# Welcome to the PyTorch setup.py. # # Environment variables you are probably interestd in: # # DEBUG # build with -O0 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_NNPACK # disables NNPACK build # # NO_DISTRIBUTED # disables THD (distributed) build # # NO_SYSTEM_NCCL # disables use of system-wide nccl (we will use our submoduled # copy in torch/lib/nccl) # # WITH_GLOO_IBVERBS # toggle features related to distributed support # # PYTORCH_BINARY_BUILD # toggle static linking against libstdc++, used when we're building # binaries for distribution # # 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 # # 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 # # CUDNN_LIB_DIR # CUDNN_INCLUDE_DIR # CUDNN_LIBRARY # specify where cuDNN 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 setuptools import setup, Extension, distutils, Command, find_packages import setuptools.command.build_ext import setuptools.command.install import setuptools.command.develop import setuptools.command.build_py import distutils.unixccompiler import distutils.command.build import distutils.command.clean import platform import subprocess import shutil import multiprocessing import sys import os import json import glob from tools.setup_helpers.env import check_env_flag from tools.setup_helpers.cuda import WITH_CUDA, CUDA_HOME, CUDA_VERSION from tools.setup_helpers.cudnn import (WITH_CUDNN, CUDNN_LIBRARY, CUDNN_LIB_DIR, CUDNN_INCLUDE_DIR) from tools.setup_helpers.nccl import WITH_NCCL, WITH_SYSTEM_NCCL, NCCL_LIB_DIR, \ NCCL_INCLUDE_DIR, NCCL_ROOT_DIR, NCCL_SYSTEM_LIB from tools.setup_helpers.nnpack import WITH_NNPACK from tools.setup_helpers.nvtoolext import NVTOOLEXT_HOME from tools.setup_helpers.split_types import split_types from tools.setup_helpers.generate_code import generate_code from tools.setup_helpers.ninja_builder import NinjaBuilder, ninja_build_ext from tools.setup_helpers.dist_check import WITH_DISTRIBUTED, \ WITH_DISTRIBUTED_MW, WITH_GLOO_IBVERBS DEBUG = check_env_flag('DEBUG') IS_WINDOWS = (platform.system() == 'Windows') IS_DARWIN = (platform.system() == 'Darwin') IS_LINUX = (platform.system() == 'Linux') NUM_JOBS = multiprocessing.cpu_count() max_jobs = os.getenv("MAX_JOBS") if max_jobs is not None: NUM_JOBS = min(NUM_JOBS, int(max_jobs)) try: import ninja WITH_NINJA = True except ImportError: WITH_NINJA = False if not WITH_NINJA: ################################################################################ # Monkey-patch setuptools to compile in parallel ################################################################################ def parallelCCompile(self, sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None): # those lines are copied from distutils.ccompiler.CCompiler directly macros, objects, extra_postargs, pp_opts, build = self._setup_compile( output_dir, macros, include_dirs, sources, depends, extra_postargs) cc_args = self._get_cc_args(pp_opts, debug, extra_preargs) # compile using a thread pool import multiprocessing.pool def _single_compile(obj): src, ext = build[obj] self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts) multiprocessing.pool.ThreadPool(NUM_JOBS).map(_single_compile, objects) return objects distutils.ccompiler.CCompiler.compile = parallelCCompile original_link = distutils.unixccompiler.UnixCCompiler.link def patched_link(self, *args, **kwargs): _cxx = self.compiler_cxx self.compiler_cxx = None result = original_link(self, *args, **kwargs) self.compiler_cxx = _cxx return result