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setup.py
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setup.py
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import os
import subprocess
import torch
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
def get_cuda_bare_metal_version(cuda_dir):
raw_output = subprocess.check_output(
[cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
)
output = raw_output.split()
release_idx = output.index("release") + 1
release = output[release_idx].split(".")
bare_metal_major = release[0]
bare_metal_minor = release[1][0]
return raw_output, bare_metal_major, bare_metal_minor
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) or (
bare_metal_minor != torch_binary_minor
):
raise RuntimeError(
"CUDA extensions are being compiled with a version of CUDA that does "
+ "not match the version used to compile Pytorch binaries. "
+ "Pytorch binaries were compiled with CUDA {}.\n".format(
torch.version.cuda
)
+ "In some cases, a minor-version mismatch will not cause later errors: "
+ "https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. "
"You can try commenting out this check (at your own risk)."
)
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
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, xTrimoMultimer 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_HOME)
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"
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(
"xTrimoMultimer requires Pytorch 1.10 or newer.\n"
+ "The latest stable release can be obtained from https://pytorch.org/get-started/locally/"
)
cmdclass = {}
ext_modules = []
# 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
version_dependent_macros = ["-DVERSION_GE_1_1", "-DVERSION_GE_1_3", "-DVERSION_GE_1_5"]
if CUDA_HOME is None:
raise RuntimeError(
"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."
)
else:
# check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
def cuda_ext_helper(
name,
sources,
extra_cuda_flags,
source_dir="xtrimomultimer/model_acc/kernel/cuda_native/csrc",
include_dir="xtrimomultimer/model_acc/kernel/cuda_native/csrc/include",
):
return CUDAExtension(
name=name,
sources=[os.path.join(source_dir, path) for path in sources],
include_dirs=[os.path.join(this_dir, include_dir)],
extra_compile_args={
"cxx": ["-O3"] + version_dependent_macros,
"nvcc": append_nvcc_threads(
["-O3", "--use_fast_math"]
+ version_dependent_macros
+ extra_cuda_flags
),
},
)
cc_flag = ["-gencode", "arch=compute_70,code=sm_70"]
_, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME)
if int(bare_metal_major) >= 11:
cc_flag.append("-gencode")
cc_flag.append("arch=compute_80,code=sm_80")
extra_cuda_flags = [
"-std=c++14",
"-maxrregcount=50",
"-U__CUDA_NO_HALF_OPERATORS__",
"-U__CUDA_NO_HALF_CONVERSIONS__",
"--expt-relaxed-constexpr",
"--expt-extended-lambda",
]
ext_modules.append(
cuda_ext_helper(
"fastfold_cuda_ops",
[
"layer_norm_cuda_kernel.cu",
"layer_norm_cuda.cpp",
"softmax_cuda_kernel.cu",
"fastfold_cuda_ops.cpp",
],
extra_cuda_flags + cc_flag,
)
)
setup(
name="xTrimoMultimer",
version="0.1.0-beta",
packages=find_packages(exclude=["tests", "scripts"]),
description="Optimizing Protein Structure Prediction Model Training and Inference on GPU Clusters",
author="BioMap",
ext_modules=ext_modules,
package_data={"xtrimomultimer": ["model_acc/kernel/cuda_native/csrc/*"]},
cmdclass={"build_ext": BuildExtension} if ext_modules else {},
install_requires=[
"torch",
"deepspeed",
"biopython",
"ml-collections",
"numpy",
"scipy",
"pytorch_lightning",
"dm-tree",
"colossalai",
],
classifiers=[
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3.7," "Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.8," "Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.9," "Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3.10," "Operating System :: POSIX :: Linux",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
)