setup.py

# Welcome to the PyTorch setup.py.
# Environment variables you are probably interested in:
#
#   DEBUG
#     build with -O0 and -g (debug symbols)
#
#   REL_WITH_DEB_INFO
#     build with optimizations and -g (debug symbols)
#
#   USE_CUSTOM_DEBINFO="path/to/file1.cpp;path/to/file2.cpp"
#     build with debug info only for specified files
#
#   MAX_JOBS
#     maximum number of compile jobs we should use to compile your code
#
#   USE_CUDA=0
#     disables CUDA build
#
#   CFLAGS
#     flags to apply to both C and C++ files to be compiled (a quirk of setup.py
#     which we have faithfully adhered to in our build system is that CFLAGS
#     also applies to C++ files (unless CXXFLAGS is set), in contrast to the
#     default behavior of autogoo and cmake build systems.)
#
#   CC
#     the C/C++ compiler to use
#
# Environment variables for feature toggles:
#
#   DEBUG_CUDA=1
#     if used in conjunction with DEBUG or REL_WITH_DEB_INFO, will also
#     build CUDA kernels with -lineinfo --source-in-ptx.  Note that
#     on CUDA 12 this may cause nvcc to OOM, so this is disabled by default.

#   USE_CUDNN=0
#     disables the cuDNN build
#
#   USE_CUSPARSELT=0
#     disables the cuSPARSELt build
#
#   USE_CUDSS=0
#     disables the cuDSS build
#
#   USE_CUFILE=0
#     disables the cuFile build
#
#   USE_FBGEMM=0
#     disables the FBGEMM build
#
#   USE_KINETO=0
#     disables usage of libkineto library for profiling
#
#   USE_NUMPY=0
#     disables the NumPy build
#
#   BUILD_TEST=0
#     disables the test build
#
#   USE_MKLDNN=0
#     disables use of MKLDNN
#
#   USE_MKLDNN_ACL
#     enables use of Compute Library backend for MKLDNN on Arm;
#     USE_MKLDNN must be explicitly enabled.
#
#   MKLDNN_CPU_RUNTIME
#     MKL-DNN threading mode: TBB or OMP (default)
#
#   USE_STATIC_MKL
#     Prefer to link with MKL statically - Unix only
#   USE_ITT=0
#     disable use of Intel(R) VTune Profiler's ITT functionality
#
#   USE_NNPACK=0
#     disables NNPACK build
#
#   USE_DISTRIBUTED=0
#     disables distributed (c10d, gloo, mpi, etc.) build
#
#   USE_TENSORPIPE=0
#     disables distributed Tensorpipe backend build
#
#   USE_GLOO=0
#     disables distributed gloo backend build
#
#   USE_MPI=0
#     disables distributed MPI backend build
#
#   USE_SYSTEM_NCCL=0
#     disables use of system-wide nccl (we will use our submoduled
#     copy in third_party/nccl)
#
#   USE_OPENMP=0
#     disables use of OpenMP for parallelization
#
#   USE_FLASH_ATTENTION=0
#     disables building flash attention for scaled dot product attention
#
#   USE_MEM_EFF_ATTENTION=0
#    disables building memory efficient attention for scaled dot product attention
#
#   BUILD_BINARY
#     enables the additional binaries/ build
#
#   ATEN_AVX512_256=TRUE
#     ATen AVX2 kernels can use 32 ymm registers, instead of the default 16.
#     This option can be used if AVX512 doesn't perform well on a machine.
#     The FBGEMM library also uses AVX512_256 kernels on Xeon D processors,
#     but it also has some (optimized) assembly code.
#
#   PYTORCH_BUILD_VERSION
#   PYTORCH_BUILD_NUMBER
#     specify the version of PyTorch, rather than the hard-coded version
#     in this file; used when we're building binaries for distribution
#
#   TORCH_CUDA_ARCH_LIST
#     specify which CUDA architectures to build for.
#     ie `TORCH_CUDA_ARCH_LIST="6.0;7.0"`
#     These are not CUDA versions, instead, they specify what
#     classes of NVIDIA hardware we should generate PTX for.
#
#   TORCH_XPU_ARCH_LIST
#     specify which XPU architectures to build for.
#     ie `TORCH_XPU_ARCH_LIST="ats-m150,lnl-m"`
#
#   PYTORCH_ROCM_ARCH
#     specify which AMD GPU targets to build for.
#     ie `PYTORCH_ROCM_ARCH="gfx900;gfx906"`
#
#   ONNX_NAMESPACE
#     specify a namespace for ONNX built here rather than the hard-coded
#     one in this file; needed to build with other frameworks that share ONNX.
#
#   BLAS
#     BLAS to be used by Caffe2. Can be MKL, Eigen, ATLAS, FlexiBLAS, or OpenBLAS. If set
#     then the build will fail if the requested BLAS is not found, otherwise
#     the BLAS will be chosen based on what is found on your system.
#
#   MKL_THREADING
#     MKL threading mode: SEQ, TBB or OMP (default)
#
#   USE_ROCM_KERNEL_ASSERT=1
#     Enable kernel assert in ROCm platform
#
# Environment variables we respect (these environment variables are
# conventional and are often understood/set by other software.)
#
#   CUDA_HOME (Linux/OS X)
#   CUDA_PATH (Windows)
#     specify where CUDA is installed; usually /usr/local/cuda or
#     /usr/local/cuda-x.y
#   CUDAHOSTCXX
#     specify a different compiler than the system one to use as the CUDA
#     host compiler for nvcc.
#
#   CUDA_NVCC_EXECUTABLE
#     Specify a NVCC to use. This is used in our CI to point to a cached nvcc
#
#   CUDNN_LIB_DIR
#   CUDNN_INCLUDE_DIR
#   CUDNN_LIBRARY
#     specify where cuDNN is installed
#
#   MIOPEN_LIB_DIR
#   MIOPEN_INCLUDE_DIR
#   MIOPEN_LIBRARY
#     specify where MIOpen is installed
#
#   NCCL_ROOT
#   NCCL_LIB_DIR
#   NCCL_INCLUDE_DIR
#     specify where nccl is installed
#
#   ACL_ROOT_DIR
#     specify where Compute Library is installed
#
#   LIBRARY_PATH
#   LD_LIBRARY_PATH
#     we will search for libraries in these paths
#
#   ATEN_THREADING
#     ATen parallel backend to use for intra- and inter-op parallelism
#     possible values:
#       OMP - use OpenMP for intra-op and native backend for inter-op tasks
#       NATIVE - use native thread pool for both intra- and inter-op tasks
#
#   USE_SYSTEM_LIBS (work in progress)
#      Use system-provided libraries to satisfy the build dependencies.
#      When turned on, the following cmake variables will be toggled as well:
#        USE_SYSTEM_CPUINFO=ON
#        USE_SYSTEM_SLEEF=ON
#        USE_SYSTEM_GLOO=ON
#        BUILD_CUSTOM_PROTOBUF=OFF
#        USE_SYSTEM_EIGEN_INSTALL=ON
#        USE_SYSTEM_FP16=ON
#        USE_SYSTEM_PTHREADPOOL=ON
#        USE_SYSTEM_PSIMD=ON
#        USE_SYSTEM_FXDIV=ON
#        USE_SYSTEM_BENCHMARK=ON
#        USE_SYSTEM_ONNX=ON
#        USE_SYSTEM_XNNPACK=ON
#        USE_SYSTEM_PYBIND11=ON
#        USE_SYSTEM_NCCL=ON
#        USE_SYSTEM_NVTX=ON
#
#   USE_MIMALLOC
#      Static link mimalloc into C10, and use mimalloc in alloc_cpu & alloc_free.
#      By default, It is only enabled on Windows.
#
#   USE_PRIORITIZED_TEXT_FOR_LD
#      Uses prioritized text form cmake/prioritized_text.txt for LD
#
#   BUILD_LIBTORCH_WHL
#      Builds libtorch.so and its dependencies as a wheel
#
#   BUILD_PYTHON_ONLY
#      Builds pytorch as a wheel using libtorch.so from a seperate wheel

