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setup.py
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setup.py
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# 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 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_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/cutlass"])
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)
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@cs.stanford.edu",
description="Flash Attention: Fast and Memory-Efficient Exact Attention",
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={"build_ext": BuildExtension} if ext_modules else {},
python_requires=">=3.7",
install_requires=[
"torch",
"einops",
"packaging",
"ninja",
],
)