distutils.unixccompiler.UnixCCompiler.link = patched_link ################################################################################ # Workaround setuptools -Wstrict-prototypes warnings # I lifted this code from https://stackoverflow.com/a/29634231/23845 ################################################################################ import distutils.sysconfig cfg_vars = distutils.sysconfig.get_config_vars() for key, value in cfg_vars.items(): if type(value) == str: cfg_vars[key] = value.replace("-Wstrict-prototypes", "") ################################################################################ # Custom build commands ################################################################################ dep_libs = [ 'nccl', 'ATen', 'libshm', 'libshm_windows', 'gloo', 'THD', 'nanopb', ] # global ninja file for building generated code stuff ninja_global = None if WITH_NINJA: ninja_global = NinjaBuilder('global') def build_libs(libs): for lib in libs: assert lib in dep_libs, 'invalid lib: {}'.format(lib) if IS_WINDOWS: build_libs_cmd = ['torch\\lib\\build_libs.bat'] else: build_libs_cmd = ['bash', 'torch/lib/build_libs.sh'] my_env = os.environ.copy() my_env["PYTORCH_PYTHON"] = sys.executable my_env["NUM_JOBS"] = str(NUM_JOBS) if not IS_WINDOWS: if WITH_NINJA: my_env["CMAKE_GENERATOR"] = '-GNinja' my_env["CMAKE_INSTALL"] = 'ninja install' else: my_env['CMAKE_GENERATOR'] = '' my_env['CMAKE_INSTALL'] = 'make install' if WITH_SYSTEM_NCCL: my_env["NCCL_ROOT_DIR"] = NCCL_ROOT_DIR if WITH_CUDA: my_env["CUDA_BIN_PATH"] = CUDA_HOME build_libs_cmd += ['--with-cuda'] if WITH_NNPACK: build_libs_cmd += ['--with-nnpack'] if WITH_CUDNN: my_env["CUDNN_LIB_DIR"] = CUDNN_LIB_DIR my_env["CUDNN_LIBRARY"] = CUDNN_LIBRARY my_env["CUDNN_INCLUDE_DIR"] = CUDNN_INCLUDE_DIR if WITH_GLOO_IBVERBS: build_libs_cmd += ['--with-gloo-ibverbs'] if subprocess.call(build_libs_cmd + libs, env=my_env) != 0: sys.exit(1) if 'ATen' in libs: from tools.nnwrap import generate_wrappers as generate_nn_wrappers generate_nn_wrappers() class build_deps(Command): user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): # Check if you remembered to check out submodules def check_file(f): if not os.path.exists(f): print("Could not find {}".format(f)) print("Did you run 'git submodule update --init'?") sys.exit(1) check_file(os.path.join(lib_path, "gloo", "CMakeLists.txt")) check_file(os.path.join(lib_path, "nanopb", "CMakeLists.txt")) check_file(os.path.join(lib_path, "pybind11", "CMakeLists.txt")) libs = [] if WITH_NCCL and not WITH_SYSTEM_NCCL: libs += ['nccl'] libs += ['ATen', 'nanopb'] if IS_WINDOWS: libs += ['libshm_windows'] else: libs += ['libshm'] if WITH_DISTRIBUTED: if sys.platform.startswith('linux'): libs += ['gloo'] libs += ['THD'] build_libs(libs) build_dep_cmds = {} for lib in dep_libs: # wrap in function to capture lib class build_dep(build_deps): description = 'Build {} external library'.format(lib) def run(self): build_libs([self.lib]) build_dep.lib = lib build_dep_cmds['build_' + lib.lower()] = build_dep class build_module(Command): user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): self.run_command('build_py') self.run_command('build_ext') class build_py(setuptools.command.build_py.build_py): def run(self): self.create_version_file() setuptools.command.build_py.build_py.run(self) @staticmethod def create_version_file(): global version, cwd print('-- 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))) class develop(setuptools.command.develop.develop): def