import os
import sys


if sys.platform == "win32" and sys.maxsize.bit_length() == 31:
    print(
        "32-bit Windows Python runtime is not supported. Please switch to 64-bit Python."
    )
    sys.exit(-1)

import platform


BUILD_LIBTORCH_WHL = os.getenv("BUILD_LIBTORCH_WHL", "0") == "1"
BUILD_PYTHON_ONLY = os.getenv("BUILD_PYTHON_ONLY", "0") == "1"

python_min_version = (3, 9, 0)
python_min_version_str = ".".join(map(str, python_min_version))
if sys.version_info < python_min_version:
    print(
        f"You are using Python {platform.python_version()}. Python >={python_min_version_str} is required."
    )
    sys.exit(-1)

import filecmp
import glob
import importlib
import importlib.util
import json
import shutil
import subprocess
import sysconfig
import time
from collections import defaultdict

import setuptools.command.build_ext
import setuptools.command.install
import setuptools.command.sdist
from setuptools import Extension, find_packages, setup
from setuptools.dist import Distribution
from tools.build_pytorch_libs import build_pytorch
from tools.generate_torch_version import get_torch_version
from tools.setup_helpers.cmake import CMake
from tools.setup_helpers.env import build_type, IS_DARWIN, IS_LINUX, IS_WINDOWS
from tools.setup_helpers.generate_linker_script import gen_linker_script


def _get_package_path(package_name):
    spec = importlib.util.find_spec(package_name)
    if spec:
        # The package might be a namespace package, so get_data may fail
        try:
            loader = spec.loader
            if loader is not None:
                file_path = loader.get_filename()  # type: ignore[attr-defined]
                return os.path.dirname(file_path)
        except AttributeError:
            pass
    return None


# set up appropriate env variables
if BUILD_LIBTORCH_WHL:
    # Set up environment variables for ONLY building libtorch.so and not libtorch_python.so
    # functorch is not supported without python
    os.environ["BUILD_FUNCTORCH"] = "OFF"


if BUILD_PYTHON_ONLY:
    os.environ["BUILD_LIBTORCHLESS"] = "ON"
    os.environ["LIBTORCH_LIB_PATH"] = f"{_get_package_path('torch')}/lib"

################################################################################
# Parameters parsed from environment
################################################################################

VERBOSE_SCRIPT = True
RUN_BUILD_DEPS = True
# see if the user passed a quiet flag to setup.py arguments and respect
# that in our parts of the build
EMIT_BUILD_WARNING = False
RERUN_CMAKE = False
CMAKE_ONLY = False
filtered_args = []
for i, arg in enumerate(sys.argv):
    if arg == "--cmake":
        RERUN_CMAKE = True
        continue
    if arg == "--cmake-only":
        # Stop once cmake terminates. Leave users a chance to adjust build
        # options.
        CMAKE_ONLY = True
        continue
    if arg == "rebuild" or arg == "build":
        arg = "build"  # rebuild is gone, make it build
        EMIT_BUILD_WARNING = True
    if arg == "--":
        filtered_args += sys.argv[i:]
        break
    if arg == "-q" or arg == "--quiet":
        VERBOSE_SCRIPT = False
    if arg in ["clean", "egg_info", "sdist"]:
        RUN_BUILD_DEPS = False
    filtered_args.append(arg)
sys.argv = filtered_args

if VERBOSE_SCRIPT:

    def report(*args):
        print(*args)

else:

    def report(*args):
        pass

    # Make distutils respect --quiet too
    setuptools.distutils.log.warn = report

# Constant known variables used throughout this file
cwd = os.path.dirname(os.path.abspath(__file__))
lib_path = os.path.join(cwd, "torch", "lib")
third_party_path = os.path.join(cwd, "third_party")

# CMAKE: full path to python library
if IS_WINDOWS:
    cmake_python_library = "{}/libs/python{}.lib".format(
        sysconfig.get_config_var("prefix"), sysconfig.get_config_var("VERSION")
    )
    # Fix virtualenv builds
    if not os.path.exists(cmake_python_library):
        cmake_python_library = "{}/libs/python{}.lib".format(
            sys.base_prefix, sysconfig.get_config_var("VERSION")
        )
else:
    cmake_python_library = "{}/{}".format(
        sysconfig.get_config_var("LIBDIR"), sysconfig.get_config_var("INSTSONAME")
    )
cmake_python_include_dir = sysconfig.get_path("include")


