from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import glob
import imp
import os
import re
import shlex
import setuptools
import subprocess
import sys
import sysconfig
import tempfile
import warnings
import collections
import torch
from .file_baton import FileBaton
from ._cpp_extension_versioner import ExtensionVersioner
from setuptools.command.build_ext import build_ext
IS_WINDOWS = sys.platform == 'win32'
def _find_cuda_home():
'''Finds the CUDA install path.'''
# Guess #1
cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
if cuda_home is None:
# Guess #2
try:
which = 'where' if IS_WINDOWS else 'which'
with open(os.devnull, 'w') as devnull:
nvcc = subprocess.check_output([which, 'nvcc'],
stderr=devnull).decode().rstrip('\r\n')
cuda_home = os.path.dirname(os.path.dirname(nvcc))
except Exception:
# Guess #3
if IS_WINDOWS:
cuda_homes = glob.glob(
'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*')
if len(cuda_homes) == 0:
cuda_home = ''
else:
cuda_home = cuda_homes[0]
else:
cuda_home = '/usr/local/cuda'
if not os.path.exists(cuda_home):
cuda_home = None
if cuda_home and not torch.cuda.is_available():
print("No CUDA runtime is found, using CUDA_HOME='{}'".format(cuda_home))
return cuda_home
def _find_rocm_home():
'''Finds the ROCm install path.'''
# Guess #1
rocm_home = os.environ.get('ROCM_HOME') or os.environ.get('ROCM_PATH')
if rocm_home is None:
# Guess #2
try:
hipcc = subprocess.check_output(
['which', 'hipcc']).decode().rstrip('\r\n')
# this will be either <ROCM_HOME>/hip/bin/hipcc or <ROCM_HOME>/bin/hipcc
rocm_home = os.path.dirname(os.path.dirname(hipcc))
if os.path.basename(rocm_home) == 'hip':
rocm_home = os.path.dirname(rocm_home)
except Exception:
# Guess #3
rocm_home = '/opt/rocm'
if not os.path.exists(rocm_home):
rocm_home = None
if rocm_home and torch.version.hip is None:
print("No ROCm runtime is found, using ROCM_HOME='{}'".format(rocm_home))
return rocm_home
def _join_rocm_home(*paths):
'''
Joins paths with ROCM_HOME, or raises an error if it ROCM_HOME is not set.
This is basically a lazy way of raising an error for missing $ROCM_HOME
only once we need to get any ROCm-specific path.
'''
if ROCM_HOME is None:
raise EnvironmentError('ROCM_HOME environment variable is not set. '
'Please set it to your ROCm install root.')
elif IS_WINDOWS:
raise EnvironmentError('Building PyTorch extensions using '
'ROCm and Windows is not supported.')
return os.path.join(ROCM_HOME, *paths)
MINIMUM_GCC_VERSION = (5, 0, 0)
MINIMUM_MSVC_VERSION = (19, 0, 24215)
ABI_INCOMPATIBILITY_WARNING = '''
!! WARNING !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Your compiler ({}) may be ABI-incompatible with PyTorch!
Please use a compiler that is ABI-compatible with GCC 5.0 and above.
See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.
See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
for instructions on how to install GCC 5 or higher.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! WARNING !!
'''
WRONG_COMPILER_WARNING = '''
!! WARNING !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Your compiler ({user_compiler}) is not compatible with the compiler Pytorch was
built with for this platform, which is {pytorch_compiler} on {platform}. Please
use {pytorch_compiler} to to compile your extension. Alternatively, you may
compile PyTorch from source using {user_compiler}, and then you can also use
{user_compiler} to compile your extension.
See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help
with compiling PyTorch from source.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! WARNING !!
'''
ROCM_HOME = _find_rocm_home()
MIOPEN_HOME = _join_rocm_home('miopen') if ROCM_HOME else None
IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False
CUDA_HOME = _find_cuda_home()
CUDNN_HOME = os.environ.get('CUDNN_HOME') or os.environ.get('CUDNN_PATH')
# PyTorch releases have the version pattern major.minor.patch, whereas when
# PyTorch is built from source, we append the git commit hash, which gives
# it the below pattern.
BUILT_FROM_SOURCE_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\w+\+\w+')
COMMON_MSVC_FLAGS = ['/MD', '/wd4819', '/EHsc']
COMMON_NVCC_FLAGS = [
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
'--expt-relaxed-constexpr'
]
COMMON_HIPCC_FLAGS = [
'-fPIC',
'-D__HIP_PLATFORM_HCC__=1',
'-DCUDA_HAS_FP16=1',
'-D__HIP_NO_HALF_OPERATORS__=1',
'-D__HIP_NO_HALF_CONVERSIONS__=1',
]
JIT_EXTENSION_VERSIONER = ExtensionVersioner()
def _is_binary_build():
return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__)
def _accepted_compilers_for_platform():
# gnu-c++ and gnu-cc are the conda gcc compilers
return ['clang++', 'clang'] if sys.platform.startswith('darwin') else ['g++', 'gcc', 'gnu-c++', 'gnu-cc']
def get_default_build_root():
'''
Returns the path to the root folder under which extensions will built.
For each extension module built, there will be one folder underneath the
folder returned by this function. For example, if ``p`` is the path
returned by this function and ``ext`` the name of an extension, the build
folder for the extension will be ``p/ext``.
'''
# tempfile.gettempdir() will be /tmp on UNIX and \TEMP on Windows.
return os.path.realpath(os.path.join(tempfile.gettempdir(), 'torch_extensions'))
def check_compiler_ok_for_platform(compiler):
'''
Verifies that the compiler is the expected one for the current platform.
Arguments:
compiler (str): The compiler executable to check.
Returns:
True if the compiler is gcc/g++ on Linux or clang/clang++ on macOS,
and always True for Windows.
'''
if IS_WINDOWS:
return True
which = subprocess.check_output(['which', compiler], stderr=subprocess.STDOUT)
# Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'.
compiler_path = os.path.realpath(which.decode().strip())
return any(name in compiler_path for name in _accepted_compilers_for_platform())
[docs]def check_compiler_abi_compatibility(compiler):
'''
Verifies that the given compiler is ABI-compatible with PyTorch.
Arguments:
compiler (str): The compiler executable name to check (e.g. ``g++``).
Must be executable in a shell process.
Returns:
False if the compiler is (likely) ABI-incompatible with PyTorch,
else True.
