Source code for torch.utils.cpp_extension
# mypy: allow-untyped-defs
import copy
import glob
import importlib
import importlib.abc
import os
import platform
import re
import shlex
import shutil
import setuptools
import subprocess
import sys
import sysconfig
import warnings
import collections
from pathlib import Path
import errno
import torch
import torch._appdirs
from .file_baton import FileBaton
from ._cpp_extension_versioner import ExtensionVersioner
from .hipify import hipify_python
from .hipify.hipify_python import GeneratedFileCleaner
from typing import Dict, List, Optional, Union, Tuple
from torch.torch_version import TorchVersion, Version
from setuptools.command.build_ext import build_ext
IS_WINDOWS = sys.platform == 'win32'
IS_MACOS = sys.platform.startswith('darwin')
IS_LINUX = sys.platform.startswith('linux')
LIB_EXT = '.pyd' if IS_WINDOWS else '.so'
EXEC_EXT = '.exe' if IS_WINDOWS else ''
CLIB_PREFIX = '' if IS_WINDOWS else 'lib'
CLIB_EXT = '.dll' if IS_WINDOWS else '.so'
SHARED_FLAG = '/DLL' if IS_WINDOWS else '-shared'
_HERE = os.path.abspath(__file__)
_TORCH_PATH = os.path.dirname(os.path.dirname(_HERE))
TORCH_LIB_PATH = os.path.join(_TORCH_PATH, 'lib')
SUBPROCESS_DECODE_ARGS = ('oem',) if IS_WINDOWS else ()
MINIMUM_GCC_VERSION = (5, 0, 0)
MINIMUM_MSVC_VERSION = (19, 0, 24215)
VersionRange = Tuple[Tuple[int, ...], Tuple[int, ...]]
VersionMap = Dict[str, VersionRange]
# The following values were taken from the following GitHub gist that
# summarizes the minimum valid major versions of g++/clang++ for each supported
# CUDA version: https://gist.github.com/ax3l/9489132
# Or from include/crt/host_config.h in the CUDA SDK
# The second value is the exclusive(!) upper bound, i.e. min <= version < max
CUDA_GCC_VERSIONS: VersionMap = {
'11.0': (MINIMUM_GCC_VERSION, (10, 0)),
'11.1': (MINIMUM_GCC_VERSION, (11, 0)),
'11.2': (MINIMUM_GCC_VERSION, (11, 0)),
'11.3': (MINIMUM_GCC_VERSION, (11, 0)),
'11.4': ((6, 0, 0), (12, 0)),
'11.5': ((6, 0, 0), (12, 0)),
'11.6': ((6, 0, 0), (12, 0)),
'11.7': ((6, 0, 0), (12, 0)),
}
MINIMUM_CLANG_VERSION = (3, 3, 0)
CUDA_CLANG_VERSIONS: VersionMap = {
'11.1': (MINIMUM_CLANG_VERSION, (11, 0)),
'11.2': (MINIMUM_CLANG_VERSION, (12, 0)),
'11.3': (MINIMUM_CLANG_VERSION, (12, 0)),
'11.4': (MINIMUM_CLANG_VERSION, (13, 0)),
'11.5': (MINIMUM_CLANG_VERSION, (13, 0)),
'11.6': (MINIMUM_CLANG_VERSION, (14, 0)),
'11.7': (MINIMUM_CLANG_VERSION, (14, 0)),
}
__all__ = ["get_default_build_root", "check_compiler_ok_for_platform", "get_compiler_abi_compatibility_and_version", "BuildExtension",
"CppExtension", "CUDAExtension", "include_paths", "library_paths", "load", "load_inline", "is_ninja_available",
"verify_ninja_availability", "remove_extension_h_precompiler_headers", "get_cxx_compiler", "check_compiler_is_gcc"]
# Taken directly from python stdlib < 3.9
# See https://github.com/pytorch/pytorch/issues/48617
def _nt_quote_args(args: Optional[List[str]]) -> List[str]:
"""Quote command-line arguments for DOS/Windows conventions.
Just wraps every argument which contains blanks in double quotes, and
returns a new argument list.
"""
# Cover None-type
if not args:
return []
return [f'"{arg}"' if ' ' in arg else arg for arg in args]
def _find_cuda_home() -> Optional[str]:
"""Find 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
nvcc_path = shutil.which("nvcc")
if nvcc_path is not None:
cuda_home = os.path.dirname(os.path.dirname(nvcc_path))
else:
# 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(f"No CUDA runtime is found, using CUDA_HOME='{cuda_home}'",
file=sys.stderr)
return cuda_home
def _find_rocm_home() -> Optional[str]:
"""Find 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
hipcc_path = shutil.which('hipcc')
if hipcc_path is not None:
rocm_home = os.path.dirname(os.path.dirname(
os.path.realpath(hipcc_path)))
# can be either <ROCM_HOME>/hip/bin/hipcc or <ROCM_HOME>/bin/hipcc
if os.path.basename(rocm_home) == 'hip':
rocm_home = os.path.dirname(rocm_home)
else:
# Guess #3
fallback_path = '/opt/rocm'
if os.path.exists(fallback_path):
rocm_home = fallback_path
if rocm_home and torch.version.hip is None:
print(f"No ROCm runtime is found, using ROCM_HOME='{rocm_home}'",
file=sys.stderr)
return rocm_home
def _find_sycl_home() -> Optional[str]:
sycl_home = None
icpx_path = shutil.which('icpx')
# Guess 1: for source code build developer/user, we'll have icpx in PATH,
# which will tell us the SYCL_HOME location.
if icpx_path is not None:
sycl_home = os.path.dirname(os.path.dirname(
os.path.realpath(icpx_path)))
# Guess 2: for users install Pytorch with XPU support, the sycl runtime is
# inside intel-sycl-rt, which is automatically installed via pip dependency.
else:
try:
files = importlib.metadata.files('intel-sycl-rt') or []
for f in files:
if f.name == "libsycl.so":
sycl_home = os.path.dirname(Path(f.locate()).parent.resolve())
break
except importlib.metadata.PackageNotFoundError:
print("Trying to find SYCL_HOME from intel-sycl-rt package, but it is not installed.",
file=sys.stderr)
return sycl_home
def _join_rocm_home(*paths) -> str:
"""
Join 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 OSError('ROCM_HOME environment variable is not set. '
'Please set it to your ROCm install root.')
elif IS_WINDOWS:
raise OSError('Building PyTorch extensions using '
'ROCm and Windows is not supported.')
return os.path.join(ROCM_HOME, *paths)
def _join_sycl_home(*paths) -> str:
"""
Join paths with SYCL_HOME, or raises an error if it SYCL_HOME is not found.
This is basically a lazy way of raising an error for missing SYCL_HOME
only once we need to get any SYCL-specific path.
"""
if SYCL_HOME is None:
raise OSError('SYCL runtime is not dected. Please setup the pytorch '
'prerequisites for Intel GPU following the instruction in '
'https://github.com/pytorch/pytorch?tab=readme-ov-file#intel-gpu-support '
'or install intel-sycl-rt via pip.')
return os.path.join(SYCL_HOME, *paths)
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 !!
'''
CUDA_MISMATCH_MESSAGE = '''
The detected CUDA version ({0}) mismatches the version that was used to compile
PyTorch ({1}). Please make sure to use the same CUDA versions.
