| # 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) |
| # |
| # 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_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_QNNPACK=0 |
| # disables QNNPACK build (quantized 8-bit operators) |
| # |
| # 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) |
| # |
| # BUILD_CAFFE2_OPS=0 |
| # disable Caffe2 operators build |
| # |
| # BUILD_CAFFE2=0 |
| # disable Caffe2 build |
| # |
| # USE_IBVERBS |
| # toggle features related to distributed support |
| # |
| # USE_OPENCV |
| # enables use of OpenCV for additional operators |
| # |
| # USE_OPENMP=0 |
| # disables use of OpenMP for parallelization |
| # |
| # USE_FFMPEG |
| # enables use of ffmpeg for additional operators |
| # |
| # 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 |
| # |
| # USE_LEVELDB |
| # enables use of LevelDB for storage |
| # |
| # USE_LMDB |
| # enables use of LMDB for storage |
| # |
| # 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. |
| # |
| # 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_REDIS |
| # Whether to use Redis for distributed workflows (Linux only) |
| # |
| # USE_ZSTD |
| # Enables use of ZSTD, if the libraries are found |
| # |
| # 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 |
| # |
| # NVFUSER_SOURCE_DIR |
| # specify nvfuser root directory |
| # |
| # NVTOOLSEXT_PATH (Windows only) |
| # specify where nvtoolsext 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 |
| # TBB - using TBB for intra- and native thread pool for inter-op parallelism |
| # |
| # USE_TBB |
| # enable TBB support |
| # |
| # USE_SYSTEM_TBB |
| # Use system-provided Intel TBB. |
| # |
| # 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 BUILD_CUSTOM_PROTOBUF=OFF |
| # |
| # USE_MIMALLOC |
| # Static link mimalloc into C10, and use mimalloc in alloc_cpu & alloc_free. |
| # By default, It is only enabled on Windows. |
| |
| 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 |
| |
| python_min_version = (3, 8, 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 json |
| import os |
| 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_caffe2 |
| 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 |
| |
| ################################################################################ |
| # 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") |
| caffe2_build_dir = os.path.join(cwd, "build") |
| |
| # 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") |
| 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", |
| "tbb", |
| "onnx", |
| "foxi", |
| "QNNPACK", |
| "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.readlines() |
| 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"], |
| ) |
| check_for_files( |
| os.path.join(third_party_path, "onnx", "third_party", "benchmark"), |
| ["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_caffe2( |
| version=version, |
| cmake_python_library=cmake_python_library, |
| build_python=True, |
| 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): |
| # Copy libiomp5.dylib inside the wheel package on OS X |
| def _embed_libiomp(self): |
| 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 = "libiomp5.dylib" |
| if os.path.join("@rpath", omp_lib_name) not in libs: |
| return |
| |
| # Copy 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) |
| 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_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") |
| |
| if cmake_cache_vars["BUILD_NVFUSER"]: |
| report("-- Building nvfuser") |
| else: |
| report("-- Not Building nvfuser") |
| |
| # 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_libiomp() |
| |
| # 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() |
| # The caffe2 extensions are created in |
| # tmp_install/lib/pythonM.m/site-packages/caffe2/python/ |
| # and need to be copied to build/lib.linux.... , which will be a |
| # platform dependent build folder created by the "build" command of |
