mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
1b71e78d13
This PR adds support for convenient CUDA integration in our C++ extension mechanism. This mainly involved figuring out how to get setuptools to use nvcc for CUDA files and the regular C++ compiler for C++ files. I've added a mixed C++/CUDA test case which works great. I've also added a CUDAExtension and CppExtension function that constructs a setuptools.Extension with "usually the right" arguments, which reduces the required boilerplate to write an extension even more. Especially for CUDA, where library_dir (CUDA_HOME/lib64) and libraries (cudart) have to be specified as well. Next step is to enable this with our "JIT" mechanism. NOTE: I've had to write a small find_cuda_home function to find the CUDA install directory. This logic is kind of a duplicate of tools/setup_helpers/cuda.py, but that's not available in the shipped PyTorch distribution. The function is also fairly short. Let me know if it's fine to duplicate this logic. * CUDA support for C++ extensions with setuptools * Remove printf in CUDA test kernel * Remove -arch flag in test/cpp_extensions/setup.py * Put wrap_compile into BuildExtension * Add guesses for CUDA_HOME directory * export PATH to CUDA location in test.sh * On Python2, sys.platform has the linux version number
375 lines
14 KiB
Python
375 lines
14 KiB
Python
import glob
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import imp
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import os
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import re
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import setuptools
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import subprocess
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import sys
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import sysconfig
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import tempfile
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import warnings
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import torch
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from setuptools.command.build_ext import build_ext
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def _find_cuda_home():
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'''Finds the CUDA install path.'''
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# Guess #1
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cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
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if cuda_home is None:
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# Guess #2
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if sys.platform == 'win32':
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cuda_home = glob.glob(
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'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v*.*')
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else:
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cuda_home = '/usr/local/cuda'
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if not os.path.exists(cuda_home):
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# Guess #3
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try:
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which = 'where' if sys.platform == 'win32' else 'which'
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nvcc = subprocess.check_output([which, 'nvcc']).decode()
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cuda_home = os.path.dirname(os.path.dirname(nvcc))
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except Exception:
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cuda_home = None
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return cuda_home
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MINIMUM_GCC_VERSION = (4, 9)
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ABI_INCOMPATIBILITY_WARNING = '''
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Your compiler ({}) may be ABI-incompatible with PyTorch.
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Please use a compiler that is ABI-compatible with GCC 4.9 and above.
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See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.'''
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CUDA_HOME = _find_cuda_home() if torch.cuda.is_available() else None
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def check_compiler_abi_compatibility(compiler):
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'''
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Verifies that the given compiler is ABI-compatible with PyTorch.
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Arguments:
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compiler (str): The compiler executable name to check (e.g. 'g++')
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Returns:
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False if the compiler is (likely) ABI-incompatible with PyTorch,
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else True.
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'''
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try:
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info = subprocess.check_output('{} --version'.format(compiler).split())
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except Exception:
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_, error, _ = sys.exc_info()
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warnings.warn('Error checking compiler version: {}'.format(error))
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else:
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info = info.decode().lower()
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if 'gcc' in info:
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# Sometimes the version is given as "major.x" instead of semver.
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version = re.search(r'(\d+)\.(\d+|x)', info)
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if version is not None:
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major, minor = version.groups()
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minor = 0 if minor == 'x' else int(minor)
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if (int(major), minor) >= MINIMUM_GCC_VERSION:
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return True
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else:
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# Append the detected version for the warning.
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compiler = '{} {}'.format(compiler, version.group(0))
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warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))
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return False
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class BuildExtension(build_ext):
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'''A custom build extension for adding compiler-specific options.'''
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def build_extensions(self):
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# On some platforms, like Windows, compiler_cxx is not available.
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if hasattr(self.compiler, 'compiler_cxx'):
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compiler = self.compiler.compiler_cxx[0]
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else:
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compiler = os.environ.get('CXX', 'c++')
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check_compiler_abi_compatibility(compiler)
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# Register .cu and .cuh as valid source extensions.
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self.compiler.src_extensions += ['.cu', '.cuh']
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# Save the original _compile method for later.
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original_compile = self.compiler._compile
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def wrap_compile(obj, src, ext, cc_args, cflags, pp_opts):
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try:
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original_compiler = self.compiler.compiler_so
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if os.path.splitext(src)[1].startswith('.cu'):
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nvcc = _join_cuda_home('bin', 'nvcc')
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self.compiler.set_executable('compiler_so', nvcc)
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if isinstance(cflags, dict):
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cflags = cflags['nvcc']
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cflags += ['-c', '--compiler-options', "'-fPIC'"]
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else:
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if isinstance(cflags, dict):
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cflags = cflags['cxx']
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cflags.append('-std=c++11')
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original_compile(obj, src, ext, cc_args, cflags, pp_opts)
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finally:
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# Put the original compiler back in place.