'''
CUDA_MISMATCH_WARN = "The detected CUDA version ({0}) has a minor version mismatch with the version that was used to compile PyTorch ({1}). Most likely this shouldn't be a problem."
CUDA_NOT_FOUND_MESSAGE = '''
CUDA was not found on the system, please set the CUDA_HOME or the CUDA_PATH
environment variable or add NVCC to your system PATH. The extension compilation will fail.
'''
ROCM_HOME = _find_rocm_home()
HIP_HOME = _join_rocm_home('hip') if ROCM_HOME else None
IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False
ROCM_VERSION = None
if torch.version.hip is not None:
ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split('.')[:2])
CUDA_HOME = _find_cuda_home() if torch.cuda._is_compiled() else None
CUDNN_HOME = os.environ.get('CUDNN_HOME') or os.environ.get('CUDNN_PATH')
SYCL_HOME = _find_sycl_home() if torch.xpu._is_compiled() else None
# 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', '/wd4251', '/wd4244', '/wd4267', '/wd4275', '/wd4018', '/wd4190', '/wd4624', '/wd4067', '/wd4068', '/EHsc']
MSVC_IGNORE_CUDAFE_WARNINGS = [
'base_class_has_different_dll_interface',
'field_without_dll_interface',
'dll_interface_conflict_none_assumed',
'dll_interface_conflict_dllexport_assumed'
]
COMMON_NVCC_FLAGS = [
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_BFLOAT16_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
'--expt-relaxed-constexpr'
]
COMMON_HIP_FLAGS = [
'-fPIC',
'-D__HIP_PLATFORM_AMD__=1',
'-DUSE_ROCM=1',
'-DHIPBLAS_V2',
]
COMMON_HIPCC_FLAGS = [
'-DCUDA_HAS_FP16=1',
'-D__HIP_NO_HALF_OPERATORS__=1',
'-D__HIP_NO_HALF_CONVERSIONS__=1',
]
JIT_EXTENSION_VERSIONER = ExtensionVersioner()
PLAT_TO_VCVARS = {
'win32' : 'x86',
'win-amd64' : 'x86_amd64',
}
def get_cxx_compiler():
if IS_WINDOWS:
compiler = os.environ.get('CXX', 'cl')
else:
compiler = os.environ.get('CXX', 'c++')
return compiler
def _is_binary_build() -> bool:
return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__)
def _accepted_compilers_for_platform() -> List[str]:
# gnu-c++ and gnu-cc are the conda gcc compilers
return ['clang++', 'clang'] if IS_MACOS else ['g++', 'gcc', 'gnu-c++', 'gnu-cc', 'clang++', 'clang']
def _maybe_write(filename, new_content):
r'''
Equivalent to writing the content into the file but will not touch the file
if it already had the right content (to avoid triggering recompile).
'''
if os.path.exists(filename):
with open(filename) as f:
content = f.read()
if content == new_content:
# The file already contains the right thing!
return
with open(filename, 'w') as source_file:
source_file.write(new_content)
def get_default_build_root() -> str:
"""
Return 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``.
This directory is **user-specific** so that multiple users on the same
machine won't meet permission issues.
"""
return os.path.realpath(torch._appdirs.user_cache_dir(appname='torch_extensions'))
def check_compiler_ok_for_platform(compiler: str) -> bool:
"""
Verify that the compiler is the expected one for the current platform.
Args:
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
compiler_path = shutil.which(compiler)
if compiler_path is None:
return False
# Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'.
compiler_path = os.path.realpath(compiler_path)
# Check the compiler name
if any(name in compiler_path for name in _accepted_compilers_for_platform()):
return True
# If compiler wrapper is used try to infer the actual compiler by invoking it with -v flag
env = os.environ.copy()
env['LC_ALL'] = 'C' # Don't localize output
version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
if IS_LINUX:
# Check for 'gcc' or 'g++' for sccache wrapper
pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
results = re.findall(pattern, version_string)
if len(results) != 1:
# Clang is also a supported compiler on Linux
# Though on Ubuntu it's sometimes called "Ubuntu clang version"
return 'clang version' in version_string
compiler_path = os.path.realpath(results[0].strip())
# On RHEL/CentOS c++ is a gcc compiler wrapper
if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
return True
return any(name in compiler_path for name in _accepted_compilers_for_platform())
if IS_MACOS:
# Check for 'clang' or 'clang++'
return version_string.startswith("Apple clang")
return False
[docs]def get_compiler_abi_compatibility_and_version(compiler) -> Tuple[bool, TorchVersion]:
"""
Determine if the given compiler is ABI-compatible with PyTorch alongside its version.
Args:
compiler (str): The compiler executable name to check (e.g. ``g++``).
Must be executable in a shell process.
Returns:
A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch,
followed by a `TorchVersion` string that contains the compiler version separated by dots.
"""
if not _is_binary_build():
return (True, TorchVersion('0.0.0'))
if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
return (True, TorchVersion('0.0.0'))
# 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, TorchVersion('0.0.0'))
if IS_MACOS:
# There is no particular minimum version we need for clang, so we're good here.
return (True, TorchVersion('0.0.0'))
try:
if IS_LINUX:
minimum_required_version = MINIMUM_GCC_VERSION
versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).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(*SUBPROCESS_DECODE_ARGS).strip())
version = ['0', '0', '0'] if match is None else list(match.groups())
except Exception:
_, error, _ = sys.exc_info()
warnings.warn(f'Error checking compiler version for {compiler}: {error}')
return (False, TorchVersion('0.0.0'))
if tuple(map(int, version)) >= minimum_required_version:
return (True, TorchVersion('.'.join(version)))
compiler = f'{compiler} {".".join(version)}'
warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))
return (False, TorchVersion('.'.join(version)))
def _check_cuda_version(compiler_name: str, compiler_version: TorchVersion) -> None:
if not CUDA_HOME:
raise RuntimeError(CUDA_NOT_FOUND_MESSAGE)
nvcc = os.path.join(CUDA_HOME, 'bin', 'nvcc')
cuda_version_str = subprocess.check_output([nvcc, '--version']).strip().decode(*SUBPROCESS_DECODE_ARGS)
cuda_version = re.search(r'release (\d+[.]\d+)', cuda_version_str)
if cuda_version is None:
return
cuda_str_version = cuda_version.group(1)
cuda_ver = Version(cuda_str_version)
if torch.version.cuda is None:
return
torch_cuda_version = Version(torch.version.cuda)
if cuda_ver != torch_cuda_version:
# major/minor attributes are only available in setuptools>=49.4.0
if getattr(cuda_ver, "major", None) is None:
raise ValueError("setuptools>=49.4.0 is required")
if cuda_ver.major != torch_cuda_version.major:
raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))
if not (sys.platform.startswith('linux') and
os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') not in ['ON', '1', 'YES', 'TRUE', 'Y'] and
_is_binary_build()):
return
cuda_compiler_bounds: VersionMap = CUDA_CLANG_VERSIONS if compiler_name.startswith('clang') else CUDA_GCC_VERSIONS
if cuda_str_version not in cuda_compiler_bounds:
warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')
else:
min_compiler_version, max_excl_compiler_version = cuda_compiler_bounds[cuda_str_version]
# Special case for 11.4.0, which has lower compiler bounds than 11.4.1
if "V11.4.48" in cuda_version_str and cuda_compiler_bounds == CUDA_GCC_VERSIONS:
max_excl_compiler_version = (11, 0)
min_compiler_version_str = '.'.join(map(str, min_compiler_version))
max_excl_compiler_version_str = '.'.join(map(str, max_excl_compiler_version))
version_bound_str = f'>={min_compiler_version_str}, <{max_excl_compiler_version_str}'
if compiler_version < TorchVersion(min_compiler_version_str):
raise RuntimeError(
f'The current installed version of {compiler_name} ({compiler_version}) is less '
f'than the minimum required version by CUDA {cuda_str_version} ({min_compiler_version_str}). '
f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
)
if compiler_version >= TorchVersion(max_excl_compiler_version_str):
raise RuntimeError(
f'The current installed version of {compiler_name} ({compiler_version}) is greater '
f'than the maximum required version by CUDA {cuda_str_version}. '
f'Please make sure to use an adequate version of {compiler_name} ({version_bound_str}).'
