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setup.py
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setup.py
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# Welcome to the PyTorch setup.py.
#
# Environment variables you are probably interested 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_TEST
# disables the test build
#
# NO_MIOPEN
# disables the MIOpen build
#
# NO_MKLDNN
# disables the MKLDNN build
#
# NO_NNPACK
# disables NNPACK build
#
# 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_LEVELDB
# enables use of LevelDB for storage
#
# USE_LMBD
# 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.
#
# 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
#
# 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
#
# MKLDNN_LIB_DIR
# MKLDNN_LIBRARY
# MKLDNN_INCLUDE_DIR
# specify where MKLDNN 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 distutils.sysconfig
import platform
import subprocess
import shutil
import multiprocessing
import sys
import os
import json
import glob
import importlib
from tools.setup_helpers.env import check_env_flag, check_negative_env_flag
def hotpatch_var(var, prefix='USE_'):
if check_env_flag('NO_' + var):
os.environ[prefix + var] = '0'
elif check_negative_env_flag('NO_' + var):
os.environ[prefix + var] = '1'
elif check_env_flag('WITH_' + var):
os.environ[prefix + var] = '1'
elif check_negative_env_flag('WITH_' + var):
os.environ[prefix + var] = '0'
# Before we run the setup_helpers, let's look for NO_* and WITH_*
# variables and hotpatch environment with the USE_* equivalent
use_env_vars = ['CUDA', 'CUDNN', 'MIOPEN', 'MKLDNN', 'NNPACK', 'DISTRIBUTED',
'OPENCV', 'SYSTEM_NCCL', 'GLOO_IBVERBS']
list(map(hotpatch_var, use_env_vars))
# Also hotpatch a few with BUILD_* equivalent
build_env_vars = ['BINARY', 'TEST', 'CAFFE2_OPS']
[hotpatch_var(v, 'BUILD_') for v in build_env_vars]
from tools.setup_helpers.cuda import USE_CUDA, CUDA_HOME, CUDA_VERSION
from tools.setup_helpers.build import (BUILD_BINARY, BUILD_TEST,
BUILD_CAFFE2_OPS, USE_LEVELDB,
USE_LMDB, USE_OPENCV)
from tools.setup_helpers.rocm import USE_ROCM, ROCM_HOME, ROCM_VERSION
from tools.setup_helpers.cudnn import (USE_CUDNN, CUDNN_LIBRARY,
CUDNN_LIB_DIR, CUDNN_INCLUDE_DIR)
from tools.setup_helpers.miopen import (USE_MIOPEN, MIOPEN_LIBRARY,
MIOPEN_LIB_DIR, MIOPEN_INCLUDE_DIR)
from tools.setup_helpers.nccl import USE_NCCL, USE_SYSTEM_NCCL, NCCL_LIB_DIR, \
NCCL_INCLUDE_DIR, NCCL_ROOT_DIR, NCCL_SYSTEM_LIB
from tools.setup_helpers.mkldnn import (USE_MKLDNN, MKLDNN_LIBRARY,
MKLDNN_LIB_DIR, MKLDNN_INCLUDE_DIR)
from tools.setup_helpers.nnpack import USE_NNPACK
from tools.setup_helpers.nvtoolext import NVTOOLEXT_HOME
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 USE_DISTRIBUTED, \
USE_GLOO_IBVERBS
################################################################################
# Parameters parsed from environment
################################################################################
DEBUG = check_env_flag('DEBUG')
IS_WINDOWS = (platform.system() == 'Windows')
IS_DARWIN = (platform.system() == 'Darwin')
IS_LINUX = (platform.system() == 'Linux')
BUILD_PYTORCH = check_env_flag('BUILD_PYTORCH')
USE_CUDA_STATIC_LINK = check_env_flag('USE_CUDA_STATIC_LINK')
RERUN_CMAKE = True
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))
ONNX_NAMESPACE = os.getenv("ONNX_NAMESPACE")
if not ONNX_NAMESPACE:
ONNX_NAMESPACE = "onnx_torch"
# Ninja
try:
import ninja
USE_NINJA = True
ninja_global = NinjaBuilder('global')
except ImportError:
USE_NINJA = False
ninja_global = None
# 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()
class PytorchCommand(setuptools.Command):
"""
Base Pytorch command to avoid implementing initialize/finalize_options in
