303 lines
13 KiB
Python
303 lines
13 KiB
Python
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
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import sys
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import warnings
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import os
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import re
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import ast
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from pathlib import Path
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from packaging.version import parse, Version
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import platform
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from setuptools import setup, find_packages
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import subprocess
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import urllib.request
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import urllib.error
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from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
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import torch
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from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
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with open("README.md", "r", encoding="utf-8") as fh:
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long_description = fh.read()
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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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PACKAGE_NAME = "flash_attn"
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BASE_WHEEL_URL = "https://github.com/Dao-AILab/flash-attention/releases/download/{tag_name}/{wheel_name}"
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# FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
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# SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to copy over raw files, without any cuda compilation
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FORCE_BUILD = os.getenv("FLASH_ATTENTION_FORCE_BUILD", "FALSE") == "TRUE"
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SKIP_CUDA_BUILD = os.getenv("FLASH_ATTENTION_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
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# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
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FORCE_CXX11_ABI = os.getenv("FLASH_ATTENTION_FORCE_CXX11_ABI", "FALSE") == "TRUE"
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# For CI, we want the option to not add "--threads 4" to nvcc, since the runner can OOM
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FORCE_SINGLE_THREAD = os.getenv("FLASH_ATTENTION_FORCE_SINGLE_THREAD", "FALSE") == "TRUE"
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def get_platform():
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"""
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Returns the platform name as used in wheel filenames.
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"""
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if sys.platform.startswith('linux'):
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return 'linux_x86_64'
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elif sys.platform == 'darwin':
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mac_version = '.'.join(platform.mac_ver()[0].split('.')[:2])
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return f'macosx_{mac_version}_x86_64'
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elif sys.platform == 'win32':
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return 'win_amd64'
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else:
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raise ValueError('Unsupported platform: {}'.format(sys.platform))
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def get_cuda_bare_metal_version(cuda_dir):
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raw_output = subprocess.check_output([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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bare_metal_version = parse(output[release_idx].split(",")[0])
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return raw_output, bare_metal_version
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def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
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raw_output, bare_metal_version = get_cuda_bare_metal_version(cuda_dir)
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torch_binary_version = parse(torch.version.cuda)
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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_version != torch_binary_version):
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raise RuntimeError(
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"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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)
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def raise_if_cuda_home_none(global_option: str) -> None:
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if CUDA_HOME is not None:
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return
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raise RuntimeError(
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f"{global_option} was requested, but nvcc was not found. Are you sure your environment has nvcc available? "
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"If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, "
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"only images whose names contain 'devel' will provide nvcc."
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)
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def append_nvcc_threads(nvcc_extra_args):
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if not FORCE_SINGLE_THREAD:
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return nvcc_extra_args + ["--threads", "4"]
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return nvcc_extra_args
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cmdclass = {}
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ext_modules = []
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# We want this even if SKIP_CUDA_BUILD because when we run python setup.py sdist we want the .hpp
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# files included in the source distribution, in case the user compiles from source.
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subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"])
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if not SKIP_CUDA_BUILD:
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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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# Check, if ATen/CUDAGeneratorImpl.h is found, otherwise use ATen/cuda/CUDAGeneratorImpl.h
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# See https://github.com/pytorch/pytorch/pull/70650
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generator_flag = []
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torch_dir = torch.__path__[0]
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if os.path.exists(os.path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")):
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generator_flag = ["-DOLD_GENERATOR_PATH"]
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raise_if_cuda_home_none("flash_attn")
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# Check, if CUDA11 is installed for compute capability 8.0
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cc_flag = []
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_, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
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if bare_metal_version < Version("11.4"):
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raise RuntimeError("FlashAttention is only supported on CUDA 11.4 and above")
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# cc_flag.append("-gencode")
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# cc_flag.append("arch=compute_75,code=sm_75")
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cc_flag.append("-gencode")
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cc_flag.append("arch=compute_80,code=sm_80")
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if bare_metal_version >= Version("11.8"):
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cc_flag.append("-gencode")
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cc_flag.append("arch=compute_90,code=sm_90")
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# HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
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# torch._C._GLIBCXX_USE_CXX11_ABI
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# https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
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if FORCE_CXX11_ABI:
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torch._C._GLIBCXX_USE_CXX11_ABI = True
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ext_modules.append(
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CUDAExtension(
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name="flash_attn_2_cuda",
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sources=[
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"csrc/flash_attn/flash_api.cpp",
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"csrc/flash_attn/src/flash_fwd_hdim32_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim32_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim64_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim64_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim96_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim96_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim128_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim128_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim160_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim160_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim192_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim192_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim224_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim224_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim256_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_fwd_hdim256_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim32_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim32_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim64_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim64_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim96_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim96_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim128_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim128_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim160_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim160_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim192_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim192_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim224_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim224_bf16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu",