)
[docs]class BuildExtension(build_ext):
"""
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++17``) 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):
"""Return a subclass with alternative constructor that extends any original keyword arguments to the original constructor with the given options."""
class cls_with_options(cls): # type: ignore[misc, valid-type]
def __init__(self, *args, **kwargs):
kwargs.update(options)
super().__init__(*args, **kwargs)
return cls_with_options
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False)
self.use_ninja = kwargs.get('use_ninja', 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 not is_ninja_available():
warnings.warn(msg.format('we could not find ninja.'))
self.use_ninja = False
def finalize_options(self) -> None:
super().finalize_options()
if self.use_ninja:
self.force = True
def build_extensions(self) -> None:
compiler_name, compiler_version = self._check_abi()
cuda_ext = False
extension_iter = iter(self.extensions)
extension = next(extension_iter, None)
while not cuda_ext and extension:
for source in extension.sources:
_, ext = os.path.splitext(source)
if ext == '.cu':
cuda_ext = True
break
extension = next(extension_iter, None)
if cuda_ext and not IS_HIP_EXTENSION:
_check_cuda_version(compiler_name, compiler_version)
for extension in self.extensions:
# Ensure at least an empty list of flags for 'cxx' and 'nvcc' when
# extra_compile_args is a dict. Otherwise, default torch flags do
# not get passed. Necessary when only one of 'cxx' and 'nvcc' is
# passed to extra_compile_args in CUDAExtension, i.e.
# CUDAExtension(..., extra_compile_args={'cxx': [...]})
# or
# CUDAExtension(..., extra_compile_args={'nvcc': [...]})
if isinstance(extension.extra_compile_args, dict):
for ext in ['cxx', 'nvcc']:
if ext not in extension.extra_compile_args:
extension.extra_compile_args[ext] = []
self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H')
# See note [Pybind11 ABI constants]
for name in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
val = getattr(torch._C, f"_PYBIND11_{name}")
if val is not None and not IS_WINDOWS:
self._add_compile_flag(extension, f'-DPYBIND11_{name}="{val}"')
self._define_torch_extension_name(extension)
self._add_gnu_cpp_abi_flag(extension)
if 'nvcc_dlink' in extension.extra_compile_args:
assert self.use_ninja, f"With dlink=True, ninja is required to build cuda extension {extension.name}."
# Register .cu, .cuh, .hip, and .mm as valid source extensions.
self.compiler.src_extensions += ['.cu', '.cuh', '.hip']
if torch.backends.mps.is_built():
self.compiler.src_extensions += ['.mm']
# 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_std17_if_no_std_present(cflags) -> None:
# 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++17'
if not any(flag.startswith(cpp_flag_prefix) for flag in cflags):
cflags.append(cpp_flag)
def unix_cuda_flags(cflags):
cflags = (COMMON_NVCC_FLAGS +
['--compiler-options', "'-fPIC'"] +
cflags + _get_cuda_arch_flags(cflags))
# NVCC does not allow multiple -ccbin/--compiler-bindir to be passed, so we avoid
# overriding the option if the user explicitly passed it.
_ccbin = os.getenv("CC")
if (
_ccbin is not None
and not any(flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags)
):
cflags.extend(['-ccbin', _ccbin])
return 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) -> None:
# 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')]
self.compiler.set_executable('compiler_so', nvcc)
if isinstance(cflags, dict):
cflags = cflags['nvcc']
if IS_HIP_EXTENSION:
cflags = COMMON_HIPCC_FLAGS + cflags + _get_rocm_arch_flags(cflags)
else:
cflags = unix_cuda_flags(cflags)
elif isinstance(cflags, dict):
cflags = cflags['cxx']
if IS_HIP_EXTENSION:
cflags = COMMON_HIP_FLAGS + cflags
append_std17_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):
r"""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)
if IS_HIP_EXTENSION:
post_cflags = COMMON_HIP_FLAGS + post_cflags
append_std17_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)
if IS_HIP_EXTENSION:
cuda_post_cflags = cuda_post_cflags + _get_rocm_arch_flags(cuda_post_cflags)
cuda_post_cflags = COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS + cuda_post_cflags
else:
cuda_post_cflags = unix_cuda_flags(cuda_post_cflags)
append_std17_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]
if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
cuda_dlink_post_cflags = unix_cuda_flags(extra_postargs['nvcc_dlink'])
else:
cuda_dlink_post_cflags = None
_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,
cuda_dlink_post_cflags=cuda_dlink_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) + ['-std=c++17', '--use-local-env']
for flag in COMMON_MSVC_FLAGS:
cflags = ['-Xcompiler', flag] + cflags
for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
cflags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cflags
cmd = [nvcc, '-c', src, '-o', obj] + include_list + cflags
elif isinstance(self.cflags, dict):
cflags = COMMON_MSVC_FLAGS + self.cflags['cxx']
append_std17_if_no_std_present(cflags)
cmd += cflags
elif isinstance(self.cflags, list):
cflags = COMMON_MSVC_FLAGS + self.cflags
append_std17_if_no_std_present(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, but instead, the build happens
# in a script-created build folder. Thus, relative paths lose 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_std17_if_no_std_present(post_cflags)
cuda_post_cflags = None
cuda_cflags = None
if with_cuda:
cuda_cflags = ['-std=c++17', '--use-local-env']
for common_cflag in common_cflags:
cuda_cflags.append('-Xcompiler')
cuda_cflags.append(common_cflag)
for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
cuda_cflags.append('-Xcudafe')
cuda_cflags.append('--diag_suppress=' + ignore_warning)
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)
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)
if isinstance(extra_postargs, dict) and 'nvcc_dlink' in extra_postargs:
cuda_dlink_post_cflags = win_cuda_flags(extra_postargs['nvcc_dlink'])
else:
cuda_dlink_post_cflags = None
_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,
cuda_dlink_post_cflags=cuda_dlink_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.
ext_filename = super().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:
# 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) -> Tuple[str, TorchVersion]:
# On some platforms, like Windows, compiler_cxx is not available.
if hasattr(self.compiler, 'compiler_cxx'):
compiler = self.compiler.compiler_cxx[0]
else:
compiler = get_cxx_compiler()
_, version = get_compiler_abi_compatibility_and_version(compiler)
# Warn user if VC env is activated but `DISTUILS_USE_SDK` is not set.
if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' in os.environ and 'DISTUTILS_USE_SDK' not in os.environ:
msg = ('It seems that the VC environment is activated but DISTUTILS_USE_SDK is not set.'
'This may lead to multiple activations of the VC env.'