| # setuptools. Only the contents of this folder are installed in the |
| # "install" command by default. |
| # We only make this copy for Caffe2's pybind extensions |
| caffe2_pybind_exts = [ |
| "caffe2.python.caffe2_pybind11_state", |
| "caffe2.python.caffe2_pybind11_state_gpu", |
| "caffe2.python.caffe2_pybind11_state_hip", |
| ] |
| i = 0 |
| while i < len(self.extensions): |
| ext = self.extensions[i] |
| if ext.name not in caffe2_pybind_exts: |
| i += 1 |
| continue |
| fullname = self.get_ext_fullname(ext.name) |
| filename = self.get_ext_filename(fullname) |
| report(f"\nCopying extension {ext.name}") |
| |
| relative_site_packages = ( |
| sysconfig.get_path("purelib") |
| .replace(sysconfig.get_path("data"), "") |
| .lstrip(os.path.sep) |
| ) |
| src = os.path.join("torch", relative_site_packages, filename) |
| if not os.path.exists(src): |
| report(f"{src} does not exist") |
| del self.extensions[i] |
| else: |
| dst = os.path.join(os.path.realpath(self.build_lib), filename) |
| 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) |
| i += 1 |
| |
| # 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) |
| |
| # Copy nvfuser extension |
| for i, ext in enumerate(self.extensions): |
| if ext.name != "nvfuser._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), "nvfuser" + 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() |
| |
| |
| 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", |
| # This is required for Python 2 declarations that are deprecated in 3. |
| "-Wno-deprecated-declarations", |
| # 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", |
| # Clang has an unfixed bug leading to spurious missing |
| # braces warnings, see |
| # https://bugs.llvm.org/show_bug.cgi?id=21629 |
| "-Wno-missing-braces", |
| ] |
| |
| library_dirs.append(lib_path) |
| |
| main_compile_args = [] |
| main_libraries = ["torch_python"] |
| main_link_args = [] |
| main_sources = ["torch/csrc/stub.c"] |
| |
| if cmake_cache_vars["USE_CUDA"]: |
| library_dirs.append(os.path.dirname(cmake_cache_vars["CUDA_CUDA_LIB"])) |
| |
| 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"] |
| |
| # special CUDA 11.7 package that requires installation of cuda runtime, cudnn and cublas |
| 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.*"] |
| if not cmake_cache_vars["BUILD_CAFFE2"]: |
| excludes.extend(["caffe2", "caffe2.*"]) |
| if not cmake_cache_vars["BUILD_FUNCTORCH"]: |
| excludes.extend(["functorch", "functorch.*"]) |
| if not cmake_cache_vars["BUILD_NVFUSER"]: |
| excludes.extend(["nvfuser", "nvfuser.*"]) |
| 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_CAFFE2"]: |
| extensions.append( |
| Extension(name="caffe2.python.caffe2_pybind11_state", sources=[]), |
| ) |
| if cmake_cache_vars["USE_CUDA"]: |
| extensions.append( |
| Extension(name="caffe2.python.caffe2_pybind11_state_gpu", sources=[]), |
| ) |
| if cmake_cache_vars["USE_ROCM"]: |
| extensions.append( |
| Extension(name="caffe2.python.caffe2_pybind11_state_hip", sources=[]), |
| ) |
| if cmake_cache_vars["BUILD_FUNCTORCH"]: |
| extensions.append( |
| Extension(name="functorch._C", sources=[]), |
| ) |
| if cmake_cache_vars["BUILD_NVFUSER"]: |
| extensions.append( |
| Extension(name="nvfuser._C", sources=[]), |
| ) |
| |
| cmdclass = { |
| "bdist_wheel": wheel_concatenate, |
| "build_ext": build_ext, |
| "clean": clean, |
| "install": install, |
| "sdist": sdist, |
| } |
| |
| entry_points = { |
| "console_scripts": [ |
| "convert-caffe2-to-onnx = caffe2.python.onnx.bin.conversion:caffe2_to_onnx", |
| "convert-onnx-to-caffe2 = caffe2.python.onnx.bin.conversion:onnx_to_caffe2", |
| "torchrun = torch.distributed.run: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(): |
| # the list of runtime dependencies required by this built package |
| install_requires = [ |
| "filelock", |
| "typing-extensions", |
| "sympy", |
| "networkx", |
| "jinja2", |
| "fsspec", |
| ] |
| |
| extras_require = {"opt-einsum": ["opt-einsum>=3.3"]} |