run(self): build_py.create_version_file() setuptools.command.develop.develop.run(self) self.create_compile_commands() 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)] with open('compile_commands.json', 'w') as f: json.dump(all_commands, f, indent=2) if not WITH_NINJA: print("WARNING: 'develop' is not building C++ code incrementally") print("because ninja is not installed. Run this to enable it:") print(" > pip install ninja") def monkey_patch_THD_link_flags(): ''' THD's dynamic link deps are not determined until after build_deps is run So, we need to monkey-patch them in later ''' # read tmp_install_path/THD_deps.txt for THD's dynamic linkage deps with open(tmp_install_path + '/THD_deps.txt', 'r') as f: thd_deps_ = f.read() thd_deps = [] # remove empty lines for l in thd_deps_.split(';'): if l != '': thd_deps.append(l) C.extra_link_args += thd_deps build_ext_parent = ninja_build_ext if WITH_NINJA \ else setuptools.command.build_ext.build_ext class build_ext(build_ext_parent): def run(self): # Print build options if WITH_NUMPY: print('-- Building with NumPy bindings') else: print('-- NumPy not found') if WITH_CUDNN: print('-- Detected cuDNN at ' + CUDNN_LIBRARY + ', ' + CUDNN_INCLUDE_DIR) else: print('-- Not using cuDNN') if WITH_CUDA: print('-- Detected CUDA at ' + CUDA_HOME) else: print('-- Not using CUDA') if WITH_NCCL and WITH_SYSTEM_NCCL: print('-- Using system provided NCCL library at ' + NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR) elif WITH_NCCL: print('-- Building NCCL library') else: print('-- Not using NCCL') if WITH_DISTRIBUTED: print('-- Building with distributed package ') monkey_patch_THD_link_flags() else: print('-- Building without distributed package') generate_code(ninja_global) if IS_WINDOWS: build_temp = self.build_temp build_dir = 'torch/csrc' ext_filename = self.get_ext_filename('_C') lib_filename = '.'.join(ext_filename.split('.')[:-1]) + '.lib' _C_LIB = os.path.join(build_temp, build_dir, lib_filename).replace('\\', '/') THNN.extra_link_args += [_C_LIB] if WITH_CUDA: THCUNN.extra_link_args += [_C_LIB] else: # To generate .obj files for those .h files for the export class # a header file cannot build, so it has to be copied to someplace as a source file temp_dir = 'torch/csrc/generated' hfile_list = ['torch/csrc/cuda/AutoGPU.h'] hname_list = [os.path.basename(hfile) for hfile in hfile_list] rname_list = [os.path.splitext(hname)[0] for hname in hname_list] cfile_list = [temp_dir + '/' + rname + '_cpu_win.cpp' for rname in rname_list] if not os.path.exists(temp_dir): os.mkdir(temp_dir) for hfile, cfile in zip(hfile_list, cfile_list): if os.path.exists(cfile): os.remove(cfile) shutil.copyfile(hfile, cfile) C.sources += cfile_list if WITH_NINJA: # before we start the normal build make sure all generated code # gets built ninja_global.run() # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self) class build(distutils.command.build.build): sub_commands = [ ('build_deps', lambda self: True), ] + distutils.command.build.build.sub_commands class install(setuptools.command.install.install): def run(self): if not self.skip_build: self.run_command('build_deps') # Copy headers necessary to compile C++ extensions. self.copy_tree('torch/csrc', 'torch/lib/include/torch/csrc/') self.copy_tree('torch/lib/pybind11/include/pybind11/', 'torch/lib/include/pybind11') self.copy_file('torch/torch.h', 'torch/lib/include/torch/torch.h') setuptools.command.install.install.run(self) class clean(distutils.command.clean.clean): def