'Please set `DISTUTILS_USE_SDK=1` and try again.')
raise UserWarning(msg)
return compiler, version
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 = f'-DTORCH_EXTENSION_NAME={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):
"""
Create 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. Full list arguments can be found at
https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
.. note::
The PyTorch python API (as provided in libtorch_python) cannot be built
with the flag ``py_limited_api=True``. When this flag is passed, it is
the user's responsibility in their library to not use APIs from
libtorch_python (in particular pytorch/python bindings) and to only use
APIs from libtorch (aten objects, operators and the dispatcher). For
example, to give access to custom ops from python, the library should
register the ops through the dispatcher.
Example:
>>> # xdoctest: +SKIP
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
>>> 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'],
... extra_link_args=['-Wl,--no-as-needed', '-lm'])
... ],
... 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')
if not kwargs.get('py_limited_api', False):
# torch_python uses more than the python limited api
libraries.append('torch_python')
if IS_WINDOWS and platform.machine().lower() != "arm64":
libraries.append("sleef")
kwargs['libraries'] = libraries
kwargs['language'] = 'c++'
return setuptools.Extension(name, sources, *args, **kwargs)
[docs]def CUDAExtension(name, sources, *args, **kwargs):
"""
Create 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. Full list arguments can be found at
https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
.. note::
The PyTorch python API (as provided in libtorch_python) cannot be built
with the flag ``py_limited_api=True``. When this flag is passed, it is
the user's responsibility in their library to not use APIs from
libtorch_python (in particular pytorch/python bindings) and to only use
APIs from libtorch (aten objects, operators and the dispatcher). For
example, to give access to custom ops from python, the library should
register the ops through the dispatcher.
Example:
>>> # xdoctest: +SKIP
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
>>> 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']},
... extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
... ],
... cmdclass={
... 'build_ext': BuildExtension
... })
Compute capabilities:
By default the extension will be compiled to run on all archs of the cards visible during the
building process of the extension, plus PTX. If down the road a new card is installed the
extension may need to be recompiled. If a visible card has a compute capability (CC) that's
newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch
will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
support (see below for details on PTX).
You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
CCs you want the extension to support:
``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
The +PTX option causes extension kernel binaries to include PTX instructions for the specified
CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
"8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
"8.0 8.6" would be better.
Note that while it's possible to include all supported archs, the more archs get included the
slower the building process will be, as it will build a separate kernel image for each arch.
Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
To workaround the issue, move python binding logic to pure C++ file.
Example use:
#include <ATen/ATen.h>
at::Tensor SigmoidAlphaBlendForwardCuda(....)
Instead of:
#include <torch/extension.h>
torch::Tensor SigmoidAlphaBlendForwardCuda(...)
Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
Relocatable device code linking:
If you want to reference device symbols across compilation units (across object files),
the object files need to be built with `relocatable device code` (-rdc=true or -dc).
An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore.
`Relocatable device code` is less optimized so it needs to be used only on object files that need it.
Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
helps reduce the protentional perf degradation of `-rdc`.
Note that it needs to be used at both steps to be useful.
If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
There is also a case where `-dlink` is used without `-rdc`:
when an extension is linked against a static lib containing rdc-compiled objects
like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
Note: Ninja is required to build a CUDA Extension with RDC linking.
Example:
>>> # xdoctest: +SKIP
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
>>> CUDAExtension(
... name='cuda_extension',
... sources=['extension.cpp', 'extension_kernel.cu'],
... dlink=True,
... dlink_libraries=["dlink_lib"],
... extra_compile_args={'cxx': ['-g'],
... 'nvcc': ['-O2', '-rdc=true']})
"""
library_dirs = kwargs.get('library_dirs', [])
library_dirs += library_paths(device_type="cuda")
kwargs['library_dirs'] = library_dirs
libraries = kwargs.get('libraries', [])
libraries.append('c10')
libraries.append('torch')
libraries.append('torch_cpu')
if not kwargs.get('py_limited_api', False):
# torch_python uses more than the python limited api
libraries.append('torch_python')
if IS_HIP_EXTENSION:
libraries.append('amdhip64')
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', [])
if IS_HIP_EXTENSION:
build_dir = os.getcwd()
hipify_result = hipify_python.hipify(
project_directory=build_dir,
output_directory=build_dir,
header_include_dirs=include_dirs,
includes=[os.path.join(build_dir, '*')], # limit scope to build_dir only
extra_files=[os.path.abspath(s) for s in sources],
show_detailed=True,
is_pytorch_extension=True,
hipify_extra_files_only=True, # don't hipify everything in includes path
)
hipified_sources = set()
for source in sources:
s_abs = os.path.abspath(source)
hipified_s_abs = (hipify_result[s_abs].hipified_path if (s_abs in hipify_result and
hipify_result[s_abs].hipified_path is not None) else s_abs)
# setup() arguments must *always* be /-separated paths relative to the setup.py directory,
# *never* absolute paths
hipified_sources.add(os.path.relpath(hipified_s_abs, build_dir))
sources = list(hipified_sources)
include_dirs += include_paths(device_type="cuda")
kwargs['include_dirs'] = include_dirs
kwargs['language'] = 'c++'
dlink_libraries = kwargs.get('dlink_libraries', [])
dlink = kwargs.get('dlink', False) or dlink_libraries
if dlink:
extra_compile_args = kwargs.get('extra_compile_args', {})
extra_compile_args_dlink = extra_compile_args.get('nvcc_dlink', [])
extra_compile_args_dlink += ['-dlink']
extra_compile_args_dlink += [f'-L{x}' for x in library_dirs]
extra_compile_args_dlink += [f'-l{x}' for x in dlink_libraries]
if (torch.version.cuda is not None) and TorchVersion(torch.version.cuda) >= '11.2':
extra_compile_args_dlink += ['-dlto'] # Device Link Time Optimization started from cuda 11.2
extra_compile_args['nvcc_dlink'] = extra_compile_args_dlink
kwargs['extra_compile_args'] = extra_compile_args
return setuptools.Extension(name, sources, *args, **kwargs)
[docs]def include_paths(device_type: str = "cpu") -> List[str]:
"""
Get the include paths required to build a C++ or CUDA or SYCL extension.
Args:
device_type: Defaults to "cpu".
Returns:
A list of include path strings.
"""
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 device_type == "cuda" and IS_HIP_EXTENSION:
paths.append(os.path.join(lib_include, 'THH'))
paths.append(_join_rocm_home('include'))
elif device_type == "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)
# Support CUDA_INC_PATH env variable supported by CMake files
if (cuda_inc_path := os.environ.get("CUDA_INC_PATH", None)) and \
cuda_inc_path != '/usr/include':
paths.append(cuda_inc_path)
if CUDNN_HOME is not None:
paths.append(os.path.join(CUDNN_HOME, 'include'))
elif device_type == "xpu":
paths.append(_join_sycl_home('include'))
paths.append(_join_sycl_home('include', 'sycl'))
return paths
def library_paths(device_type: str = "cpu") -> List[str]:
"""
Get the library paths required to build a C++ or CUDA extension.
Args:
device_type: Defaults to "cpu".
Returns:
A list of library path strings.