| if platform.system() == "Linux": |
| cmake_cache_vars = get_cmake_cache_vars() |
| if cmake_cache_vars["USE_ROCM"]: |
| triton_text_file = "triton-rocm.txt" |
| triton_package_name = "pytorch-triton-rocm" |
| else: |
| triton_text_file = "triton.txt" |
| triton_package_name = "pytorch-triton" |
| triton_pin_file = os.path.join( |
| cwd, ".ci", "docker", "ci_commit_pins", triton_text_file |
| ) |
| triton_version_file = os.path.join(cwd, ".ci", "docker", "triton_version.txt") |
| if os.path.exists(triton_pin_file) and os.path.exists(triton_version_file): |
| with open(triton_pin_file) as f: |
| triton_pin = f.read().strip() |
| with open(triton_version_file) as f: |
| triton_version = f.read().strip() |
| extras_require["dynamo"] = [ |
| triton_package_name + "==" + triton_version + "+" + triton_pin[:10], |
| "jinja2", |
| ] |
| |
| # 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 |
| |
| # 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], 10) + 1 |
| torch_package_data = [ |
| "py.typed", |
| "bin/*", |
| "test/*", |
| "*.pyi", |
| "_C/*.pyi", |
| "cuda/*.pyi", |
| "fx/*.pyi", |
| "optim/*.pyi", |
| "autograd/*.pyi", |
| "nn/*.pyi", |
| "nn/modules/*.pyi", |
| "nn/parallel/*.pyi", |
| "utils/data/*.pyi", |
| "utils/data/datapipes/*.pyi", |
| "lib/*.so*", |
| "lib/*.dylib*", |
| "lib/*.dll", |
| "lib/*.lib", |
| "lib/*.pdb", |
| "lib/torch_shm_manager", |
| "lib/*.h", |
| "include/*.h", |
| "include/ATen/*.h", |
| "include/ATen/cpu/*.h", |
| "include/ATen/cpu/vec/vec256/*.h", |
| "include/ATen/cpu/vec/vec256/vsx/*.h", |
| "include/ATen/cpu/vec/vec512/*.h", |
| "include/ATen/cpu/vec/*.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/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/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/quantized/*.h", |
| "include/ATen/native/quantized/cpu/*.h", |
| "include/ATen/quantized/*.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/core/impl/cow/*.h", |
| "include/c10/util/*.h", |
| "include/c10/cuda/*.h", |
| "include/c10/cuda/impl/*.h", |
| "include/c10/hip/*.h", |
| "include/c10/hip/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/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/jit/codegen/cuda/ops/*.h", |
| "include/torch/csrc/jit/codegen/cuda/scheduler/*.h", |
| "include/torch/csrc/onnx/*.h", |
| "include/torch/csrc/profiler/*.h", |
| "include/torch/csrc/profiler/orchestration/*.h", |
| "include/torch/csrc/profiler/stubs/*.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/pybind11/*.h", |
| "include/pybind11/detail/*.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/*.cpp", |
| "_inductor/codegen/*.h", |
| "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 get_cmake_cache_vars()["BUILD_NVFUSER"]: |
| torch_package_data.extend( |
| [ |
| "share/cmake/nvfuser/*.cmake", |
| "include/nvfuser/*.h", |
| "include/nvfuser/kernel_db/*.h", |
| "include/nvfuser/multidevice/*.h", |
| "include/nvfuser/ops/*.h", |
| "include/nvfuser/python_frontend/*.h", |
| "include/nvfuser/scheduler/*.h", |
| ] |
| ) |
| |
| if get_cmake_cache_vars()["BUILD_CAFFE2"]: |
| torch_package_data.extend( |
| [ |
| "include/caffe2/**/*.h", |
| "include/caffe2/utils/*.h", |
| "include/caffe2/utils/**/*.h", |
| ] |
| ) |
| 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", |
| ] |
| ) |
| 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/*", |
| ] |
| 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={ |
| "torch": torch_package_data, |
| "torchgen": torchgen_package_data, |
| "caffe2": [ |
| "python/serialized_test/data/operator_test/*.zip", |
| ], |
| }, |
| url="https://pytorch.org/", |
| download_url="https://github.com/pytorch/pytorch/tags", |
| author="PyTorch Team", |
| author_email="[email protected]", |
| 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", |
| keywords="pytorch, machine learning", |
| ) |
| if EMIT_BUILD_WARNING: |
| print_box(build_update_message) |
| |
| |
| if __name__ == "__main__": |
| main() |
