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e43ff32192
pytorch/setup.py
Zachary DeVito e43ff32192
Add a JIT interpreter (#3634) 
* Add a JIT interpreter

The separate interpreter is used to graphs with a lower overhead than
converting them to autograd graphs. Some notes:

* does not support Handles/PythonOp/CppOp, these will be in a future commit
* jit_closure.cpp still exists and we fall back to it for now when
  cannot handle something because of PythonOp/CppOp
* In order to support retain_graph=True, the interpreter can be cloned,
  creating a copy that can be run with different arguments. This is
  assumed to be the non-standard case so cloning is not particularly optimized.
  No tensor _data_ is copied, but the at::Tensor list in the interpreter is.
  If we hit problems, there is a lot we could do (such as register allocation)
  to minimize the stuff that needs to be copied.
* Uses a pImpl pattern to keep implementation details out of its header file.
* Modifies the way getTensorOp works so that it reads/writes to already-existing
  vectors, this prevents needing to realloc these buffers each time.
* Timings are here: https://gist.github.com/zdevito/5a20ac29fb1b9e449e693b67dc478127
  This reduces overhead to about the same as running it in python.
  It is about 10us faster to run the same thing using ATen directly.

* Code Mod

Interpreter -> InterpreterState
Function -> Code

Add other requested comments.

* RegList -> ListHandle<T>

Change the RegList functions to be safer by identifying the type of
each argument list, and checking that list insert does not try
to add to two different lists at once.