'''
if not _is_binary_build():
return True
if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
return True
# First check if the compiler is one of the expected ones for the particular platform.
if not check_compiler_ok_for_platform(compiler):
warnings.warn(WRONG_COMPILER_WARNING.format(
user_compiler=compiler,
pytorch_compiler=_accepted_compilers_for_platform()[0],
platform=sys.platform))
return False
if sys.platform.startswith('darwin'):
# There is no particular minimum version we need for clang, so we're good here.
return True
try:
if sys.platform.startswith('linux'):
minimum_required_version = MINIMUM_GCC_VERSION
version = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
version = version.decode().strip().split('.')
else:
minimum_required_version = MINIMUM_MSVC_VERSION
compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT)
match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode().strip())
version = (0, 0, 0) if match is None else match.groups()
except Exception:
_, error, _ = sys.exc_info()
warnings.warn('Error checking compiler version for {}: {}'.format(compiler, error))
return False
if tuple(map(int, version)) >= minimum_required_version:
return True
compiler = '{} {}'.format(compiler, ".".join(version))
warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))
return False
# See below for why we inherit BuildExtension from object.
# https://stackoverflow.com/questions/1713038/super-fails-with-error-typeerror-argument-1-must-be-type-not-classobj-when
[docs]class BuildExtension(build_ext, object):
'''
A custom :mod:`setuptools` build extension .
This :class:`setuptools.build_ext` subclass takes care of passing the
minimum required compiler flags (e.g. ``-std=c++14``) as well as mixed
C++/CUDA compilation (and support for CUDA files in general).
When using :class:`BuildExtension`, it is allowed to supply a dictionary
for ``extra_compile_args`` (rather than the usual list) that maps from
languages (``cxx`` or ``nvcc``) to a list of additional compiler flags to
supply to the compiler. This makes it possible to supply different flags to
the C++ and CUDA compiler during mixed compilation.
``use_ninja`` (bool): If ``use_ninja`` is ``True`` (default), then we
attempt to build using the Ninja backend. Ninja greatly speeds up
compilation compared to the standard ``setuptools.build_ext``.
Fallbacks to the standard distutils backend if Ninja is not available.
.. note::
By default, the Ninja backend uses #CPUS + 2 workers to build the
extension. This may use up too many resources on some systems. One
can control the number of workers by setting the `MAX_JOBS` environment
variable to a non-negative number.
'''
@classmethod
def with_options(cls, **options):
'''
Returns an alternative constructor that extends any original keyword
arguments to the original constructor with the given options.
'''
def init_with_options(*args, **kwargs):
kwargs = kwargs.copy()
kwargs.update(options)
return cls(*args, **kwargs)
return init_with_options
def __init__(self, *args, **kwargs):
super(BuildExtension, self).__init__(*args, **kwargs)
self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False)
self.use_ninja = kwargs.get('use_ninja', False if IS_HIP_EXTENSION else True)
if self.use_ninja:
# Test if we can use ninja. Fallback otherwise.
msg = ('Attempted to use ninja as the BuildExtension backend but '
'{}. Falling back to using the slow distutils backend.')
if IS_HIP_EXTENSION:
warnings.warn(msg.format('HIP extensions is not supported yet for ninja.'))
self.use_ninja = False
elif not _is_ninja_available():
warnings.warn(msg.format('we could not find ninja.'))
self.use_ninja = False
def build_extensions(self):
self._check_abi()
for extension in self.extensions:
self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H')
self._define_torch_extension_name(extension)
self._add_gnu_cpp_abi_flag(extension)
# Register .cu, .cuh and .hip as valid source extensions.
self.compiler.src_extensions += ['.cu', '.cuh', '.hip']
# Save the original _compile method for later.
if self.compiler.compiler_type == 'msvc':
self.compiler._cpp_extensions += ['.cu', '.cuh']
original_compile = self.compiler.compile
original_spawn = self.compiler.spawn
else:
original_compile = self.compiler._compile
def append_std14_if_no_std_present(cflags):
# NVCC does not allow multiple -std to be passed, so we avoid
# overriding the option if the user explicitly passed it.
cpp_format_prefix = '/{}:' if self.compiler.compiler_type == 'msvc' else '-{}='
cpp_flag_prefix = cpp_format_prefix.format('std')
cpp_flag = cpp_flag_prefix + 'c++14'
if not any(flag.startswith(cpp_flag_prefix) for flag in cflags):
cflags.append(cpp_flag)
def unix_cuda_flags(cflags):
return (COMMON_NVCC_FLAGS +
['--compiler-options', "'-fPIC'"] +
cflags + _get_cuda_arch_flags(cflags))
def convert_to_absolute_paths_inplace(paths):
# Helper function. See Note [Absolute include_dirs]
if paths is not None:
for i in range(len(paths)):
if not os.path.isabs(paths[i]):
paths[i] = os.path.abspath(paths[i])
def unix_wrap_single_compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
# Copy before we make any modifications.
cflags = copy.deepcopy(extra_postargs)
try:
original_compiler = self.compiler.compiler_so
if _is_cuda_file(src):
nvcc = (_join_rocm_home('bin', 'hipcc') if IS_HIP_EXTENSION else _join_cuda_home('bin', 'nvcc'))
if not isinstance(nvcc, list):
nvcc = [nvcc]
self.compiler.set_executable('compiler_so', nvcc)
if isinstance(cflags, dict):
cflags = cflags['nvcc']
if IS_HIP_EXTENSION:
cflags = cflags + _get_rocm_arch_flags(cflags)
else:
cflags = unix_cuda_flags(cflags)
elif isinstance(cflags, dict):
cflags = cflags['cxx']
if IS_HIP_EXTENSION:
cflags = cflags + COMMON_HIPCC_FLAGS
append_std14_if_no_std_present(cflags)
original_compile(obj, src, ext, cc_args, cflags, pp_opts)
finally:
# Put the original compiler back in place.
self.compiler.set_executable('compiler_so', original_compiler)
def unix_wrap_ninja_compile(sources,
output_dir=None,
macros=None,
include_dirs=None,
debug=0,
extra_preargs=None,
extra_postargs=None,
depends=None):
"""Compiles sources by outputting a ninja file and running it."""
# NB: I copied some lines from self.compiler (which is an instance
# of distutils.UnixCCompiler). See the following link.
# https://github.com/python/cpython/blob/f03a8f8d5001963ad5b5b28dbd95497e9cc15596/Lib/distutils/ccompiler.py#L564-L567
# This can be fragile, but a lot of other repos also do this
# (see https://github.com/search?q=_setup_compile&type=Code)
# so it is probably OK; we'll also get CI signal if/when
# we update our python version (which is when distutils can be
# upgraded)
# Use absolute path for output_dir so that the object file paths
# (`objects`) get generated with absolute paths.
output_dir = os.path.abspath(output_dir)
# See Note [Absolute include_dirs]
convert_to_absolute_paths_inplace(self.compiler.include_dirs)
_, objects, extra_postargs, pp_opts, _ = \
self.compiler._setup_compile(output_dir, macros,
include_dirs, sources,
depends, extra_postargs)
common_cflags = self.compiler._get_cc_args(pp_opts, debug, extra_preargs)
extra_cc_cflags = self.compiler.compiler_so[1:]
with_cuda = any(map(_is_cuda_file, sources))
