper import register_targets, RichHelp, TargetSpec
-from cli.lib.core.vllm.vllm_build import VllmBuildRunner
+from cli.lib.core.vllm import VllmBuildRunner
 
 
 logger = logging.getLogger(__name__)

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

@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import logging
-import os
-import textwrap
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-from cli.lib.common.utils import get_wheels
-from jinja2 import Template
-
-
-if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping
-
-
-logger = logging.getLogger(__name__)
-
-_TPL_CONTENT = Template(
-    textwrap.dedent("""\
-    ## {{ title }}
-
-    ```{{ lang }}
-    {{ content }}
-    ```
-""")
-)
-
-_TPL_LIST_ITEMS = Template(
-    textwrap.dedent("""\
-    ## {{ title }}
-    {% for it in items %}
-    - {{ it.pkg }}: {{ it.relpath }}
-    {% else %}
-    _(no item found)_
-    {% endfor %}
-    """)
-)
-
-_TPL_TABLE = Template(
-    textwrap.dedent("""\
-    {%- if rows %}
-    | {{ cols | join(' | ') }} |
-    |{%- for _ in cols %} --- |{%- endfor %}
-    {%- for r in rows %}
-    | {%- for c in cols %} {{ r.get(c, "") }} |{%- endfor %}
-    {%- endfor %}
-    {%- else %}
-    _(no data)_
-    {%- endif %}
-""")
-)
-
-
-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 Path(p) if p else None
-
-
-def write_gh_step_summary(md: str, *, append_content: bool = True) -> bool:
-    """
-    Write Markdown content to the GitHub Step Summary file if GITHUB_STEP_SUMMARY is set.
-    append_content: default true, if True, append to the end of the file, else overwrite the whole file
-
-    Returns:
-        True if written successfully (in GitHub Actions environment),
-        False if skipped (e.g., running locally where the variable is not set).
-    """
-    sp = gh_summary_path()
-    if not sp:
-        logger.info("[gh-summary] GITHUB_STEP_SUMMARY not set, skipping write.")
-        return False
-
-    md_clean = textwrap.dedent(md).strip() + "\n"
-
-    mode = "a" if append_content else "w"
-    with sp.open(mode, encoding="utf-8") as f:
-        f.write(md_clean)
-    return True
-
-
-def md_heading(text: str, level: int = 2) -> str:
-    """Generate a Markdown heading string with the given level (1-6)."""
-    return f"{'#' * max(1, min(level, 6))} {text}\n"
-
-
-def md_details(summary: str, content: str) -> str:
-    """Generate a collapsible <details> block with a summary and inner content."""
-    return f"<details>\n<summary>{summary}</summary>\n\n{content}\n\n</details>\n"
-
-
-def summarize_content_from_file(
-    output_dir: Path,
-    freeze_file: str,
-    title: str = "Content from file",
-    code_lang: str = "",  # e.g. "text" or "ini"
-) -> bool:
-    f = Path(output_dir) / freeze_file
-    if not f.exists():
-        return False
-    content = f.read_text(encoding="utf-8").strip()
-    md = render_content(content, title=title, lang=code_lang)
-    return write_gh_step_summary(md)
-
-
-def summarize_wheels(path: Path, title: str = "Wheels", max_depth: int = 3):
-    items = get_wheels(path, max_depth=max_depth)
-    if not items:
-        return False
-    md = render_list(items, title=title)
-    return write_gh_step_summary(md)
-
-
-def md_kv_table(rows: Iterable[Mapping[str, str | int | float]]) -> str:
-    """
-    Render a list of dicts as a Markdown table using Jinja template.
-    """
-    rows = list(rows)
-    cols = list({k for r in rows for k in r.keys()})
-    md = _TPL_TABLE.render(cols=cols, rows=rows).strip() + "\n"
-    return md
-
-
-def render_list(
-    items: Iterable[str],
-    *,
-    title: str = "List",
-) -> str:
-    tpl = _TPL_LIST_ITEMS
-    md = tpl.render(title=title, items=items)
-    return md
-
-
-def render_content(
-    content: str,
-    *,
-    title: str = "Content",
-    lang: str = "text",
-) -> str:
-    tpl = _TPL_CONTENT
-    md = tpl.render(title=title, content=content, lang=lang)
-    return md

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

@@ -45,7 +45,7 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
 
         # Checkout pinned commit
         commit = get_post_build_pinned_commit(target)
-        logger.info("Checking out pinned %s commit %s", target, commit)
+        logger.info("Checking out pinned commit %s", commit)
         r.git.checkout(commit)
 
         # Update submodules if requested
@@ -55,7 +55,7 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
                 sm.update(init=True, recursive=True, progress=PrintProgress())
 
         logger.info("Successfully cloned %s", target)
-        return r, commit
+        return r
 
     except GitCommandError as e:
         logger.error("Git operation failed: %s", e)

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

@@ -1,71 +0,0 @@
-import glob
-import logging
-import shlex
-import shutil
-import sys
-from collections.abc import Iterable
-from importlib.metadata import PackageNotFoundError, version  # noqa: UP035
-from typing import Optional, Union
-
-from cli.lib.common.utils import run_command
-
-
-logger = logging.getLogger(__name__)
-
-
-def pip_install_packages(
-    packages: Iterable[str] = (),
-    env=None,
-    *,
-    requirements: Optional[str] = None,
-    constraints: Optional[str] = None,
-    prefer_uv: bool = False,
-) -> None:
-    use_uv = prefer_uv and shutil.which("uv") is not None
-    base = (
-        [sys.executable, "-m", "uv", "pip", "install"]
-        if use_uv
-        else [sys.executable, "-m", "pip", "install"]
-    )
-    cmd = base[:]
-    if requirements:
-        cmd += ["-r", requirements]
-    if constraints:
-        cmd += ["-c", constraints]
-    cmd += list(packages)
-    logger.info("pip installing packages: %s", " ".join(map(shlex.quote, cmd)))
-    run_command(" ".join(map(shlex.quote, cmd)), env=env)
-
-
-def pip_install_first_match(pattern: str, extras: Optional[str] = None, pref_uv=False):
-    wheel = first_matching_pkg(pattern)
-    target = f"{wheel}[{extras}]" if extras else wheel
-    logger.info("Installing %s...", target)
-    pip_install_packages([target], prefer_uv=pref_uv)
-
-
-def run_python(args: Union[str, list[str]], env=None):
-    """
-    Run the python in the current environment.
-    """
-    if isinstance(args, str):
-        args = shlex.split(args)
-    cmd = [sys.executable] + args
-    run_command(" ".join(map(shlex.quote, cmd)), env=env)
-
-
-def pkg_exists(name: str) -> bool:
-    try:
-        pkg_version = version(name)
-        logger.info("%s already exist with version: %s", name, pkg_version)
-        return True
-    except PackageNotFoundError:
-        logger.info("%s is not installed", name)
-        return False
-
-
-def first_matching_pkg(pattern: str) -> str:
-    matches = sorted(glob.glob(pattern))
-    if not matches:
-        raise FileNotFoundError(f"No wheel matching: {pattern}")
-    return matches[0]

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

@@ -7,8 +7,6 @@ import os
 import shlex
 import subprocess
 import sys
-from contextlib import contextmanager
-from pathlib import Path
 from typing import Optional
 
 
@@ -79,61 +77,3 @@ def str2bool(value: Optional[str]) -> bool:
     if value in false_value_set:
         return False
     raise ValueError(f"Invalid string value for boolean conversion: {value}")
-
-
-@contextmanager
-def temp_environ(updates: dict[str, str]):
-    """
-    Temporarily set environment variables and restore them after the block.
-    Args:
-        updates: Dict of environment variables to set.
-    """
-    missing = object()
-    old: dict[str, str | object] = {k: os.environ.get(k, missing) for k in updates}
-    try:
-        os.environ.update(updates)
-        yield
-    finally:
-        for k, v in old.items():
-            if v is missing:
-                os.environ.pop(k, None)
-            else:
-                os.environ[k] = v  # type: ignore[arg-type]
-
-
-@contextmanager
-def working_directory(path: str):
-    """
-    Temporarily change the working directory inside a context.
-    """
-    if not path:
-        # No-op context
-        yield
-        return
-    prev_cwd = os.getcwd()
-    try:
-        os.chdir(path)
-        yield
-    finally:
-        os.chdir(prev_cwd)
-
-
-def get_wheels(
-    output_dir: Path,
-    max_depth: Optional[int] = None,
-) -> list[str]:
-    """Return a list of wheels found in the given output directory."""
-    root = Path(output_dir)
-    if not root.exists():
-        return []
-    items = []
-    for dirpath, _, filenames in os.walk(root):
-        depth = Path(dirpath).relative_to(root).parts
-        if max_depth is not None and len(depth) > max_depth:
-            continue
-        for fname in sorted(filenames):
-            if fname.endswith(".whl"):
-                pkg = fname.split("-")[0]
-                relpath = str((Path(dirpath) / fname).relative_to(root))
-                items.append({"pkg": pkg, "relpath": relpath})
-    return items

