# 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 # # NO_CUDA # 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, in contrast to the default behavior of autogoo # and cmake build systems.) # # CC # the C/C++ compiler to use (NB: the CXX flag has no effect for distutils # compiles, because distutils always uses CC to compile, even for C++ # files. # # Environment variables for feature toggles: # # NO_CUDNN # disables the cuDNN build # # NO_FBGEMM # disables the FBGEMM build # # NO_TEST # disables the test build # # NO_MIOPEN # disables the MIOpen build # # NO_MKLDNN # disables use of MKLDNN # # NO_NNPACK # disables NNPACK build # # NO_QNNPACK # disables QNNPACK build (quantized 8-bit operators) # # NO_DISTRIBUTED # disables distributed (c10d, gloo, mpi, etc.) build # # NO_SYSTEM_NCCL # disables use of system-wide nccl (we will use our submoduled # copy in third_party/nccl) # # NO_CAFFE2_OPS # disable Caffe2 operators build # # USE_GLOO_IBVERBS # toggle features related to distributed support # # USE_OPENCV # enables use of OpenCV for additional operators # # USE_FFMPEG # enables use of ffmpeg for additional operators # # USE_LEVELDB # enables use of LevelDB for storage # # USE_LMDB # enables use of LMDB for storage # # BUILD_BINARY # enables the additional binaries/ build # # 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. # # 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, 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. # # USE_FBGEMM # Enables use of FBGEMM # # 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_DIR # NCCL_LIB_DIR # NCCL_INCLUDE_DIR # specify where nccl is installed # # NVTOOLSEXT_PATH (Windows only) # specify where nvtoolsext is installed # # LIBRARY_PATH # LD_LIBRARY_PATH # we will search for libraries in these paths from __future__ import print_function from setuptools import setup, Extension, distutils, Command, find_packages from distutils import dir_util import setuptools.command.build_ext import setuptools.command.install import distutils.command.clean import distutils.sysconfig import filecmp import platform import subprocess import shutil import sys import os import json import glob import importlib from tools.build_pytorch_libs import build_caffe2 from tools.setup_helpers.env import (IS_WINDOWS, IS_DARWIN, IS_LINUX, check_env_flag, DEBUG, REL_WITH_DEB_INFO, USE_MKLDNN) from tools.setup_helpers.cuda import USE_CUDA, CUDA_HOME, CUDA_VERSION from tools.setup_helpers.cudnn import USE_CUDNN, CUDNN_LIBRARY, CUDNN_INCLUDE_DIR from tools.setup_helpers.rocm import USE_ROCM from tools.setup_helpers.miopen import USE_MIOPEN, MIOPEN_LIBRARY, MIOPEN_INCLUDE_DIR from tools.setup_helpers.nccl import USE_NCCL, USE_SYSTEM_NCCL, NCCL_SYSTEM_LIB, NCCL_INCLUDE_DIR from tools.setup_helpers.dist_check import USE_DISTRIBUTED ################################################################################ # 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 filtered_args = [] for i, arg in enumerate(sys.argv): if arg == '--cmake': RERUN_CMAKE = 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 == 'clean': 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 # 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") tmp_install_path = lib_path + "/tmp_install" caffe2_build_dir = os.path.join(cwd, "build") # lib/pythonx.x/site-packages rel_site_packages = distutils.sysconfig.get_python_lib(prefix='') # full absolute path to the dir above full_site_packages = distutils.sysconfig.get_python_lib() # CMAKE: full path to python library if IS_WINDOWS: cmake_python_library = "{}/libs/python{}.lib".format( distutils.sysconfig.get_config_var("prefix"), distutils.sysconfig.get_config_var("VERSION")) else: cmake_python_library = "{}/{}".format( distutils.sysconfig.get_config_var("LIBDIR"), distutils.sysconfig.get_config_var("INSTSONAME")) cmake_python_include_dir = distutils.sysconfig.get_python_inc() ################################################################################ # Version, create_version_file, and package_name ################################################################################ package_name = os.getenv('TORCH_PACKAGE_NAME', 'torch') version = '1.1.0a0' if os.getenv('PYTORCH_BUILD_VERSION'): assert os.getenv('PYTORCH_BUILD_NUMBER') is not None build_number = int(os.getenv('PYTORCH_BUILD_NUMBER')) version = os.getenv('PYTORCH_BUILD_VERSION') if