# extra_postargs can be either:
# - a dict mapping cxx/nvcc to extra flags
# - a list of extra flags.
if isinstance(extra_postargs, dict):
post_cflags = extra_postargs['cxx']
else:
post_cflags = list(extra_postargs)
append_std14_if_no_std_present(post_cflags)
cuda_post_cflags = None
cuda_cflags = None
if with_cuda:
cuda_cflags = common_cflags
if isinstance(extra_postargs, dict):
cuda_post_cflags = extra_postargs['nvcc']
else:
cuda_post_cflags = list(extra_postargs)
cuda_post_cflags = unix_cuda_flags(cuda_post_cflags)
append_std14_if_no_std_present(cuda_post_cflags)
cuda_cflags = [shlex.quote(f) for f in cuda_cflags]
cuda_post_cflags = [shlex.quote(f) for f in cuda_post_cflags]
_write_ninja_file_and_compile_objects(
sources=sources,
objects=objects,
cflags=[shlex.quote(f) for f in extra_cc_cflags + common_cflags],
post_cflags=[shlex.quote(f) for f in post_cflags],
cuda_cflags=cuda_cflags,
cuda_post_cflags=cuda_post_cflags,
build_directory=output_dir,
verbose=True,
with_cuda=with_cuda)
# Return *all* object filenames, not just the ones we just built.
return objects
def win_cuda_flags(cflags):
return (COMMON_NVCC_FLAGS +
cflags + _get_cuda_arch_flags(cflags))
def win_wrap_single_compile(sources,
output_dir=None,
macros=None,
include_dirs=None,
debug=0,
extra_preargs=None,
extra_postargs=None,
depends=None):
self.cflags = copy.deepcopy(extra_postargs)
extra_postargs = None
def spawn(cmd):
# Using regex to match src, obj and include files
src_regex = re.compile('/T(p|c)(.*)')
src_list = [
m.group(2) for m in (src_regex.match(elem) for elem in cmd)
if m
]
obj_regex = re.compile('/Fo(.*)')
obj_list = [
m.group(1) for m in (obj_regex.match(elem) for elem in cmd)
if m
]
include_regex = re.compile(r'((\-|\/)I.*)')
include_list = [
m.group(1)
for m in (include_regex.match(elem) for elem in cmd) if m
]
if len(src_list) >= 1 and len(obj_list) >= 1:
src = src_list[0]
obj = obj_list[0]
if _is_cuda_file(src):
nvcc = _join_cuda_home('bin', 'nvcc')
if isinstance(self.cflags, dict):
cflags = self.cflags['nvcc']
elif isinstance(self.cflags, list):
cflags = self.cflags
else:
cflags = []
cflags = win_cuda_flags(cflags)
for flag in COMMON_MSVC_FLAGS:
cflags = ['-Xcompiler', flag] + cflags
cmd = [nvcc, '-c', src, '-o', obj] + include_list + cflags
elif isinstance(self.cflags, dict):
cflags = COMMON_MSVC_FLAGS + self.cflags['cxx']
cmd += cflags
elif isinstance(self.cflags, list):
cflags = COMMON_MSVC_FLAGS + self.cflags
cmd += cflags
return original_spawn(cmd)
try:
self.compiler.spawn = spawn
return original_compile(sources, output_dir, macros,
include_dirs, debug, extra_preargs,
extra_postargs, depends)
finally:
self.compiler.spawn = original_spawn
def win_wrap_ninja_compile(sources,
output_dir=None,
macros=None,
include_dirs=None,
debug=0,
extra_preargs=None,
extra_postargs=None,
depends=None):
if not self.compiler.initialized:
self.compiler.initialize()
output_dir = os.path.abspath(output_dir)
# Note [Absolute include_dirs]
# Convert relative path in self.compiler.include_dirs to absolute path if any,
# For ninja build, the build location is not local, the build happens
# in a in script created build folder, relative path lost their correctness.
# To be consistent with jit extension, we allow user to enter relative include_dirs
# in setuptools.setup, and we convert the relative path to absolute path here
convert_to_absolute_paths_inplace(self.compiler.include_dirs)
_, objects, extra_postargs, pp_opts, _ = \
self.compiler._setup_compile(output_dir, macros,
include_dirs, sources,
depends, extra_postargs)
common_cflags = extra_preargs or []
cflags = []
if debug:
cflags.extend(self.compiler.compile_options_debug)
else:
cflags.extend(self.compiler.compile_options)
common_cflags.extend(COMMON_MSVC_FLAGS)
cflags = cflags + common_cflags + pp_opts
with_cuda = any(map(_is_cuda_file, sources))
# extra_postargs can be either:
# - a dict mapping cxx/nvcc to extra flags
# - a list of extra flags.
if isinstance(extra_postargs, dict):
post_cflags = extra_postargs['cxx']
else:
post_cflags = list(extra_postargs)
append_std14_if_no_std_present(post_cflags)
cuda_post_cflags = None
cuda_cflags = None
if with_cuda:
cuda_cflags = []
for common_cflag in common_cflags:
cuda_cflags.append('-Xcompiler')
cuda_cflags.append(common_cflag)
cuda_cflags.extend(pp_opts)
if isinstance(extra_postargs, dict):
cuda_post_cflags = extra_postargs['nvcc']
else:
cuda_post_cflags = list(extra_postargs)
cuda_post_cflags = win_cuda_flags(cuda_post_cflags)
from distutils.spawn import _nt_quote_args
cflags = _nt_quote_args(cflags)
post_cflags = _nt_quote_args(post_cflags)
if with_cuda:
cuda_cflags = _nt_quote_args(cuda_cflags)
cuda_post_cflags = _nt_quote_args(cuda_post_cflags)
_write_ninja_file_and_compile_objects(
sources=sources,
objects=objects,
cflags=cflags,
post_cflags=post_cflags,
cuda_cflags=cuda_cflags,
cuda_post_cflags=cuda_post_cflags,
build_directory=output_dir,
verbose=True,
with_cuda=with_cuda)
# Return *all* object filenames, not just the ones we just built.
return objects
# Monkey-patch the _compile or compile method.
# https://github.com/python/cpython/blob/dc0284ee8f7a270b6005467f26d8e5773d76e959/Lib/distutils/ccompiler.py#L511
if self.compiler.compiler_type == 'msvc':
if self.use_ninja:
self.compiler.compile = win_wrap_ninja_compile
else:
self.compiler.compile = win_wrap_single_compile
else:
if self.use_ninja:
self.compiler.compile = unix_wrap_ninja_compile
else:
self.compiler._compile = unix_wrap_single_compile
build_ext.build_extensions(self)
def get_ext_filename(self, ext_name):
# Get the original shared library name. For Python 3, this name will be
# suffixed with "<SOABI>.so", where <SOABI> will be something like
# cpython-37m-x86_64-linux-gnu. On Python 2, there is no such ABI name.
# The final extension, .so, would be .lib/.dll on Windows of course.
ext_filename = super(BuildExtension, self).get_ext_filename(ext_name)
# If `no_python_abi_suffix` is `True`, we omit the Python 3 ABI
# component. This makes building shared libraries with setuptools that
# aren't Python modules nicer.
if self.no_python_abi_suffix and sys.version_info >= (3, 0):
# The parts will be e.g. ["my_extension", "cpython-37m-x86_64-linux-gnu", "so"].
ext_filename_parts = ext_filename.split('.')