################################################################################
# Version, create_version_file, and package_name
################################################################################

package_name = os.getenv("TORCH_PACKAGE_NAME", "torch")
LIBTORCH_PKG_NAME = os.getenv("LIBTORCH_PACKAGE_NAME", "torch_no_python")
if BUILD_LIBTORCH_WHL:
    package_name = LIBTORCH_PKG_NAME


package_type = os.getenv("PACKAGE_TYPE", "wheel")
version = get_torch_version()
report(f"Building wheel {package_name}-{version}")

cmake = CMake()


def get_submodule_folders():
    git_modules_path = os.path.join(cwd, ".gitmodules")
    default_modules_path = [
        os.path.join(third_party_path, name)
        for name in [
            "gloo",
            "cpuinfo",
            "onnx",
            "fbgemm",
            "cutlass",
        ]
    ]
    if not os.path.exists(git_modules_path):
        return default_modules_path
    with open(git_modules_path) as f:
        return [
            os.path.join(cwd, line.split("=", 1)[1].strip())
            for line in f
            if line.strip().startswith("path")
        ]


def check_submodules():
    def check_for_files(folder, files):
        if not any(os.path.exists(os.path.join(folder, f)) for f in files):
            report("Could not find any of {} in {}".format(", ".join(files), folder))
            report("Did you run 'git submodule update --init --recursive'?")
            sys.exit(1)

    def not_exists_or_empty(folder):
        return not os.path.exists(folder) or (
            os.path.isdir(folder) and len(os.listdir(folder)) == 0
        )

    if bool(os.getenv("USE_SYSTEM_LIBS", False)):
        return
    folders = get_submodule_folders()
    # If none of the submodule folders exists, try to initialize them
    if all(not_exists_or_empty(folder) for folder in folders):
        try:
            print(" --- Trying to initialize submodules")
            start = time.time()
            subprocess.check_call(
                ["git", "submodule", "update", "--init", "--recursive"], cwd=cwd
            )
            end = time.time()
            print(f" --- Submodule initialization took {end - start:.2f} sec")
        except Exception:
            print(" --- Submodule initalization failed")
            print("Please run:\n\tgit submodule update --init --recursive")
            sys.exit(1)
    for folder in folders:
        check_for_files(
            folder,
            [
                "CMakeLists.txt",
                "Makefile",
                "setup.py",
                "LICENSE",
                "LICENSE.md",
                "LICENSE.txt",
            ],
        )
    check_for_files(
        os.path.join(third_party_path, "fbgemm", "third_party", "asmjit"),
        ["CMakeLists.txt"],
    )


# Windows has very bad support for symbolic links.
# Instead of using symlinks, we're going to copy files over
def mirror_files_into_torchgen():
    # (new_path, orig_path)
    # Directories are OK and are recursively mirrored.
    paths = [
        (
            "torchgen/packaged/ATen/native/native_functions.yaml",
            "aten/src/ATen/native/native_functions.yaml",
        ),
        ("torchgen/packaged/ATen/native/tags.yaml", "aten/src/ATen/native/tags.yaml"),
        ("torchgen/packaged/ATen/templates", "aten/src/ATen/templates"),
        ("torchgen/packaged/autograd", "tools/autograd"),
        ("torchgen/packaged/autograd/templates", "tools/autograd/templates"),
    ]
    for new_path, orig_path in paths:
        # Create the dirs involved in new_path if they don't exist
        if not os.path.exists(new_path):
            os.makedirs(os.path.dirname(new_path), exist_ok=True)

        # Copy the files from the orig location to the new location
        if os.path.isfile(orig_path):
            shutil.copyfile(orig_path, new_path)
            continue
        if os.path.isdir(orig_path):
            if os.path.exists(new_path):
                # copytree fails if the tree exists already, so remove it.
                shutil.rmtree(new_path)
            shutil.copytree(orig_path, new_path)
            continue
        raise RuntimeError("Check the file paths in `mirror_files_into_torchgen()`")


# all the work we need to do _before_ setup runs
def build_deps():
    report("-- Building version " + version)

    check_submodules()
    check_pydep("yaml", "pyyaml")
    build_python = not BUILD_LIBTORCH_WHL
    build_pytorch(
        version=version,
        cmake_python_library=cmake_python_library,
        build_python=build_python,
        rerun_cmake=RERUN_CMAKE,
        cmake_only=CMAKE_ONLY,
        cmake=cmake,
    )

    if CMAKE_ONLY:
        report(
            'Finished running cmake. Run "ccmake build" or '
            '"cmake-gui build" to adjust build options and '
            '"python setup.py install" to build.'
        )
        sys.exit()

    # Use copies instead of symbolic files.
    # Windows has very poor support for them.
    sym_files = [
        "tools/shared/_utils_internal.py",
        "torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h",
        "torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h",
    ]
    orig_files = [
        "torch/_utils_internal.py",
        "third_party/valgrind-headers/callgrind.h",
        "third_party/valgrind-headers/valgrind.h",
    ]
    for sym_file, orig_file in zip(sym_files, orig_files):
        same = False
        if os.path.exists(sym_file):
            if filecmp.cmp(sym_file, orig_file):
                same = True
            else:
                os.remove(sym_file)
        if not same:
            shutil.copyfile(orig_file, sym_file)


################################################################################
# Building dependent libraries
################################################################################

missing_pydep = """
Missing build dependency: Unable to `import {importname}`.
Please install it via `conda install {module}` or `pip install {module}`
""".strip()


def check_pydep(importname, module):
    try:
        importlib.import_module(importname)
    except ImportError as e:
        raise RuntimeError(
            missing_pydep.format(importname=importname, module=module)
        ) from e


class build_ext(setuptools.command.build_ext.build_ext):
    def _embed_libomp(self):
        # Copy libiomp5.dylib/libomp.dylib inside the wheel package on MacOS
        lib_dir = os.path.join(self.build_lib, "torch", "lib")
        libtorch_cpu_path = os.path.join(lib_dir, "libtorch_cpu.dylib")
        if not os.path.exists(libtorch_cpu_path):
            return
        # Parse libtorch_cpu load commands
        otool_cmds = (
            subprocess.check_output(["otool", "-l", libtorch_cpu_path])
            .decode("utf-8")
            .split("\n")
        )
        rpaths, libs = [], []
        for idx, line in enumerate(otool_cmds):
            if line.strip() == "cmd LC_LOAD_DYLIB":
                lib_name = otool_cmds[idx + 2].strip()
                assert lib_name.startswith("name ")
                libs.append(lib_name.split(" ", 1)[1].rsplit("(", 1)[0][:-1])

            if line.strip() == "cmd LC_RPATH":
                rpath = otool_cmds[idx + 2].strip()
                assert rpath.startswith("path ")
                rpaths.append(rpath.split(" ", 1)[1].rsplit("(", 1)[0][:-1])

        omp_lib_name = (
            "libomp.dylib" if os.uname().machine == "arm64" else "libiomp5.dylib"
        )
        omp_rpath_lib_path = os.path.join("@rpath", omp_lib_name)
        if omp_rpath_lib_path not in libs:
            return

