/
setup.py
143 lines (117 loc) · 6.1 KB
/
setup.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
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():
print("======== NOTICE: torch.cuda.is_available == False")
# # 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, FastFold 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("FastFold requires Pytorch 1.10 or newer.\n" +
"The latest stable release can be obtained from https://pytorch.org/")
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:
# check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
def cuda_ext_helper(name, sources, extra_cuda_flags):
return CUDAExtension(
name=name,
sources=[
os.path.join('fastfold/model/fastnn/kernel/cuda_native/csrc', path) for path in sources
],
include_dirs=[
os.path.join(this_dir, 'fastfold/model/fastnn/kernel/cuda_native/csrc/include')
],
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_layer_norm_cuda',
['layer_norm_cuda.cpp', 'layer_norm_cuda_kernel.cu'],
extra_cuda_flags + cc_flag))
ext_modules.append(
cuda_ext_helper('fastfold_softmax_cuda', ['softmax_cuda.cpp', 'softmax_cuda_kernel.cu'],
extra_cuda_flags + cc_flag))
else:
print("======== NOTICE: install without cuda kernel")
setup(
name='fastfold',
version='0.2.0',
packages=find_packages(exclude=(
'assets',
'benchmark',
'*.egg-info',
)),
description=
'Optimizing Protein Structure Prediction Model Training and Inference on GPU Clusters',
ext_modules=ext_modules,
package_data={'fastfold': ['model/fastnn/kernel/cuda_native/csrc/*']},
cmdclass={'build_ext': BuildExtension} if ext_modules else {},
install_requires=['einops', 'colossalai'],
)