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

@@ -13,11 +13,7 @@ from cli.lib.common.envs_helper import (
     env_str_field,
     with_params_help,
 )
-from cli.lib.common.gh_summary import (
-    gh_summary_path,
-    summarize_content_from_file,
-    summarize_wheels,
-)
+from cli.lib.common.git_helper import clone_external_repo
 from cli.lib.common.path_helper import (
     copy,
     ensure_dir_exists,
@@ -26,7 +22,6 @@ from cli.lib.common.path_helper import (
     is_path_exist,
 )
 from cli.lib.common.utils import run_command
-from cli.lib.core.vllm.lib import clone_vllm, summarize_build_info
 
 
 logger = logging.getLogger(__name__)
@@ -47,7 +42,7 @@ class VllmBuildParameters:
     """
 
     # USE_TORCH_WHEEL: when true, use local Torch wheels; requires TORCH_WHEELS_PATH.
-    # Otherwise docker build pull torch nightly during build
+    #  Otherwise docker build pull torch nightly during build
     # TORCH_WHEELS_PATH: directory containing local torch wheels when use_torch_whl is True
     use_torch_whl: bool = env_bool_field("USE_TORCH_WHEEL", True)
     torch_whls_path: Path = env_path_field("TORCH_WHEELS_PATH", "./dist")
@@ -66,11 +61,6 @@ class VllmBuildParameters:
         "DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile.tmp_vllm"
     )
 
-    # 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"
-    )
-
     # OUTPUT_DIR: where docker buildx (local exporter) will write artifacts
     output_dir: Path = env_path_field("OUTPUT_DIR", "external/vllm")
 
@@ -162,45 +152,18 @@ class VllmBuildRunner(BaseRunner):
         3. run docker build
         """
         inputs = VllmBuildParameters()
-        logger.info("Running vllm build with inputs: %s", inputs)
-        vllm_commit = clone_vllm()
+        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_dir_exists(inputs.output_dir)
 
         cmd = self._generate_docker_build_cmd(inputs)
         logger.info("Running docker build: \n %s", cmd)
-
-        try:
-            run_command(cmd, cwd="vllm", env=os.environ.copy())
-        finally:
-            self.genearte_vllm_build_summary(vllm_commit, inputs)
-
-    def genearte_vllm_build_summary(
-        self, vllm_commit: str, inputs: VllmBuildParameters
-    ):
-        if not gh_summary_path():
-            return logger.info("Skipping, not detect GH Summary env var....")
-        logger.info("Generate GH Summary ...")
-        # summarize vllm build info
-        summarize_build_info(vllm_commit)
-
-        # summarize vllm build artifacts
-        vllm_artifact_dir = inputs.output_dir / "wheels"
-        summarize_content_from_file(
-            vllm_artifact_dir,
-            "build_summary.txt",
-            title="Vllm build env pip package summary",
-        )
-        summarize_wheels(
-            inputs.torch_whls_path, max_depth=3, title="Torch Wheels Artifacts"
-        )
-        summarize_wheels(vllm_artifact_dir, max_depth=3, title="Vllm Wheels Artifacts")
+        run_command(cmd, cwd="vllm", env=os.environ.copy())
 
     def cp_torch_whls_if_exist(self, inputs: VllmBuildParameters) -> str:
         if not inputs.use_torch_whl:
@@ -211,11 +174,6 @@ 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")
@@ -294,3 +252,12 @@ class VllmBuildRunner(BaseRunner):
                 --progress=plain .
         """
         ).strip()
+
+
+def clone_vllm():
+    clone_external_repo(
+        target="vllm",
+        repo="https://github.com/vllm-project/vllm.git",
+        dst="vllm",
+        update_submodules=True,
+    )

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

@@ -1,292 +0,0 @@
-import logging
-import os
-import textwrap
-from typing import Any
-
-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 jinja2 import Template
-
-
-logger = logging.getLogger(__name__)
-
-_TPL_VLLM_INFO = Template(
-    textwrap.dedent("""\
-    ##  Vllm against Pytorch CI Test Summary
-    **Vllm Commit**: [{{ vllm_commit }}](https://github.com/vllm-project/vllm/commit/{{ vllm_commit }})
-    {%- if torch_sha %}
-    **Pytorch Commit**: [{{ torch_sha }}](https://github.com/pytorch/pytorch/commit/{{ torch_sha }})
-    {%- endif %}
-""")
-)
-
-
-def sample_vllm_test_library():
-    """
-    Simple sample to unblock the vllm ci development, which is mimic to
-    https://github.com/vllm-project/vllm/blob/main/.buildkite/test-pipeline.yaml
-    see run_test_plan for more details
-    """
-    # TODO(elainewy): Read from yaml file to handle the env and tests for vllm
-    return {
-        "vllm_basic_correctness_test": {
-            "title": "Basic Correctness Test",
-            "id": "vllm_basic_correctness_test",
-            "env_vars": {
-                "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
-            },
-            "steps": [
-                "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_basic_models_test": {
-            "title": "Basic models test",
-            "id": "vllm_basic_models_test",
-            "steps": [
-                "pytest -v -s models/test_transformers.py",
-                "pytest -v -s models/test_registry.py",
-                "pytest -v -s models/test_utils.py",
-                "pytest -v -s models/test_vision.py",
-                "pytest -v -s models/test_initialization.py",
-            ],
-        },
-        "vllm_entrypoints_test": {
-            "title": "Entrypoints Test ",
-            "id": "vllm_entrypoints_test",
-            "env_vars": {
-                "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
-            },
-            "steps": [
-                " ".join(
-                    [
-                        "pytest",
-                        "-v",
-                        "-s",
-                        "entrypoints/llm",
-                        "--ignore=entrypoints/llm/test_generate.py",
-                        "--ignore=entrypoints/llm/test_collective_rpc.py",
-                    ]
-                ),
-                "pytest -v -s entrypoints/llm/test_generate.py",
-                "pytest -v -s entrypoints/offline_mode",
-            ],
-        },
-        "vllm_regression_test": {
-            "title": "Regression Test",
-            "id": "vllm_regression_test",
-            "package_install": ["modelscope"],
-            "steps": [
-                "pytest -v -s test_regression.py",
-            ],
-        },
-        "vllm_lora_tp_test_distributed": {
-            "title": "LoRA TP Test (Distributed)",
-            "id": "vllm_lora_tp_test_distributed",
-            "env_vars": {
-                "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
-            },
-            "num_gpus": 4,
-            "steps": [
-                "pytest -v -s -x lora/test_chatglm3_tp.py",
-                "pytest -v -s -x lora/test_llama_tp.py",
-                "pytest -v -s -x lora/test_llm_with_multi_loras.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",
-            "steps": ["pytest -v lora/test_quant_model.py"],
-        },
-        "vllm_multi_model_processor_test": {
-            "title": "Multi-Modal Processor Test",
-            "id": "vllm_multi_model_processor_test",
-            "package_install": ["git+https://github.com/TIGER-AI-Lab/Mantis.git"],
-            "steps": [
-                "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",
-            "steps": [
-                "pytest -v -s compile/test_pass_manager.py",
-                "pytest -v -s compile/test_fusion.py",
-                "pytest -v -s compile/test_fusion_attn.py",
-                "pytest -v -s compile/test_silu_mul_quant_fusion.py",
-                "pytest -v -s compile/test_sequence_parallelism.py",
-                "pytest -v -s compile/test_async_tp.py",
-                "pytest -v -s compile/test_fusion_all_reduce.py",
-                "pytest -v -s compile/test_decorator.py",
-            ],
-        },
-        "vllm_languagde_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",
-            "id": "lora_test",
-            "parallelism": 4,
-            "steps": [
-                "echo '[checking] list sharded lora tests:'",
-                " ".join(
-                    [
-                        "pytest -q --collect-only lora",
-                        "--shard-id=$$BUILDKITE_PARALLEL_JOB",
-                        "--num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT",
-                        "--ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py",
-                    ]
-                ),
-                "echo '[checking] Done. list lora tests'",
-                " ".join(
-                    [
-                        "pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB",
-                        "--num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT",
-                        "--ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py",
-                    ]
-                ),
-            ],
-        },
-    }
-
-
-def check_parallelism(tests: Any, title: str, shard_id: int = 0, num_shards: int = 0):
-    """
-    a method to check if the test plan is parallelism or not.
-    """
-    parallelism = int(tests.get("parallelism", "0"))
-    is_parallel = parallelism and parallelism > 1
-
-    if not is_parallel:
-        return False
-
-    if shard_id > num_shards:
-        raise RuntimeError(
-            f"Test {title} expects {num_shards} shards, but invalid {shard_id} is provided"
-        )
-
-    if num_shards != parallelism:
-        raise RuntimeError(
-            f"Test {title} expects {parallelism} shards, but invalid {num_shards} is provided"
-        )
-
-    return True
-
-
-def run_test_plan(
-    test_plan: str,
-    test_target: str,
-    tests_map: dict[str, Any],
-    shard_id: int = 0,
-    num_shards: int = 0,
-):
-    """
-    a method to run list of tests based on the test plan.
-    """
-    logger.info("run %s tests.....", test_target)
-    if test_plan not in tests_map:
-        raise RuntimeError(
-            f"test {test_plan} not found, please add it to test plan pool"
-        )
-    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}")
-
-    logger.info("Running tests: %s", title)
-    if pkgs:
-        logger.info("Installing packages: %s", pkgs)
-        pip_install_packages(packages=pkgs, prefer_uv=True)
-    with (
-        working_directory(tests.get("working_directory", "tests")),
-        temp_environ(tests.get("env_vars", {})),
-    ):
-        failures = []
-        for step in tests["steps"]:
-            logger.info("Running step: %s", step)
-            if is_parallel:
-                step = replace_buildkite_placeholders(step, shard_id, num_shards)
-                logger.info("Running parallel step: %s", step)
-            code = run_command(cmd=step, check=False, use_shell=True)
-            if code != 0:
-                failures.append(step)
-            logger.info("Finish running step: %s", step)
-        if failures:
-            logger.error("Failed tests: %s", failures)
-            raise RuntimeError(f"{len(failures)} pytest runs failed: {failures}")
-        logger.info("Done. All tests passed")
-
-
-def clone_vllm(dst: str = "vllm"):
-    _, commit = clone_external_repo(
-        target="vllm",
-        repo="https://github.com/vllm-project/vllm.git",
-        dst=dst,
-        update_submodules=True,
-    )
-    return commit
-
-
-def replace_buildkite_placeholders(step: str, shard_id: int, num_shards: int) -> str:
-    mapping = {
-        "$$BUILDKITE_PARALLEL_JOB_COUNT": str(num_shards),
-        "$$BUILDKITE_PARALLEL_JOB": str(shard_id),
-    }
-    for k in sorted(mapping, key=len, reverse=True):
-        step = step.replace(k, mapping[k])
-    return step
-
-
-def summarize_build_info(vllm_commit: str) -> bool:
-    torch_sha = os.getenv("GITHUB_SHA")
-    md = (
-        _TPL_VLLM_INFO.render(vllm_commit=vllm_commit, torch_sha=torch_sha).strip()
-        + "\n"
-    )
-    return write_gh_step_summary(md)

