296 lines
11 KiB
Python
296 lines
11 KiB
Python
# Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao.
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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 shutil
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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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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 = "flashattn-hopper"
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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("FAHOPPER_FORCE_BUILD", "FALSE") == "TRUE"
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SKIP_CUDA_BUILD = os.getenv("FAHOPPER_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("FAHOPPER_FORCE_CXX11_ABI", "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_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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# warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary
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# in that case.
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warnings.warn(
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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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return nvcc_extra_args + ["--threads", "4"]
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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_cuda_home_none("--fahopper")
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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("12.3"):
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raise RuntimeError("FA Hopper is only supported on CUDA 12.3 and above")
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cc_flag.append("-gencode")
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cc_flag.append("arch=compute_90a,code=sm_90a")
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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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repo_dir = Path(this_dir).parent
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cutlass_dir = repo_dir / "csrc" / "cutlass"
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sources = [
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"flash_api.cpp",
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"flash_fwd_hdim64_fp16_sm90.cu",
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"flash_fwd_hdim64_bf16_sm90.cu",
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"flash_fwd_hdim128_fp16_sm90.cu",
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"flash_fwd_hdim128_bf16_sm90.cu",
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"flash_fwd_hdim256_fp16_sm90.cu",
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"flash_fwd_hdim256_bf16_sm90.cu",
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"flash_bwd_hdim64_fp16_sm90.cu",
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"flash_bwd_hdim96_fp16_sm90.cu",
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"flash_bwd_hdim128_fp16_sm90.cu",
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# "flash_bwd_hdim256_fp16_sm90.cu",
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"flash_bwd_hdim64_bf16_sm90.cu",
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"flash_bwd_hdim96_bf16_sm90.cu",
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"flash_bwd_hdim128_bf16_sm90.cu",
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"flash_fwd_hdim64_e4m3_sm90.cu",
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"flash_fwd_hdim128_e4m3_sm90.cu",
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"flash_fwd_hdim256_e4m3_sm90.cu"
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]
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nvcc_flags = [
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"-O3",
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# "-O0",
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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_BFLOAT16_OPERATORS__",
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"-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
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"-U__CUDA_NO_BFLOAT162_OPERATORS__",
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"-U__CUDA_NO_BFLOAT162_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", # printing out number of registers
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"--ptxas-options=--verbose,--register-usage-level=10,--warn-on-local-memory-usage", # printing out number of registers
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"-lineinfo",
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"-DCUTLASS_DEBUG_TRACE_LEVEL=0", # Can toggle for debugging
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"-DNDEBUG", # Important, otherwise performance is severely impacted
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]
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include_dirs = [
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# Path(this_dir) / "fmha-pipeline",
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# repo_dir / "lib",
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# repo_dir / "include",
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cutlass_dir / "include",
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# cutlass_dir / "examples" / "common",
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# cutlass_dir / "tools" / "util" / "include",
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]
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ext_modules.append(
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CUDAExtension(
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name="flashattn_hopper_cuda",
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sources=sources,
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extra_compile_args={
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"cxx": ["-O3", "-std=c++17"],
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# "cxx": ["-O0", "-std=c++17"],
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"nvcc": append_nvcc_threads(
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nvcc_flags + cc_flag
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),
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},
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include_dirs=include_dirs,
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# Without this we get and error about cuTensorMapEncodeTiled not defined
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libraries=["cuda"]
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)
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)
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# ext_modules.append(
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# CUDAExtension(
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# name="flashattn_hopper_cuda_ws",
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# sources=sources,
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# extra_compile_args={
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# "cxx": ["-O3", "-std=c++17"],
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# "nvcc": append_nvcc_threads(
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# nvcc_flags + ["-DEXECMODE=1"] + cc_flag
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# ),
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# },
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# include_dirs=include_dirs,
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# # Without this we get and error about cuTensorMapEncodeTiled not defined
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# libraries=["cuda"]
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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) / "__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("FLASHATTN_HOPPER_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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def get_wheel_url():
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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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# For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.2
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# to save CI time. Minor versions should be compatible.
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torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.2")
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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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package_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}-{package_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(tag_name=f"v{package_version}", wheel_name=wheel_filename)
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return wheel_url, wheel_filename
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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 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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wheel_url, wheel_filename = get_wheel_url()
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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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shutil.move(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=(
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"build",
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"csrc",
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"include",
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"tests",
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"dist",
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"docs",
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"benchmarks",
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)
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),
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py_modules=["flash_attn_interface"],
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description="FlashAttention-3",
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long_description=long_description,
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long_description_content_type="text/markdown",
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classifiers=[
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: Apache Software 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={"bdist_wheel": CachedWheelsCommand, "build_ext": BuildExtension}
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if ext_modules
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else {
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"bdist_wheel": CachedWheelsCommand,
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},
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python_requires=">=3.8",
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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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