# Omit the second to last element.
without_abi = ext_filename_parts[:-2] + ext_filename_parts[-1:]
ext_filename = '.'.join(without_abi)
return ext_filename
def _check_abi(self):
# On some platforms, like Windows, compiler_cxx is not available.
if hasattr(self.compiler, 'compiler_cxx'):
compiler = self.compiler.compiler_cxx[0]
elif IS_WINDOWS:
compiler = os.environ.get('CXX', 'cl')
else:
compiler = os.environ.get('CXX', 'c++')
check_compiler_abi_compatibility(compiler)
def _add_compile_flag(self, extension, flag):
extension.extra_compile_args = copy.deepcopy(extension.extra_compile_args)
if isinstance(extension.extra_compile_args, dict):
for args in extension.extra_compile_args.values():
args.append(flag)
else:
extension.extra_compile_args.append(flag)
def _define_torch_extension_name(self, extension):
# pybind11 doesn't support dots in the names
# so in order to support extensions in the packages
# like torch._C, we take the last part of the string
# as the library name
names = extension.name.split('.')
name = names[-1]
define = '-DTORCH_EXTENSION_NAME={}'.format(name)
self._add_compile_flag(extension, define)
def _add_gnu_cpp_abi_flag(self, extension):
# use the same CXX ABI as what PyTorch was compiled with
self._add_compile_flag(extension, '-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI)))
[docs]def CppExtension(name, sources, *args, **kwargs):
'''
Creates a :class:`setuptools.Extension` for C++.
Convenience method that creates a :class:`setuptools.Extension` with the
bare minimum (but often sufficient) arguments to build a C++ extension.
All arguments are forwarded to the :class:`setuptools.Extension`
constructor.
Example:
>>> from setuptools import setup
>>> from torch.utils.cpp_extension import BuildExtension, CppExtension
>>> setup(
name='extension',
ext_modules=[
CppExtension(
name='extension',
sources=['extension.cpp'],
extra_compile_args=['-g']),
],
cmdclass={
'build_ext': BuildExtension
})
'''
include_dirs = kwargs.get('include_dirs', [])
include_dirs += include_paths()
kwargs['include_dirs'] = include_dirs
library_dirs = kwargs.get('library_dirs', [])
library_dirs += library_paths()
kwargs['library_dirs'] = library_dirs
libraries = kwargs.get('libraries', [])
libraries.append('c10')
libraries.append('torch')
libraries.append('torch_cpu')
libraries.append('torch_python')
kwargs['libraries'] = libraries
kwargs['language'] = 'c++'
return setuptools.Extension(name, sources, *args, **kwargs)
[docs]def CUDAExtension(name, sources, *args, **kwargs):
'''
Creates a :class:`setuptools.Extension` for CUDA/C++.
Convenience method that creates a :class:`setuptools.Extension` with the
bare minimum (but often sufficient) arguments to build a CUDA/C++
extension. This includes the CUDA include path, library path and runtime
library.
All arguments are forwarded to the :class:`setuptools.Extension`
constructor.
Example:
>>> from setuptools import setup
>>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
>>> setup(
name='cuda_extension',
ext_modules=[
CUDAExtension(
name='cuda_extension',
sources=['extension.cpp', 'extension_kernel.cu'],
extra_compile_args={'cxx': ['-g'],
'nvcc': ['-O2']})
],
cmdclass={
'build_ext': BuildExtension
})
'''
library_dirs = kwargs.get('library_dirs', [])
library_dirs += library_paths(cuda=True)
kwargs['library_dirs'] = library_dirs
libraries = kwargs.get('libraries', [])
libraries.append('c10')
libraries.append('torch')
libraries.append('torch_cpu')
libraries.append('torch_python')
if IS_HIP_EXTENSION:
libraries.append('c10_hip')
libraries.append('torch_hip')
else:
libraries.append('cudart')
libraries.append('c10_cuda')
libraries.append('torch_cuda')
kwargs['libraries'] = libraries
include_dirs = kwargs.get('include_dirs', [])
include_dirs += include_paths(cuda=True)
kwargs['include_dirs'] = include_dirs
kwargs['language'] = 'c++'
return setuptools.Extension(name, sources, *args, **kwargs)
[docs]def include_paths(cuda=False):
'''
Get the include paths required to build a C++ or CUDA extension.
Args:
cuda: If `True`, includes CUDA-specific include paths.
Returns:
A list of include path strings.
'''
here = os.path.abspath(__file__)
torch_path = os.path.dirname(os.path.dirname(here))
lib_include = os.path.join(torch_path, 'include')
paths = [
lib_include,
# Remove this once torch/torch.h is officially no longer supported for C++ extensions.
os.path.join(lib_include, 'torch', 'csrc', 'api', 'include'),
# Some internal (old) Torch headers don't properly prefix their includes,
# so we need to pass -Itorch/lib/include/TH as well.
os.path.join(lib_include, 'TH'),
os.path.join(lib_include, 'THC')
]
if cuda and IS_HIP_EXTENSION:
paths.append(os.path.join(lib_include, 'THH'))
paths.append(_join_rocm_home('include'))
if MIOPEN_HOME is not None:
paths.append(os.path.join(MIOPEN_HOME, 'include'))
elif cuda:
cuda_home_include = _join_cuda_home('include')
# if we have the Debian/Ubuntu packages for cuda, we get /usr as cuda home.
# but gcc doesn't like having /usr/include passed explicitly
if cuda_home_include != '/usr/include':
paths.append(cuda_home_include)
if CUDNN_HOME is not None:
paths.append(os.path.join(CUDNN_HOME, 'include'))
return paths
def library_paths(cuda=False):
'''
Get the library paths required to build a C++ or CUDA extension.
Args:
cuda: If `True`, includes CUDA-specific library paths.
Returns:
A list of library path strings.
'''
paths = []
# We need to link against libtorch.so
here = os.path.abspath(__file__)
torch_path = os.path.dirname(os.path.dirname(here))
lib_path = os.path.join(torch_path, 'lib')
paths.append(lib_path)
if cuda and IS_HIP_EXTENSION:
lib_dir = 'lib'
paths.append(_join_rocm_home(lib_dir))
elif cuda:
if IS_WINDOWS:
lib_dir = 'lib/x64'
else:
lib_dir = 'lib64'
if (not os.path.exists(_join_cuda_home(lib_dir)) and
os.path.exists(_join_cuda_home('lib'))):
# 64-bit CUDA may be installed in 'lib' (see e.g. gh-16955)
# Note that it's also possible both don't exist (see
# _find_cuda_home) - in that case we stay with 'lib64'.
lib_dir = 'lib'
paths.append(_join_cuda_home(lib_dir))
if CUDNN_HOME is not None:
paths.append(os.path.join(CUDNN_HOME, lib_dir))
return paths
[docs]def load(name,
sources,
extra_cflags=None,
extra_cuda_cflags=None,
extra_ldflags=None,
extra_include_paths=None,
build_directory=None,
verbose=False,
with_cuda=None,
is_python_module=True):
'''
Loads a PyTorch C++ extension just-in-time (JIT).