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

@@ -1,280 +0,0 @@
-import logging
-import os
-import re
-import subprocess
-import sys
-from collections.abc import Iterable
-from dataclasses import dataclass
-from enum import Enum
-from pathlib import Path
-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.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
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class VllmTestParameters:
-    """
-    Parameters defining the vllm external test input
-
-    !!!DO NOT ADD SECRETS IN THIS CLASS!!!
-    you can put environment variable name in VllmTestParameters if it's not the same as the secret one
-    fetch secrests directly from env variables during runtime
-    """
-
-    torch_whls_path: Path = env_path_field("WHEELS_PATH", "./dist")
-
-    vllm_whls_path: Path = env_path_field(
-        "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"
-    )
-
-    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")
-
-
-class TestInpuType(Enum):
-    TEST_PLAN = "test_plan"
-    UNKNOWN = "unknown"
-
-
-class VllmTestRunner(BaseRunner):
-    def __init__(self, args: Any):
-        self.work_directory = "vllm"
-        self.test_plan = ""
-        self.test_type = TestInpuType.UNKNOWN
-
-        self.shard_id = args.shard_id
-        self.num_shards = args.num_shards
-
-        if args.test_plan:
-            self.test_plan = args.test_plan
-            self.test_type = TestInpuType.TEST_PLAN
-
-        # Matches the structeur in the artifacts.zip from torcb build
-        self.TORCH_WHL_PATH_REGEX = "torch*.whl"
-        self.TORCH_WHL_EXTRA = "opt-einsum"
-        self.TORCH_ADDITIONAL_WHLS_REGEX = [
-            "vision/torchvision*.whl",
-            "audio/torchaudio*.whl",
-        ]
-
-        # Match the structure of the artifacts.zip from vllm external build
-        self.VLLM_TEST_WHLS_REGEX = [
-            "xformers/*.whl",
-            "vllm/vllm*.whl",
-            "flashinfer-python/flashinfer*.whl",
-        ]
-
-    def prepare(self):
-        """
-        prepare test environment for vllm. This includes clone vllm repo, install all wheels, test dependencies and set env
-        """
-        params = VllmTestParameters()
-        logger.info("Display VllmTestParameters %s", params)
-        self._set_envs(params)
-
-        clone_vllm(dst=self.work_directory)
-        self.cp_torch_cleaning_script(params)
-        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()
-
-    def run(self):
-        """
-        main function to run vllm test
-        """
-        self.prepare()
-        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()
-                        )
-                else:
-                    raise ValueError(f"Unknown test type {self.test_type}")
-        finally:
-            # double check the torches are not overridden by other packages
-            check_versions()
-
-    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 _install_wheels(self, params: VllmTestParameters):
-        logger.info("Running vllm test with inputs: %s", params)
-        if not pkg_exists("torch"):
-            # install torch from local whls if it's not installed yet.
-            torch_p = f"{str(params.torch_whls_path)}/{self.TORCH_WHL_PATH_REGEX}"
-            pip_install_first_match(torch_p, self.TORCH_WHL_EXTRA)
-
-        torch_whls_path = [
-            f"{str(params.torch_whls_path)}/{whl_path}"
-            for whl_path in self.TORCH_ADDITIONAL_WHLS_REGEX
-        ]
-        for torch_whl in torch_whls_path:
-            pip_install_first_match(torch_whl)
-        logger.info("Done. Installed torch and other torch-related wheels ")
-
-        logger.info("Installing vllm wheels")
-        vllm_whls_path = [
-            f"{str(params.vllm_whls_path)}/{whl_path}"
-            for whl_path in self.VLLM_TEST_WHLS_REGEX
-        ]
-        for vllm_whl in vllm_whls_path:
-            pip_install_first_match(vllm_whl)
-        logger.info("Done. Installed vllm wheels")
-
-    def _install_test_dependencies(self):
-        """
-        This method replaces torch dependencies with local torch wheel info in
-        requirements/test.in file from vllm repo. then generates the test.txt
-        in runtime
-        """
-        logger.info("generate test.txt from requirements/test.in with local torch whls")
-        preprocess_test_in()
-        copy("requirements/test.txt", "snapshot_constraint.txt")
-
-        run_command(
-            f"{sys.executable} -m uv pip compile requirements/test.in "
-            "-o test.txt "
-            "--index-strategy unsafe-best-match "
-            "--constraint snapshot_constraint.txt "
-            "--torch-backend cu128"
-        )
-        pip_install_packages(requirements="test.txt", prefer_uv=True)
-        logger.info("Done. installed requirements for test dependencies")
-
-    def _install_dependencies(self):
-        pip_install_packages(packages=["-e", "tests/vllm_test_utils"], prefer_uv=True)
-        pip_install_packages(packages=["hf_transfer"], prefer_uv=True)
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-
-        # using script from vllm repo to remove all torch packages from requirements txt
-        run_python("use_existing_torch.py")
-
-        # install common packages
-        for requirements in ["requirements/common.txt", "requirements/build.txt"]:
-            pip_install_packages(
-                requirements=requirements,
-                prefer_uv=True,
-            )
-        # install test packages
-        self._install_test_dependencies()
-
-    def _set_envs(self, inputs: VllmTestParameters):
-        os.environ["TORCH_CUDA_ARCH_LIST"] = inputs.torch_cuda_arch_list
-        if not validate_cuda(get_env("TORCH_CUDA_ARCH_LIST")):
-            logger.warning(
-                "Missing supported TORCH_CUDA_ARCH_LIST. "
-                "Currently support TORCH_CUDA_ARCH_LIST env var "
-                "with supported arch [8.0, 8.9, 9.0]"
-            )
-
-        os.environ["HF_TOKEN"] = os.getenv("VLLM_TEST_HUGGING_FACE_TOKEN", "")
-        if not get_env("HF_TOKEN"):
-            raise ValueError(
-                "missing required HF_TOKEN, please set VLLM_TEST_HUGGING_FACE_TOKEN env var"
-            )
-        if not get_env("TORCH_CUDA_ARCH_LIST"):
-            raise ValueError(
-                "missing required TORCH_CUDA_ARCH_LIST, please set TORCH_CUDA_ARCH_LIST env var"
-            )
-
-
-def preprocess_test_in(
-    target_file: str = "requirements/test.in", additional_packages: Iterable[str] = ()
-):
-    """
-    This modifies the target_file file in place in vllm work directory.
-    It removes torch and unwanted packages in target_file and replace with local torch whls
-    package  with format "$WHEEL_PACKAGE_NAME @ file://<LOCAL_PATH>"
-    """
-    additional_package_to_move = list(additional_packages or ())
-    pkgs_to_remove = [
-        "torch",
-        "torchvision",
-        "torchaudio",
-        "xformers",
-        "mamba_ssm",
-    ] + additional_package_to_move
-    # Read current requirements
-    target_path = Path(target_file)
-    lines = target_path.read_text().splitlines()
-
-    pkgs_to_add = []
-
-    # Remove lines starting with the package names (==, @, >=) — case-insensitive
-    pattern = re.compile(rf"^({'|'.join(pkgs_to_remove)})\s*(==|@|>=)", re.IGNORECASE)
-    kept_lines = [line for line in lines if not pattern.match(line)]
-
-    # Get local installed torch/vision/audio from pip freeze
-    # This is hacky, but it works
-    pip_freeze = subprocess.check_output(["pip", "freeze"], text=True)
-    header_lines = [
-        line
-        for line in pip_freeze.splitlines()
-        if re.match(
-            r"^(torch|torchvision|torchaudio)\s*@\s*file://", line, re.IGNORECASE
-        )
-    ]
-
-    # Write back: header_lines + blank + kept_lines
-    out_lines = header_lines + [""] + kept_lines
-    if pkgs_to_add:
-        out_lines += [""] + pkgs_to_add
-
-    out = "\n".join(out_lines) + "\n"
-    target_path.write_text(out)
-    logger.info("[INFO] Updated %s", target_file)
-
-
-def validate_cuda(value: str) -> bool:
-    VALID_VALUES = {"8.0", "8.9", "9.0"}
-    return all(v in VALID_VALUES for v in value.split())
-
-
-def check_versions():
-    """
-    check installed packages version
-    """
-    logger.info("Double check installed packages")
-    patterns = ["torch", "xformers", "torchvision", "torchaudio", "vllm"]
-    for pkg in patterns:
-        pkg_exists(pkg)
-    logger.info("Done. checked installed packages")