build_number > 1: version += '.post' + str(build_number) else: try: sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=cwd).decode('ascii').strip() version += '+' + sha[:7] except Exception: pass report("Building wheel {}-{}".format(package_name, version)) # all the work we need to do _before_ setup runs def build_deps(): report('-- Building version ' + version) version_path = os.path.join(cwd, 'torch', 'version.py') with open(version_path, 'w') as f: f.write("__version__ = '{}'\n".format(version)) # NB: This is not 100% accurate, because you could have built the # library code with DEBUG, but csrc without DEBUG (in which case # this would claim to be a release build when it's not.) f.write("debug = {}\n".format(repr(DEBUG))) f.write("cuda = {}\n".format(repr(CUDA_VERSION))) def check_file(f): if not os.path.exists(f): report("Could not find {}".format(f)) report("Did you run 'git submodule update --init --recursive'?") sys.exit(1) check_file(os.path.join(third_party_path, "gloo", "CMakeLists.txt")) check_file(os.path.join(third_party_path, "pybind11", "CMakeLists.txt")) check_file(os.path.join(third_party_path, 'cpuinfo', 'CMakeLists.txt')) check_file(os.path.join(third_party_path, 'onnx', 'CMakeLists.txt')) check_file(os.path.join(third_party_path, 'QNNPACK', 'CMakeLists.txt')) check_file(os.path.join(third_party_path, 'fbgemm', 'CMakeLists.txt')) check_pydep('yaml', 'pyyaml') check_pydep('typing', 'typing') build_caffe2(version=version, cmake_python_library=cmake_python_library, build_python=True, rerun_cmake=RERUN_CMAKE, build_dir='build') # Use copies instead of symbolic files. # Windows has very poor support for them. sym_files = ['tools/shared/cwrap_common.py', 'tools/shared/_utils_internal.py'] orig_files = ['aten/src/ATen/common_with_cwrap.py', 'torch/_utils_internal.py'] 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) dir_util.copy_tree('torch/lib/tmp_install/share', 'torch/share') dir_util.copy_tree('third_party/pybind11/include/pybind11/', 'torch/lib/include/pybind11') ################################################################################ # 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: raise RuntimeError(missing_pydep.format(importname=importname, module=module)) class build_ext(setuptools.command.build_ext.build_ext): def run(self): # report build options if USE_NUMPY: report('-- Building with NumPy bindings') else: report('-- NumPy not found') if USE_CUDNN: report('-- Detected cuDNN at ' + CUDNN_LIBRARY + ', ' + CUDNN_INCLUDE_DIR) else: report('-- Not using cuDNN') if USE_MIOPEN: report('-- Detected MIOpen at ' + MIOPEN_LIBRARY + ', ' + MIOPEN_INCLUDE_DIR) else: report('-- Not using MIOpen') if USE_CUDA: report('-- Detected CUDA at ' + CUDA_HOME) else: report('-- Not using CUDA') if USE_MKLDNN: report('-- Using MKLDNN') else: report('-- Not using MKLDNN') if USE_NCCL and USE_SYSTEM_NCCL: report('-- Using system provided NCCL library at ' + NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR) elif USE_NCCL: report('-- Building NCCL library') else: report('-- Not using NCCL') if USE_DISTRIBUTED: report('-- Building with THD distributed package ') if IS_LINUX: report('-- Building with c10d distributed package ') else: report('-- Building without c10d distributed package') else: report('-- Building without distributed package') # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self) # 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('\\', '/') 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("\nCopying extension {}".format(ext.name)) src = os.path.join(tmp_install_path, rel_site_packages, filename) if not os.path.exists(src): report("{} does not exist".format(src)) del self.extensions[i] else: dst = os.path.join(os.path.realpath(self.build_lib), filename) report("Copying {} from {} to {}".format(ext.name, src, 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 distutils.command.build_ext.build_ext.build_extensions(self) def get_outputs(self): outputs = distutils.command.build_ext.build_ext.get_outputs(self) outputs.append(os.path.join(self.build_lib, "caffe2")) report("setup.py::get_outputs returning {}".format(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', 'r') as f: contents = f.read() if contents != new_contents: with open('compile_commands.json', 'w') as f: f.write(new_contents) class install(setuptools.command.install.install): def