import os
import re
from datetime import datetime
from setuptools import find_packages, setup
from op_builder.utils import get_cuda_bare_metal_version
try:
import torch
from torch.utils.cpp_extension import CUDA_HOME, BuildExtension, CUDAExtension
print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
TORCH_MAJOR = int(torch.__version__.split('.')[0])
TORCH_MINOR = int(torch.__version__.split('.')[1])
if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 10):
raise RuntimeError("Colossal-AI requires Pytorch 1.10 or newer.\n"
"The latest stable release can be obtained from https://pytorch.org/")
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
CUDA_HOME = None
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
build_cuda_ext = False
ext_modules = []
is_nightly = int(os.environ.get('NIGHTLY', '0')) == 1
if int(os.environ.get('CUDA_EXT', '0')) == 1:
if not TORCH_AVAILABLE:
raise ModuleNotFoundError(
"PyTorch is not found while CUDA_EXT=1. You need to install PyTorch first in order to build CUDA extensions"
)
if not CUDA_HOME:
raise RuntimeError(
"CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment vairable or install CUDA Toolkit first in order to build CUDA extensions"
)
build_cuda_ext = True
def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
raw_output, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
torch_binary_major = torch.version.cuda.split(".")[0]
torch_binary_minor = torch.version.cuda.split(".")[1]
print("\nCompiling cuda extensions with")
print(raw_output + "from " + cuda_dir + "/bin\n")
if bare_metal_major != torch_binary_major:
print(f'The detected CUDA version ({raw_output}) mismatches the version that was used to compile PyTorch '
f'({torch.version.cuda}). CUDA extension will not be installed.')
return False
if bare_metal_minor != torch_binary_minor:
print("\nWarning: Cuda extensions are being compiled with a version of Cuda that does "
"not match the version used to compile Pytorch binaries. "
f"Pytorch binaries were compiled with Cuda {torch.version.cuda}.\n"
"In some cases, a minor-version mismatch will not cause later errors: "
"https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. ")
return True
def check_cuda_availability(cuda_dir):
if not torch.cuda.is_available():
# https://github.com/NVIDIA/apex/issues/486
# Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query
# torch.cuda.get_device_capability(), which will fail if you are compiling in an environment
# without visible GPUs (e.g. during an nvidia-docker build command).
print(
'\nWarning: Torch did not find available GPUs on this system.\n',
'If your intention is to cross-compile, this is not an error.\n'
'By default, Colossal-AI will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
'If you wish to cross-compile for a single specific architecture,\n'
'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
_, bare_metal_major, _ = get_cuda_bare_metal_version(cuda_dir)
if int(bare_metal_major) == 11:
os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
else:
os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
return False
if cuda_dir is None:
print("nvcc was not found. CUDA extension will not be installed. If you're installing within a container from "
"https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
return False
return True
def append_nvcc_threads(nvcc_extra_args):
_, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
return nvcc_extra_args + ["--threads", "4"]
return nvcc_extra_args
def fetch_requirements(path):
with open(path, 'r') as fd:
return [r.strip() for r in fd.readlines()]
def fetch_readme():
with open('README.md', encoding='utf-8') as f:
return f.read()
def get_version():
setup_file_path = os.path.abspath(__file__)
project_path = os.path.dirname(setup_file_path)
version_txt_path = os.path.join(project_path, 'version.txt')
version_py_path = os.path.join(project_path, 'colossalai/version.py')
with open(version_txt_path) as f:
version = f.read().strip()
if build_cuda_ext:
torch_version = '.'.join(torch.__version__.split('.')[:2])
cuda_version = '.'.join(get_cuda_bare_metal_version(CUDA_HOME)[1:])
version += f'+torch{torch_version}cu{cuda_version}'
# write version into version.py
with open(version_py_path, 'w') as f:
f.write(f"__version__ = '{version}'\n")
return version
if build_cuda_ext:
build_cuda_ext = check_cuda_availability(CUDA_HOME) and check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
if build_cuda_ext:
# Set up macros for forward/backward compatibility hack around
# https://github.com/pytorch/pytorch/commit/4404762d7dd955383acee92e6f06b48144a0742e
# and
# https://github.com/NVIDIA/apex/issues/456
# https://github.com/pytorch/pytorch/commit/eb7b39e02f7d75c26d8a795ea8c7fd911334da7e#diff-4632522f237f1e4e728cb824300403ac
from op_builder import ALL_OPS
for name, builder_cls in ALL_OPS.items():
print(f'===== Building Extension {name} =====')
ext_modules.append(builder_cls().builder())
# always put not nightly branch as the if branch
# otherwise github will treat colossalai-nightly as the project name
# and it will mess up with the dependency graph insights
if not is_nightly:
version = get_version()
package_name = 'colossalai'
else:
# use date as the nightly version
version = datetime.today().strftime('%Y.%m.%d')
package_name = 'colossalai-nightly'
setup(name=package_name,
version=version,
packages=find_packages(exclude=(
'benchmark',
'docker',
'tests',
'docs',
'examples',
'tests',
'scripts',
'requirements',
'*.egg-info',
)),
description='An integrated large-scale model training system with efficient parallelization techniques',
long_description=fetch_readme(),
long_description_content_type='text/markdown',
license='Apache Software License 2.0',
url='https://www.colossalai.org',
project_urls={
'Forum': 'https://github.com/hpcaitech/ColossalAI/discussions',
'Bug Tracker': 'https://github.com/hpcaitech/ColossalAI/issues',
'Examples': 'https://github.com/hpcaitech/ColossalAI-Examples',
'Documentation': 'http://colossalai.readthedocs.io',
'Github': 'https://github.com/hpcaitech/ColossalAI',
},
ext_modules=ext_modules,
cmdclass={'build_ext': BuildExtension} if ext_modules else {},
install_requires=fetch_requirements('requirements/requirements.txt'),
entry_points='''
[console_scripts]
colossalai=colossalai.cli:cli
''',
python_requires='>=3.6',
classifiers=[
'Programming Language :: Python :: 3',
'License :: OSI Approved :: Apache Software License',
'Environment :: GPU :: NVIDIA CUDA',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: System :: Distributed Computing',
],
package_data={
'colossalai': [
'_C/*.pyi', 'kernel/cuda_native/csrc/*', 'kernel/cuda_native/csrc/kernel/*',
'kernel/cuda_native/csrc/kernels/include/*'
]
})