To load an extension, a Ninja build file is emitted, which is used to
compile the given sources into a dynamic library. This library is
subsequently loaded into the current Python process as a module and
returned from this function, ready for use.
By default, the directory to which the build file is emitted and the
resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
``<tmp>`` is the temporary folder on the current platform and ``<name>``
the name of the extension. This location can be overridden in two ways.
First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
into subfolders of this directory. Second, if the ``build_directory``
argument to this function is supplied, it overrides the entire path, i.e.
the library will be compiled into that folder directly.
To compile the sources, the default system compiler (``c++``) is used,
which can be overridden by setting the ``CXX`` environment variable. To pass
additional arguments to the compilation process, ``extra_cflags`` or
``extra_ldflags`` can be provided. For example, to compile your extension
with optimizations, pass ``extra_cflags=['-O3']``. You can also use
``extra_cflags`` to pass further include directories.
CUDA support with mixed compilation is provided. Simply pass CUDA source
files (``.cu`` or ``.cuh``) along with other sources. Such files will be
detected and compiled with nvcc rather than the C++ compiler. This includes
passing the CUDA lib64 directory as a library directory, and linking
``cudart``. You can pass additional flags to nvcc via
``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
heuristics for finding the CUDA install directory are used, which usually
work fine. If not, setting the ``CUDA_HOME`` environment variable is the
safest option.
Args:
name: The name of the extension to build. This MUST be the same as the
name of the pybind11 module!
sources: A list of relative or absolute paths to C++ source files.
extra_cflags: optional list of compiler flags to forward to the build.
extra_cuda_cflags: optional list of compiler flags to forward to nvcc
when building CUDA sources.
extra_ldflags: optional list of linker flags to forward to the build.
extra_include_paths: optional list of include directories to forward
to the build.
build_directory: optional path to use as build workspace.
verbose: If ``True``, turns on verbose logging of load steps.
with_cuda: Determines whether CUDA headers and libraries are added to
the build. If set to ``None`` (default), this value is
automatically determined based on the existence of ``.cu`` or
``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
and libraries to be included.
is_python_module: If ``True`` (default), imports the produced shared
library as a Python module. If ``False``, loads it into the process
as a plain dynamic library.
Returns:
If ``is_python_module`` is ``True``, returns the loaded PyTorch
extension as a Python module. If ``is_python_module`` is ``False``
returns nothing (the shared library is loaded into the process as a side
effect).
Example:
>>> from torch.utils.cpp_extension import load
>>> module = load(
name='extension',
sources=['extension.cpp', 'extension_kernel.cu'],
extra_cflags=['-O2'],
verbose=True)
'''
return _jit_compile(
name,
[sources] if isinstance(sources, str) else sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory or _get_build_directory(name, verbose),
verbose,
with_cuda,
is_python_module)
[docs]def load_inline(name,
cpp_sources,
cuda_sources=None,
functions=None,
extra_cflags=None,
extra_cuda_cflags=None,
extra_ldflags=None,
extra_include_paths=None,
build_directory=None,
verbose=False,
with_cuda=None,
is_python_module=True,
with_pytorch_error_handling=True):
'''
Loads a PyTorch C++ extension just-in-time (JIT) from string sources.
This function behaves exactly like :func:`load`, but takes its sources as
strings rather than filenames. These strings are stored to files in the
build directory, after which the behavior of :func:`load_inline` is
identical to :func:`load`.
See `the
tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions.py>`_
for good examples of using this function.
Sources may omit two required parts of a typical non-inline C++ extension:
the necessary header includes, as well as the (pybind11) binding code. More
precisely, strings passed to ``cpp_sources`` are first concatenated into a
single ``.cpp`` file. This file is then prepended with ``#include
<torch/extension.h>``.
Furthermore, if the ``functions`` argument is supplied, bindings will be
automatically generated for each function specified. ``functions`` can
either be a list of function names, or a dictionary mapping from function
names to docstrings. If a list is given, the name of each function is used
as its docstring.
The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
file and prepended with ``torch/types.h``, ``cuda.h`` and
``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
separately, but ultimately linked into a single library. Note that no
bindings are generated for functions in ``cuda_sources`` per se. To bind
to a CUDA kernel, you must create a C++ function that calls it, and either
declare or define this C++ function in one of the ``cpp_sources`` (and
include its name in ``functions``).
See :func:`load` for a description of arguments omitted below.
Args:
cpp_sources: A string, or list of strings, containing C++ source code.
cuda_sources: A string, or list of strings, containing CUDA source code.
functions: A list of function names for which to generate function
bindings. If a dictionary is given, it should map function names to
docstrings (which are otherwise just the function names).
with_cuda: Determines whether CUDA headers and libraries are added to
the build. If set to ``None`` (default), this value is
automatically determined based on whether ``cuda_sources`` is
provided. Set it to ``True`` to force CUDA headers
and libraries to be included.
with_pytorch_error_handling: Determines whether pytorch error and
warning macros are handled by pytorch instead of pybind. To do
this, each function ``foo`` is called via an intermediary ``_safe_foo``
function. This redirection might cause issues in obscure cases
of cpp. This flag should be set to ``False`` when this redirect
causes issues.
Example:
>>> from torch.utils.cpp_extension import load_inline
>>> source = \'\'\'
at::Tensor sin_add(at::Tensor x, at::Tensor y) {
return x.sin() + y.sin();
}
\'\'\'
>>> module = load_inline(name='inline_extension',
cpp_sources=[source],
functions=['sin_add'])
.. note::
By default, the Ninja backend uses #CPUS + 2 workers to build the
extension. This may use up too many resources on some systems. One
can control the number of workers by setting the `MAX_JOBS` environment
variable to a non-negative number.
'''
build_directory = build_directory or _get_build_directory(name, verbose)
if isinstance(cpp_sources, str):
cpp_sources = [cpp_sources]
cuda_sources = cuda_sources or []
if isinstance(cuda_sources, str):
cuda_sources = [cuda_sources]
cpp_sources.insert(0, '#include <torch/extension.h>')