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"csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu",
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],
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extra_compile_args={
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"cxx": ["-O3", "-std=c++17"] + generator_flag,
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"nvcc": append_nvcc_threads(
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[
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"-O3",
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"-std=c++17",
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"-U__CUDA_NO_HALF_OPERATORS__",
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"-U__CUDA_NO_HALF_CONVERSIONS__",
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"-U__CUDA_NO_HALF2_OPERATORS__",
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"-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
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"--expt-relaxed-constexpr",
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"--expt-extended-lambda",
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"--use_fast_math",
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"--ptxas-options=-v",
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# "--ptxas-options=-O2",
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"-lineinfo"
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]
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+ generator_flag
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+ cc_flag
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),
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},
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include_dirs=[
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Path(this_dir) / 'csrc' / 'flash_attn',
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Path(this_dir) / 'csrc' / 'flash_attn' / 'src',
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Path(this_dir) / 'csrc' / 'cutlass' / 'include',
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],
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)
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)
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def get_package_version():
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with open(Path(this_dir) / "flash_attn" / "__init__.py", "r") as f:
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version_match = re.search(r"^__version__\s*=\s*(.*)$", f.read(), re.MULTILINE)
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public_version = ast.literal_eval(version_match.group(1))
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local_version = os.environ.get("FLASH_ATTN_LOCAL_VERSION")
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if local_version:
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return f"{public_version}+{local_version}"
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else:
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return str(public_version)
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class CachedWheelsCommand(_bdist_wheel):
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"""
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The CachedWheelsCommand plugs into the default bdist wheel, which is ran by pip when it cannot
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find an existing wheel (which is currently the case for all flash attention installs). We use
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the environment parameters to detect whether there is already a pre-built version of a compatible
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wheel available and short-circuits the standard full build pipeline.
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"""
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def run(self):
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if FORCE_BUILD:
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return super().run()
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# Determine the version numbers that will be used to determine the correct wheel
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# We're using the CUDA version used to build torch, not the one currently installed
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# _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
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torch_cuda_version = parse(torch.version.cuda)
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torch_version_raw = parse(torch.__version__)
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python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
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platform_name = get_platform()
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flash_version = get_package_version()
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# cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}"
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cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
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torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
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cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()
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# Determine wheel URL based on CUDA version, torch version, python version and OS
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wheel_filename = f'{PACKAGE_NAME}-{flash_version}+cu{cuda_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl'
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wheel_url = BASE_WHEEL_URL.format(
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tag_name=f"v{flash_version}",
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wheel_name=wheel_filename
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)
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print("Guessing wheel URL: ", wheel_url)
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try:
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urllib.request.urlretrieve(wheel_url, wheel_filename)
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# Make the archive
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# Lifted from the root wheel processing command
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# https://github.com/pypa/wheel/blob/cf71108ff9f6ffc36978069acb28824b44ae028e/src/wheel/bdist_wheel.py#LL381C9-L381C85
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if not os.path.exists(self.dist_dir):
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os.makedirs(self.dist_dir)
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impl_tag, abi_tag, plat_tag = self.get_tag()
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archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
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wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
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print("Raw wheel path", wheel_path)
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os.rename(wheel_filename, wheel_path)
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except urllib.error.HTTPError:
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print("Precompiled wheel not found. Building from source...")
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# If the wheel could not be downloaded, build from source
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super().run()
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setup(
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name=PACKAGE_NAME,
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version=get_package_version(),
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packages=find_packages(
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exclude=("build", "csrc", "include", "tests", "dist", "docs", "benchmarks", "flash_attn.egg-info",)
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),
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author="Tri Dao",
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author_email="trid@cs.stanford.edu",
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description="Flash Attention: Fast and Memory-Efficient Exact Attention",
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long_description=long_description,
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long_description_content_type="text/markdown",
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url="https://github.com/Dao-AILab/flash-attention",
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classifiers=[
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: BSD License",
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"Operating System :: Unix",
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],
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ext_modules=ext_modules,
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cmdclass={
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'bdist_wheel': CachedWheelsCommand,
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"build_ext": BuildExtension
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} if ext_modules else {
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'bdist_wheel': CachedWheelsCommand,
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},
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python_requires=">=3.7",
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install_requires=[
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"torch",
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"einops",
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"packaging",
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"ninja",
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],
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)
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