every subclass
"""
user_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
################################################################################
# Patches and workarounds
################################################################################
# Monkey-patch setuptools to compile in parallel
if not USE_NINJA:
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
# Patch for linking with ccache
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
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", "")
################################################################################
# Version, create_version_file, and package_name
################################################################################
package_name = os.getenv('TORCH_PACKAGE_NAME', 'torch')
version = '0.5.0a0'
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
print("Building wheel {}-{}".format(package_name, version))
class create_version_file(PytorchCommand):
def run(self):
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)))
################################################################################
# Building dependent libraries
################################################################################
# All libraries that torch could depend on
dep_libs = [
'nccl', 'caffe2',
'libshm', 'libshm_windows', 'gloo', 'THD', 'c10d',
]
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))
# Calls build_pytorch_libs.sh/bat with the correct env variables
def build_libs(libs):
for lib in libs:
assert lib in dep_libs, 'invalid lib: {}'.format(lib)
if IS_WINDOWS:
build_libs_cmd = ['tools\\build_pytorch_libs.bat']
else:
build_libs_cmd = ['bash', os.path.join('..', 'tools', 'build_pytorch_libs.sh')]
my_env = os.environ.copy()
my_env["PYTORCH_PYTHON"] = sys.executable
my_env["CMAKE_PREFIX_PATH"] = full_site_packages
my_env["NUM_JOBS"] = str(NUM_JOBS)
my_env["ONNX_NAMESPACE"] = ONNX_NAMESPACE
if not IS_WINDOWS:
if USE_NINJA:
my_env["CMAKE_GENERATOR"] = '-GNinja'
my_env["CMAKE_INSTALL"] = 'ninja install'
else:
my_env['CMAKE_GENERATOR'] = ''
my_env['CMAKE_INSTALL'] = 'make install'
if USE_SYSTEM_NCCL:
my_env["NCCL_ROOT_DIR"] = NCCL_ROOT_DIR
if USE_CUDA:
my_env["CUDA_BIN_PATH"] = CUDA_HOME
build_libs_cmd += ['--use-cuda']
if IS_WINDOWS:
my_env["NVTOOLEXT_HOME"] = NVTOOLEXT_HOME
if USE_CUDA_STATIC_LINK:
build_libs_cmd += ['--cuda-static-link']
if USE_ROCM:
build_libs_cmd += ['--use-rocm']
if USE_NNPACK:
build_libs_cmd += ['--use-nnpack']
if USE_CUDNN:
my_env["CUDNN_LIB_DIR"] = CUDNN_LIB_DIR
my_env["CUDNN_LIBRARY"] = CUDNN_LIBRARY
my_env["CUDNN_INCLUDE_DIR"] = CUDNN_INCLUDE_DIR
if USE_MIOPEN:
my_env["MIOPEN_LIB_DIR"] = MIOPEN_LIB_DIR
my_env["MIOPEN_LIBRARY"] = MIOPEN_LIBRARY
my_env["MIOPEN_INCLUDE_DIR"] = MIOPEN_INCLUDE_DIR
if USE_MKLDNN:
my_env["MKLDNN_LIB_DIR"] = MKLDNN_LIB_DIR
my_env["MKLDNN_LIBRARY"] = MKLDNN_LIBRARY
my_env["MKLDNN_INCLUDE_DIR"] = MKLDNN_INCLUDE_DIR
build_libs_cmd += ['--use-mkldnn']
if USE_GLOO_IBVERBS:
build_libs_cmd += ['--use-gloo-ibverbs']
if not RERUN_CMAKE:
build_libs_cmd += ['--dont-rerun-cmake']
my_env["BUILD_TORCH"] = "ON"
my_env["BUILD_PYTHON"] = "ON"
my_env["BUILD_BINARY"] = "ON" if BUILD_BINARY else "OFF"
my_env["BUILD_TEST"] = "ON" if BUILD_TEST else "OFF"
my_env["BUILD_CAFFE2_OPS"] = "ON" if BUILD_CAFFE2_OPS else "OFF"
my_env["INSTALL_TEST"] = "ON" if BUILD_TEST else "OFF"
my_env["USE_LEVELDB"] = "ON" if USE_LEVELDB else "OFF"
my_env["USE_LMDB"] = "ON" if USE_LMDB else "OFF"
my_env["USE_OPENCV"] = "ON" if USE_OPENCV else "OFF"
try:
os.mkdir('build')
except OSError:
pass
kwargs = {'cwd': 'build'} if not IS_WINDOWS else {}
if subprocess.call(build_libs_cmd + libs, env=my_env, **kwargs) != 0:
print("Failed to run '{}'".format(' '.join(build_libs_cmd + libs)))
sys.exit(1)
# Copy Caffe2's Python proto files (generated during the build with the
# protobuf python compiler) from the build folder to the root folder
# cp root/build/caffe2/proto/proto.py root/caffe2/proto/proto.py
def copy_protos():
for src in glob.glob(
os.path.join(caffe2_build_dir, 'caffe2', 'proto', '*.py')):
dst = os.path.join(
cwd, os.path.relpath(src, caffe2_build_dir))
shutil.copyfile(src, dst)
# Build all dependent libraries
class build_deps(PytorchCommand):
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(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, 'catch', 'CMakeLists.txt'))
check_file(os.path.join(third_party_path, 'onnx', 'CMakeLists.txt'))
check_pydep('yaml', 'pyyaml')
check_pydep('typing', 'typing')
libs = []
if USE_NCCL and not USE_SYSTEM_NCCL:
libs += ['nccl']
libs += ['caffe2']
if IS_WINDOWS:
libs += ['libshm_windows']
else:
libs += ['libshm']
if USE_DISTRIBUTED:
if IS_LINUX:
libs += ['gloo']
libs += ['c10d']
libs += ['THD']
build_libs(libs)
# 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):
if os.path.exists(sym_file):
os.remove(sym_file)
shutil.copyfile(orig_file, sym_file)
# Copy headers necessary to compile C++ extensions.
#
# This is not perfect solution as build does not depend on any of
# the auto-generated code and auto-generated files will not be
# included in this copy. If we want to use auto-generated files,
# we need to find a better way to do this.
# More information can be found in conversation thread of PR #5772
self.copy_tree('torch/lib/tmp_install/share', 'torch/share')
self.copy_tree('third_party/pybind11/include/pybind11/',
'torch/lib/include/pybind11')
self.copy_file('torch/csrc/torch.h', 'torch/lib/include/torch/torch.h')
build_dep_cmds = {}
rebuild_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 rebuild_dep(build_deps):
description = 'Rebuild {} external library'.format(lib)
def run(self):
global RERUN_CMAKE
RERUN_CMAKE = False
build_libs([self.lib])
rebuild_dep.lib = lib
rebuild_dep_cmds['rebuild_' + lib.lower()] = rebuild_dep
class build_module(PytorchCommand):
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.run_command('create_version_file')
setuptools.command.build_py.build_py.run(self)
class develop(setuptools.command.develop.develop):
def run(self):
self.run_command('create_version_file')
setuptools.command.develop.develop.run(self)
self.create_compile_commands()
copy_protos()
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)
if not USE_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
def monkey_patch_C10D_inc_flags():
'''
C10D's include deps are not determined until after build c10d is run, so
we need to monkey-patch it.
'''
mpi_include_path_file = tmp_install_path + "/include/c10d/mpi_include_path"
if os.path.exists(mpi_include_path_file):
with open(mpi_include_path_file, 'r') as f:
mpi_include_paths = f.readlines()
mpi_include_paths = [p.strip() for p in mpi_include_paths]
C.include_dirs += mpi_include_paths
print("-- For c10d, will include MPI paths: {}".format(mpi_include_paths))
build_ext_parent = ninja_build_ext if USE_NINJA \
else setuptools.command.build_ext.build_ext
class build_ext(build_ext_parent):
def run(self):
# Print build options
if USE_NUMPY:
print('-- Building with NumPy bindings')
else:
print('-- NumPy not found')
if USE_CUDNN:
print('-- Detected cuDNN at ' + CUDNN_LIBRARY + ', ' + CUDNN_INCLUDE_DIR)
else:
print('-- Not using cuDNN')
if USE_MIOPEN:
print('-- Detected MIOpen at ' + MIOPEN_LIBRARY + ', ' + MIOPEN_INCLUDE_DIR)
else:
print('-- Not using MIOpen')
if USE_CUDA:
print('-- Detected CUDA at ' + CUDA_HOME)
else:
print('-- Not using CUDA')
if USE_MKLDNN:
print('-- Detected MKLDNN at ' + MKLDNN_LIBRARY + ', ' + MKLDNN_INCLUDE_DIR)
else:
print('-- Not using MKLDNN')
if USE_NCCL and USE_SYSTEM_NCCL:
print('-- Using system provided NCCL library at ' +
NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR)
elif USE_NCCL:
print('-- Building NCCL library')
else:
print('-- Not using NCCL')
if USE_DISTRIBUTED:
print('-- Building with THD distributed package ')
monkey_patch_THD_link_flags()
if IS_LINUX:
print('-- Building with c10d distributed package ')
monkey_patch_C10D_inc_flags()
else:
print('-- Building without c10d distributed package')
else:
print('-- Building without distributed package')
generate_code(ninja_global)
if USE_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)
# 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):
# 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)
print("\nCopying extension {}".format(ext.name))
src = os.path.join(tmp_install_path, rel_site_packages, filename)
if not os.path.exists(src):
print("{} does not exist".format(src))
del self.extensions[i]
else:
dst = os.path.join(os.path.realpath(self.build_lib), filename)
print("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"))
print("setup.py::get_outputs returning {}".format(outputs))
return outputs
class build(distutils.command.build.build):
sub_commands = [
('build_deps', lambda self: True),
] + distutils.command.build.build.sub_commands
class rebuild(distutils.command.build.build):
sub_commands = [
('build_deps', lambda self: True),
] + distutils.command.build.build.sub_commands
def run(self):
global RERUN_CMAKE
RERUN_CMAKE = False
distutils.command.build.build.run(self)
class install(setuptools.command.install.install):
def run(self):
if not self.skip_build:
self.run_command('build_deps')
copy_protos()
setuptools.command.install.install.run(self)
class rebuild_libtorch(distutils.command.build.build):
def run(self):
if subprocess.call(['ninja', 'install'], cwd='build') != 0:
print("Failed to run `ninja install` for the `rebuild_libtorch` command")
sys.exit(1)
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
################################################################################
include_dirs = []
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'):
print('The support for PyTorch with Python 2.7 on Windows is very experimental.')
print('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-zero-length-array',
'-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',
# gcc7 seems to report spurious warnings with this enabled
"-Wno-stringop-overflow",
]
if check_env_flag('WERROR'):
extra_compile_args.append('-Werror')
include_dirs += [
cwd,
tmp_install_path + "/include",
tmp_install_path + "/include/TH",
tmp_install_path + "/include/THNN",
tmp_install_path + "/include/ATen",
third_party_path + "/pybind11/include",
os.path.join(cwd, "torch", "csrc"),
"build/third_party",
]
library_dirs.append(lib_path)
# we specify exact lib names to avoid conflict with lua-torch installs
CAFFE2_LIBS = [os.path.join(lib_path, 'libcaffe2.so')]
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'])
THD_LIB = os.path.join(lib_path, 'libTHD.a')
NCCL_LIB = os.path.join(lib_path, 'libnccl.so.1')
C10D_LIB = os.path.join(lib_path, 'libc10d.a')
# static library only
if DEBUG:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobufd.a')
else:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobuf.a')
if IS_DARWIN:
CAFFE2_LIBS = [os.path.join(lib_path, 'libcaffe2.dylib')]
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'))
NCCL_LIB = os.path.join(lib_path, 'libnccl.1.dylib')
if IS_WINDOWS:
CAFFE2_LIBS = [os.path.join(lib_path, 'caffe2.lib')]