# If `functions` is supplied, we create the pybind11 bindings for the user.
# Here, `functions` is (or becomes, after some processing) a map from
# function names to function docstrings.
if functions is not None:
module_def = []
module_def.append('PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {')
if isinstance(functions, str):
functions = [functions]
if isinstance(functions, list):
# Make the function docstring the same as the function name.
functions = dict((f, f) for f in functions)
elif not isinstance(functions, dict):
raise ValueError(
"Expected 'functions' to be a list or dict, but was {}".format(
type(functions)))
for function_name, docstring in functions.items():
if with_pytorch_error_handling:
module_def.append(
'm.def("{0}", torch::wrap_pybind_function({0}), "{1}");'
.format(function_name, docstring))
else:
module_def.append('m.def("{0}", {0}, "{1}");'.format(function_name, docstring))
module_def.append('}')
cpp_sources += module_def
cpp_source_path = os.path.join(build_directory, 'main.cpp')
with open(cpp_source_path, 'w') as cpp_source_file:
cpp_source_file.write('\n'.join(cpp_sources))
sources = [cpp_source_path]
if cuda_sources:
cuda_sources.insert(0, '#include <torch/types.h>')
cuda_sources.insert(1, '#include <cuda.h>')
cuda_sources.insert(2, '#include <cuda_runtime.h>')
cuda_source_path = os.path.join(build_directory, 'cuda.cu')
with open(cuda_source_path, 'w') as cuda_source_file:
cuda_source_file.write('\n'.join(cuda_sources))
sources.append(cuda_source_path)
return _jit_compile(
name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory,
verbose,
with_cuda,
is_python_module)
def _jit_compile(name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory,
verbose,
with_cuda,
is_python_module):
old_version = JIT_EXTENSION_VERSIONER.get_version(name)
version = JIT_EXTENSION_VERSIONER.bump_version_if_changed(
name,
sources,
build_arguments=[extra_cflags, extra_cuda_cflags, extra_ldflags, extra_include_paths],
build_directory=build_directory,
with_cuda=with_cuda
)
if version > 0:
if version != old_version and verbose:
print('The input conditions for extension module {} have changed. '.format(name) +
'Bumping to version {0} and re-building as {1}_v{0}...'.format(version, name))
name = '{}_v{}'.format(name, version)
if version != old_version:
baton = FileBaton(os.path.join(build_directory, 'lock'))
if baton.try_acquire():
try:
_write_ninja_file_and_build_library(
name=name,
sources=sources,
extra_cflags=extra_cflags or [],
extra_cuda_cflags=extra_cuda_cflags or [],
extra_ldflags=extra_ldflags or [],
extra_include_paths=extra_include_paths or [],
build_directory=build_directory,
verbose=verbose,
with_cuda=with_cuda)
finally:
baton.release()
else:
baton.wait()
elif verbose:
print('No modifications detected for re-loaded extension '
'module {}, skipping build step...'.format(name))
if verbose:
print('Loading extension module {}...'.format(name))
return _import_module_from_library(name, build_directory, is_python_module)
def _write_ninja_file_and_compile_objects(
sources,
objects,
cflags,
post_cflags,
cuda_cflags,
cuda_post_cflags,
build_directory,
verbose,
with_cuda):
verify_ninja_availability()
if IS_WINDOWS:
compiler = os.environ.get('CXX', 'cl')
else:
compiler = os.environ.get('CXX', 'c++')
check_compiler_abi_compatibility(compiler)
if with_cuda is None:
with_cuda = any(map(_is_cuda_file, sources))
build_file_path = os.path.join(build_directory, 'build.ninja')
if verbose:
print(
'Emitting ninja build file {}...'.format(build_file_path))
_write_ninja_file(
path=build_file_path,
cflags=cflags,
post_cflags=post_cflags,
cuda_cflags=cuda_cflags,
cuda_post_cflags=cuda_post_cflags,
sources=sources,
objects=objects,
ldflags=None,
library_target=None,
with_cuda=with_cuda)
if verbose:
print('Compiling objects...')
_run_ninja_build(
build_directory,
verbose,
# It would be better if we could tell users the name of the extension
# that failed to build but there isn't a good way to get it here.
error_prefix='Error compiling objects for extension')
def _write_ninja_file_and_build_library(
name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory,
verbose,
with_cuda):
verify_ninja_availability()
if IS_WINDOWS:
compiler = os.environ.get('CXX', 'cl')
else:
compiler = os.environ.get('CXX', 'c++')
check_compiler_abi_compatibility(compiler)
if with_cuda is None:
with_cuda = any(map(_is_cuda_file, sources))
extra_ldflags = _prepare_ldflags(
extra_ldflags or [],
with_cuda,
verbose)
build_file_path = os.path.join(build_directory, 'build.ninja')
if verbose:
print(
'Emitting ninja build file {}...'.format(build_file_path))
# NOTE: Emitting a new ninja build file does not cause re-compilation if
# the sources did not change, so it's ok to re-emit (and it's fast).
_write_ninja_file_to_build_library(
path=build_file_path,
name=name,
sources=sources,
extra_cflags=extra_cflags or [],
extra_cuda_cflags=extra_cuda_cflags or [],
extra_ldflags=extra_ldflags or [],
extra_include_paths=extra_include_paths or [],
with_cuda=with_cuda)
if verbose:
print('Building extension module {}...'.format(name))
_run_ninja_build(
build_directory,
verbose,
error_prefix="Error building extension '{}'".format(name))
def _is_ninja_available():
with open(os.devnull, 'wb') as devnull:
try:
subprocess.check_call('ninja --version'.split(), stdout=devnull)
except OSError:
return False
else:
return True
[docs]def verify_ninja_availability():
'''
Returns ``True`` if the `ninja <https://ninja-build.org/>`_ build system is
available on the system.
'''
if not _is_ninja_available():
raise RuntimeError("Ninja is required to load C++ extensions")
def _prepare_ldflags(extra_ldflags, with_cuda, verbose):
here = os.path.abspath(__file__)
torch_path = os.path.dirname(os.path.dirname(here))
lib_path = os.path.join(torch_path, 'lib')
if IS_WINDOWS:
python_path = os.path.dirname(sys.executable)
python_lib_path = os.path.join(python_path, 'libs')
extra_ldflags.append('c10.lib')
if with_cuda:
extra_ldflags.append('c10_cuda.lib')
extra_ldflags.append('torch_cpu.lib')
if with_cuda:
extra_ldflags.append('torch_cuda.lib')
# /INCLUDE is used to ensure torch_cuda is linked against in a project that relies on it.
# Related issue: https://github.com/pytorch/pytorch/issues/31611
extra_ldflags.append('-INCLUDE:?warp_size@cuda@at@@YAHXZ')
extra_ldflags.append('torch.lib')
extra_ldflags.append('torch_python.lib')
extra_ldflags.append('/LIBPATH:{}'.format(python_lib_path))
extra_ldflags.append('/LIBPATH:{}'.format(lib_path))
else:
extra_ldflags.append('-L{}'.format(lib_path))
extra_ldflags.append('-lc10')
if with_cuda:
extra_ldflags.append('-lc10_cuda')
extra_ldflags.append('-ltorch_cpu')
if with_cuda:
extra_ldflags.append('-ltorch_cuda')
extra_ldflags.append('-ltorch')
extra_ldflags.append('-ltorch_python')
if with_cuda:
if verbose:
print('Detected CUDA files, patching ldflags')
if IS_WINDOWS:
extra_ldflags.append('/LIBPATH:{}'.format(
_join_cuda_home('lib/x64')))
extra_ldflags.append('cudart.lib')
if CUDNN_HOME is not None:
extra_ldflags.append(os.path.join(CUDNN_HOME, 'lib/x64'))
else:
extra_ldflags.append('-L{}'.format(_join_cuda_home('lib64')))
extra_ldflags.append('-lcudart')
if CUDNN_HOME is not None:
extra_ldflags.append('-L{}'.format(os.path.join(CUDNN_HOME, 'lib64')))
return extra_ldflags
def _get_cuda_arch_flags(cflags=None):
'''
Determine CUDA arch flags to use.