run(self): setuptools.command.install.install.run(self) class clean(distutils.command.clean.clean): def run(self): import glob import re with open('.gitignore', 'r') 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: for filename in glob.glob(wildcard): try: os.remove(filename) except OSError: shutil.rmtree(filename, ignore_errors=True) # It's an old-style class in Python 2.7... distutils.command.clean.clean.run(self) ################################################################################ # Configure compile flags ################################################################################ library_dirs = [] 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 # /Z7 turns on symbolic debugging information in .obj files # /EHa is about native C++ catch support for asynchronous # structured exception handling (SEH) # /DNOMINMAX removes builtin min/max functions # /wdXXXX disables warning no. XXXX extra_compile_args = ['/MD', '/Z7', '/EHa', '/DNOMINMAX', '/wd4267', '/wd4251', '/wd4522', '/wd4522', '/wd4838', '/wd4305', '/wd4244', '/wd4190', '/wd4101', '/wd4996', '/wd4275'] if sys.version_info[0] == 2: if not check_env_flag('FORCE_PY27_BUILD'): report('The support for PyTorch with Python 2.7 on Windows is very experimental.') report('Please set the flag `FORCE_PY27_BUILD` to 1 to continue build.') sys.exit(1) # /bigobj increases number of sections in .obj file, which is needed to link # against libaries in Python 2.7 under Windows extra_compile_args.append('/bigobj') else: extra_link_args = [] extra_compile_args = [ '-std=c++11', '-Wall', '-Wextra', '-Wno-strict-overflow', '-Wno-unused-parameter', '-Wno-missing-field-initializers', '-Wno-write-strings', '-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', ] if check_env_flag('WERROR'): extra_compile_args.append('-Werror') library_dirs.append(lib_path) # we specify exact lib names to avoid conflict with lua-torch installs CAFFE2_LIBS = [] if USE_CUDA: CAFFE2_LIBS.extend(['-Wl,--no-as-needed', os.path.join(lib_path, 'libcaffe2_gpu.so'), '-Wl,--as-needed']) if USE_ROCM: CAFFE2_LIBS.extend(['-Wl,--no-as-needed', os.path.join(lib_path, 'libcaffe2_hip.so'), '-Wl,--as-needed']) # static library only if IS_DARWIN: CAFFE2_LIBS = [] if USE_CUDA: CAFFE2_LIBS.append(os.path.join(lib_path, 'libcaffe2_gpu.dylib')) if USE_ROCM: CAFFE2_LIBS.append(os.path.join(lib_path, 'libcaffe2_hip.dylib')) if IS_WINDOWS: CAFFE2_LIBS = [] if USE_CUDA: CAFFE2_LIBS.append(os.path.join(lib_path, 'caffe2_gpu.lib')) if USE_ROCM: CAFFE2_LIBS.append(os.path.join(lib_path, 'caffe2_hip.lib')) main_compile_args = [] main_libraries = ['shm', 'torch_python'] main_link_args = [] main_sources = ["torch/csrc/stub.cpp"] # Before the introduction of stub.cpp, _C.so and libcaffe2.so defined # some of the same symbols, and it was important for _C.so to be # loaded before libcaffe2.so so that the versions in _C.so got # used. This happened automatically because we loaded _C.so directly, # and libcaffe2.so was brought in as a dependency (though I suspect it # may have been possible to break by importing caffe2 first in the # same process). # # Now, libtorch_python.so and libcaffe2.so define some of the same # symbols. We directly load the _C.so stub, which brings both of these # in as dependencies. We have to make sure that symbols continue to be # looked up in libtorch_python.so first, by making sure it comes # before libcaffe2.so in the linker command. main_link_args.extend(CAFFE2_LIBS) try: import numpy as np NUMPY_INCLUDE_DIR = np.get_include() USE_NUMPY = True except ImportError: USE_NUMPY = False if USE_CUDA: if IS_WINDOWS: cuda_lib_path = CUDA_HOME + '/lib/x64/' else: cuda_lib_dirs = ['lib64', 'lib'] for lib_dir in cuda_lib_dirs: cuda_lib_path = os.path.join(CUDA_HOME, lib_dir) if os.path.exists(cuda_lib_path): break library_dirs.append(cuda_lib_path) if DEBUG: if IS_WINDOWS: extra_link_args.append('/DEBUG:FULL') else: extra_compile_args += ['-O0', '-g'] extra_link_args += ['-O0', '-g'] if REL_WITH_DEB_INFO: if IS_WINDOWS: extra_link_args.append('/DEBUG:FULL') else: extra_compile_args += ['-g'] extra_link_args += ['-g'] def make_relative_rpath(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 = [] packages = find_packages(exclude=('tools', 'tools.