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136 lines
6.3 KiB
136 lines
6.3 KiB
import os
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import subprocess
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import sys
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import torch
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from setuptools import setup, find_packages
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from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
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# ninja build does not work unless include_dirs are abs path
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this_dir = os.path.dirname(os.path.abspath(__file__))
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def get_cuda_bare_metal_version(cuda_dir):
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raw_output = subprocess.check_output(
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[cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
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output = raw_output.split()
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release_idx = output.index("release") + 1
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release = output[release_idx].split(".")
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bare_metal_major = release[0]
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bare_metal_minor = release[1][0]
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return raw_output, bare_metal_major, bare_metal_minor
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def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
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raw_output, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(
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cuda_dir)
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torch_binary_major = torch.version.cuda.split(".")[0]
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torch_binary_minor = torch.version.cuda.split(".")[1]
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print("\nCompiling cuda extensions with")
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print(raw_output + "from " + cuda_dir + "/bin\n")
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if (bare_metal_major != torch_binary_major) or (bare_metal_minor != torch_binary_minor):
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raise RuntimeError("Cuda extensions are being compiled with a version of Cuda that does " +
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"not match the version used to compile Pytorch binaries. " +
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"Pytorch binaries were compiled with Cuda {}.\n".format(torch.version.cuda) +
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"In some cases, a minor-version mismatch will not cause later errors: " +
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"https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. "
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"You can try commenting out this check (at your own risk).")
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def fetch_requirements(path):
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with open(path, 'r') as fd:
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return [r.strip() for r in fd.readlines()]
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if not torch.cuda.is_available():
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# https://github.com/NVIDIA/apex/issues/486
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# Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query torch.cuda.get_device_capability(),
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# which will fail if you are compiling in an environment without visible GPUs (e.g. during an nvidia-docker build command).
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print('\nWarning: Torch did not find available GPUs on this system.\n',
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'If your intention is to cross-compile, this is not an error.\n'
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'By default, Colossal-AI will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
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'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
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'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
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'If you wish to cross-compile for a single specific architecture,\n'
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'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
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if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
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_, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME)
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if int(bare_metal_major) == 11:
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os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
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else:
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os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
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print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
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TORCH_MAJOR = int(torch.__version__.split('.')[0])
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TORCH_MINOR = int(torch.__version__.split('.')[1])
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if TORCH_MAJOR == 0 and TORCH_MINOR < 4:
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raise RuntimeError("Colossal-AI requires Pytorch 0.4 or newer.\n" +
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"The latest stable release can be obtained from https://pytorch.org/")
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cmdclass = {}
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ext_modules = []
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# Set up macros for forward/backward compatibility hack around
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# https://github.com/pytorch/pytorch/commit/4404762d7dd955383acee92e6f06b48144a0742e
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# and
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# https://github.com/NVIDIA/apex/issues/456
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# https://github.com/pytorch/pytorch/commit/eb7b39e02f7d75c26d8a795ea8c7fd911334da7e#diff-4632522f237f1e4e728cb824300403ac
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version_ge_1_1 = []
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if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR > 0):
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version_ge_1_1 = ['-DVERSION_GE_1_1']
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version_ge_1_3 = []
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if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR > 2):
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version_ge_1_3 = ['-DVERSION_GE_1_3']
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version_ge_1_5 = []
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if (TORCH_MAJOR > 1) or (TORCH_MAJOR == 1 and TORCH_MINOR > 4):
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version_ge_1_5 = ['-DVERSION_GE_1_5']
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version_dependent_macros = version_ge_1_1 + version_ge_1_3 + version_ge_1_5
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if "--cuda_ext" in sys.argv:
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if TORCH_MAJOR == 0:
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raise RuntimeError("--cuda_ext requires Pytorch 1.0 or later, "
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"found torch.__version__ = {}".format(torch.__version__))
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sys.argv.remove("--cuda_ext")
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if CUDA_HOME is None:
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raise RuntimeError(
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"--cuda_ext was requested, but nvcc was not found. Are you sure your environment has nvcc available? If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
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else:
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check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
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ext_modules.append(
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CUDAExtension(name='colossal_C',
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sources=['csrc/colossal_C_frontend.cpp',
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'csrc/multi_tensor_sgd_kernel.cu',
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'csrc/multi_tensor_scale_kernel.cu',
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'csrc/multi_tensor_adam.cu',
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'csrc/multi_tensor_l2norm_kernel.cu',
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'csrc/multi_tensor_lamb.cu'],
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extra_compile_args={'cxx': ['-O3'] + version_dependent_macros,
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'nvcc': ['-lineinfo',
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'-O3',
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# '--resource-usage',
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'--use_fast_math'] + version_dependent_macros}))
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install_requires = fetch_requirements('requirements/requirements.txt')
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setup(
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name='colossalai',
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version='0.0.1-beta',
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packages=find_packages(exclude=('csrc',
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'tests',
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'docs',
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'tests',
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'*.egg-info',)),
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description='An integrated large-scale model training system with efficient parallelization techniques',
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ext_modules=ext_modules,
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cmdclass={'build_ext': BuildExtension} if ext_modules else {},
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package_data={'colossalai': ['kernel/cuda_native/csrc/*']},
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install_requires=install_requires,
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) |