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'], )