For an arch, say "6.1", the added compile flag will be
``-gencode=arch=compute_61,code=sm_61``.
For an added "+PTX", an additional
``-gencode=arch=compute_xx,code=compute_xx`` is added.
See select_compute_arch.cmake for corresponding named and supported arches
when building with CMake.
'''
# If cflags is given, there may already be user-provided arch flags in it
# (from `extra_compile_args`)
if cflags is not None:
for flag in cflags:
if 'arch' in flag:
return []
# Note: keep combined names ("arch1+arch2") above single names, otherwise
# string replacement may not do the right thing
named_arches = collections.OrderedDict([
('Kepler+Tesla', '3.7'),
('Kepler', '3.5+PTX'),
('Maxwell+Tegra', '5.3'),
('Maxwell', '5.0;5.2+PTX'),
('Pascal', '6.0;6.1+PTX'),
('Volta', '7.0+PTX'),
('Turing', '7.5+PTX'),
])
supported_arches = ['3.5', '3.7', '5.0', '5.2', '5.3', '6.0', '6.1', '6.2',
'7.0', '7.2', '7.5']
valid_arch_strings = supported_arches + [s + "+PTX" for s in supported_arches]
# The default is sm_30 for CUDA 9.x and 10.x
# First check for an env var (same as used by the main setup.py)
# Can be one or more architectures, e.g. "6.1" or "3.5;5.2;6.0;6.1;7.0+PTX"
# See cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake
arch_list = os.environ.get('TORCH_CUDA_ARCH_LIST', None)
# If not given, determine what's needed for the GPU that can be found
if not arch_list:
capability = torch.cuda.get_device_capability()
arch_list = ['{}.{}'.format(capability[0], capability[1])]
else:
# Deal with lists that are ' ' separated (only deal with ';' after)
arch_list = arch_list.replace(' ', ';')
# Expand named arches
for named_arch, archval in named_arches.items():
arch_list = arch_list.replace(named_arch, archval)
arch_list = arch_list.split(';')
flags = []
for arch in arch_list:
if arch not in valid_arch_strings:
raise ValueError("Unknown CUDA arch ({}) or GPU not supported".format(arch))
else:
num = arch[0] + arch[2]
flags.append('-gencode=arch=compute_{},code=sm_{}'.format(num, num))
if arch.endswith('+PTX'):
flags.append('-gencode=arch=compute_{},code=compute_{}'.format(num, num))
return list(set(flags))
def _get_rocm_arch_flags(cflags=None):
# If cflags is given, there may already be user-provided arch flags in it
# (from `extra_compile_args`)
if cflags is not None:
for flag in cflags:
if 'amdgpu-target' in flag:
return ['-fno-gpu-rdc']
return [
'--amdgpu-target=gfx803',
'--amdgpu-target=gfx900',
'--amdgpu-target=gfx906',
'--amdgpu-target=gfx908',
'-fno-gpu-rdc'
]
def _get_build_directory(name, verbose):
root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
if root_extensions_directory is None:
root_extensions_directory = get_default_build_root()
if verbose:
print('Using {} as PyTorch extensions root...'.format(
root_extensions_directory))
build_directory = os.path.join(root_extensions_directory, name)
if not os.path.exists(build_directory):
if verbose:
print('Creating extension directory {}...'.format(build_directory))
# This is like mkdir -p, i.e. will also create parent directories.
os.makedirs(build_directory)
return build_directory
def _get_num_workers(verbose):
max_jobs = os.environ.get('MAX_JOBS')
if max_jobs is not None and max_jobs.isdigit():
if verbose:
print('Using envvar MAX_JOBS ({}) as the number of workers...'.format(max_jobs))
return int(max_jobs)
if verbose:
print('Allowing ninja to set a default number of workers... '
'(overridable by setting the environment variable MAX_JOBS=N)')
return None
def _run_ninja_build(build_directory, verbose, error_prefix):
command = ['ninja', '-v']
num_workers = _get_num_workers(verbose)
if num_workers is not None:
command.extend(['-j', str(num_workers)])
try:
sys.stdout.flush()
sys.stderr.flush()
if sys.version_info >= (3, 5):
# Warning: don't pass stdout=None to subprocess.run to get output.
# subprocess.run assumes that sys.__stdout__ has not been modified and
# attempts to write to it by default. However, when we call _run_ninja_build
# from ahead-of-time cpp extensions, the following happens:
# 1) If the stdout encoding is not utf-8, setuptools detachs __stdout__.
# https://github.com/pypa/setuptools/blob/7e97def47723303fafabe48b22168bbc11bb4821/setuptools/dist.py#L1110
# (it probably shouldn't do this)
# 2) subprocess.run (on POSIX, with no stdout override) relies on
# __stdout__ not being detached:
# https://github.com/python/cpython/blob/c352e6c7446c894b13643f538db312092b351789/Lib/subprocess.py#L1214
# To work around this, we pass in the fileno directly and hope that
# it is valid.
stdout_fileno = 1
subprocess.run(
command,
stdout=stdout_fileno if verbose else subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=build_directory,
check=True)
else:
subprocess.check_output(
command,
stderr=subprocess.STDOUT,
cwd=build_directory)
except subprocess.CalledProcessError:
# Python 2 and 3 compatible way of getting the error object.
_, error, _ = sys.exc_info()
# error.output contains the stdout and stderr of the build attempt.
message = error_prefix
if hasattr(error, 'output') and error.output:
message += ": {}".format(error.output.decode())
raise RuntimeError(message)
def _import_module_from_library(module_name, path, is_python_module):
# https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
file, path, description = imp.find_module(module_name, [path])
# Close the .so file after load.
with file:
if is_python_module:
return imp.load_module(module_name, file, path, description)
else:
torch.ops.load_library(path)
def _write_ninja_file_to_build_library(path,
name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
with_cuda):
extra_cflags = [flag.strip() for flag in extra_cflags]
extra_cuda_cflags = [flag.strip() for flag in extra_cuda_cflags]
extra_ldflags = [flag.strip() for flag in extra_ldflags]
extra_include_paths = [flag.strip() for flag in extra_include_paths]
# Turn into absolute paths so we can emit them into the ninja build
# file wherever it is.
user_includes = [os.path.abspath(file) for file in extra_include_paths]
# include_paths() gives us the location of torch/extension.h
system_includes = include_paths(with_cuda)
# sysconfig.get_paths()['include'] gives us the location of Python.h
system_includes.append(sysconfig.get_paths()['include'])