"""
# We need to link against libtorch.so
paths = [TORCH_LIB_PATH]
if device_type == "cuda" and IS_HIP_EXTENSION:
lib_dir = 'lib'
paths.append(_join_rocm_home(lib_dir))
if HIP_HOME is not None:
paths.append(os.path.join(HIP_HOME, 'lib'))
elif device_type == "cuda":
if IS_WINDOWS:
lib_dir = os.path.join('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))
elif device_type == "xpu":
if IS_WINDOWS:
lib_dir = os.path.join('lib', 'x64')
else:
lib_dir = 'lib64'
if (not os.path.exists(_join_sycl_home(lib_dir)) and
os.path.exists(_join_sycl_home('lib'))):
lib_dir = 'lib'
paths.append(_join_sycl_home(lib_dir))
return paths
[docs]def load(name,
sources: Union[str, List[str]],
extra_cflags=None,
extra_cuda_cflags=None,
extra_ldflags=None,
extra_include_paths=None,
build_directory=None,
verbose=False,
with_cuda: Optional[bool] = None,
is_python_module=True,
is_standalone=False,
keep_intermediates=True):
"""
Load 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``, behavior depends on
``is_standalone``.
is_standalone: If ``False`` (default) loads the constructed extension
into the process as a plain dynamic library. If ``True``, build a
standalone executable.
Returns:
If ``is_python_module`` is ``True``:
Returns the loaded PyTorch extension as a Python module.
If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
Returns nothing. (The shared library is loaded into the process as
a side effect.)
If ``is_standalone`` is ``True``.
Return the path to the executable. (On Windows, TORCH_LIB_PATH is
added to the PATH environment variable as a side effect.)
Example:
>>> # xdoctest: +SKIP
>>> 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,
is_standalone,
keep_intermediates=keep_intermediates)
def _get_pybind11_abi_build_flags():
# Note [Pybind11 ABI constants]
#
# Pybind11 before 2.4 used to build an ABI strings using the following pattern:
# f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_BUILD_TYPE}__"
# Since 2.4 compier type, stdlib and build abi parameters are also encoded like this:
# f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_COMPILER_TYPE}{PYBIND11_STDLIB}{PYBIND11_BUILD_ABI}{PYBIND11_BUILD_TYPE}__"
#
# This was done in order to further narrow down the chances of compiler ABI incompatibility
# that can cause a hard to debug segfaults.
# For PyTorch extensions we want to relax those restrictions and pass compiler, stdlib and abi properties
# captured during PyTorch native library compilation in torch/csrc/Module.cpp
#
# Note that these flags don't have side effects even if the PyTorch extension does not
# require nor use pybind, so we do not do anything differently for them in the py_limited_api
# case.
abi_cflags = []
for pname in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
pval = getattr(torch._C, f"_PYBIND11_{pname}")
if pval is not None and not IS_WINDOWS:
abi_cflags.append(f'-DPYBIND11_{pname}=\\"{pval}\\"')
return abi_cflags
def _get_glibcxx_abi_build_flags():
glibcxx_abi_cflags = ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))]
return glibcxx_abi_cflags
def check_compiler_is_gcc(compiler):
if not IS_LINUX:
return False
env = os.environ.copy()
env['LC_ALL'] = 'C' # Don't localize output
try:
version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
except Exception:
try:
version_string = subprocess.check_output([compiler, '--version'], stderr=subprocess.STDOUT, env=env).decode(*SUBPROCESS_DECODE_ARGS)
except Exception:
return False
# Check for 'gcc' or 'g++' for sccache wrapper
pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
results = re.findall(pattern, version_string)
if len(results) != 1:
return False
compiler_path = os.path.realpath(results[0].strip())
# On RHEL/CentOS c++ is a gcc compiler wrapper
if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
return True
return False
def _check_and_build_extension_h_precompiler_headers(
extra_cflags,
extra_include_paths,
is_standalone=False):
r'''
Precompiled Headers(PCH) can pre-build the same headers and reduce build time for pytorch load_inline modules.
GCC offical manual: https://gcc.gnu.org/onlinedocs/gcc-4.0.4/gcc/Precompiled-Headers.html
PCH only works when built pch file(header.h.gch) and build target have the same build parameters. So, We need
add a signature file to record PCH file parameters. If the build parameters(signature) changed, it should rebuild
PCH file.
Note:
1. Windows and MacOS have different PCH mechanism. We only support Linux currently.
2. It only works on GCC/G++.
'''
if not IS_LINUX:
return
compiler = get_cxx_compiler()
b_is_gcc = check_compiler_is_gcc(compiler)
if b_is_gcc is False:
return
head_file = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h')
head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
def listToString(s):
# initialize an empty string
string = ""
if s is None:
return string
# traverse in the string
for element in s:
string += (element + ' ')
# return string
return string
def format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags, torch_include_dirs, extra_cflags, extra_include_paths):
return re.sub(
r"[ \n]+",
" ",
f"""
{compiler} -x c++-header {head_file} -o {head_file_pch} {torch_include_dirs} {extra_include_paths} {extra_cflags} {common_cflags}
""",
).strip()
def command_to_signature(cmd):
signature = cmd.replace(' ', '_')
return signature
def check_pch_signature_in_file(file_path, signature):
b_exist = os.path.isfile(file_path)
if b_exist is False:
return False
with open(file_path) as file:
# read all content of a file
content = file.read()
# check if string present in a file
return signature == content
def _create_if_not_exist(path_dir):
if not os.path.exists(path_dir):
try:
Path(path_dir).mkdir(parents=True, exist_ok=True)
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise RuntimeError(f"Fail to create path {path_dir}") from exc
def write_pch_signature_to_file(file_path, pch_sign):
_create_if_not_exist(os.path.dirname(file_path))
with open(file_path, "w") as f:
f.write(pch_sign)
f.close()
def build_precompile_header(pch_cmd):
try:
subprocess.check_output(pch_cmd, shell=True, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as e:
raise RuntimeError(f"Compile PreCompile Header fail, command: {pch_cmd}") from e
extra_cflags_str = listToString(extra_cflags)
extra_include_paths_str = " ".join(
[f"-I{include}" for include in extra_include_paths] if extra_include_paths else []
)
lib_include = os.path.join(_TORCH_PATH, 'include')
torch_include_dirs = [
f"-I {lib_include}",
# Python.h
"-I {}".format(sysconfig.get_path("include")),
# torch/all.h
"-I {}".format(os.path.join(lib_include, 'torch', 'csrc', 'api', 'include')),
]
torch_include_dirs_str = listToString(torch_include_dirs)
common_cflags = []
if not is_standalone:
common_cflags += ['-DTORCH_API_INCLUDE_EXTENSION_H']
common_cflags += ['-std=c++17', '-fPIC']
common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
common_cflags_str = listToString(common_cflags)
pch_cmd = format_precompiler_header_cmd(compiler, head_file, head_file_pch, common_cflags_str, torch_include_dirs_str, extra_cflags_str, extra_include_paths_str)
pch_sign = command_to_signature(pch_cmd)
if os.path.isfile(head_file_pch) is not True:
build_precompile_header(pch_cmd)
write_pch_signature_to_file(head_file_signature, pch_sign)
else:
b_same_sign = check_pch_signature_in_file(head_file_signature, pch_sign)
if b_same_sign is False:
build_precompile_header(pch_cmd)
write_pch_signature_to_file(head_file_signature, pch_sign)
def remove_extension_h_precompiler_headers():
def _remove_if_file_exists(path_file):
if os.path.exists(path_file):
os.remove(path_file)
head_file_pch = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.gch')
head_file_signature = os.path.join(_TORCH_PATH, 'include', 'torch', 'extension.h.sign')
_remove_if_file_exists(head_file_pch)
_remove_if_file_exists(head_file_signature)
[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,
keep_intermediates=True,
use_pch=False):
r'''
Load 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_jit.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:
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
>>> 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::
Since load_inline will just-in-time compile the source code, please ensure
that you have the right toolchains installed in the runtime. For example,
when loading C++, make sure a C++ compiler is available. If you're loading
a CUDA extension, you will need to additionally install the corresponding CUDA
toolkit (nvcc and any other dependencies your code has). Compiling toolchains
are not included when you install torch and must be additionally installed.