.ci/lumen_cli/cli/run.py

@@ -5,7 +5,6 @@ import logging
 
 from cli.build_cli.register_build import register_build_commands
 from cli.lib.common.logger import setup_logging
-from cli.test_cli.register_test import register_test_commands
 
 
 logger = logging.getLogger(__name__)
@@ -21,7 +20,6 @@ def main():
 
     # registers second-level subcommands
     register_build_commands(subparsers)
-    register_test_commands(subparsers)
 
     # parse args after all options are registered
     args = parser.parse_args()

.ci/lumen_cli/cli/test_cli/register_test.py

@@ -1,62 +0,0 @@
-import argparse
-import logging
-
-from cli.lib.common.cli_helper import register_targets, RichHelp, TargetSpec
-from cli.lib.core.vllm.vllm_test import VllmTestRunner
-
-
-logger = logging.getLogger(__name__)
-
-# Maps targets to their argparse configuration and runner
-# it adds new target to path python -m cli.run build external {target} with buildrunner
-_TARGETS: dict[str, TargetSpec] = {
-    "vllm": {
-        "runner": VllmTestRunner,
-        "help": "test vLLM with pytorch main",
-    }
-    # add yours ...
-}
-
-
-def common_args(parser: argparse.ArgumentParser) -> None:
-    """
-    Add common CLI arguments to the given parser.
-    """
-    parser.add_argument(
-        "--shard-id",
-        type=int,
-        default=1,
-        help="a shard id to run, e.g. '0,1,2,3'",
-    )
-    parser.add_argument(
-        "--num-shards",
-        type=int,
-        default=1,
-        help="a number of shards to run, e.g. '4'",
-    )
-    group = parser.add_mutually_exclusive_group(required=True)
-    group.add_argument(
-        "-tp",
-        "--test-plan",
-        type=str,
-        help="a pre-defined test plan to run, e.g. 'basic_correctness_test'",
-    )
-
-
-def register_test_commands(subparsers: argparse._SubParsersAction) -> None:
-    build_parser = subparsers.add_parser(
-        "test",
-        help="test related commands",
-        formatter_class=RichHelp,
-    )
-    build_subparsers = build_parser.add_subparsers(dest="test_command", required=True)
-    overview = "\n".join(
-        f"  {name:12} {spec.get('help', '')}" for name, spec in _TARGETS.items()
-    )
-    external_parser = build_subparsers.add_parser(
-        "external",
-        help="Test external targets",
-        description="Test third-party targets.\n\nAvailable targets:\n" + overview,
-        formatter_class=RichHelp,
-    )
-    register_targets(external_parser, _TARGETS, common_args=common_args)

.ci/lumen_cli/pyproject.toml

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

.ci/lumen_cli/tests/test_run_plan.py

@@ -1,185 +0,0 @@
-# tests/test_run_test_plan.py
-import importlib
-from contextlib import nullcontext
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import pytest
-
-
-MOD = "cli.lib.core.vllm.lib"
-
-# We import inside tests so the MOD override above applies everywhere
-run_test_plan_import_path = f"{MOD}.run_test_plan"
-
-
-def _get_cmd(c):
-    # Support both kwargs and positional args
-    return c.kwargs.get("cmd", c.args[0] if c.args else None)
-
-
-def _get_check(c):
-    if "check" in c.kwargs:
-        return c.kwargs["check"]
-    # If positional, assume second arg is 'check' when present; default False
-    return c.args[1] if len(c.args) > 1 else False
-
-
-@pytest.fixture
-def patch_module(monkeypatch):
-    """
-    Patch helpers ('pip_install_packages', 'temp_environ', 'working_directory',
-    'run_command', 'logger') inside the target module and expose them.
-    """
-    module = importlib.import_module(MOD)
-
-    # Create fakes/mocks
-    pip_install_packages = MagicMock(name="pip_install_packages")
-    run_command = MagicMock(name="run_command", return_value=0)
-
-    # temp_environ / working_directory: record calls but act as context managers
-    temp_calls: list[dict] = []
-    workdir_calls: list[str] = []
-
-    def fake_working_directory(path: str):
-        workdir_calls.append(path)
-        return nullcontext()
-
-    def fake_temp_env(map: dict[str, str]):
-        temp_calls.append(map)
-        return nullcontext()
-
-    logger = SimpleNamespace(
-        info=MagicMock(name="logger.info"),
-        error=MagicMock(name="logger.error"),
-    )
-
-    # Apply patches (raise if attribute doesn't exist)
-    monkeypatch.setattr(
-        module, "pip_install_packages", pip_install_packages, raising=True
-    )
-    monkeypatch.setattr(module, "run_command", run_command, raising=True)
-    monkeypatch.setattr(
-        module, "working_directory", fake_working_directory, raising=True
-    )
-    monkeypatch.setattr(module, "temp_environ", fake_temp_env, raising=True)
-    monkeypatch.setattr(module, "logger", logger, raising=True)
-
-    return SimpleNamespace(
-        module=module,
-        run_test_plan=module.run_test_plan,  # expose to avoid getattr("constant") (Ruff B009)
-        pip_install_packages=pip_install_packages,
-        run_command=run_command,
-        temp_calls=temp_calls,
-        workdir_calls=workdir_calls,
-        logger=logger,
-    )
-
-
-def test_success_runs_all_steps_and_uses_env_and_workdir(monkeypatch, patch_module):
-    run_test_plan = patch_module.run_test_plan
-
-    tests_map = {
-        "basic": {
-            "title": "Basic suite",
-            "package_install": [],
-            "working_directory": "tests",
-            "env_vars": {"GLOBAL_FLAG": "1"},
-            "steps": [
-                "export A=x && pytest -q",
-                "export B=y && pytest -q tests/unit",
-            ],
-        }
-    }
-
-    # One exit code per step (export + two pytest)
-    patch_module.run_command.side_effect = [0, 0, 0]
-
-    run_test_plan("basic", "cpu", tests_map)
-
-    calls = patch_module.run_command.call_args_list
-    cmds = [_get_cmd(c) for c in calls]
-    checks = [_get_check(c) for c in calls]
-
-    assert cmds == [
-        "export A=x && pytest -q",
-        "export B=y && pytest -q tests/unit",
-    ]
-    assert all(chk is False for chk in checks)
-
-    assert patch_module.workdir_calls == ["tests"]
-    assert patch_module.temp_calls == [{"GLOBAL_FLAG": "1"}]
-
-
-def test_installs_packages_when_present(monkeypatch, patch_module):
-    run_test_plan = patch_module.module.run_test_plan
-
-    tests_map = {
-        "with_pkgs": {
-            "title": "Needs deps",
-            "package_install": ["timm==1.0.0", "flash-attn"],
-            "steps": ["pytest -q"],
-        }
-    }
-
-    patch_module.run_command.return_value = 0
-
-    run_test_plan("with_pkgs", "gpu", tests_map)
-
-    patch_module.pip_install_packages.assert_called_once_with(
-        packages=["timm==1.0.0", "flash-attn"],
-        prefer_uv=True,
-    )
-
-
-def test_raises_on_missing_plan(patch_module):
-    run_test_plan = patch_module.module.run_test_plan
-    with pytest.raises(RuntimeError) as ei:
-        run_test_plan("nope", "cpu", tests_map={})
-
-    assert "test nope not found" in str(ei.value)
-
-
-def test_aggregates_failures_and_raises(monkeypatch, patch_module):
-    run_test_plan = patch_module.module.run_test_plan
-
-    tests_map = {
-        "mix": {
-            "title": "Some pass some fail",
-            "steps": [
-                "pytest test_a.py",  # 0 → pass
-                "pytest test_b.py",  # 1 → fail
-                "pytest test_c.py",  # 2 → fail
-            ],
-        }
-    }
-
-    # Simulate pass, fail, fail
-    patch_module.run_command.side_effect = [0, 1, 2]
-
-    with pytest.raises(RuntimeError) as ei:
-        run_test_plan("mix", "cpu", tests_map)
-
-    msg = str(ei.value)
-    assert "2 pytest runs failed" in msg
-    # Ensure logger captured failed tests list
-    patch_module.logger.error.assert_called_once()
-    # And we attempted all three commands
-    assert patch_module.run_command.call_count == 3
-
-
-def test_custom_working_directory_used(patch_module):
-    run_test_plan = patch_module.module.run_test_plan
-
-    tests_map = {
-        "customwd": {
-            "title": "Custom wd",
-            "working_directory": "examples/ci",
-            "steps": ["pytest -q"],
-        }
-    }
-
-    patch_module.run_command.return_value = 0
-    run_test_plan("customwd", "cpu", tests_map)
-
-    assert patch_module.workdir_calls == ["examples/ci"]

