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

from setuptools import setup, find_packages
import subprocess

import urllib
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__))


def get_platform():
    """
    Returns the platform string.
    """
    if sys.platform.startswith('linux'):
        return 'linux_x86_64'
    elif sys.platform == 'darwin':
        return 'macosx_10_9_x86_64'
    elif sys.platform == 'win32':
        return 'win_amd64'
    else:
        raise ValueError('Unsupported platform: {}'.format(sys.platform))

from setuptools.command.install import install

# @pierce - TODO: Remove for proper release
BASE_WHEEL_URL = "https://github.com/piercefreeman/flash-attention/releases/download/{tag_name}/{wheel_name}"

class CustomInstallCommand(install):
    def run(self):
        # Determine the version numbers that will be used to determine the correct wheel
        _, cuda_version = get_cuda_bare_metal_version()
        torch_version = torch.__version__
        python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
        platform_name = get_platform()
        flash_version = get_package_version()

        # Determine wheel URL based on CUDA version, torch version, python version and OS
        wheel_filename = f'flash_attn-{flash_version}+cu{cuda_version}torch{torch_version}-{python_version}-{python_version}-{platform_name}.whl'
        wheel_url = BASE_WHEEL_URL.format(
            tag_name=f"v{flash_version}",
            wheel_name=wheel_filename
        )
        
        try:
            urllib.request.urlretrieve(wheel_url, wheel_filename)
            os.system(f'pip install {wheel_filename}')
            os.remove(wheel_filename)
        except urllib.error.HTTPError:
            print("Precompiled wheel not found. Building from source...")
            # If the wheel could not be downloaded, build from source
            install.run(self)


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"):
        return nvcc_extra_args + ["--threads", "4"]
    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("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("11.0"):
    raise RuntimeError("FlashAttention is only supported on CUDA 11 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")

subprocess.run(["git", "submodule", "update", "--init", "csrc/flash_attn/cutlass"])
ext_modules.append(
    CUDAExtension(
        name="flash_attn_cuda",
        sources=[
            "csrc/flash_attn/fmha_api.cpp",
            "csrc/flash_attn/src/fmha_fwd_hdim32.cu",
            "csrc/flash_attn/src/fmha_fwd_hdim64.cu",
            "csrc/flash_attn/src/fmha_fwd_hdim128.cu",
            "csrc/flash_attn/src/fmha_bwd_hdim32.cu",
            "csrc/flash_attn/src/fmha_bwd_hdim64.cu",
            "csrc/flash_attn/src/fmha_bwd_hdim128.cu",
            "csrc/flash_attn/src/fmha_block_fprop_fp16_kernel.sm80.cu",
            "csrc/flash_attn/src/fmha_block_dgrad_fp16_kernel_loop.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",
                    "-lineinfo"
                ]
                + generator_flag
                + cc_flag
            ),
        },
        include_dirs=[
            Path(this_dir) / 'csrc' / 'flash_attn',
            Path(this_dir) / 'csrc' / 'flash_attn' / 'src',
            Path(this_dir) / 'csrc' / 'flash_attn' / '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)

setup(
    name="flash_attn",
    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@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/HazyResearch/flash-attention",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: BSD License",
        "Operating System :: Unix",
    ],
    ext_modules=ext_modules,
    cmdclass={
        'install': CustomInstallCommand,
        "build_ext": BuildExtension
    } if ext_modules else {
        'install': CustomInstallCommand,
    },
    python_requires=">=3.7",
    install_requires=[
        "torch",
        "einops",
        "packaging",
        "ninja",
    ],
)