        # Copy libomp/libiomp5 from rpath locations
        for rpath in rpaths:
            source_lib = os.path.join(rpath, omp_lib_name)
            if not os.path.exists(source_lib):
                continue
            target_lib = os.path.join(self.build_lib, "torch", "lib", omp_lib_name)
            self.copy_file(source_lib, target_lib)
            # Delete old rpath and add @loader_lib to the rpath
            # This should prevent delocate from attempting to package another instance
            # of OpenMP library in torch wheel as well as loading two libomp.dylib into
            # the address space, as libraries are cached by their unresolved names
            subprocess.check_call(
                [
                    "install_name_tool",
                    "-rpath",
                    rpath,
                    "@loader_path",
                    libtorch_cpu_path,
                ]
            )
            break

        # Copy omp.h from OpenMP_C_FLAGS and copy it into include folder
        omp_cflags = get_cmake_cache_vars()["OpenMP_C_FLAGS"]
        if not omp_cflags:
            return
        for include_dir in [f[2:] for f in omp_cflags.split(" ") if f.startswith("-I")]:
            omp_h = os.path.join(include_dir, "omp.h")
            if not os.path.exists(omp_h):
                continue
            target_omp_h = os.path.join(self.build_lib, "torch", "include", "omp.h")
            self.copy_file(omp_h, target_omp_h)
            break

    def run(self):
        # Report build options. This is run after the build completes so # `CMakeCache.txt` exists and we can get an
        # accurate report on what is used and what is not.
        cmake_cache_vars = defaultdict(lambda: False, cmake.get_cmake_cache_variables())
        if cmake_cache_vars["USE_NUMPY"]:
            report("-- Building with NumPy bindings")
        else:
            report("-- NumPy not found")
        if cmake_cache_vars["USE_CUDNN"]:
            report(
                "-- Detected cuDNN at "
                + cmake_cache_vars["CUDNN_LIBRARY"]
                + ", "
                + cmake_cache_vars["CUDNN_INCLUDE_DIR"]
            )
        else:
            report("-- Not using cuDNN")
        if cmake_cache_vars["USE_CUDA"]:
            report("-- Detected CUDA at " + cmake_cache_vars["CUDA_TOOLKIT_ROOT_DIR"])
        else:
            report("-- Not using CUDA")
        if cmake_cache_vars["USE_XPU"]:
            report("-- Detected XPU runtime at " + cmake_cache_vars["SYCL_LIBRARY_DIR"])
        else:
            report("-- Not using XPU")
        if cmake_cache_vars["USE_MKLDNN"]:
            report("-- Using MKLDNN")
            if cmake_cache_vars["USE_MKLDNN_ACL"]:
                report("-- Using Compute Library for the Arm architecture with MKLDNN")
            else:
                report(
                    "-- Not using Compute Library for the Arm architecture with MKLDNN"
                )
            if cmake_cache_vars["USE_MKLDNN_CBLAS"]:
                report("-- Using CBLAS in MKLDNN")
            else:
                report("-- Not using CBLAS in MKLDNN")
        else:
            report("-- Not using MKLDNN")
        if cmake_cache_vars["USE_NCCL"] and cmake_cache_vars["USE_SYSTEM_NCCL"]:
            report(
                "-- Using system provided NCCL library at {}, {}".format(
                    cmake_cache_vars["NCCL_LIBRARIES"],
                    cmake_cache_vars["NCCL_INCLUDE_DIRS"],
                )
            )
        elif cmake_cache_vars["USE_NCCL"]:
            report("-- Building NCCL library")
        else:
            report("-- Not using NCCL")
        if cmake_cache_vars["USE_DISTRIBUTED"]:
            if IS_WINDOWS:
                report("-- Building without distributed package")
            else:
                report("-- Building with distributed package: ")
                report(
                    "  -- USE_TENSORPIPE={}".format(cmake_cache_vars["USE_TENSORPIPE"])
                )
                report("  -- USE_GLOO={}".format(cmake_cache_vars["USE_GLOO"]))
                report("  -- USE_MPI={}".format(cmake_cache_vars["USE_OPENMPI"]))
        else:
            report("-- Building without distributed package")
        if cmake_cache_vars["STATIC_DISPATCH_BACKEND"]:
            report(
                "-- Using static dispatch with backend {}".format(
                    cmake_cache_vars["STATIC_DISPATCH_BACKEND"]
                )
            )
        if cmake_cache_vars["USE_LIGHTWEIGHT_DISPATCH"]:
            report("-- Using lightweight dispatch")
        if cmake_cache_vars["BUILD_EXECUTORCH"]:
            report("-- Building Executorch")

        if cmake_cache_vars["USE_ITT"]:
            report("-- Using ITT")
        else:
            report("-- Not using ITT")

        # Do not use clang to compile extensions if `-fstack-clash-protection` is defined
        # in system CFLAGS
        c_flags = str(os.getenv("CFLAGS", ""))
        if (
            IS_LINUX
            and "-fstack-clash-protection" in c_flags
            and "clang" in os.environ.get("CC", "")
        ):
            os.environ["CC"] = str(os.environ["CC"])

        # It's an old-style class in Python 2.7...
        setuptools.command.build_ext.build_ext.run(self)

        if IS_DARWIN and package_type != "conda":
            self._embed_libomp()

        # Copy the essential export library to compile C++ extensions.
        if IS_WINDOWS:
            build_temp = self.build_temp

            ext_filename = self.get_ext_filename("_C")
            lib_filename = ".".join(ext_filename.split(".")[:-1]) + ".lib"

            export_lib = os.path.join(
                build_temp, "torch", "csrc", lib_filename
            ).replace("\\", "/")

            build_lib = self.build_lib

            target_lib = os.path.join(build_lib, "torch", "lib", "_C.lib").replace(
                "\\", "/"
            )

            # Create "torch/lib" directory if not exists.
            # (It is not created yet in "develop" mode.)
            target_dir = os.path.dirname(target_lib)
            if not os.path.exists(target_dir):
                os.makedirs(target_dir)

            self.copy_file(export_lib, target_lib)

    def build_extensions(self):
        self.create_compile_commands()