During compiling, 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 use_pch is True:
# Using PreCompile Header('torch/extension.h') to reduce compile time.
_check_and_build_extension_h_precompiler_headers(extra_cflags, extra_include_paths)
else:
remove_extension_h_precompiler_headers()
# 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 = {f: f for f in functions}
elif not isinstance(functions, dict):
raise ValueError(f"Expected 'functions' to be a list or dict, but was {type(functions)}")
for function_name, docstring in functions.items():
if with_pytorch_error_handling:
module_def.append(f'm.def("{function_name}", torch::wrap_pybind_function({function_name}), "{docstring}");')
else:
module_def.append(f'm.def("{function_name}", {function_name}, "{docstring}");')
module_def.append('}')
cpp_sources += module_def
cpp_source_path = os.path.join(build_directory, 'main.cpp')
_maybe_write(cpp_source_path, "\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')
_maybe_write(cuda_source_path, "\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,
is_standalone=False,
keep_intermediates=keep_intermediates)
def _jit_compile(name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory: str,
verbose: bool,
with_cuda: Optional[bool],
is_python_module,
is_standalone,
keep_intermediates=True) -> None:
if is_python_module and is_standalone:
raise ValueError("`is_python_module` and `is_standalone` are mutually exclusive.")
if with_cuda is None:
with_cuda = any(map(_is_cuda_file, sources))
with_cudnn = any('cudnn' in f for f in extra_ldflags or [])
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,
is_python_module=is_python_module,
is_standalone=is_standalone,
)
if version > 0:
if version != old_version and verbose:
print(f'The input conditions for extension module {name} have changed. ' +
f'Bumping to version {version} and re-building as {name}_v{version}...',
file=sys.stderr)
name = f'{name}_v{version}'
baton = FileBaton(os.path.join(build_directory, 'lock'))
if baton.try_acquire():
try:
if version != old_version:
with GeneratedFileCleaner(keep_intermediates=keep_intermediates) as clean_ctx:
if IS_HIP_EXTENSION and (with_cuda or with_cudnn):
hipify_result = hipify_python.hipify(
project_directory=build_directory,
output_directory=build_directory,
header_include_dirs=(extra_include_paths if extra_include_paths is not None else []),
extra_files=[os.path.abspath(s) for s in sources],
ignores=[_join_rocm_home('*'), os.path.join(_TORCH_PATH, '*')], # no need to hipify ROCm or PyTorch headers
show_detailed=verbose,
show_progress=verbose,
is_pytorch_extension=True,
clean_ctx=clean_ctx
)
hipified_sources = set()
for source in sources:
s_abs = os.path.abspath(source)
hipified_sources.add(hipify_result[s_abs].hipified_path if s_abs in hipify_result else s_abs)
sources = list(hipified_sources)
_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,
is_standalone=is_standalone)
elif verbose:
print('No modifications detected for re-loaded extension '
f'module {name}, skipping build step...', file=sys.stderr)
finally:
baton.release()
else:
baton.wait()
if verbose:
print(f'Loading extension module {name}...', file=sys.stderr)
if is_standalone:
return _get_exec_path(name, build_directory)
return _import_module_from_library(name, build_directory, is_python_module)
def _write_ninja_file_and_compile_objects(
sources: List[str],
objects,
cflags,
post_cflags,
cuda_cflags,
cuda_post_cflags,
cuda_dlink_post_cflags,
build_directory: str,
verbose: bool,
with_cuda: Optional[bool]) -> None:
verify_ninja_availability()
compiler = get_cxx_compiler()
get_compiler_abi_compatibility_and_version(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(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
# Create build_directory if it does not exist
if not os.path.exists(build_directory):
if verbose:
print(f'Creating directory {build_directory}...', file=sys.stderr)
# This is like mkdir -p, i.e. will also create parent directories.
os.makedirs(build_directory, exist_ok=True)
_write_ninja_file(
path=build_file_path,
cflags=cflags,
post_cflags=post_cflags,
cuda_cflags=cuda_cflags,
cuda_post_cflags=cuda_post_cflags,
cuda_dlink_post_cflags=cuda_dlink_post_cflags,
sources=sources,
objects=objects,
ldflags=None,
library_target=None,
with_cuda=with_cuda)
if verbose:
print('Compiling objects...', file=sys.stderr)
_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: List[str],
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
build_directory: str,
verbose: bool,
with_cuda: Optional[bool],
is_standalone: bool = False) -> None:
verify_ninja_availability()
compiler = get_cxx_compiler()
get_compiler_abi_compatibility_and_version(compiler)
if with_cuda is None:
with_cuda = any(map(_is_cuda_file, sources))
extra_ldflags = _prepare_ldflags(
extra_ldflags or [],
with_cuda,
verbose,
is_standalone)
build_file_path = os.path.join(build_directory, 'build.ninja')
if verbose:
print(f'Emitting ninja build file {build_file_path}...', file=sys.stderr)
# Create build_directory if it does not exist
if not os.path.exists(build_directory):
if verbose:
print(f'Creating directory {build_directory}...', file=sys.stderr)
# This is like mkdir -p, i.e. will also create parent directories.
os.makedirs(build_directory, exist_ok=True)
# 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,
is_standalone=is_standalone)
if verbose:
print(f'Building extension module {name}...', file=sys.stderr)
_run_ninja_build(
build_directory,
verbose,
error_prefix=f"Error building extension '{name}'")
[docs]def is_ninja_available():
"""Return ``True`` if the `ninja <https://ninja-build.org/>`_ build system is available on the system, ``False`` otherwise."""
try:
subprocess.check_output('ninja --version'.split())
except Exception:
return False
else:
return True
[docs]def verify_ninja_availability():
"""Raise ``RuntimeError`` if `ninja <https://ninja-build.org/>`_ build system is not available on the system, does nothing otherwise."""