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self.compiler.set_executable('compiler_so', original_compiler)
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# Monkey-patch the _compile method.
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self.compiler._compile = wrap_compile
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build_ext.build_extensions(self)
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def CppExtension(name, sources, *args, **kwargs):
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'''
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Create a setuptools.Extension instance for C++.
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Convenience method that creates a `setuptools.Extension` with the bare
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minimum (but often sufficient) arguments to build a C++ extension.
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All arguments are forwarded to the setuptools.Extension constructor.
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'''
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include_dirs = kwargs.get('include_dirs', [])
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include_dirs += include_paths()
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kwargs['include_dirs'] = include_dirs
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kwargs['language'] = 'c++'
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return setuptools.Extension(name, sources, *args, **kwargs)
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def CUDAExtension(name, sources, *args, **kwargs):
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'''
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Create a setuptools.Extension instance for CUDA/C++.
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Convenience method that creates a `setuptools.Extension` with the bare
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minimum (but often sufficient) arguments to build a CUDA/C++ extension.
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This includes the CUDA include path, library path and runtime library.
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All arguments are forwarded to the setuptools.Extension constructor.
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'''
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library_dirs = kwargs.get('library_dirs', [])
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library_dirs.append(_join_cuda_home('lib64'))
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kwargs['library_dirs'] = library_dirs
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libraries = kwargs.get('libraries', [])
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libraries.append('cudart')
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kwargs['libraries'] = libraries
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include_dirs = kwargs.get('include_dirs', [])
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include_dirs += include_paths(cuda=True)
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kwargs['include_dirs'] = include_dirs
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kwargs['language'] = 'c++'
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return setuptools.Extension(name, sources, *args, **kwargs)
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def include_paths(cuda=False):
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'''
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Return include paths required to build a C++ extension.
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Args:
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cuda: If `True`, includes CUDA include paths.
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Returns:
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A list of include path strings.
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'''
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here = os.path.abspath(__file__)
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torch_path = os.path.dirname(os.path.dirname(here))
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paths = [os.path.join(torch_path, 'lib', 'include')]
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if cuda:
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paths.append(_join_cuda_home('include'))
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return paths
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def load(name,
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sources,
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extra_cflags=None,
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extra_ldflags=None,
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extra_include_paths=None,
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build_directory=None,
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verbose=False):
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'''
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Loads a C++ PyTorch extension.
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To load an extension, a Ninja build file is emitted, which is used to
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compile the given sources into a dynamic library. This library is
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subsequently loaded into the current Python process as a module and
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returned from this function, ready for use.
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By default, the directory to which the build file is emitted and the
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resulting library compiled to is `<tmp>/torch_extensions`, where `<tmp>` is
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the temporary folder on the current platform. This location can be
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overriden in two ways. First, if the `TORCH_EXTENSIONS_DIR` environment
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variable is set, it replaces `<tmp>` and all extensions will be compiled
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into subfolders of this directory. Second, if the `build_directory`
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argument to this function is supplied, it overrides the entire path, i.e.
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the library will be compiled into that folder directly.
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To compile the sources, the default system compiler (`c++`) is used, which
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can be overriden by setting the CXX environment variable. To pass
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additional arguments to the compilation process, `extra_cflags` or
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`extra_ldflags` can be provided. For example, to compile your extension
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with optimizations, pass `extra_cflags=['-O3']`. You can also use
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`extra_cflags` to pass further include directories (`-I`).
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Args:
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name: The name of the module to build.
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sources: A list of relative or absolute paths to C++ source files.
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extra_cflags: optional list of compiler flags to forward to the build.
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extra_ldflags: optional list of linker flags to forward to the build.
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extra_include_paths: optional list of include directories to forward
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to the build.
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build_directory: optional path to use as build workspace.
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verbose: If `True`, turns on verbose logging of load steps.
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Returns:
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The loaded PyTorch extension as a Python module.
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'''
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verify_ninja_availability()
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# Allows sources to be a single path or a list of paths.
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if isinstance(sources, str):
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sources = [sources]
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if build_directory is None:
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build_directory = _get_build_directory(name, verbose)
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build_file_path = os.path.join(build_directory, 'build.ninja')
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if verbose:
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print('Emitting ninja build file {}...'.format(build_file_path))
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# NOTE: Emitting a new ninja build file does not cause re-compilation if
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# the sources did not change, so it's ok to re-emit (and it's fast).