        # Copy functorch extension
        for i, ext in enumerate(self.extensions):
            if ext.name != "functorch._C":
                continue
            fullname = self.get_ext_fullname(ext.name)
            filename = self.get_ext_filename(fullname)
            fileext = os.path.splitext(filename)[1]
            src = os.path.join(os.path.dirname(filename), "functorch" + fileext)
            dst = os.path.join(os.path.realpath(self.build_lib), filename)
            if os.path.exists(src):
                report(f"Copying {ext.name} from {src} to {dst}")
                dst_dir = os.path.dirname(dst)
                if not os.path.exists(dst_dir):
                    os.makedirs(dst_dir)
                self.copy_file(src, dst)

        setuptools.command.build_ext.build_ext.build_extensions(self)

    def get_outputs(self):
        outputs = setuptools.command.build_ext.build_ext.get_outputs(self)
        outputs.append(os.path.join(self.build_lib, "caffe2"))
        report(f"setup.py::get_outputs returning {outputs}")
        return outputs

    def create_compile_commands(self):
        def load(filename):
            with open(filename) as f:
                return json.load(f)

        ninja_files = glob.glob("build/*compile_commands.json")
        cmake_files = glob.glob("torch/lib/build/*/compile_commands.json")
        all_commands = [entry for f in ninja_files + cmake_files for entry in load(f)]

        # cquery does not like c++ compiles that start with gcc.
        # It forgets to include the c++ header directories.
        # We can work around this by replacing the gcc calls that python
        # setup.py generates with g++ calls instead
        for command in all_commands:
            if command["command"].startswith("gcc "):
                command["command"] = "g++ " + command["command"][4:]

        new_contents = json.dumps(all_commands, indent=2)
        contents = ""
        if os.path.exists("compile_commands.json"):
            with open("compile_commands.json") as f:
                contents = f.read()
        if contents != new_contents:
            with open("compile_commands.json", "w") as f:
                f.write(new_contents)


class concat_license_files:
    """Merge LICENSE and LICENSES_BUNDLED.txt as a context manager

    LICENSE is the main PyTorch license, LICENSES_BUNDLED.txt is auto-generated
    from all the licenses found in ./third_party/. We concatenate them so there
    is a single license file in the sdist and wheels with all of the necessary
    licensing info.
    """

    def __init__(self, include_files=False):
        self.f1 = "LICENSE"
        self.f2 = "third_party/LICENSES_BUNDLED.txt"
        self.include_files = include_files

    def __enter__(self):
        """Concatenate files"""

        old_path = sys.path
        sys.path.append(third_party_path)
        try:
            from build_bundled import create_bundled
        finally:
            sys.path = old_path

        with open(self.f1) as f1:
            self.bsd_text = f1.read()

        with open(self.f1, "a") as f1:
            f1.write("\n\n")
            create_bundled(
                os.path.relpath(third_party_path), f1, include_files=self.include_files
            )

    def __exit__(self, exception_type, exception_value, traceback):
        """Restore content of f1"""
        with open(self.f1, "w") as f:
            f.write(self.bsd_text)


try:
    from wheel.bdist_wheel import bdist_wheel
except ImportError:
    # This is useful when wheel is not installed and bdist_wheel is not
    # specified on the command line. If it _is_ specified, parsing the command
    # line will fail before wheel_concatenate is needed
    wheel_concatenate = None
else:
    # Need to create the proper LICENSE.txt for the wheel
    class wheel_concatenate(bdist_wheel):
        """check submodules on sdist to prevent incomplete tarballs"""

        def run(self):
            with concat_license_files(include_files=True):
                super().run()

        def write_wheelfile(self, *args, **kwargs):
            super().write_wheelfile(*args, **kwargs)

            if BUILD_LIBTORCH_WHL:
                # Remove extraneneous files in the libtorch wheel
                for root, dirs, files in os.walk(self.bdist_dir):
                    for file in files:
                        if file.endswith((".a", ".so")) and os.path.isfile(
                            os.path.join(self.bdist_dir, file)
                        ):
                            os.remove(os.path.join(root, file))
                        elif file.endswith(".py"):
                            os.remove(os.path.join(root, file))
                # need an __init__.py file otherwise we wouldn't have a package
                open(os.path.join(self.bdist_dir, "torch", "__init__.py"), "w").close()


class install(setuptools.command.install.install):
    def run(self):
        super().run()


class clean(setuptools.Command):
    user_options = []

    def initialize_options(self):
        pass

    def finalize_options(self):
        pass

    def run(self):
        import glob
        import re

        with open(".gitignore") as f:
            ignores = f.read()
            pat = re.compile(r"^#( BEGIN NOT-CLEAN-FILES )?")
            for wildcard in filter(None, ignores.split("\n")):
                match = pat.match(wildcard)
                if match:
                    if match.group(1):
                        # Marker is found and stop reading .gitignore.
                        break
                    # Ignore lines which begin with '#'.
                else:
                    # Don't remove absolute paths from the system
                    wildcard = wildcard.lstrip("./")

                    for filename in glob.glob(wildcard):
                        try:
                            os.remove(filename)
                        except OSError:
                            shutil.rmtree(filename, ignore_errors=True)


class sdist(setuptools.command.sdist.sdist):
    def run(self):
        with concat_license_files():
            super().run()


def get_cmake_cache_vars():
    try:
        return defaultdict(lambda: False, cmake.get_cmake_cache_variables())
    except FileNotFoundError:
        # CMakeCache.txt does not exist. Probably running "python setup.py clean" over a clean directory.
        return defaultdict(lambda: False)


def configure_extension_build():
    r"""Configures extension build options according to system environment and user's choice.

    Returns:
      The input to parameters ext_modules, cmdclass, packages, and entry_points as required in setuptools.setup.
    """

    cmake_cache_vars = get_cmake_cache_vars()

    ################################################################################
    # Configure compile flags
    ################################################################################

    library_dirs = []
    extra_install_requires = []

    if IS_WINDOWS:
        # /NODEFAULTLIB makes sure we only link to DLL runtime
        # and matches the flags set for protobuf and ONNX
        extra_link_args = ["/NODEFAULTLIB:LIBCMT.LIB"]
        # /MD links against DLL runtime
        # and matches the flags set for protobuf and ONNX
        # /EHsc is about standard C++ exception handling
        extra_compile_args = ["/MD", "/FS", "/EHsc"]
    else:
        extra_link_args = []
        extra_compile_args = [
            "-Wall",
            "-Wextra",
            "-Wno-strict-overflow",
            "-Wno-unused-parameter",
            "-Wno-missing-field-initializers",
            "-Wno-unknown-pragmas",
            # Python 2.6 requires -fno-strict-aliasing, see
            # http://legacy.python.org/dev/peps/pep-3123/
            # We also depend on it in our code (even Python 3).
            "-fno-strict-aliasing",
        ]

    library_dirs.append(lib_path)

    main_compile_args = []
    main_libraries = ["torch_python"]

    main_link_args = []
    main_sources = ["torch/csrc/stub.c"]

    if BUILD_LIBTORCH_WHL:
        main_libraries = ["torch"]
        main_sources = []

    if build_type.is_debug():
        if IS_WINDOWS:
            extra_compile_args.append("/Z7")
            extra_link_args.append("/DEBUG:FULL")
        else:
            extra_compile_args += ["-O0", "-g"]
            extra_link_args += ["-O0", "-g"]

    if build_type.is_rel_with_deb_info():
        if IS_WINDOWS:
            extra_compile_args.append("/Z7")
            extra_link_args.append("/DEBUG:FULL")
        else:
            extra_compile_args += ["-g"]
            extra_link_args += ["-g"]