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'))
# Windows needs direct access to ONNX libraries as well
# as through Caffe2 library
CAFFE2_LIBS += [
os.path.join(lib_path, 'onnx.lib'),
os.path.join(lib_path, 'onnx_proto.lib'),
]
if DEBUG:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobufd.lib')
else:
PROTOBUF_STATIC_LIB = os.path.join(lib_path, 'libprotobuf.lib')
main_compile_args = ['-D_THP_CORE', '-DONNX_NAMESPACE=' + ONNX_NAMESPACE]
main_libraries = ['shm']
main_link_args = CAFFE2_LIBS + [PROTOBUF_STATIC_LIB]
if IS_WINDOWS:
main_link_args.append(os.path.join(lib_path, 'torch.lib'))
elif IS_DARWIN:
main_link_args.append(os.path.join(lib_path, 'libtorch.dylib'))
else:
main_link_args.append(os.path.join(lib_path, 'libtorch.so'))
main_sources = [
"torch/csrc/DataLoader.cpp",
"torch/csrc/Device.cpp",
"torch/csrc/Dtype.cpp",
"torch/csrc/DynamicTypes.cpp",
"torch/csrc/Exceptions.cpp",
"torch/csrc/Generator.cpp",
"torch/csrc/Layout.cpp",
"torch/csrc/Module.cpp",
"torch/csrc/PtrWrapper.cpp",
"torch/csrc/Size.cpp",
"torch/csrc/Storage.cpp",
"torch/csrc/autograd/functions/init.cpp",
"torch/csrc/autograd/generated/python_functions.cpp",
"torch/csrc/autograd/generated/python_nn_functions.cpp",
"torch/csrc/autograd/generated/python_torch_functions.cpp",
"torch/csrc/autograd/generated/python_variable_methods.cpp",
"torch/csrc/autograd/init.cpp",
"torch/csrc/autograd/python_anomaly_mode.cpp",
"torch/csrc/autograd/python_cpp_function.cpp",
"torch/csrc/autograd/python_engine.cpp",
"torch/csrc/autograd/python_function.cpp",
"torch/csrc/autograd/python_hook.cpp",
"torch/csrc/autograd/python_legacy_variable.cpp",
"torch/csrc/autograd/python_variable.cpp",
"torch/csrc/autograd/python_variable_indexing.cpp",
"torch/csrc/byte_order.cpp",
"torch/csrc/jit/batched/BatchTensor.cpp",
"torch/csrc/jit/init.cpp",
"torch/csrc/jit/ivalue.cpp",
"torch/csrc/jit/passes/onnx.cpp",
"torch/csrc/jit/passes/onnx/fixup_onnx_loop.cpp",
"torch/csrc/jit/passes/onnx/prepare_division_for_onnx.cpp",
"torch/csrc/jit/passes/onnx/peephole.cpp",
"torch/csrc/jit/passes/to_batch.cpp",
"torch/csrc/jit/python_arg_flatten.cpp",
"torch/csrc/jit/python_interpreter.cpp",
"torch/csrc/jit/python_ir.cpp",
"torch/csrc/jit/python_tracer.cpp",
"torch/csrc/jit/script/init.cpp",
"torch/csrc/jit/script/lexer.cpp",
"torch/csrc/jit/script/module.cpp",
"torch/csrc/jit/script/python_tree_views.cpp",
"torch/csrc/nn/THNN.cpp",
"torch/csrc/onnx/init.cpp",
"torch/csrc/serialization.cpp",
"torch/csrc/tensor/python_tensor.cpp",
"torch/csrc/utils.cpp",
"torch/csrc/utils/cuda_lazy_init.cpp",
"torch/csrc/utils/invalid_arguments.cpp",
"torch/csrc/utils/object_ptr.cpp",
"torch/csrc/utils/python_arg_parser.cpp",
"torch/csrc/utils/tensor_apply.cpp",
"torch/csrc/utils/tensor_conversion_dispatch.cpp",
"torch/csrc/utils/tensor_dtypes.cpp",
"torch/csrc/utils/tensor_flatten.cpp",
"torch/csrc/utils/tensor_layouts.cpp",
"torch/csrc/utils/tensor_list.cpp",
"torch/csrc/utils/tensor_new.cpp",
"torch/csrc/utils/tensor_numpy.cpp",
"torch/csrc/utils/tensor_types.cpp",
"torch/csrc/utils/tuple_parser.cpp",
]
try:
import numpy as np
include_dirs.append(np.get_include())
extra_compile_args.append('-DUSE_NUMPY')
USE_NUMPY = True
except ImportError:
USE_NUMPY = False
if USE_DISTRIBUTED:
extra_compile_args += ['-DUSE_DISTRIBUTED']
main_sources += [
"torch/csrc/distributed/Module.cpp",
]
include_dirs += [tmp_install_path + "/include/THD"]
main_link_args += [THD_LIB]
if IS_LINUX:
extra_compile_args.append('-DUSE_C10D')
main_sources.append('torch/csrc/distributed/c10d/init.cpp')
if USE_CUDA:
main_sources.append('torch/csrc/distributed/c10d/ddp.cpp')
main_link_args.append(C10D_LIB)
if USE_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")