*')) 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('lib')], ) extensions.append(C) if not IS_WINDOWS: DL = Extension("torch._dl", sources=["torch/csrc/dl.c"], language='c' ) extensions.append(DL) if USE_CUDA: thnvrtc_link_flags = extra_link_args + [make_relative_rpath('lib')] if IS_LINUX: thnvrtc_link_flags = thnvrtc_link_flags + ['-Wl,--no-as-needed'] # these have to be specified as -lcuda in link_flags because they # have to come right after the `no-as-needed` option if IS_WINDOWS: thnvrtc_link_flags += ['cuda.lib', 'nvrtc.lib'] else: thnvrtc_link_flags += ['-lcuda', '-lnvrtc'] cuda_stub_path = [cuda_lib_path + '/stubs'] if IS_DARWIN: # on macOS this is where the CUDA stub is installed according to the manual cuda_stub_path = ["/usr/local/cuda/lib"] THNVRTC = Extension("torch._nvrtc", sources=['torch/csrc/nvrtc.cpp'], language='c++', extra_compile_args=main_compile_args + extra_compile_args, include_dirs=[cwd], library_dirs=library_dirs + cuda_stub_path, extra_link_args=thnvrtc_link_flags, ) extensions.append(THNVRTC) # These extensions are built by cmake and copied manually in build_extensions() # inside the build_ext implementaiton extensions.append( Extension( name=str('caffe2.python.caffe2_pybind11_state'), sources=[]), ) if USE_CUDA: extensions.append( Extension( name=str('caffe2.python.caffe2_pybind11_state_gpu'), sources=[]), ) if USE_ROCM: extensions.append( Extension( name=str('caffe2.python.caffe2_pybind11_state_hip'), sources=[]), ) cmdclass = { 'build_ext': build_ext, 'clean': clean, 'install': install, } 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', ] } # 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-run (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)) if __name__ == '__main__': if RUN_BUILD_DEPS: build_deps() setup( name=package_name, version=version, description=("Tensors and Dynamic neural networks in " "Python with strong GPU acceleration"), ext_modules=extensions, cmdclass=cmdclass, packages=packages, entry_points=entry_points, package_data={ 'torch': [ '__init__.pyi', 'lib/*.so*', 'lib/*.dylib*', 'lib/*.dll', 'lib/*.lib', 'lib/*.pdb', 'lib/torch_shm_manager', 'lib/*.h', 'lib/include/ATen/*.h', 'lib/include/ATen/cpu/*.h', 'lib/include/ATen/core/*.h', 'lib/include/ATen/cuda/*.cuh', 'lib/include/ATen/cuda/*.h', 'lib/include/ATen/cuda/detail/*.cuh', 'lib/include/ATen/cuda/detail/*.h', 'lib/include/ATen/cudnn/*.h', 'lib/include/ATen/detail/*.h', 'lib/include/caffe2/utils/*.h', 'lib/include/c10/*.h', 'lib/include/c10/macros/*.h', 'lib/include/c10/core/*.h', 'lib/include/ATen/core/dispatch/*.h', 'lib/include/c10/core/impl/*.h', 'lib/include/ATen/core/opschema/*.h', 'lib/include/c10/util/*.h', 'lib/include/c10/cuda/*.h', 'lib/include/c10/cuda/impl/*.h', 'lib/include/c10/hip/*.h', 'lib/include/c10/hip/impl/*.h', 'lib/include/caffe2/**/*.h', 'lib/include/torch/*.h', 'lib/include/torch/csrc/*.h', 'lib/include/torch/csrc/api/include/torch/*.h', 'lib/include/torch/csrc/api/include/torch/data/*.h', 'lib/include/torch/csrc/api/include/torch/data/dataloader/*.h', 'lib/include/torch/csrc/api/include/torch/data/datasets/*.h', 'lib/include/torch/csrc/api/include/torch/data/detail/*.h', 'lib/include/torch/csrc/api/include/torch/data/samplers/*.h', 'lib/include/torch/csrc/api/include/torch/data/transforms/*.h', 'lib/include/torch/csrc/api/include/torch/detail/*.h', 'lib/include/torch/csrc/api/include/torch/detail/ordered_dict.h', 'lib/include/torch/csrc/api/include/torch/nn/*.h', 'lib/include/torch/csrc/api/include/torch/nn/modules/*.h', 'lib/include/torch/csrc/api/include/torch/nn/parallel/*.h', 'lib/include/torch/csrc/api/include/torch/optim/*.h', 'lib/include/torch/csrc/api/include/torch/serialize/*.h', 'lib/include/torch/csrc/autograd/*.h', 'lib/include/torch/csrc/autograd/generated/*.h', 'lib/include/torch/csrc/cuda/*.h', 'lib/include/torch/csrc/jit/*.h', 'lib/include/torch/csrc/jit/generated/*.h', 'lib/include/torch/csrc/jit/passes/*.h', 'lib/include/torch/csrc/jit/script/*.h', 'lib/include/torch/csrc/utils/*.h', 'lib/include/pybind11/*.h', 'lib/include/pybind11/detail/*.h', 'lib/include/TH/*.h*', 'lib/include/TH/generic/*.h*', 'lib/include/THC/*.cuh', 'lib/include/THC/*.h*', 'lib/include/THC/generic/*.h', 'lib/include/THCUNN/*.cuh', 'lib/include/THNN/*.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/Torch/*.cmake', ], 'caffe2': [ 'cpp_test/*', 'python/serialized_test/data/operator_test/*.zip', ] }, ) if EMIT_BUILD_WARNING: print_box(build_update_message)