* Use exactly equal for interp tests
2017-11-13 22:09:53 -08:00

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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 sys
import os
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_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, NNPACK_LIB_PATHS, \
NNPACK_INCLUDE_DIRS
from tools.setup_helpers.nvtoolext import NVTOOLEXT_HOME
from tools.setup_helpers.split_types import split_types
DEBUG = check_env_flag('DEBUG')
IS_WINDOWS = (platform.system() == 'Windows')
IS_DARWIN = (platform.system() == 'Darwin')
IS_LINUX = (platform.system() == 'Linux')
WITH_DISTRIBUTED = not check_env_flag('NO_DISTRIBUTED') and not IS_WINDOWS
WITH_DISTRIBUTED_MW = WITH_DISTRIBUTED and check_env_flag('WITH_DISTRIBUTED_MW')
################################################################################
# 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", "")
################################################################################
# Monkey-patch setuptools to compile in parallel
################################################################################
original_link = distutils.unixccompiler.UnixCCompiler.link
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)
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))
multiprocessing.pool.ThreadPool(num_jobs).map(_single_compile, objects)
return objects
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.ccompiler.CCompiler.compile = parallelCCompile
distutils.unixccompiler.UnixCCompiler.link = patched_link
################################################################################
# Custom build commands
################################################################################
dep_libs = [
'nccl', 'ATen',
'libshm', 'libshm_windows', 'gloo', 'THD', 'nanopb',
]
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
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_CUDNN:
my_env["CUDNN_LIB_DIR"] = CUDNN_LIB_DIR
my_env["CUDNN_INCLUDE_DIR"] = CUDNN_INCLUDE_DIR
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):
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)
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
class build_ext(setuptools.command.build_ext.build_ext):
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_LIB_DIR + ', ' + 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')
# Do we actually need this here?
if WITH_NNPACK:
nnpack_dir = NNPACK_LIB_PATHS[0]
print('-- Detected NNPACK at ' + nnpack_dir)
else:
print('-- Not using NNPACK')
# cwrap depends on pyyaml, so we can't import it earlier
from tools.cwrap import cwrap
from tools.cwrap.plugins.THPPlugin import THPPlugin
from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin
from tools.cwrap.plugins.AutoGPU import AutoGPU
from tools.cwrap.plugins.BoolOption import BoolOption
from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin
from tools.cwrap.plugins.NullableArguments import NullableArguments
from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin
from tools.cwrap.plugins.WrapDim import WrapDim
from tools.cwrap.plugins.AssertNDim import AssertNDim
from tools.cwrap.plugins.Broadcast import Broadcast
from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin
from tools.autograd.gen_variable_type import gen_variable_type
from tools.jit.gen_jit_dispatch import gen_jit_dispatch
thp_plugin = THPPlugin()
cwrap('torch/csrc/generic/TensorMethods.cwrap', plugins=[
ProcessorSpecificPlugin(), BoolOption(), thp_plugin,
AutoGPU(condition='IS_CUDA'), ArgcountSortPlugin(), KwargsPlugin(),
AssertNDim(), WrapDim(), Broadcast()
])
cwrap('torch/csrc/cudnn/cuDNN.cwrap', plugins=[
CuDNNPlugin(), NullableArguments()
])
# Build ATen based Variable classes
autograd_gen_dir = 'torch/csrc/autograd/generated'
jit_gen_dir = 'torch/csrc/jit/generated'
for d in (autograd_gen_dir, jit_gen_dir):
if not os.path.exists(d):
os.mkdir(d)
gen_variable_type(
'torch/lib/tmp_install/share/ATen/Declarations.yaml',
autograd_gen_dir)
gen_jit_dispatch(
'torch/lib/tmp_install/share/ATen/Declarations.yaml',
jit_gen_dir)
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 AutoGPU for the export class
# a header file cannot build, so it has to be copied to someplace as a source file
if os.path.exists("torch/csrc/generated/AutoGPU_cpu_win.cpp"):
os.remove("torch/csrc/generated/AutoGPU_cpu_win.cpp")
shutil.copyfile("torch/csrc/cuda/AutoGPU.h", "torch/csrc/generated/AutoGPU_cpu_win.cpp")
# 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')
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
]
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")
# 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"))
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/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_geometry.cpp",
"torch/csrc/utils/tuple_parser.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/python_ir.cpp",
"torch/csrc/jit/test_jit.cpp",
"torch/csrc/jit/tracer.cpp",
"torch/csrc/jit/python_tracer.cpp",
"torch/csrc/jit/interned_strings.cpp",
"torch/csrc/jit/type.cpp",
"torch/csrc/jit/export.cpp",
"torch/csrc/jit/python_arg_flatten.cpp",
"torch/csrc/jit/python_compiled_function.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/onnx/peephole.cpp",
"torch/csrc/jit/generated/aten_dispatch.cpp",
"torch/csrc/autograd/init.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_engine.cpp",
"torch/csrc/autograd/python_hook.cpp",
"torch/csrc/autograd/functions/jit_closure.cpp",
"torch/csrc/autograd/generated/VariableType.cpp",
"torch/csrc/autograd/generated/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/batch_normalization.cpp",
"torch/csrc/autograd/functions/convolution.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/autograd/functions/onnx/convolution.cpp",
"torch/csrc/autograd/functions/onnx/batch_normalization.cpp",
"torch/csrc/autograd/functions/onnx/basic_ops.cpp",
"torch/csrc/onnx/onnx.pb.cpp",
"torch/csrc/onnx/onnx.cpp",
]
main_sources += split_types("torch/csrc/Tensor.cpp")
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 IS_WINDOWS and not WITH_CUDA:
main_sources += ["torch/csrc/generated/AutoGPU_cpu_win.cpp"]
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/expand_utils.cpp",
"torch/csrc/cuda/serialization.cpp",
"torch/csrc/jit/fusion_compiler.cpp",
]
main_sources += split_types("torch/csrc/cuda/Tensor.cpp")
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",
]
if WITH_CUDNN:
main_libraries += ['cudnn']
library_dirs.append(CUDNN_LIB_DIR)
# 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)
main_sources += [
"torch/csrc/cudnn/BatchNorm.cpp",
"torch/csrc/cudnn/Conv.cpp",
"torch/csrc/cudnn/cuDNN.cpp",
"torch/csrc/cudnn/GridSampler.cpp",
"torch/csrc/cudnn/AffineGridGenerator.cpp",
"torch/csrc/cudnn/Types.cpp",
"torch/csrc/cudnn/Handles.cpp",
]
extra_compile_args += ['-DWITH_CUDNN']
if WITH_NNPACK:
include_dirs.extend(NNPACK_INCLUDE_DIRS)
main_link_args.extend(NNPACK_LIB_PATHS)
main_sources += [
"torch/csrc/nnpack/NNPACK.cpp",
]
extra_compile_args += ['-DWITH_NNPACK']
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']
THNVRTC = Extension("torch._nvrtc",
sources=['torch/csrc/nvrtc.cpp'],
language='c++',
include_dirs=include_dirs,
library_dirs=library_dirs + [cuda_lib_path + '/stubs'],
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
version = os.getenv('PYTORCH_BUILD_VERSION') \
+ '_' + os.getenv('PYTORCH_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)
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/torch_shm_manager',
'lib/*.h',
'lib/include/TH/*.h', 'lib/include/TH/generic/*.h',
'lib/include/THC/*.h', 'lib/include/THC/generic/*.h',
'lib/include/ATen/*.h',
]},
install_requires=['pyyaml', 'numpy'],
)
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