setup.py

# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import sys
import warnings
import os
import re
import ast
from pathlib import Path
from packaging.version import parse, Version
import platform

from setuptools import setup, find_packages
import subprocess

import urllib.request
import urllib.error
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME


with open("README.md", "r", encoding="utf-8") as fh:
    long_description = fh.read()


# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))

PACKAGE_NAME = "flash_attn"

BASE_WHEEL_URL = "https://github.com/Dao-AILab/flash-attention/releases/download/{tag_name}/{wheel_name}"

# FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
# 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
FORCE_BUILD = os.getenv("FLASH_ATTENTION_FORCE_BUILD", "FALSE") == "TRUE"
SKIP_CUDA_BUILD = os.getenv("FLASH_ATTENTION_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("FLASH_ATTENTION_FORCE_CXX11_ABI", "FALSE") == "TRUE"
# For CI, we want the option to not add "--threads 4" to nvcc, since the runner can OOM
FORCE_SINGLE_THREAD = os.getenv("FLASH_ATTENTION_FORCE_SINGLE_THREAD", "FALSE") == "TRUE"


def get_platform():
    """
    Returns the platform name as used in wheel filenames.
    """
    if sys.platform.startswith('linux'):
        return 'linux_x86_64'
    elif sys.platform == 'darwin':
        mac_version = '.'.join(platform.mac_ver()[0].split('.')[:2])
        return f'macosx_{mac_version}_x86_64'
    elif sys.platform == 'win32':
        return 'win_amd64'
    else:
        raise ValueError('Unsupported platform: {}'.format(sys.platform))


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):
    if not FORCE_SINGLE_THREAD:
        return nvcc_extra_args + ["--threads", "4"]
    return nvcc_extra_args


cmdclass = {}
ext_modules = []

# We want this even if SKIP_CUDA_BUILD because when we run python setup.py sdist we want the .hpp
# files included in the source distribution, in case the user compiles from source.
subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"])

if not SKIP_CUDA_BUILD:
    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
    TORCH_MAJOR = int(torch.__version__.split(".")[0])
    TORCH_MINOR = int(torch.__version__.split(".")[1])

    # 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("flash_attn")
    # 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("11.4"):
        raise RuntimeError("FlashAttention is only supported on CUDA 11.4 and above")
    # cc_flag.append("-gencode")
    # cc_flag.append("arch=compute_75,code=sm_75")
    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")

    # HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
    # torch._C._GLIBCXX_USE_CXX11_ABI
    # https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
    if FORCE_CXX11_ABI:
        torch._C._GLIBCXX_USE_CXX11_ABI = True
    ext_modules.append(
        CUDAExtension(
            name="flash_attn_2_cuda",
            sources=[
                "csrc/flash_attn/flash_api.cpp",
                "csrc/flash_attn/src/flash_fwd_hdim32_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim32_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim64_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim64_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim96_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim96_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim128_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim128_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim160_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim160_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim192_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim192_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim224_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim224_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim256_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_fwd_hdim256_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim32_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim32_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim64_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim64_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim96_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim96_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim128_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim128_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim160_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim160_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim192_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim192_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim224_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim224_bf16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu",
                "csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu",
            ],
            extra_compile_args={
                "cxx": ["-O3", "-std=c++17"] + generator_flag,
                "nvcc": append_nvcc_threads(
                    [
                        "-O3",
                        "-std=c++17",
                        "-U__CUDA_NO_HALF_OPERATORS__",
                        "-U__CUDA_NO_HALF_CONVERSIONS__",
                        "-U__CUDA_NO_HALF2_OPERATORS__",
                        "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
                        "--expt-relaxed-constexpr",
                        "--expt-extended-lambda",
                        "--use_fast_math",
                        "--ptxas-options=-v",
                        # "--ptxas-options=-O2",
                        "-lineinfo"
                    ]
                    + generator_flag
                    + cc_flag
                ),
            },
            include_dirs=[
                Path(this_dir) / 'csrc' / 'flash_attn',
                Path(this_dir) / 'csrc' / 'flash_attn' / 'src',
                Path(this_dir) / 'csrc' / 'cutlass' / 'include',
            ],
        )
    )


def get_package_version():
    with open(Path(this_dir) / "flash_attn" / "__init__.py", "r") as f:
        version_match = re.search(r"^__version__\s*=\s*(.*)$", f.read(), re.MULTILINE)
    public_version = ast.literal_eval(version_match.group(1))
    local_version = os.environ.get("FLASH_ATTN_LOCAL_VERSION")
    if local_version:
        return f"{public_version}+{local_version}"
    else:
        return str(public_version)


class CachedWheelsCommand(_bdist_wheel):
    """
    The CachedWheelsCommand plugs into the default bdist wheel, which is ran by pip when it cannot
    find an existing wheel (which is currently the case for all flash attention installs). We use
    the environment parameters to detect whether there is already a pre-built version of a compatible
    wheel available and short-circuits the standard full build pipeline.
    """
    def run(self):
        if FORCE_BUILD:
            return super().run()

        # Determine the version numbers that will be used to determine the correct wheel
        # We're using the CUDA version used to build torch, not the one currently installed
        # _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
        torch_cuda_version = parse(torch.version.cuda)
        torch_version_raw = parse(torch.__version__)
        python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
        platform_name = get_platform()
        flash_version = get_package_version()
        # cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}"
        cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
        torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
        cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()

        # Determine wheel URL based on CUDA version, torch version, python version and OS
        wheel_filename = f'{PACKAGE_NAME}-{flash_version}+cu{cuda_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl'
        wheel_url = BASE_WHEEL_URL.format(
            tag_name=f"v{flash_version}",
            wheel_name=wheel_filename
        )
        print("Guessing wheel URL: ", wheel_url)

        try:
            urllib.request.urlretrieve(wheel_url, wheel_filename)

            # Make the archive
            # Lifted from the root wheel processing command
            # https://github.com/pypa/wheel/blob/cf71108ff9f6ffc36978069acb28824b44ae028e/src/wheel/bdist_wheel.py#LL381C9-L381C85
            if not os.path.exists(self.dist_dir):
                os.makedirs(self.dist_dir)

            impl_tag, abi_tag, plat_tag = self.get_tag()
            archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"

            wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
            print("Raw wheel path", wheel_path)
            os.rename(wheel_filename, wheel_path)
        except urllib.error.HTTPError:
            print("Precompiled wheel not found. Building from source...")
            # If the wheel could not be downloaded, build from source
            super().run()


setup(
    name=PACKAGE_NAME,
    version=get_package_version(),
    packages=find_packages(
        exclude=("build", "csrc", "include", "tests", "dist", "docs", "benchmarks", "flash_attn.egg-info",)
    ),
    author="Tri Dao",
    author_email="trid@cs.stanford.edu",
    description="Flash Attention: Fast and Memory-Efficient Exact Attention",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/Dao-AILab/flash-attention",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: BSD License",
        "Operating System :: Unix",
    ],
    ext_modules=ext_modules,
    cmdclass={
        'bdist_wheel': CachedWheelsCommand,
        "build_ext": BuildExtension
    } if ext_modules else {
        'bdist_wheel': CachedWheelsCommand,
    },
    python_requires=">=3.7",
    install_requires=[
        "torch",
        "einops",
        "packaging",
        "ninja",
    ],
)