if not is_ninja_available():
raise RuntimeError("Ninja is required to load C++ extensions")
def _prepare_ldflags(extra_ldflags, with_cuda, verbose, is_standalone):
if IS_WINDOWS:
python_lib_path = os.path.join(sys.base_exec_prefix, '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(f'/LIBPATH:{TORCH_LIB_PATH}')
if not is_standalone:
extra_ldflags.append('torch_python.lib')
extra_ldflags.append(f'/LIBPATH:{python_lib_path}')
else:
extra_ldflags.append(f'-L{TORCH_LIB_PATH}')
extra_ldflags.append('-lc10')
if with_cuda:
extra_ldflags.append('-lc10_hip' if IS_HIP_EXTENSION else '-lc10_cuda')
extra_ldflags.append('-ltorch_cpu')
if with_cuda:
extra_ldflags.append('-ltorch_hip' if IS_HIP_EXTENSION else '-ltorch_cuda')
extra_ldflags.append('-ltorch')
if not is_standalone:
extra_ldflags.append('-ltorch_python')
if is_standalone:
extra_ldflags.append(f"-Wl,-rpath,{TORCH_LIB_PATH}")
if with_cuda:
if verbose:
print('Detected CUDA files, patching ldflags', file=sys.stderr)
if IS_WINDOWS:
extra_ldflags.append(f'/LIBPATH:{_join_cuda_home("lib", "x64")}')
extra_ldflags.append('cudart.lib')
if CUDNN_HOME is not None:
extra_ldflags.append(f'/LIBPATH:{os.path.join(CUDNN_HOME, "lib", "x64")}')
elif not IS_HIP_EXTENSION:
extra_lib_dir = "lib64"
if (not os.path.exists(_join_cuda_home(extra_lib_dir)) and
os.path.exists(_join_cuda_home("lib"))):
# 64-bit CUDA may be installed in "lib"
# Note that it's also possible both don't exist (see _find_cuda_home) - in that case we stay with "lib64"
extra_lib_dir = "lib"
extra_ldflags.append(f'-L{_join_cuda_home(extra_lib_dir)}')
extra_ldflags.append('-lcudart')
if CUDNN_HOME is not None:
extra_ldflags.append(f'-L{os.path.join(CUDNN_HOME, "lib64")}')
elif IS_HIP_EXTENSION:
extra_ldflags.append(f'-L{_join_rocm_home("lib")}')
extra_ldflags.append('-lamdhip64')
return extra_ldflags
def _get_cuda_arch_flags(cflags: Optional[List[str]] = None) -> List[str]:
"""
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 'TORCH_EXTENSION_NAME' in flag:
continue
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+Tegra', '7.2'),
('Volta', '7.0+PTX'),
('Turing', '7.5+PTX'),
('Ampere+Tegra', '8.7'),
('Ampere', '8.0;8.6+PTX'),
('Ada', '8.9+PTX'),
('Hopper', '9.0+PTX'),
('Blackwell', '10.0+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', '8.0', '8.6', '8.7', '8.9', '9.0', '9.0a',
'10.0']
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 best for the GPU / CUDA version that can be found
if not _arch_list:
warnings.warn(
"TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n"
"If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].")
arch_list = []
# the assumption is that the extension should run on any of the currently visible cards,
# which could be of different types - therefore all archs for visible cards should be included
for i in range(torch.cuda.device_count()):
capability = torch.cuda.get_device_capability(i)
supported_sm = [int(arch.split('_')[1])
for arch in torch.cuda.get_arch_list() if 'sm_' in arch]
max_supported_sm = max((sm // 10, sm % 10) for sm in supported_sm)
# Capability of the device may be higher than what's supported by the user's
# NVCC, causing compilation error. User's NVCC is expected to match the one
# used to build pytorch, so we use the maximum supported capability of pytorch
# to clamp the capability.
capability = min(max_supported_sm, capability)
arch = f'{capability[0]}.{capability[1]}'
if arch not in arch_list:
arch_list.append(arch)
arch_list = sorted(arch_list)
arch_list[-1] += '+PTX'
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(f"Unknown CUDA arch ({arch}) or GPU not supported")
else:
# Handle both single and double-digit architecture versions
version = arch.split('+')[0] # Remove "+PTX" if present
major, minor = version.split('.')
num = f"{major}{minor}"
flags.append(f'-gencode=arch=compute_{num},code=sm_{num}')
if arch.endswith('+PTX'):
flags.append(f'-gencode=arch=compute_{num},code=compute_{num}')
return sorted(set(flags))
def _get_rocm_arch_flags(cflags: Optional[List[str]] = None) -> List[str]:
# 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 or 'offload-arch' in flag:
return ['-fno-gpu-rdc']
# Use same defaults as used for building PyTorch
# Allow env var to override, just like during initial cmake build.
_archs = os.environ.get('PYTORCH_ROCM_ARCH', None)
if not _archs:
archFlags = torch._C._cuda_getArchFlags()
if archFlags:
archs = archFlags.split()
else:
archs = []
else:
archs = _archs.replace(' ', ';').split(';')
flags = [f'--offload-arch={arch}' for arch in archs]
flags += ['-fno-gpu-rdc']
return flags
def _get_build_directory(name: str, verbose: bool) -> str:
root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
if root_extensions_directory is None:
root_extensions_directory = get_default_build_root()
cu_str = ('cpu' if torch.version.cuda is None else
f'cu{torch.version.cuda.replace(".", "")}')
python_version = f'py{sys.version_info.major}{sys.version_info.minor}{getattr(sys, "abiflags", "")}'
build_folder = f'{python_version}_{cu_str}'
root_extensions_directory = os.path.join(
root_extensions_directory, build_folder)
if verbose:
print(f'Using {root_extensions_directory} as PyTorch extensions root...', file=sys.stderr)
build_directory = os.path.join(root_extensions_directory, name)
if not os.path.exists(build_directory):
if verbose:
print(f'Creating extension directory {build_directory}...', file=sys.stderr)
# This is like mkdir -p, i.e. will also create parent directories.
os.makedirs(build_directory, exist_ok=True)
return build_directory
def _get_num_workers(verbose: bool) -> Optional[int]:
max_jobs = os.environ.get('MAX_JOBS')
if max_jobs is not None and max_jobs.isdigit():
if verbose:
print(f'Using envvar MAX_JOBS ({max_jobs}) as the number of workers...',
file=sys.stderr)
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)',
file=sys.stderr)
return None
def _get_vc_env(vc_arch: str) -> dict[str, str]:
try:
from setuptools import distutils
return distutils._msvccompiler._get_vc_env(vc_arch)
except AttributeError:
from setuptools._distutils import _msvccompiler
return _msvccompiler._get_vc_env(vc_arch)
def _run_ninja_build(build_directory: str, verbose: bool, error_prefix: str) -> None:
command = ['ninja', '-v']
num_workers = _get_num_workers(verbose)
if num_workers is not None:
command.extend(['-j', str(num_workers)])
env = os.environ.copy()
# Try to activate the vc env for the users
if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' not in env:
from setuptools import distutils
plat_name = distutils.util.get_platform()
plat_spec = PLAT_TO_VCVARS[plat_name]
vc_env = {k.upper(): v for k, v in _get_vc_env(plat_spec).items()}
for k, v in env.items():
uk = k.upper()
if uk not in vc_env:
vc_env[uk] = v
env = vc_env
try:
sys.stdout.flush()
sys.stderr.flush()
# 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,
env=env)
except subprocess.CalledProcessError as e:
# 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
# `error` is a CalledProcessError (which has an `output`) attribute, but
# mypy thinks it's Optional[BaseException] and doesn't narrow
if hasattr(error, 'output') and error.output: # type: ignore[union-attr]
message += f": {error.output.decode(*SUBPROCESS_DECODE_ARGS)}" # type: ignore[union-attr]
raise RuntimeError(message) from e
def _get_exec_path(module_name, path):
if IS_WINDOWS and TORCH_LIB_PATH not in os.getenv('PATH', '').split(';'):
torch_lib_in_path = any(
os.path.exists(p) and os.path.samefile(p, TORCH_LIB_PATH)
for p in os.getenv('PATH', '').split(';')
)
if not torch_lib_in_path:
os.environ['PATH'] = f"{TORCH_LIB_PATH};{os.getenv('PATH', '')}"
return os.path.join(path, f'{module_name}{EXEC_EXT}')
def _import_module_from_library(module_name, path, is_python_module):
filepath = os.path.join(path, f"{module_name}{LIB_EXT}")
if is_python_module:
# https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
spec = importlib.util.spec_from_file_location(module_name, filepath)
assert spec is not None
module = importlib.util.module_from_spec(spec)
assert isinstance(spec.loader, importlib.abc.Loader)
spec.loader.exec_module(module)
return module
else:
torch.ops.load_library(filepath)
return filepath
def _write_ninja_file_to_build_library(path,
name,
sources,
extra_cflags,
extra_cuda_cflags,
extra_ldflags,
extra_include_paths,
with_cuda,
is_standalone) -> None:
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
# TODO generalize with_cuda as specific device type.
if with_cuda:
system_includes = include_paths("cuda")
else:
system_includes = include_paths("cpu")
# sysconfig.get_path('include') gives us the location of Python.h
# Explicitly specify 'posix_prefix' scheme on non-Windows platforms to workaround error on some MacOS
# installations where default `get_path` points to non-existing `/Library/Python/M.m/include` folder
python_include_path = sysconfig.get_path('include', scheme='nt' if IS_WINDOWS else 'posix_prefix')
if python_include_path is not None:
system_includes.append(python_include_path)
common_cflags = []
if not is_standalone:
common_cflags.append(f'-DTORCH_EXTENSION_NAME={name}')
common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H')
common_cflags += [f"{x}" for x in _get_pybind11_abi_build_flags()]
# Windows does not understand `-isystem` and quotes flags later.
if IS_WINDOWS:
common_cflags += [f'-I{include}' for include in user_includes + system_includes]
else:
common_cflags += [f'-I{shlex.quote(include)}' for include in user_includes]
common_cflags += [f'-isystem {shlex.quote(include)}' for include in system_includes]
common_cflags += [f"{x}" for x in _get_glibcxx_abi_build_flags()]
if IS_WINDOWS:
cflags = common_cflags + COMMON_MSVC_FLAGS + ['/std:c++17'] + extra_cflags
cflags = _nt_quote_args(cflags)
else:
cflags = common_cflags + ['-fPIC', '-std=c++17'] + extra_cflags
if with_cuda and IS_HIP_EXTENSION:
cuda_flags = ['-DWITH_HIP'] + cflags + COMMON_HIP_FLAGS + COMMON_HIPCC_FLAGS
cuda_flags += extra_cuda_cflags
cuda_flags += _get_rocm_arch_flags(cuda_flags)
elif 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
for ignore_warning in MSVC_IGNORE_CUDAFE_WARNINGS:
cuda_flags = ['-Xcudafe', '--diag_suppress=' + ignore_warning] + cuda_flags
cuda_flags = cuda_flags + ['-std=c++17']
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++17')
cc_env = os.getenv("CC")
if cc_env is not None:
cuda_flags = ['-ccbin', cc_env] + cuda_flags
else:
cuda_flags = None
def object_file_path(source_file: str) -> str:
# '/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 = f'{file_name}.cuda.o'
else:
target = f'{file_name}.o'
return target
objects = [object_file_path(src) for src in sources]
ldflags = ([] if is_standalone else [SHARED_FLAG]) + extra_ldflags
# The darwin linker needs explicit consent to ignore unresolved symbols.
if IS_MACOS:
ldflags.append('-undefined dynamic_lookup')
elif IS_WINDOWS:
ldflags = _nt_quote_args(ldflags)
ext = EXEC_EXT if is_standalone else LIB_EXT
library_target = f'{name}{ext}'
_write_ninja_file(
path=path,
cflags=cflags,
post_cflags=None,
cuda_cflags=cuda_flags,
cuda_post_cflags=None,
cuda_dlink_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,
cuda_dlink_post_cflags,
sources,
objects,
ldflags,
library_target,
with_cuda) -> None:
r"""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)
cuda_dlink_post_cflags = sanitize_flags(cuda_dlink_post_cflags)
ldflags = sanitize_flags(ldflags)
# Sanity checks...
assert len(sources) == len(objects)
assert len(sources) > 0
compiler = get_cxx_compiler()
# Version 1.3 is required for the `deps` directive.
config = ['ninja_required_version = 1.3']
config.append(f'cxx = {compiler}')
if with_cuda or cuda_dlink_post_cflags:
if "PYTORCH_NVCC" in os.environ:
nvcc = os.getenv("PYTORCH_NVCC") # user can set nvcc compiler with ccache using the environment variable here
else:
if IS_HIP_EXTENSION:
nvcc = _join_rocm_home('bin', 'hipcc')
else:
nvcc = _join_cuda_home('bin', 'nvcc')
config.append(f'nvcc = {nvcc}')
if IS_HIP_EXTENSION:
post_cflags = COMMON_HIP_FLAGS + post_cflags
flags = [f'cflags = {" ".join(cflags)}']
flags.append(f'post_cflags = {" ".join(post_cflags)}')
if with_cuda:
flags.append(f'cuda_cflags = {" ".join(cuda_cflags)}')
flags.append(f'cuda_post_cflags = {" ".join(cuda_post_cflags)}')
flags.append(f'cuda_dlink_post_cflags = {" ".join(cuda_dlink_post_cflags)}')
flags.append(f'ldflags = {" ".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']
nvcc_gendeps = ''
# --generate-dependencies-with-compile is not supported by ROCm
# Nvcc flag `--generate-dependencies-with-compile` is not supported by sccache, which may increase build time.
if torch.version.cuda is not None and os.getenv('TORCH_EXTENSION_SKIP_NVCC_GEN_DEPENDENCIES', '0') != '1':
cuda_compile_rule.append(' depfile = $out.d')
cuda_compile_rule.append(' deps = gcc')
# Note: non-system deps with nvcc are only supported
# on Linux so use --generate-dependencies-with-compile
# to make this work on Windows too.
nvcc_gendeps = '--generate-dependencies-with-compile --dependency-output $out.d'
cuda_compile_rule.append(
f' command = $nvcc {nvcc_gendeps} $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(f'build {object_file}: {rule} {source_file}')
if cuda_dlink_post_cflags:
devlink_out = os.path.join(os.path.dirname(objects[0]), 'dlink.o')
devlink_rule = ['rule cuda_devlink']
devlink_rule.append(' command = $nvcc $in -o $out $cuda_dlink_post_cflags')
devlink = [f'build {devlink_out}: cuda_devlink {" ".join(objects)}']
objects += [devlink_out]
else:
devlink_rule, devlink = [], []
if library_target is not None:
link_rule = ['rule link']
if IS_WINDOWS:
cl_paths = subprocess.check_output(['where',
'cl']).decode(*SUBPROCESS_DECODE_ARGS).split('\r\n')
if len(cl_paths) >= 1:
cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:')
else:
raise RuntimeError("MSVC is required to load C++ extensions")
link_rule.append(f' command = "{cl_path}/link.exe" $in /nologo $ldflags /out:$out')
else:
link_rule.append(' command = $cxx $in $ldflags -o $out')
link = [f'build {library_target}: link {" ".join(objects)}']
default = [f'default {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) # type: ignore[possibly-undefined]
blocks += [devlink_rule, link_rule, build, devlink, link, default]
content = "\n\n".join("\n".join(b) for b in blocks)
# Ninja requires a new lines at the end of the .ninja file
content += "\n"
_maybe_write(path, content)
def _join_cuda_home(*paths) -> str:
"""
Join 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 OSError('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: str) -> bool:
valid_ext = ['.cu', '.cuh']
if IS_HIP_EXTENSION:
valid_ext.append('.hip')
return os.path.splitext(path)[1] in valid_ext