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_write_ninja_file(build_file_path, name, sources, extra_cflags or [],
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extra_ldflags or [], extra_include_paths or [])
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if verbose:
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print('Building extension module {}...'.format(name))
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_build_extension_module(name, build_directory)
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if verbose:
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print('Loading extension module {}...'.format(name))
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return _import_module_from_library(name, build_directory)
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def verify_ninja_availability():
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with open(os.devnull, 'wb') as devnull:
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try:
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subprocess.check_call('ninja --version'.split(), stdout=devnull)
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except OSError:
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raise RuntimeError("Ninja is required to load C++ extensions")
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def _get_build_directory(name, verbose):
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root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
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if root_extensions_directory is None:
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# tempfile.gettempdir() will be /tmp on UNIX and \TEMP on Windows.
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root_extensions_directory = os.path.join(tempfile.gettempdir(),
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'torch_extensions')
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if verbose:
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print('Using {} as PyTorch extensions root...'.format(
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root_extensions_directory))
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build_directory = os.path.join(root_extensions_directory, name)
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if not os.path.exists(build_directory):
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if verbose:
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print('Creating extension directory {}...'.format(build_directory))
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# This is like mkdir -p, i.e. will also create parent directories.
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os.makedirs(build_directory)
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return build_directory
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def _build_extension_module(name, build_directory):
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try:
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subprocess.check_output(
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['ninja', '-v'], stderr=subprocess.STDOUT, cwd=build_directory)
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except subprocess.CalledProcessError:
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# Python 2 and 3 compatible way of getting the error object.
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_, error, _ = sys.exc_info()
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# error.output contains the stdout and stderr of the build attempt.
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raise RuntimeError("Error building extension '{}': {}".format(
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name, error.output.decode()))
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def _import_module_from_library(module_name, path):
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# https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
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file, path, description = imp.find_module(module_name, [path])
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# Close the .so file after load.
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with file:
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return imp.load_module(module_name, file, path, description)
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def _write_ninja_file(path, name, sources, extra_cflags, extra_ldflags,
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extra_include_paths):
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# Version 1.3 is required for the `deps` directive.
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config = ['ninja_required_version = 1.3']
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config.append('cxx = {}'.format(os.environ.get('CXX', 'c++')))
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# Turn into absolute paths so we can emit them into the ninja build
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# file wherever it is.
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sources = [os.path.abspath(file) for file in sources]
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includes = [os.path.abspath(file) for file in extra_include_paths]
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# include_paths() gives us the location of torch/torch.h
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includes += include_paths()
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# sysconfig.get_paths()['include'] gives us the location of Python.h
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includes.append(sysconfig.get_paths()['include'])
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cflags = ['-fPIC', '-std=c++11']
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cflags += ['-I{}'.format(include) for include in includes]
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cflags += extra_cflags
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flags = ['cflags = {}'.format(' '.join(cflags))]
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ldflags = ['-shared'] + extra_ldflags
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# The darwin linker needs explicit consent to ignore unresolved symbols
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if sys.platform == 'darwin':
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ldflags.append('-undefined dynamic_lookup')
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flags.append('ldflags = {}'.format(' '.join(ldflags)))
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# See https://ninja-build.org/build.ninja.html for reference.
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compile_rule = ['rule compile']
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compile_rule.append(
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' command = $cxx -MMD -MF $out.d $cflags -c $in -o $out')
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compile_rule.append(' depfile = $out.d')
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compile_rule.append(' deps = gcc')
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compile_rule.append('')
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link_rule = ['rule link']
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link_rule.append(' command = $cxx $ldflags $in -o $out')
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# Emit one build rule per source to enable incremental build.
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object_files = []
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build = []
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for source_file in sources:
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# '/path/to/file.cpp' -> 'file'
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file_name = os.path.splitext(os.path.basename(source_file))[0]
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target = '{}.o'.format(file_name)
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object_files.append(target)
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build.append('build {}: compile {}'.format(target, source_file))
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library_target = '{}.so'.format(name)
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link = ['build {}: link {}'.format(library_target, ' '.join(object_files))]
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default = ['default {}'.format(library_target)]
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# 'Blocks' should be separated by newlines, for visual benefit.
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blocks = [config, flags, compile_rule, link_rule, build, link, default]
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with open(path, 'w') as build_file:
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for block in blocks:
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lines = '\n'.join(block)
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build_file.write('{}\n\n'.format(lines))
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def _join_cuda_home(*paths):
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'''
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Joins paths with CUDA_HOME, or raises an error if it CUDA_HOME is not set.
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This is basically a lazy way of raising an error for missing $CUDA_HOME
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only once we need to get any CUDA-specific path.
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'''
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if CUDA_HOME is None:
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raise EnvironmentError('CUDA_HOME environment variable is not set. '
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'Please set it to your CUDA install root.')
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return os.path.join(CUDA_HOME, *paths)
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