    # pypi cuda package that requires installation of cuda runtime, cudnn and cublas
    # should be included in all wheels uploaded to pypi
    pytorch_extra_install_requirements = os.getenv(
        "PYTORCH_EXTRA_INSTALL_REQUIREMENTS", ""
    )
    if pytorch_extra_install_requirements:
        report(
            f"pytorch_extra_install_requirements: {pytorch_extra_install_requirements}"
        )
        extra_install_requires += pytorch_extra_install_requirements.split("|")

    # Cross-compile for M1
    if IS_DARWIN:
        macos_target_arch = os.getenv("CMAKE_OSX_ARCHITECTURES", "")
        if macos_target_arch in ["arm64", "x86_64"]:
            macos_sysroot_path = os.getenv("CMAKE_OSX_SYSROOT")
            if macos_sysroot_path is None:
                macos_sysroot_path = (
                    subprocess.check_output(
                        ["xcrun", "--show-sdk-path", "--sdk", "macosx"]
                    )
                    .decode("utf-8")
                    .strip()
                )
            extra_compile_args += [
                "-arch",
                macos_target_arch,
                "-isysroot",
                macos_sysroot_path,
            ]
            extra_link_args += ["-arch", macos_target_arch]

    def make_relative_rpath_args(path):
        if IS_DARWIN:
            return ["-Wl,-rpath,@loader_path/" + path]
        elif IS_WINDOWS:
            return []
        else:
            return ["-Wl,-rpath,$ORIGIN/" + path]

    ################################################################################
    # Declare extensions and package
    ################################################################################

    extensions = []
    excludes = ["tools", "tools.*", "caffe2", "caffe2.*"]
    if not cmake_cache_vars["BUILD_FUNCTORCH"]:
        excludes.extend(["functorch", "functorch.*"])
    packages = find_packages(exclude=excludes)
    C = Extension(
        "torch._C",
        libraries=main_libraries,
        sources=main_sources,
        language="c",
        extra_compile_args=main_compile_args + extra_compile_args,
        include_dirs=[],
        library_dirs=library_dirs,
        extra_link_args=extra_link_args
        + main_link_args
        + make_relative_rpath_args("lib"),
    )
    extensions.append(C)

    # These extensions are built by cmake and copied manually in build_extensions()
    # inside the build_ext implementation
    if cmake_cache_vars["BUILD_FUNCTORCH"]:
        extensions.append(
            Extension(name="functorch._C", sources=[]),
        )

    cmdclass = {
        "bdist_wheel": wheel_concatenate,
        "build_ext": build_ext,
        "clean": clean,
        "install": install,
        "sdist": sdist,
    }

    entry_points = {
        "console_scripts": [
            "torchrun = torch.distributed.run:main",
        ],
        "torchrun.logs_specs": [
            "default = torch.distributed.elastic.multiprocessing:DefaultLogsSpecs",
        ],
    }

    if cmake_cache_vars["USE_DISTRIBUTED"]:
        # Only enable fr_trace command if distributed is enabled
        entry_points["console_scripts"].append(
            "torchfrtrace = tools.flight_recorder.fr_trace:main",
        )
    return extensions, cmdclass, packages, entry_points, extra_install_requires


# post run, warnings, printed at the end to make them more visible
build_update_message = """
    It is no longer necessary to use the 'build' or 'rebuild' targets

    To install:
      $ python setup.py install
    To develop locally:
      $ python setup.py develop
    To force cmake to re-generate native build files (off by default):
      $ python setup.py develop --cmake
"""


def print_box(msg):
    lines = msg.split("\n")
    size = max(len(l) + 1 for l in lines)
    print("-" * (size + 2))
    for l in lines:
        print("|{}{}|".format(l, " " * (size - len(l))))
    print("-" * (size + 2))


def main():
    if BUILD_LIBTORCH_WHL and BUILD_PYTHON_ONLY:
        raise RuntimeError(
            "Conflict: 'BUILD_LIBTORCH_WHL' and 'BUILD_PYTHON_ONLY' can't both be 1. Set one to 0 and rerun."
        )
    install_requires = [
        "filelock",
        "typing-extensions>=4.10.0",
        'setuptools ; python_version >= "3.12"',
        'sympy==1.13.1 ; python_version >= "3.9"',
        "networkx",
        "jinja2",
        "fsspec",
    ]

    if BUILD_PYTHON_ONLY:
        install_requires.append(f"{LIBTORCH_PKG_NAME}=={get_torch_version()}")

    use_prioritized_text = str(os.getenv("USE_PRIORITIZED_TEXT_FOR_LD", ""))
    if (
        use_prioritized_text == ""
        and platform.system() == "Linux"
        and platform.processor() == "aarch64"
    ):
        print_box(
            """
            WARNING: we strongly recommend enabling linker script optimization for ARM + CUDA.
            To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
            """
        )
    if use_prioritized_text == "1" or use_prioritized_text == "True":
        gen_linker_script(
            filein="cmake/prioritized_text.txt", fout="cmake/linker_script.ld"
        )
        linker_script_path = os.path.abspath("cmake/linker_script.ld")
        os.environ["LDFLAGS"] = os.getenv("LDFLAGS", "") + f" -T{linker_script_path}"
        os.environ["CFLAGS"] = (
            os.getenv("CFLAGS", "") + " -ffunction-sections -fdata-sections"
        )
        os.environ["CXXFLAGS"] = (
            os.getenv("CXXFLAGS", "") + " -ffunction-sections -fdata-sections"
        )

    # Parse the command line and check the arguments before we proceed with
    # building deps and setup. We need to set values so `--help` works.
    dist = Distribution()
    dist.script_name = os.path.basename(sys.argv[0])
    dist.script_args = sys.argv[1:]
    try:
        dist.parse_command_line()
    except setuptools.distutils.errors.DistutilsArgError as e:
        print(e)
        sys.exit(1)

    mirror_files_into_torchgen()
    if RUN_BUILD_DEPS:
        build_deps()