run(self): import glob with open('.gitignore', 'r') as f: ignores = f.read() for wildcard in filter(bool, ignores.split('\n')): 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 ################################################################################ include_dirs = [] library_dirs = [] extra_link_args = [] if IS_WINDOWS: extra_compile_args = ['/Z7', '/EHa', '/DNOMINMAX' # /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 ] if sys.version_info[0] == 2: # /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_compile_args = ['-std=c++11', '-Wno-write-strings', # Python 2.6 requires -fno-strict-aliasing, see # http://legacy.python.org/dev/peps/pep-3123/ '-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'] cwd = os.path.dirname(os.path.abspath(__file__)) lib_path = os.path.join(cwd, "torch", "lib") tmp_install_path = lib_path + "/tmp_install" include_dirs += [ cwd, os.path.join(cwd, "torch", "csrc"), lib_path + "/pybind11/include", tmp_install_path + "/include", tmp_install_path + "/include/TH", tmp_install_path + "/include/THNN", tmp_install_path + "/include/ATen", ] library_dirs.append(lib_path) # we specify exact lib names to avoid conflict with lua-torch installs ATEN_LIB = os.path.join(lib_path, 'libATen.so.1') THD_LIB = os.path.join(lib_path, 'libTHD.a') NCCL_LIB = os.path.join(lib_path, 'libnccl.so.1') # static library only NANOPB_STATIC_LIB = os.path.join(lib_path, 'libprotobuf-nanopb.a') if IS_DARWIN: ATEN_LIB = os.path.join(lib_path, 'libATen.1.dylib') NCCL_LIB = os.path.join(lib_path, 'libnccl.1.dylib') if IS_WINDOWS: ATEN_LIB = os.path.join(lib_path, 'ATen.lib') NANOPB_STATIC_LIB = os.path.join(lib_path, 'protobuf-nanopb.lib') main_compile_args = ['-D_THP_CORE'] main_libraries = ['shm'] main_link_args = [ATEN_LIB, NANOPB_STATIC_LIB] main_sources = [ "torch/csrc/PtrWrapper.cpp", "torch/csrc/Module.cpp", "torch/csrc/Generator.cpp", "torch/csrc/Size.cpp", "torch/csrc/Dtype.cpp", "torch/csrc/Exceptions.cpp", "torch/csrc/Storage.cpp", "torch/csrc/DynamicTypes.cpp", "torch/csrc/assertions.cpp", "torch/csrc/byte_order.cpp", "torch/csrc/utils.cpp", "torch/csrc/expand_utils.cpp", "torch/csrc/utils/invalid_arguments.cpp", "torch/csrc/utils/object_ptr.cpp", "torch/csrc/utils/python_arg_parser.cpp", "torch/csrc/utils/tensor_list.cpp", "torch/csrc/utils/tensor_new.cpp", "torch/csrc/utils/tensor_numpy.cpp", "torch/csrc/utils/tensor_dtypes.cpp", "torch/csrc/utils/tensor_types.cpp", "torch/csrc/utils/tuple_parser.cpp", "torch/csrc/utils/tensor_apply.cpp", "torch/csrc/utils/tensor_flatten.cpp", "torch/csrc/utils/variadic.cpp", "torch/csrc/allocators.cpp", "torch/csrc/serialization.cpp", "torch/csrc/jit/init.cpp", "torch/csrc/jit/interpreter.cpp", "torch/csrc/jit/ir.cpp", "torch/csrc/jit/fusion_compiler.cpp", "torch/csrc/jit/graph_executor.cpp", "torch/csrc/jit/python_ir.cpp", "torch/csrc/jit/test_jit.cpp", "torch/csrc/jit/tracer.cpp", "torch/csrc/jit/tracer_state.cpp", "torch/csrc/jit/python_tracer.cpp", "torch/csrc/jit/passes/shape_analysis.cpp", "torch/csrc/jit/interned_strings.cpp", "torch/csrc/jit/type.cpp", "torch/csrc/jit/export.cpp", "torch/csrc/jit/autodiff.cpp", "torch/csrc/jit/interpreter_autograd_function.cpp", "torch/csrc/jit/python_arg_flatten.cpp", "torch/csrc/jit/python_compiled_function.cpp", "torch/csrc/jit/variable_flags.cpp", "torch/csrc/jit/passes/create_autodiff_subgraphs.cpp", "torch/csrc/jit/passes/graph_fuser.cpp", "torch/csrc/jit/passes/onnx.cpp", "torch/csrc/jit/passes/dead_code_elimination