.ci/lumen_cli/tests/test_utils.py

@@ -1,143 +0,0 @@
-import os
-import tempfile
-import unittest
-from pathlib import Path
-
-from cli.lib.common.utils import temp_environ, working_directory  # <-- replace import
-
-
-class EnvIsolatedTestCase(unittest.TestCase):
-    """Base class that snapshots os.environ and CWD for isolation."""
-
-    def setUp(self):
-        import os
-        import tempfile
-
-        self._env_backup = dict(os.environ)
-
-        # Snapshot/repair CWD if it's gone
-        try:
-            self._cwd_backup = os.getcwd()
-        except FileNotFoundError:
-            # If CWD no longer exists, switch to a safe place and record that
-            self._cwd_backup = tempfile.gettempdir()
-            os.chdir(self._cwd_backup)
-
-        # Create a temporary directory for the test to run in
-        self._temp_dir = tempfile.mkdtemp()
-        os.chdir(self._temp_dir)
-
-    def tearDown(self):
-        import os
-        import shutil
-        import tempfile
-
-        # Restore cwd first (before cleaning up temp dir)
-        try:
-            os.chdir(self._cwd_backup)
-        except OSError:
-            os.chdir(tempfile.gettempdir())
-
-        # Clean up temporary directory
-        try:
-            shutil.rmtree(self._temp_dir, ignore_errors=True)
-        except Exception:
-            pass  # Ignore cleanup errors
-
-        # Restore env
-        to_del = set(os.environ.keys()) - set(self._env_backup.keys())
-        for k in to_del:
-            os.environ.pop(k, None)
-        for k, v in self._env_backup.items():
-            os.environ[k] = v
-
-
-class TestTempEnviron(EnvIsolatedTestCase):
-    def test_sets_and_restores_new_var(self):
-        var = "TEST_TMP_ENV_NEW"
-        self.assertNotIn(var, os.environ)
-
-        with temp_environ({var: "123"}):
-            self.assertEqual(os.environ[var], "123")
-
-        self.assertNotIn(var, os.environ)  # removed after exit
-
-    def test_overwrites_and_restores_existing_var(self):
-        var = "TEST_TMP_ENV_OVERWRITE"
-        os.environ[var] = "orig"
-
-        with temp_environ({var: "override"}):
-            self.assertEqual(os.environ[var], "override")
-
-        self.assertEqual(os.environ[var], "orig")  # restored
-
-    def test_multiple_vars_and_missing_cleanup(self):
-        v1, v2 = "TEST_ENV_V1", "TEST_ENV_V2"
-        os.environ.pop(v1, None)
-        os.environ[v2] = "keep"
-
-        with temp_environ({v1: "a", v2: "b"}):
-            self.assertEqual(os.environ[v1], "a")
-            self.assertEqual(os.environ[v2], "b")
-
-        self.assertNotIn(v1, os.environ)  # newly-added -> removed
-        self.assertEqual(os.environ[v2], "keep")  # pre-existing -> restored
-
-    def test_restores_even_on_exception(self):
-        var = "TEST_TMP_ENV_EXCEPTION"
-        self.assertNotIn(var, os.environ)
-
-        with self.assertRaises(RuntimeError):
-            with temp_environ({var: "x"}):
-                self.assertEqual(os.environ[var], "x")
-                raise RuntimeError("boom")
-
-        self.assertNotIn(var, os.environ)  # removed after exception
-
-
-class TestWorkingDirectory(EnvIsolatedTestCase):
-    def test_changes_and_restores(self):
-        start = Path.cwd()
-        with tempfile.TemporaryDirectory() as td:
-            target = Path(td) / "wd"
-            target.mkdir()
-
-            with working_directory(str(target)):
-                self.assertEqual(Path.cwd().resolve(), target.resolve())
-
-        self.assertEqual(Path.cwd(), start)
-
-    def test_noop_when_empty_path(self):
-        start = Path.cwd()
-        with working_directory(""):
-            self.assertEqual(Path.cwd(), start)
-        self.assertEqual(Path.cwd(), start)
-
-    def test_restores_on_exception(self):
-        start = Path.cwd()
-
-        with tempfile.TemporaryDirectory() as td:
-            target = Path(td) / "wd_exc"
-            target.mkdir()
-
-            with self.assertRaises(ValueError):
-                with working_directory(str(target)):
-                    # Normalize both sides to handle /var -> /private/var
-                    self.assertEqual(Path.cwd().resolve(), target.resolve())
-                    raise ValueError("boom")
-
-        self.assertEqual(Path.cwd().resolve(), start.resolve())
-
-    def test_raises_for_missing_dir(self):
-        start = Path.cwd()
-        with tempfile.TemporaryDirectory() as td:
-            missing = Path(td) / "does_not_exist"
-            with self.assertRaises(FileNotFoundError):
-                # os.chdir should raise before yielding
-                with working_directory(str(missing)):
-                    pass
-        self.assertEqual(Path.cwd(), start)
-
-
-if __name__ == "__main__":
-    unittest.main(verbosity=2)

.ci/lumen_cli/tests/test_vllm.py

@@ -4,15 +4,12 @@ import unittest
 from pathlib import Path
 from unittest.mock import MagicMock, patch
 
-import cli.lib.core.vllm.vllm_build as vllm_build
-
-
-_VLLM_BUILD_MODULE = "cli.lib.core.vllm.vllm_build"
+import cli.lib.core.vllm as vllm
 
 
 class TestVllmBuildParameters(unittest.TestCase):
-    @patch(f"{_VLLM_BUILD_MODULE}.local_image_exists", return_value=True)
-    @patch(f"{_VLLM_BUILD_MODULE}.is_path_exist", return_value=True)
+    @patch("cli.lib.core.vllm.local_image_exists", return_value=True)
+    @patch("cli.lib.core.vllm.is_path_exist", return_value=True)
     @patch(
         "cli.lib.common.envs_helper.env_path_optional",
         side_effect=lambda name, default=None, resolve=True: {
@@ -37,13 +34,13 @@ class TestVllmBuildParameters(unittest.TestCase):
     def test_params_success_normalizes_and_validates(
         self, mock_env_path, mock_is_path, mock_local_img
     ):
-        params = vllm_build.VllmBuildParameters()
+        params = vllm.VllmBuildParameters()
         self.assertEqual(params.torch_whls_path, Path("/abs/dist"))
         self.assertEqual(params.dockerfile_path, Path("/abs/vllm/Dockerfile"))
         self.assertEqual(params.output_dir, Path("/abs/shared"))
         self.assertEqual(params.base_image, "my/image:tag")
 
-    @patch(f"{_VLLM_BUILD_MODULE}.is_path_exist", return_value=False)
+    @patch("cli.lib.core.vllm.is_path_exist", return_value=False)
     @patch.dict(
         os.environ, {"USE_TORCH_WHEEL": "1", "TORCH_WHEELS_PATH": "dist"}, clear=True
     )
@@ -51,14 +48,14 @@ class TestVllmBuildParameters(unittest.TestCase):
         with tempfile.TemporaryDirectory() as td:
             os.chdir(td)
             with self.assertRaises(ValueError) as cm:
-                vllm_build.VllmBuildParameters(
+                vllm.VllmBuildParameters(
                     use_local_base_image=False,
                     use_local_dockerfile=False,
                 )
         err = cm.exception
         self.assertIn("TORCH_WHEELS_PATH", str(err))
 
-    @patch(f"{_VLLM_BUILD_MODULE}.local_image_exists", return_value=False)
+    @patch("cli.lib.core.vllm.local_image_exists", return_value=False)
     @patch.dict(
         os.environ, {"USE_LOCAL_BASE_IMAGE": "1", "BASE_IMAGE": "img:tag"}, clear=True
     )
@@ -66,14 +63,14 @@ class TestVllmBuildParameters(unittest.TestCase):
         with tempfile.TemporaryDirectory() as td:
             os.chdir(td)
             with self.assertRaises(ValueError) as cm:
-                vllm_build.VllmBuildParameters(
+                vllm.VllmBuildParameters(
                     use_torch_whl=False,
                     use_local_dockerfile=False,
                 )
         err = cm.exception
         self.assertIn("BASE_IMAGE", str(err))
 