    (
        extensions,
        cmdclass,
        packages,
        entry_points,
        extra_install_requires,
    ) = configure_extension_build()
    install_requires += extra_install_requires

    extras_require = {
        "optree": ["optree>=0.13.0"],
        "opt-einsum": ["opt-einsum>=3.3"],
    }

    # Read in README.md for our long_description
    with open(os.path.join(cwd, "README.md"), encoding="utf-8") as f:
        long_description = f.read()

    version_range_max = max(sys.version_info[1], 12) + 1
    torch_package_data = [
        "py.typed",
        "bin/*",
        "test/*",
        "*.pyi",
        "_C/*.pyi",
        "cuda/*.pyi",
        "fx/*.pyi",
        "optim/*.pyi",
        "autograd/*.pyi",
        "jit/*.pyi",
        "nn/*.pyi",
        "nn/modules/*.pyi",
        "nn/parallel/*.pyi",
        "utils/data/*.pyi",
        "utils/data/datapipes/*.pyi",
        "lib/*.pdb",
        "lib/*shm*",
        "lib/torch_shm_manager",
        "lib/*.h",
        "include/*.h",
        "include/ATen/*.h",
        "include/ATen/cpu/*.h",
        "include/ATen/cpu/vec/vec128/*.h",
        "include/ATen/cpu/vec/vec256/*.h",
        "include/ATen/cpu/vec/vec256/vsx/*.h",
        "include/ATen/cpu/vec/vec256/zarch/*.h",
        "include/ATen/cpu/vec/vec512/*.h",
        "include/ATen/cpu/vec/*.h",
        "include/ATen/cpu/vec/sve/*.h",
        "include/ATen/core/*.h",
        "include/ATen/cuda/*.cuh",
        "include/ATen/cuda/*.h",
        "include/ATen/cuda/detail/*.cuh",
        "include/ATen/cuda/detail/*.h",
        "include/ATen/cuda/tunable/*.h",
        "include/ATen/cudnn/*.h",
        "include/ATen/functorch/*.h",
        "include/ATen/ops/*.h",
        "include/ATen/hip/*.cuh",
        "include/ATen/hip/*.h",
        "include/ATen/hip/detail/*.cuh",
        "include/ATen/hip/detail/*.h",
        "include/ATen/hip/impl/*.h",
        "include/ATen/hip/tunable/*.h",
        "include/ATen/mps/*.h",
        "include/ATen/miopen/*.h",
        "include/ATen/detail/*.h",
        "include/ATen/native/*.h",
        "include/ATen/native/cpu/*.h",
        "include/ATen/native/cuda/*.h",
        "include/ATen/native/cuda/*.cuh",
        "include/ATen/native/hip/*.h",
        "include/ATen/native/hip/*.cuh",
        "include/ATen/native/mps/*.h",
        "include/ATen/native/mkldnn/xpu/*.h",
        "include/ATen/native/mkldnn/xpu/detail/*.h",
        "include/ATen/native/nested/*.h",
        "include/ATen/native/quantized/*.h",
        "include/ATen/native/quantized/cpu/*.h",
        "include/ATen/native/transformers/*.h",
        "include/ATen/native/sparse/*.h",
        "include/ATen/native/utils/*.h",
        "include/ATen/quantized/*.h",
        "include/ATen/xpu/*.h",
        "include/ATen/xpu/detail/*.h",
        "include/caffe2/serialize/*.h",
        "include/c10/*.h",
        "include/c10/macros/*.h",
        "include/c10/core/*.h",
        "include/ATen/core/boxing/*.h",
        "include/ATen/core/boxing/impl/*.h",
        "include/ATen/core/dispatch/*.h",
        "include/ATen/core/op_registration/*.h",
        "include/c10/core/impl/*.h",
        "include/c10/util/*.h",
        "include/c10/cuda/*.h",
        "include/c10/cuda/impl/*.h",
        "include/c10/hip/*.h",
        "include/c10/hip/impl/*.h",
        "include/c10/xpu/*.h",
        "include/c10/xpu/impl/*.h",
        "include/torch/*.h",
        "include/torch/csrc/*.h",
        "include/torch/csrc/api/include/torch/*.h",
        "include/torch/csrc/api/include/torch/data/*.h",
        "include/torch/csrc/api/include/torch/data/dataloader/*.h",
        "include/torch/csrc/api/include/torch/data/datasets/*.h",
        "include/torch/csrc/api/include/torch/data/detail/*.h",
        "include/torch/csrc/api/include/torch/data/samplers/*.h",
        "include/torch/csrc/api/include/torch/data/transforms/*.h",
        "include/torch/csrc/api/include/torch/detail/*.h",
        "include/torch/csrc/api/include/torch/detail/ordered_dict.h",
        "include/torch/csrc/api/include/torch/nn/*.h",
        "include/torch/csrc/api/include/torch/nn/functional/*.h",
        "include/torch/csrc/api/include/torch/nn/options/*.h",
        "include/torch/csrc/api/include/torch/nn/modules/*.h",
        "include/torch/csrc/api/include/torch/nn/modules/container/*.h",
        "include/torch/csrc/api/include/torch/nn/parallel/*.h",
        "include/torch/csrc/api/include/torch/nn/utils/*.h",
        "include/torch/csrc/api/include/torch/optim/*.h",
        "include/torch/csrc/api/include/torch/optim/schedulers/*.h",
        "include/torch/csrc/api/include/torch/serialize/*.h",
        "include/torch/csrc/autograd/*.h",
        "include/torch/csrc/autograd/functions/*.h",
        "include/torch/csrc/autograd/generated/*.h",
        "include/torch/csrc/autograd/utils/*.h",
        "include/torch/csrc/cuda/*.h",
        "include/torch/csrc/distributed/c10d/*.h",
        "include/torch/csrc/distributed/c10d/*.hpp",
        "include/torch/csrc/distributed/rpc/*.h",
        "include/torch/csrc/distributed/autograd/context/*.h",
        "include/torch/csrc/distributed/autograd/functions/*.h",
        "include/torch/csrc/distributed/autograd/rpc_messages/*.h",
        "include/torch/csrc/dynamo/*.h",
        "include/torch/csrc/inductor/*.h",
        "include/torch/csrc/inductor/aoti_package/*.h",
        "include/torch/csrc/inductor/aoti_runner/*.h",
        "include/torch/csrc/inductor/aoti_runtime/*.h",
        "include/torch/csrc/inductor/aoti_torch/*.h",
        "include/torch/csrc/inductor/aoti_torch/c/*.h",
        "include/torch/csrc/inductor/aoti_torch/generated/*.h",
        "include/torch/csrc/inductor/aoti_torch/generated/extend/*.h",
        "include/torch/csrc/jit/*.h",
        "include/torch/csrc/jit/backends/*.h",
        "include/torch/csrc/jit/generated/*.h",
        "include/torch/csrc/jit/passes/*.h",
        "include/torch/csrc/jit/passes/quantization/*.h",
        "include/torch/csrc/jit/passes/utils/*.h",
        "include/torch/csrc/jit/runtime/*.h",
        "include/torch/csrc/jit/ir/*.h",
        "include/torch/csrc/jit/frontend/*.h",
        "include/torch/csrc/jit/api/*.h",
        "include/torch/csrc/jit/serialization/*.h",
        "include/torch/csrc/jit/python/*.h",
        "include/torch/csrc/jit/mobile/*.h",
        "include/torch/csrc/jit/testing/*.h",
        "include/torch/csrc/jit/tensorexpr/*.h",
        "include/torch/csrc/jit/tensorexpr/operators/*.h",
        "include/torch/csrc/jit/codegen/cuda/*.h",
        "include/torch/csrc/onnx/*.h",
        "include/torch/csrc/profiler/*.h",
        "include/torch/csrc/profiler/orchestration/*.h",
        "include/torch/csrc/profiler/standalone/*.h",
        "include/torch/csrc/profiler/stubs/*.h",
        "include/torch/csrc/profiler/unwind/*.h",
        "include/torch/csrc/profiler/python/*.h",
        "include/torch/csrc/utils/*.h",
        "include/torch/csrc/tensor/*.h",
        "include/torch/csrc/lazy/backend/*.h",
        "include/torch/csrc/lazy/core/*.h",
        "include/torch/csrc/lazy/core/internal_ops/*.h",
        "include/torch/csrc/lazy/core/ops/*.h",
        "include/torch/csrc/lazy/python/python_util.h",
        "include/torch/csrc/lazy/ts_backend/*.h",
        "include/torch/csrc/xpu/*.h",
        "include/pybind11/*.h",
        "include/pybind11/detail/*.h",
        "include/pybind11/eigen/*.h",
        "include/TH/*.h*",
        "include/TH/generic/*.h*",
        "include/THC/*.cuh",
        "include/THC/*.h*",
        "include/THC/generic/*.h",
        "include/THH/*.cuh",
        "include/THH/*.h*",
        "include/THH/generic/*.h",
        "include/sleef.h",
        "_inductor/codegen/*.h",
        "_inductor/codegen/aoti_runtime/*.cpp",
        "_export/serde/*.yaml",
        "share/cmake/ATen/*.cmake",
        "share/cmake/Caffe2/*.cmake",
        "share/cmake/Caffe2/public/*.cmake",
        "share/cmake/Caffe2/Modules_CUDA_fix/*.cmake",
        "share/cmake/Caffe2/Modules_CUDA_fix/upstream/*.cmake",
        "share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA/*.cmake",
        "share/cmake/Gloo/*.cmake",
        "share/cmake/Tensorpipe/*.cmake",
        "share/cmake/Torch/*.cmake",
        "utils/benchmark/utils/*.cpp",
        "utils/benchmark/utils/valgrind_wrapper/*.cpp",
        "utils/benchmark/utils/valgrind_wrapper/*.h",
        "utils/model_dump/skeleton.html",
        "utils/model_dump/code.js",
        "utils/model_dump/*.mjs",
    ]