# Windows does not understand `-isystem`.
if IS_WINDOWS:
user_includes += system_includes
system_includes.clear()
common_cflags = ['-DTORCH_EXTENSION_NAME={}'.format(name)]
common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H')
common_cflags += ['-I{}'.format(include) for include in user_includes]
common_cflags += ['-isystem {}'.format(include) for include in system_includes]
common_cflags += ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))]
if IS_WINDOWS:
cflags = common_cflags + COMMON_MSVC_FLAGS + extra_cflags
from distutils.spawn import _nt_quote_args
cflags = _nt_quote_args(cflags)
else:
cflags = common_cflags + ['-fPIC', '-std=c++14'] + extra_cflags
if with_cuda:
cuda_flags = common_cflags + COMMON_NVCC_FLAGS + _get_cuda_arch_flags()
if IS_WINDOWS:
for flag in COMMON_MSVC_FLAGS:
cuda_flags = ['-Xcompiler', flag] + cuda_flags
cuda_flags = _nt_quote_args(cuda_flags)
cuda_flags += _nt_quote_args(extra_cuda_cflags)
else:
cuda_flags += ['--compiler-options', "'-fPIC'"]
cuda_flags += extra_cuda_cflags
if not any(flag.startswith('-std=') for flag in cuda_flags):
cuda_flags.append('-std=c++14')
else:
cuda_flags = None
def object_file_path(source_file):
# '/path/to/file.cpp' -> 'file'
file_name = os.path.splitext(os.path.basename(source_file))[0]
if _is_cuda_file(source_file) and with_cuda:
# Use a different object filename in case a C++ and CUDA file have
# the same filename but different extension (.cpp vs. .cu).
target = '{}.cuda.o'.format(file_name)
else:
target = '{}.o'.format(file_name)
return target
objects = list(map(object_file_path, sources))
if IS_WINDOWS:
ldflags = ['/DLL'] + extra_ldflags
else:
ldflags = ['-shared'] + extra_ldflags
# The darwin linker needs explicit consent to ignore unresolved symbols.
if sys.platform.startswith('darwin'):
ldflags.append('-undefined dynamic_lookup')
elif IS_WINDOWS:
ldflags = _nt_quote_args(ldflags)
ext = 'pyd' if IS_WINDOWS else 'so'
library_target = '{}.{}'.format(name, ext)
_write_ninja_file(
path=path,
cflags=cflags,
post_cflags=None,
cuda_cflags=cuda_flags,
cuda_post_cflags=None,
sources=sources,
objects=objects,
ldflags=ldflags,
library_target=library_target,
with_cuda=with_cuda)
def _write_ninja_file(path,
cflags,
post_cflags,
cuda_cflags,
cuda_post_cflags,
sources,
objects,
ldflags,
library_target,
with_cuda):
"""Write a ninja file that does the desired compiling and linking.
`path`: Where to write this file
`cflags`: list of flags to pass to $cxx. Can be None.
`post_cflags`: list of flags to append to the $cxx invocation. Can be None.
`cuda_cflags`: list of flags to pass to $nvcc. Can be None.
`cuda_postflags`: list of flags to append to the $nvcc invocation. Can be None.
`sources`: list of paths to source files
`objects`: list of desired paths to objects, one per source.
`ldflags`: list of flags to pass to linker. Can be None.
`library_target`: Name of the output library. Can be None; in that case,
we do no linking.
`with_cuda`: If we should be compiling with CUDA.
"""
def sanitize_flags(flags):
if flags is None:
return []
else:
return [flag.strip() for flag in flags]
cflags = sanitize_flags(cflags)
post_cflags = sanitize_flags(post_cflags)
cuda_cflags = sanitize_flags(cuda_cflags)
cuda_post_cflags = sanitize_flags(cuda_post_cflags)
ldflags = sanitize_flags(ldflags)
# Sanity checks...
assert len(sources) == len(objects)
assert len(sources) > 0
if IS_WINDOWS:
compiler = os.environ.get('CXX', 'cl')
else:
compiler = os.environ.get('CXX', 'c++')
# Version 1.3 is required for the `deps` directive.
config = ['ninja_required_version = 1.3']
config.append('cxx = {}'.format(compiler))
if with_cuda:
config.append('nvcc = {}'.format(_join_cuda_home('bin', 'nvcc')))
flags = ['cflags = {}'.format(' '.join(cflags))]
flags.append('post_cflags = {}'.format(' '.join(post_cflags)))
if with_cuda:
flags.append('cuda_cflags = {}'.format(' '.join(cuda_cflags)))
flags.append('cuda_post_cflags = {}'.format(' '.join(cuda_post_cflags)))
flags.append('ldflags = {}'.format(' '.join(ldflags)))
# Turn into absolute paths so we can emit them into the ninja build
# file wherever it is.
sources = [os.path.abspath(file) for file in sources]
# See https://ninja-build.org/build.ninja.html for reference.
compile_rule = ['rule compile']
if IS_WINDOWS:
compile_rule.append(
' command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags')
compile_rule.append(' deps = msvc')
else:
compile_rule.append(
' command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags')
compile_rule.append(' depfile = $out.d')
compile_rule.append(' deps = gcc')
if with_cuda:
cuda_compile_rule = ['rule cuda_compile']
cuda_compile_rule.append(
' command = $nvcc $cuda_cflags -c $in -o $out $cuda_post_cflags')
# Emit one build rule per source to enable incremental build.
build = []
for source_file, object_file in zip(sources, objects):
is_cuda_source = _is_cuda_file(source_file) and with_cuda
rule = 'cuda_compile' if is_cuda_source else 'compile'
if IS_WINDOWS:
source_file = source_file.replace(':', '$:')
object_file = object_file.replace(':', '$:')
source_file = source_file.replace(" ", "$ ")
object_file = object_file.replace(" ", "$ ")
build.append('build {}: {} {}'.format(object_file, rule, source_file))
if library_target is not None:
link_rule = ['rule link']
if IS_WINDOWS:
cl_paths = subprocess.check_output(['where',
'cl']).decode().split('\r\n')
if len(cl_paths) >= 1:
cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:')
else:
raise RuntimeError("MSVC is required to load C++ extensions")
link_rule.append(
' command = "{}/link.exe" $in /nologo $ldflags /out:$out'.format(
cl_path))
else:
link_rule.append(' command = $cxx $in $ldflags -o $out')
link = ['build {}: link {}'.format(library_target, ' '.join(objects))]
default = ['default {}'.format(library_target)]
else:
link_rule, link, default = [], [], []
# 'Blocks' should be separated by newlines, for visual benefit.
blocks = [config, flags, compile_rule]
if with_cuda:
blocks.append(cuda_compile_rule)
blocks += [link_rule, build, link, default]
with open(path, 'w') as build_file:
for block in blocks:
lines = '\n'.join(block)
build_file.write('{}\n\n'.format(lines))
def _join_cuda_home(*paths):
'''
Joins paths with CUDA_HOME, or raises an error if it CUDA_HOME is not set.
This is basically a lazy way of raising an error for missing $CUDA_HOME
only once we need to get any CUDA-specific path.
'''
if CUDA_HOME is None:
raise EnvironmentError('CUDA_HOME environment variable is not set. '
'Please set it to your CUDA install root.')
return os.path.join(CUDA_HOME, *paths)
def _is_cuda_file(path):
valid_ext = ['.cu', '.cuh']
if IS_HIP_EXTENSION:
valid_ext.append('.hip')
return os.path.splitext(path)[1] in valid_ext