-    @patch(f"{_VLLM_BUILD_MODULE}.is_path_exist", return_value=False)
+    @patch("cli.lib.core.vllm.is_path_exist", return_value=False)
     @patch.dict(
         os.environ,
         {"USE_LOCAL_DOCKERFILE": "1", "DOCKERFILE_PATH": "Dockerfile"},
@@ -83,14 +80,14 @@ class TestVllmBuildParameters(unittest.TestCase):
         with tempfile.TemporaryDirectory() as td:
             os.chdir(td)
             with self.assertRaises(ValueError) as cm:
-                vllm_build.VllmBuildParameters(
+                vllm.VllmBuildParameters(
                     use_torch_whl=False,
                     use_local_base_image=False,
                 )
         err = cm.exception
         self.assertIn("DOCKERFILE_PATH", str(err))
 
-    @patch(f"{_VLLM_BUILD_MODULE}.is_path_exist", return_value=False)
+    @patch("cli.lib.core.vllm.is_path_exist", return_value=False)
     @patch.dict(
         os.environ,
         {"OUTPUT_DIR": ""},
@@ -98,13 +95,14 @@ class TestVllmBuildParameters(unittest.TestCase):
     )
     def test_params_missing_output_dir(self, _is_path):
         with self.assertRaises(FileNotFoundError):
-            vllm_build.VllmBuildParameters()
+            vllm.VllmBuildParameters()
 
 
 class TestBuildCmdAndRun(unittest.TestCase):
-    @patch(f"{_VLLM_BUILD_MODULE}.local_image_exists", return_value=True)
+    @patch("cli.lib.core.vllm.local_image_exists", return_value=True)
     def test_generate_docker_build_cmd_includes_bits(self, _exists):
-        runner = vllm_build.VllmBuildRunner()
+        runner = vllm.VllmBuildRunner()
+        # Craft inputs that simulate a prepared build
         inputs = MagicMock()
         inputs.output_dir = Path("/abs/out")
         inputs.use_local_base_image = True
@@ -120,7 +118,7 @@ class TestBuildCmdAndRun(unittest.TestCase):
         inputs.tag_name = "vllm-wheels"
 
         cmd = runner._generate_docker_build_cmd(inputs)
-        squashed = " ".join(cmd.split())
+        squashed = " ".join(cmd.split())  # normalize whitespace for matching
 
         self.assertIn("--output type=local,dest=/abs/out", squashed)
         self.assertIn("-f docker/Dockerfile.nightly_torch", squashed)
@@ -138,17 +136,18 @@ class TestBuildCmdAndRun(unittest.TestCase):
         self.assertIn("--target export-wheels", squashed)
         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}.clone_vllm")
+    @patch("cli.lib.core.vllm.run_command")
+    @patch("cli.lib.core.vllm.ensure_dir_exists")
+    @patch("cli.lib.core.vllm.clone_vllm")
     @patch.object(
-        vllm_build.VllmBuildRunner,
+        vllm.VllmBuildRunner,
         "_generate_docker_build_cmd",
         return_value="docker buildx ...",
     )
     @patch.dict(
         os.environ,
         {
+            # Make __post_init__ validations pass cheaply
             "USE_TORCH_WHEEL": "0",
             "USE_LOCAL_BASE_IMAGE": "0",
             "USE_LOCAL_DOCKERFILE": "0",
@@ -159,18 +158,24 @@ class TestBuildCmdAndRun(unittest.TestCase):
     def test_run_calls_clone_prepare_and_build(
         self, mock_gen, mock_clone, mock_ensure, mock_run
     ):
+        # Stub parameters instance so we avoid FS/Docker accesses in run()
         params = MagicMock()
         params.output_dir = Path("shared")
         params.use_local_dockerfile = False
         params.use_torch_whl = False
 
-        with patch(f"{_VLLM_BUILD_MODULE}.VllmBuildParameters", return_value=params):
-            runner = vllm_build.VllmBuildRunner()
+        with patch("cli.lib.core.vllm.VllmBuildParameters", return_value=params):
+            runner = vllm.VllmBuildRunner()
             runner.run()
 
         mock_clone.assert_called_once()
         mock_ensure.assert_called_once_with(Path("shared"))
         mock_gen.assert_called_once_with(params)
         mock_run.assert_called_once()
+        # ensure we run in vllm workdir
         _, kwargs = mock_run.call_args
         assert kwargs.get("cwd") == "vllm"
+
+
+if __name__ == "__main__":
+    unittest.main()

.ci/magma-rocm/Makefile

@@ -1,11 +1,11 @@
 SHELL=/usr/bin/env bash
 
 DOCKER_CMD ?= docker
-DESIRED_ROCM ?= 7.0
+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;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 \
@@ -16,7 +16,6 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	magma-rocm/build_magma.sh
 
 .PHONY: all
-all: magma-rocm70
 all: magma-rocm64
 all: magma-rocm63
 
@@ -25,11 +24,6 @@ clean:
 	$(RM) -r magma-*
 	$(RM) -r output
 
-.PHONY: magma-rocm70
-magma-rocm70: DESIRED_ROCM := 7.0
-magma-rocm70:
-	$(DOCKER_RUN)
-
 .PHONY: magma-rocm64
 magma-rocm64: DESIRED_ROCM := 6.4
 magma-rocm64:

.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/magma/Makefile

@@ -16,7 +16,6 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
 	magma/build_magma.sh
 
 .PHONY: all
-all: magma-cuda130
 all: magma-cuda129
 all: magma-cuda128
 all: magma-cuda126
@@ -26,12 +25,6 @@ clean:
 	$(RM) -r magma-*
 	$(RM) -r output
 
-.PHONY: magma-cuda130
-magma-cuda130: DESIRED_CUDA := 13.0
-magma-cuda130: CUDA_ARCH_LIST := -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90 -gencode arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120
-magma-cuda130:
-	$(DOCKER_RUN)
-
 .PHONY: magma-cuda129
 magma-cuda129: DESIRED_CUDA := 12.9
 magma-cuda129: CUDA_ARCH_LIST += -gencode arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120

.ci/magma/build_magma.sh

@@ -28,7 +28,6 @@ pushd ${PACKAGE_DIR}/magma-${MAGMA_VERSION}
 patch < ${PACKAGE_FILES}/CMake.patch
 patch < ${PACKAGE_FILES}/cmakelists.patch
 patch -p0 < ${PACKAGE_FILES}/thread_queue.patch
-patch -p1 < ${PACKAGE_FILES}/cuda13.patch
 patch -p1 < ${PACKAGE_FILES}/getrf_shfl.patch
 patch -p1 < ${PACKAGE_FILES}/getrf_nbparam.patch
 # The build.sh script expects to be executed from the sources root folder
@@ -38,7 +37,6 @@ popd
 # Package recipe, license and tarball
 # Folder and package name are backward compatible for the build workflow
 cp ${PACKAGE_FILES}/build.sh ${PACKAGE_RECIPE}/build.sh
-cp ${PACKAGE_FILES}/cuda13.patch ${PACKAGE_RECIPE}/cuda13.patch
 cp ${PACKAGE_FILES}/thread_queue.patch ${PACKAGE_RECIPE}/thread_queue.patch
 cp ${PACKAGE_FILES}/cmakelists.patch ${PACKAGE_RECIPE}/cmakelists.patch
 cp ${PACKAGE_FILES}/getrf_shfl.patch ${PACKAGE_RECIPE}/getrf_shfl.patch

.ci/magma/package_files/cuda13.patch

@@ -1,26 +0,0 @@
-diff --git a/interface_cuda/interface.cpp b/interface_cuda/interface.cpp
-index 73fed1b20..e77519bfe 100644
---- a/interface_cuda/interface.cpp
-+++ b/interface_cuda/interface.cpp
-@@ -438,14 +438,20 @@ magma_print_environment()
-         cudaDeviceProp prop;
-         err = cudaGetDeviceProperties( &prop, dev );
-         check_error( err );
-+        #ifdef MAGMA_HAVE_CUDA
-+#if CUDA_VERSION < 13000
-         printf( "%% device %d: %s, %.1f MHz clock, %.1f MiB memory, capability %d.%d\n",
-                 dev,
-                 prop.name,
-                 prop.clockRate / 1000.,
-+#else
-+        printf( "%% device %d: %s, ??? MHz clock, %.1f MiB memory, capability %d.%d\n",
-+                dev,
-+                prop.name,
-+#endif
-                 prop.totalGlobalMem / (1024.*1024.),
-                 prop.major,
-                 prop.minor );
--        #ifdef MAGMA_HAVE_CUDA
-         int arch = prop.major*100 + prop.minor*10;
-         if ( arch < MAGMA_CUDA_ARCH_MIN ) {
-             printf("\n"

.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

@@ -66,9 +66,6 @@ case ${CUDA_VERSION} in
             TORCH_CUDA_ARCH_LIST="7.5;8.0;9.0;10.0;12.0+PTX"
         fi
         ;;
-    13.0)
-        TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;9.0;10.0;12.0+PTX"
-        ;;
     12.6)
         TORCH_CUDA_ARCH_LIST="5.0;6.0;7.0;7.5;8.0;8.6;9.0"
         ;;
@@ -113,18 +110,13 @@ DEPS_SONAME=(
 )
 