    if not BUILD_LIBTORCH_WHL:
        torch_package_data.extend(
            [
                "lib/libtorch_python.so",
                "lib/libtorch_python.dylib",
                "lib/libtorch_python.dll",
            ]
        )
    if not BUILD_PYTHON_ONLY:
        torch_package_data.extend(
            [
                "lib/*.so*",
                "lib/*.dylib*",
                "lib/*.dll",
                "lib/*.lib",
            ]
        )
    if get_cmake_cache_vars()["USE_TENSORPIPE"]:
        torch_package_data.extend(
            [
                "include/tensorpipe/*.h",
                "include/tensorpipe/channel/*.h",
                "include/tensorpipe/channel/basic/*.h",
                "include/tensorpipe/channel/cma/*.h",
                "include/tensorpipe/channel/mpt/*.h",
                "include/tensorpipe/channel/xth/*.h",
                "include/tensorpipe/common/*.h",
                "include/tensorpipe/core/*.h",
                "include/tensorpipe/transport/*.h",
                "include/tensorpipe/transport/ibv/*.h",
                "include/tensorpipe/transport/shm/*.h",
                "include/tensorpipe/transport/uv/*.h",
            ]
        )
    if get_cmake_cache_vars()["USE_KINETO"]:
        torch_package_data.extend(
            [
                "include/kineto/*.h",
            ]
        )
    torchgen_package_data = [
        # Recursive glob doesn't work in setup.py,
        # https://github.com/pypa/setuptools/issues/1806
        # To make this robust we should replace it with some code that
        # returns a list of everything under packaged/
        "packaged/ATen/*",
        "packaged/ATen/native/*",
        "packaged/ATen/templates/*",
        "packaged/autograd/*",
        "packaged/autograd/templates/*",
    ]
    package_data = {
        "torch": torch_package_data,
    }

    if not BUILD_LIBTORCH_WHL:
        package_data["torchgen"] = torchgen_package_data
    else:
        # no extensions in BUILD_LIBTORCH_WHL mode
        extensions = []

    setup(
        name=package_name,
        version=version,
        description=(
            "Tensors and Dynamic neural networks in "
            "Python with strong GPU acceleration"
        ),
        long_description=long_description,
        long_description_content_type="text/markdown",
        ext_modules=extensions,
        cmdclass=cmdclass,
        packages=packages,
        entry_points=entry_points,
        install_requires=install_requires,
        extras_require=extras_require,
        package_data=package_data,
        url="https://pytorch.org/",
        download_url="https://github.com/pytorch/pytorch/tags",
        author="PyTorch Team",
        author_email="packages@pytorch.org",
        python_requires=f">={python_min_version_str}",
        # PyPI package information.
        classifiers=[
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3",
        ]
        + [
            f"Programming Language :: Python :: 3.{i}"
            for i in range(python_min_version[1], version_range_max)
        ],
        license="BSD-3-Clause",
        keywords="pytorch, machine learning",
    )
    if EMIT_BUILD_WARNING:
        print_box(build_update_message)


if __name__ == "__main__":
    main()