# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py import sys import warnings import os from packaging.version import parse, Version from setuptools import setup, find_packages import subprocess import torch from torch.utils.cpp_extension import BuildExtension, CppExtension, 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 bare_metal_version = parse(output[release_idx].split(",")[0]) return raw_output, bare_metal_version def check_cuda_torch_binary_vs_bare_metal(cuda_dir): raw_output, bare_metal_version = get_cuda_bare_metal_version(cuda_dir) torch_binary_version = parse(torch.version.cuda) print("\nCompiling cuda extensions with") print(raw_output + "from " + cuda_dir + "/bin\n") if (bare_metal_version != torch_binary_version): 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 raise_if_cuda_home_none(global_option: str) -> None: if CUDA_HOME is not None: return raise RuntimeError( f"{global_option} 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." ) def append_nvcc_threads(nvcc_extra_args): _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME) if bare_metal_version >= Version("11.2"): nvcc_threads = os.getenv("NVCC_THREADS") or "4" return nvcc_extra_args + ["--threads", nvcc_threads] 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, Apex 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 and CUDA_HOME is not None: _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME) if bare_metal_version >= Version("11.8"): os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0;8.6;9.0" elif bare_metal_version >= Version("11.1"): os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0;8.6" elif bare_metal_version == Version("11.0"): 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]) cmdclass = {} ext_modules = [] # Check, if ATen/CUDAGeneratorImpl.h is found, otherwise use ATen/cuda/CUDAGeneratorImpl.h # See https://github.com/pytorch/pytorch/pull/70650 generator_flag = [] torch_dir = torch.__path__[0] if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")): generator_flag = ["-DOLD_GENERATOR_PATH"] raise_if_cuda_home_none("--ft_attention") # Check, if CUDA11 is installed for compute capability 8.0 cc_flag = [] _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME) if bare_metal_version < Version("11.0"): raise RuntimeError("ft_attention is only supported on CUDA 11 and above") cc_flag.append("-gencode") cc_flag.append("arch=compute_70,code=sm_70") cc_flag.append("-gencode") cc_flag.append("arch=compute_80,code=sm_80") if bare_metal_version >= Version("11.8"): cc_flag.append("-gencode") cc_flag.append("arch=compute_90,code=sm_90") ext_modules.append( CUDAExtension( name="ft_attention", sources=[ "ft_attention.cpp", "decoder_masked_multihead_attention.cu", ], extra_compile_args={ "cxx": ["-O3", "-DENABLE_BF16"] + generator_flag, "nvcc": append_nvcc_threads( [ "-DENABLE_BF16", # TODO "-O3", "-U__CUDA_NO_HALF_OPERATORS__", "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_BFLOAT16_OPERATORS__", "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "-U__CUDA_NO_BFLOAT162_OPERATORS__", "-U__CUDA_NO_BFLOAT162_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda", "--use_fast_math", ] + generator_flag + cc_flag ), }, include_dirs=[this_dir], ) ) setup( name="ft_attention", version="0.1", description="Attention for single query from FasterTransformer", ext_modules=ext_modules, cmdclass={"build_ext": BuildExtension} if ext_modules else {}, )