 
-# CUDA_VERSION 12.*, 13.*
-if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
+# CUDA_VERSION 12.6, 12.8, 12.9
+if [[ $CUDA_VERSION == 12* ]]; then
     export USE_STATIC_CUDNN=0
     # Try parallelizing nvcc as well
-    TORCH_NVCC_FLAGS="-Xfatbin -compress-all --threads 2"
-    # Compress the fatbin with -compress-mode=size for CUDA 13
-    if [[ $CUDA_VERSION == 13* ]]; then
-        export TORCH_NVCC_FLAGS="$TORCH_NVCC_FLAGS -compress-mode=size"
-    fi
+    export TORCH_NVCC_FLAGS="-Xfatbin -compress-all --threads 2"
     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 +126,16 @@ 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+=(
@@ -150,83 +147,42 @@ 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/libnvToolsExt.so.1"
-                "/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+=(
-                "libnvToolsExt.so.1"
-                "libcublas.so.12"
-                "libcublasLt.so.12"
-                "libcudart.so.12"
-                "libnvrtc.so.12"
-                "libcupti.so.12")
+        if [[ $CUDA_VERSION != 12.9* ]]; then
+            DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
+            DEPS_SONAME+=("libnvToolsExt.so.1")
         fi
     else
         echo "Using nvidia libs from pypi."
         CUDA_RPATHS=(
+            '$ORIGIN/../../nvidia/cublas/lib'
+            '$ORIGIN/../../nvidia/cuda_cupti/lib'
+            '$ORIGIN/../../nvidia/cuda_nvrtc/lib'
+            '$ORIGIN/../../nvidia/cuda_runtime/lib'
             '$ORIGIN/../../nvidia/cudnn/lib'
-            '$ORIGIN/../../nvidia/nvshmem/lib'
-            '$ORIGIN/../../nvidia/nccl/lib'
+            '$ORIGIN/../../nvidia/cufft/lib'
+            '$ORIGIN/../../nvidia/curand/lib'
+            '$ORIGIN/../../nvidia/cusolver/lib'
+            '$ORIGIN/../../nvidia/cusparse/lib'
             '$ORIGIN/../../nvidia/cusparselt/lib'
+            '$ORIGIN/../../cusparselt/lib'
+            '$ORIGIN/../../nvidia/nccl/lib'
+            '$ORIGIN/../../nvidia/nvshmem/lib'
+            '$ORIGIN/../../nvidia/nvtx/lib'
+            '$ORIGIN/../../nvidia/cufile/lib'
         )
-        if [[ $CUDA_VERSION == 13* ]]; then
-            CUDA_RPATHS+=('$ORIGIN/../../nvidia/cu13/lib')
-        else
-            CUDA_RPATHS+=(
-                '$ORIGIN/../../nvidia/cublas/lib'
-                '$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'
-                '$ORIGIN/../../cusparselt/lib'
-                '$ORIGIN/../../nvidia/nvtx/lib'
-                '$ORIGIN/../../nvidia/cufile/lib'
-            )
-        fi
-
         CUDA_RPATHS=$(IFS=: ; echo "${CUDA_RPATHS[*]}")
         export C_SO_RPATH=$CUDA_RPATHS':$ORIGIN:$ORIGIN/lib'
         export LIB_SO_RPATH=$CUDA_RPATHS':$ORIGIN'

.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/manywheel/build_xpu.sh

@@ -25,7 +25,6 @@ source /opt/intel/oneapi/mpi/latest/env/vars.sh
 export USE_STATIC_MKL=1
 export USE_ONEMKL=1
 export USE_XCCL=1
-export USE_MPI=0
 
 WHEELHOUSE_DIR="wheelhousexpu"
 LIBTORCH_HOUSE_DIR="libtorch_housexpu"

.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 [[ "$BUILD_ENVIRONMENT" == *riscv64* ]]; then
@@ -173,7 +173,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
   # Enable XCCL build
   export USE_XCCL=1
-  export USE_MPI=0
   # XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
   export USE_KINETO=0
   export TORCH_XPU_ARCH_LIST=pvc
@@ -195,16 +194,8 @@ fi
 
 # We only build FlashAttention files for CUDA 8.0+, and they require large amounts of
 # memory to build and will OOM
-
 if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && echo "${TORCH_CUDA_ARCH_LIST}" | tr ' ' '\n' | sed 's/$/>= 8.0/' | bc | grep -q 1; then
-  J=2  # default to 2 jobs
-  case "$RUNNER" in
-    linux.12xlarge.memory|linux.24xlarge.memory)
-      J=24
-      ;;
-  esac
-  echo "Building FlashAttention with job limit $J"
-  export BUILD_CUSTOM_STEP="ninja -C build flash_attention -j ${J}"
+  export BUILD_CUSTOM_STEP="ninja -C build flash_attention -j 2"
 fi
 
 if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
@@ -290,13 +281,13 @@ 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)"
 

.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

@@ -149,19 +149,6 @@ function get_pinned_commit() {
   cat .github/ci_commit_pins/"${1}".txt
 }
 
-function detect_cuda_arch() {
-  if [[ "${BUILD_ENVIRONMENT}" == *cuda* ]]; then
-    if command -v nvidia-smi; then
-      TORCH_CUDA_ARCH_LIST=$(nvidia-smi --query-gpu=compute_cap --format=csv | tail -n 1)
-    elif [[ "${TEST_CONFIG}" == *nogpu* ]]; then
-      # There won't be nvidia-smi in nogpu tests, so just set TORCH_CUDA_ARCH_LIST to the default
-      # minimum supported value here
-      TORCH_CUDA_ARCH_LIST=8.0
-    fi
-    export TORCH_CUDA_ARCH_LIST
-  fi
-}
-
 function install_torchaudio() {
   local commit
   commit=$(get_pinned_commit audio)
@@ -258,19 +245,11 @@ 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
@@ -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=(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)
-
-  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 [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
     time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
-    time python test/run_test.py --verbose -i distributed/test_aten_comm_compute_reordering
     time python test/run_test.py --verbose -i distributed/test_store
     time python test/run_test.py --verbose -i distributed/test_symmetric_memory
     time python test/run_test.py --verbose -i distributed/test_pg_wrapper
@@ -46,7 +45,6 @@ if [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
     # DTensor tests
     time python test/run_test.py --verbose -i distributed/tensor/test_random_ops
     time python test/run_test.py --verbose -i distributed/tensor/test_dtensor_compile
-    time python test/run_test.py --verbose -i distributed/tensor/test_utils.py
 
     # DeviceMesh test
     time python test/run_test.py --verbose -i distributed/test_device_mesh

.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/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

@@ -32,16 +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
-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
-
 echo "Environment variables:"
 env
 
@@ -101,7 +91,6 @@ if [[ "$BUILD_ENVIRONMENT" == *clang9* || "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   export VALGRIND=OFF
 fi
 
-detect_cuda_arch
 
 if [[ "$BUILD_ENVIRONMENT" == *s390x* ]]; then
   # There are additional warnings on s390x, maybe due to newer gcc.
@@ -322,29 +311,23 @@ 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
+  # 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
 }
 
-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 inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
-  assert_git_not_dirty
-}
-
 test_h100_distributed() {
   # Distributed tests at H100
   time python test/run_test.py --include distributed/_composable/test_composability/test_pp_composability.py  $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
@@ -390,7 +373,6 @@ 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
@@ -435,7 +417,7 @@ 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
 }
 
@@ -513,14 +495,6 @@ 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
@@ -1163,12 +1137,6 @@ 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
@@ -1415,7 +1383,7 @@ 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::"
 
@@ -1563,10 +1531,14 @@ 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
@@ -1580,14 +1552,17 @@ 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
@@ -1617,7 +1592,7 @@ test_operator_benchmark() {
   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" \
@@ -1630,25 +1605,6 @@ test_operator_benchmark() {
       --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; 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())")
@@ -1673,16 +1629,10 @@ elif [[ "${TEST_CONFIG}" == *xla* ]]; then
   install_torchvision
   build_xla
   test_xla
-elif [[ "$TEST_CONFIG" == *vllm* ]]; then
-    echo "vLLM CI uses TORCH_CUDA_ARCH_LIST: $TORCH_CUDA_ARCH_LIST"
-    (cd .ci/lumen_cli && python -m pip install -e .)
-    python -m cli.run test external vllm --test-plan "$TEST_CONFIG" --shard-id "$SHARD_NUMBER" --num-shards "$NUM_TEST_SHARDS"
 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"
@@ -1705,8 +1655,6 @@ 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 [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
@@ -1760,6 +1708,11 @@ elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper* ]]; then
 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
@@ -1809,14 +1762,10 @@ 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_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" "pynvml==11.4.1" "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/cuda130.bat