.cpp", "torch/csrc/jit/passes/common_subexpression_elimination.cpp", "torch/csrc/jit/passes/peephole.cpp", "torch/csrc/jit/passes/inplace_check.cpp", "torch/csrc/jit/passes/canonicalize.cpp", "torch/csrc/jit/passes/batch_mm.cpp", "torch/csrc/jit/passes/onnx/peephole.cpp", "torch/csrc/jit/generated/aten_dispatch.cpp", "torch/csrc/jit/script/lexer.cpp", "torch/csrc/jit/script/compiler.cpp", "torch/csrc/jit/script/init.cpp", "torch/csrc/jit/script/python_tree_views.cpp", "torch/csrc/autograd/init.cpp", "torch/csrc/autograd/grad_mode.cpp", "torch/csrc/autograd/engine.cpp", "torch/csrc/autograd/function.cpp", "torch/csrc/autograd/variable.cpp", "torch/csrc/autograd/saved_variable.cpp", "torch/csrc/autograd/input_buffer.cpp", "torch/csrc/autograd/profiler.cpp", "torch/csrc/autograd/python_function.cpp", "torch/csrc/autograd/python_cpp_function.cpp", "torch/csrc/autograd/python_variable.cpp", "torch/csrc/autograd/python_variable_indexing.cpp", "torch/csrc/autograd/python_engine.cpp", "torch/csrc/autograd/python_hook.cpp", "torch/csrc/autograd/generated/VariableType.cpp", "torch/csrc/autograd/generated/Functions.cpp", "torch/csrc/autograd/generated/python_torch_functions.cpp", "torch/csrc/autograd/generated/python_variable_methods.cpp", "torch/csrc/autograd/generated/python_functions.cpp", "torch/csrc/autograd/generated/python_nn_functions.cpp", "torch/csrc/autograd/functions/basic_ops.cpp", "torch/csrc/autograd/functions/tensor.cpp", "torch/csrc/autograd/functions/accumulate_grad.cpp", "torch/csrc/autograd/functions/special.cpp", "torch/csrc/autograd/functions/utils.cpp", "torch/csrc/autograd/functions/init.cpp", "torch/csrc/tensor/python_tensor.cpp", "torch/csrc/onnx/onnx.pb.cpp", "torch/csrc/onnx/onnx.cpp", ] main_sources += split_types("torch/csrc/Tensor.cpp", ninja_global) try: import numpy as np include_dirs += [np.get_include()] extra_compile_args += ['-DWITH_NUMPY'] WITH_NUMPY = True except ImportError: WITH_NUMPY = False if WITH_DISTRIBUTED: extra_compile_args += ['-DWITH_DISTRIBUTED'] main_sources += [ "torch/csrc/distributed/Module.cpp", ] if WITH_DISTRIBUTED_MW: main_sources += [ "torch/csrc/distributed/Tensor.cpp", "torch/csrc/distributed/Storage.cpp", ] extra_compile_args += ['-DWITH_DISTRIBUTED_MW'] include_dirs += [tmp_install_path + "/include/THD"] main_link_args += [THD_LIB] if WITH_CUDA: nvtoolext_lib_name = None if IS_WINDOWS: cuda_lib_path = CUDA_HOME + '/lib/x64/' nvtoolext_lib_path = NVTOOLEXT_HOME + '/lib/x64/' nvtoolext_include_path = os.path.join(NVTOOLEXT_HOME, 'include') library_dirs.append(nvtoolext_lib_path) include_dirs.append(nvtoolext_include_path) nvtoolext_lib_name = 'nvToolsExt64_1' # MSVC doesn't support runtime symbol resolving, `nvrtc` and `cuda` should be linked main_libraries += ['nvrtc', 'cuda'] 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 extra_link_args.append('-Wl,-rpath,' + cuda_lib_path) nvtoolext_lib_name = 'nvToolsExt' library_dirs.append(cuda_lib_path) cuda_include_path = os.path.join(CUDA_HOME, 'include') include_dirs.append(cuda_include_path) include_dirs.append(tmp_install_path + "/include/THCUNN") extra_compile_args += ['-DWITH_CUDA'] extra_compile_args += ['-DCUDA_LIB_PATH=' + cuda_lib_path] main_libraries += ['cudart', nvtoolext_lib_name] main_sources += [ "torch/csrc/cuda/Module.cpp", "torch/csrc/cuda/Storage.cpp", "torch/csrc/cuda/Stream.cpp", "torch/csrc/cuda/AutoGPU.cpp", "torch/csrc/cuda/utils.cpp", "torch/csrc/cuda/comm.cpp", "torch/csrc/cuda/python_comm.cpp", "torch/csrc/cuda/expand_utils.cpp", "torch/csrc/cuda/lazy_init.cpp", "torch/csrc/cuda/serialization.cpp", ] main_sources += split_types("torch/csrc/cuda/Tensor.cpp", ninja_global) if WITH_NCCL: if WITH_SYSTEM_NCCL: main_link_args += [NCCL_SYSTEM_LIB] include_dirs.append(NCCL_INCLUDE_DIR) else: main_link_args += [NCCL_LIB] extra_compile_args += ['-DWITH_NCCL'] main_sources += [ "torch/csrc/cuda/nccl.cpp", "torch/csrc/cuda/python_nccl.cpp", ] if WITH_CUDNN: main_libraries += [CUDNN_LIBRARY] # NOTE: these are at the front, in case there's another cuDNN in CUDA path include_dirs.insert(0, CUDNN_INCLUDE_DIR) if not IS_WINDOWS: extra_link_args.insert(0, '-Wl,-rpath,' + CUDNN_LIB_DIR) extra_compile_args += ['-DWITH_CUDNN'] if DEBUG: if IS_WINDOWS: extra_link_args.append('/DEBUG:FULL') else: extra_compile_args += ['-O0', '-g'] extra_link_args += ['-O0', '-g'] if os.getenv('PYTORCH_BINARY_BUILD') and platform.system() == 'Linux': print('PYTORCH_BINARY_BUILD found. Static linking libstdc++ on Linux') # get path of libstdc++ and link manually. # for reasons unknown, -static-libstdc++ doesn't fully link some symbols CXXNAME = os.getenv('CXX', 'g++') STDCPP_LIB = subprocess.check_output([CXXNAME, '-print-file-name=libstdc++.a']) STDCPP_LIB = STDCPP_LIB[:-1] if type(STDCPP_LIB) != str: # python 3 STDCPP_LIB = STDCPP_LIB.decode(sys.stdout.encoding) main_link_args += [STDCPP_LIB] version_script = os.path.abspath("tools/pytorch.version") extra_link_args += ['-Wl,--version-script=' + version_script] 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=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) THNN = Extension("torch._thnn._THNN", sources=['torch/csrc/nn/THNN.cpp'], language='c++', extra_compile_args=extra_compile_args, include_dirs=include_dirs, extra_link_args=extra_link_args + [ ATEN_LIB, make_relative_rpath('../lib'), ] ) extensions.append(THNN) if WITH_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++', include_dirs=include_dirs, library_dirs=library_dirs + cuda_stub_path, extra_link_args=thnvrtc_link_flags, ) extensions.append(THNVRTC) THCUNN = Extension("torch._thnn._THCUNN", sources=['torch/csrc/nn/THCUNN.cpp'], language='c++', extra_compile_args=extra_compile_args, include_dirs=include_dirs, extra_link_args=extra_link_args + [ ATEN_LIB, make_relative_rpath('../lib'), ] ) extensions.append(THCUNN) version = '0.4.0a0' 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 cmdclass = { 'build': build, 'build_py': build_py, 'build_ext': build_ext, 'build_deps': build_deps, 'build_module': build_module, 'develop': develop, 'install': install, 'clean': clean, } cmdclass.update(build_dep_cmds) if __name__ == '__main__': setup( name="torch", version=version, description=("Tensors and Dynamic neural networks in " "Python with strong GPU acceleration"), ext_modules=extensions, cmdclass=cmdclass, packages=packages, package_data={ 'torch': [ 'lib/*.so*', 'lib/*.dylib*', 'lib/*.dll', 'lib/*.lib', 'lib/torch_shm_manager', 'lib/*.h', 'lib/include/ATen/*.h', 'lib/include/ATen/cuda/*.cuh', 'lib/include/ATen/cudnn/*.h', 'lib/include/ATen/cuda/detail/*.cuh', 'lib/include/pybind11/*.h', 'lib/include/pybind11/detail/*.h', 'lib/include/TH/*.h', 'lib/include/TH/generic/*.h', 'lib/include/THC/*.h', 'lib/include/THC/*.cuh', 'lib/include/THC/generic/*.h', 'lib/include/torch/csrc/*.h', 'lib/include/torch/csrc/autograd/*.h', 'lib/include/torch/csrc/jit/*.h', 'lib/include/torch/csrc/utils/*.h', 'lib/include/torch/torch.h', ] }, install_requires=['pyyaml', 'numpy'], )