@@ -1,59 +0,0 @@
-@echo off
-
-set MODULE_NAME=pytorch
-
-IF NOT EXIST "setup.py" IF NOT EXIST "%MODULE_NAME%" (
-    call internal\clone.bat
-    cd %~dp0
-) ELSE (
-    call internal\clean.bat
-)
-IF ERRORLEVEL 1 goto :eof
-
-call internal\check_deps.bat
-IF ERRORLEVEL 1 goto :eof
-
-REM Check for optional components
-
-set USE_CUDA=
-set CMAKE_GENERATOR=Visual Studio 15 2017 Win64
-
-IF "%NVTOOLSEXT_PATH%"=="" (
-    IF EXIST "C:\Program Files\NVIDIA Corporation\NvToolsExt\lib\x64\nvToolsExt64_1.lib"  (
-        set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-    ) ELSE (
-        echo NVTX ^(Visual Studio Extension ^for CUDA^) ^not installed, failing
-        exit /b 1
-    )
-)
-
-IF "%CUDA_PATH_V130%"=="" (
-    IF EXIST "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0\bin\nvcc.exe" (
-        set "CUDA_PATH_V130=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0"
-    ) ELSE (
-        echo CUDA 13.0 not found, failing
-        exit /b 1
-    )
-)
-
-IF "%BUILD_VISION%" == "" (
-    set TORCH_CUDA_ARCH_LIST=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_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_V130%"
-set "PATH=%CUDA_PATH_V130%\bin;%PATH%"
-
-:optcheck
-
-call internal\check_opts.bat
-IF ERRORLEVEL 1 goto :eof
-
-if exist "%NIGHTLIES_PYTORCH_ROOT%" cd %NIGHTLIES_PYTORCH_ROOT%\..
-call  %~dp0\internal\copy.bat
-IF ERRORLEVEL 1 goto :eof
-
-call  %~dp0\internal\setup.bat
-IF ERRORLEVEL 1 goto :eof

.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/cuda_install.bat

@@ -26,7 +26,6 @@ if exist "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION_STR%
 if %CUDA_VER% EQU 126 goto cuda126
 if %CUDA_VER% EQU 128 goto cuda128
 if %CUDA_VER% EQU 129 goto cuda129
-if %CUDA_VER% EQU 130 goto cuda130
 
 echo CUDA %CUDA_VERSION_STR% is not supported
 exit /b 1
@@ -114,33 +113,6 @@ xcopy /Y "%SRC_DIR%\temp_build\zlib\dll_x64\*.dll" "C:\Windows\System32"
 
 goto cuda_common
 
-:cuda130
-
-set CUDA_INSTALL_EXE=cuda_13.0.0_windows.exe
-if not exist "%SRC_DIR%\temp_build\%CUDA_INSTALL_EXE%" (
-    curl -k -L "https://ossci-windows.s3.amazonaws.com/%CUDA_INSTALL_EXE%" --output "%SRC_DIR%\temp_build\%CUDA_INSTALL_EXE%" & REM @lint-ignore
-    if errorlevel 1 exit /b 1
-    set "CUDA_SETUP_FILE=%SRC_DIR%\temp_build\%CUDA_INSTALL_EXE%"
-    set "ARGS="
-)
-
-set CUDNN_FOLDER=cudnn-windows-x86_64-9.12.0.46_cuda13-archive
-set CUDNN_LIB_FOLDER="lib"
-set "CUDNN_INSTALL_ZIP=%CUDNN_FOLDER%.zip"
-if not exist "%SRC_DIR%\temp_build\%CUDNN_INSTALL_ZIP%" (
-    curl -k -L "http://s3.amazonaws.com/ossci-windows/%CUDNN_INSTALL_ZIP%" --output "%SRC_DIR%\temp_build\%CUDNN_INSTALL_ZIP%" & REM @lint-ignore
-    if errorlevel 1 exit /b 1
-    set "CUDNN_SETUP_FILE=%SRC_DIR%\temp_build\%CUDNN_INSTALL_ZIP%"
-)
-
-@REM cuDNN 8.3+ required zlib to be installed on the path
-echo Installing ZLIB dlls
-curl -k -L "http://s3.amazonaws.com/ossci-windows/zlib123dllx64.zip" --output "%SRC_DIR%\temp_build\zlib123dllx64.zip"
-7z x "%SRC_DIR%\temp_build\zlib123dllx64.zip" -o"%SRC_DIR%\temp_build\zlib"
-xcopy /Y "%SRC_DIR%\temp_build\zlib\dll_x64\*.dll" "C:\Windows\System32"
-
-goto cuda_common
-
 :cuda_common
 :: NOTE: We only install CUDA if we don't have it installed already.
 :: With GHA runners these should be pre-installed as part of our AMI process

.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

@@ -1,22 +1,12 @@
 set ADDITIONAL_OPTIONS=""
 set PYTHON_EXEC="python"
-
-
 if "%DESIRED_PYTHON%" == "3.13t" (
     echo Python version is set to 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-3.14.0rc1-amd64.exe"
-    set ADDITIONAL_OPTIONS="Include_freethreaded=1"
-    set PYTHON_EXEC="python3.14t"
 ) else (
-    echo Python version is set to %DESIRED_PYTHON%
+    echo DESIRED_PYTHON not defined, Python version is set to %DESIRED_PYTHON%
     set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/%DESIRED_PYTHON%.0/python-%DESIRED_PYTHON%.0-amd64.exe" %= @lint-ignore =%
 )
 
@@ -28,5 +18,5 @@ 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

@@ -7,8 +7,6 @@ call "internal\install_python.bat"
 
 %PYTHON_EXEC% --version
 set "PATH=%CD%\Python\Lib\site-packages\cmake\data\bin;%CD%\Python\Scripts;%CD%\Python;%PATH%"
-if "%DESIRED_PYTHON%" == "3.14t" %PYTHON_EXEC% -m pip install numpy==2.3.2 cmake
-if "%DESIRED_PYTHON%" == "3.14" %PYTHON_EXEC% -m pip install numpy==2.3.2 cmake
 if "%DESIRED_PYTHON%" == "3.13t" %PYTHON_EXEC% -m pip install numpy==2.2.1 cmake
 if "%DESIRED_PYTHON%" == "3.13" %PYTHON_EXEC% -m pip install numpy==2.1.2 cmake
 if "%DESIRED_PYTHON%" == "3.12" %PYTHON_EXEC% -m pip install numpy==2.0.2 cmake
@@ -18,7 +16,7 @@ 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 +124,88 @@ 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'
-        NUMPY_PINNED_VERSION="==2.1.0"
+        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'
-        NUMPY_PINNED_VERSION="==2.1.0"
+        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'
-        NUMPY_PINNED_VERSION="==2.1.0"
+        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"
-        NUMPY_PINNED_VERSION="==2.1.0"
+        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"
-        NUMPY_PINNED_VERSION="==2.0.2"
+        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"
-        NUMPY_PINNED_VERSION="==2.0.2"
+        SETUPTOOLS_PINNED_VERSION=">=70.1.0"
+        PYYAML_PINNED_VERSION=">=5.3"
+        NUMPY_PINNED_VERSION="=2.0.2"
         ;;
     3.10)
         echo "Using 3.10 deps"
-        NUMPY_PINNED_VERSION="==2.0.2"
+        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
 
-PINNED_PACKAGES=(
-    "numpy${NUMPY_PINNED_VERSION}"
-)
-python -mvenv ~/${desired_python}-build
-source ~/${desired_python}-build/bin/activate
-retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements.txt"
+# Install into a fresh env
+tmp_env_name="wheel_py$python_nodot"
+conda create ${EXTRA_CONDA_INSTALL_FLAGS} -yn "$tmp_env_name" python="$desired_python" ${CONDA_ENV_CREATE_FLAGS}
+source activate "$tmp_env_name"
+
+retry pip install -r "${pytorch_rootdir}/requirements-build.txt"
+pip install "numpy=${NUMPY_PINNED_VERSION}"  "pyyaml${PYYAML_PINNED_VERSION}" requests ninja "setuptools${SETUPTOOLS_PINNED_VERSION}" 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 +216,11 @@ 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

@@ -75,8 +75,8 @@ TRITON_VERSION=$(cat $PYTORCH_ROOT/.ci/docker/triton_version.txt)
 # 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 12.9/13.0 builds have triton for Linux and Linux aarch64 binaries.
-if [[ "$DESIRED_CUDA" == "cu129" ]] || [[ "$DESIRED_CUDA" == "cu130" ]]; then
+# CUDA 12.9 builds have triton for Linux and Linux aarch64 binaries.
+if [[ "$DESIRED_CUDA" == "cu129" ]]; then
   TRITON_CONSTRAINT="platform_system == 'Linux'"
 fi
 

.circleci/scripts/binary_upload.sh

@@ -51,12 +51,16 @@ s3_upload() {
     s3_upload_dir="${s3_root_dir}/${UPLOAD_SUBFOLDER}/"
   fi
   (
+    cache_control_flag=""
+    if [[ "${UPLOAD_CHANNEL}" = "test" ]]; then
+      cache_control_flag="--cache-control='no-cache,no-store,must-revalidate'"
+    fi
     for pkg in ${PKG_DIR}/*.${extension}; do
       (
         set -x
         shm_id=$(sha256sum "${pkg}" | awk '{print $1}')
         ${AWS_S3_CP} --no-progress --acl public-read "${pkg}" "${s3_upload_dir}" \
-          --metadata "checksum-sha256=${shm_id}"
+          --metadata "checksum-sha256=${shm_id}" ${cache_control_flag}
       )
     done
   )

.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 ===================