# Copyright (c) 2023, Tri Dao. import sys import functools import warnings import os import re import ast import glob import shutil 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, ROCM_HOME, IS_HIP_EXTENSION, ) 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__)) BUILD_TARGET = os.environ.get("BUILD_TARGET", "auto") if BUILD_TARGET == "auto": if IS_HIP_EXTENSION: IS_ROCM = True else: IS_ROCM = False else: if BUILD_TARGET == "cuda": IS_ROCM = False elif BUILD_TARGET == "rocm": IS_ROCM = True 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" USE_TRITON_ROCM = os.getenv("FLASH_ATTENTION_TRITON_AMD_ENABLE", "FALSE") == "TRUE" @functools.lru_cache(maxsize=None) def cuda_archs() -> str: return os.getenv("FLASH_ATTN_CUDA_ARCHS", "80;90;100;120").split(";") def get_platform(): """ Returns the platform name as used in wheel filenames. """ if sys.platform.startswith("linux"): return f'linux_{platform.uname().machine}' 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 get_hip_version(): return parse(torch.version.hip.split()[-1].rstrip('-').replace('-', '+')) def check_if_cuda_home_none(global_option: str) -> None: if CUDA_HOME is not None: return # warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary # in that case. warnings.warn( 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 check_if_rocm_home_none(global_option: str) -> None: if ROCM_HOME is not None: return # warn instead of error because user could be downloading prebuilt wheels, so hipcc won't be necessary # in that case. warnings.warn( f"{global_option} was requested, but hipcc was not found." ) def append_nvcc_threads(nvcc_extra_args): nvcc_threads = os.getenv("NVCC_THREADS") or "2" return nvcc_extra_args + ["--threads", nvcc_threads] def rename_cpp_to_cu(cpp_files): for entry in cpp_files: shutil.copy(entry, os.path.splitext(entry)[0] + ".cu") def validate_and_update_archs(archs): # List of allowed architectures allowed_archs = ["native", "gfx90a", "gfx940", "gfx941", "gfx942"] # Validate if each element in archs is in allowed_archs assert all( arch in allowed_archs for arch in archs ), f"One of GPU archs of {archs} is invalid or not supported by Flash-Attention" 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. if os.path.isdir(".git"): subprocess.run(["git", "submodule", "update", "--init", "csrc/composable_kernel"], check=True) subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"], check=True) else: if IS_ROCM: if not USE_TRITON_ROCM: assert ( os.path.exists("csrc/composable_kernel/example/ck_tile/01_fmha/generate.py") ), "csrc/composable_kernel is missing, please use source distribution or git clone" else: assert ( os.path.exists("csrc/cutlass/include/cutlass/cutlass.h") ), "csrc/cutlass is missing, please use source distribution or git clone" if not SKIP_CUDA_BUILD and not IS_ROCM: 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_cuda_home_none("flash_attn") # Check, if CUDA11 is installed for compute capability 8.0 cc_flag = [] if CUDA_HOME is not None: _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME) if bare_metal_version < Version("11.7"): raise RuntimeError( "FlashAttention is only supported on CUDA 11.7 and above. " "Note: make sure nvcc has a supported version by running nvcc -V." ) if "80" in cuda_archs(): cc_flag.append("-gencode") cc_flag.append("arch=compute_80,code=sm_80") if CUDA_HOME is not None: if bare_metal_version >= Version("11.8") and "90" in cuda_archs(): cc_flag.append("-gencode") cc_flag.append("arch=compute_90,code=sm_90") if bare_metal_version >= Version("12.8") and "100" in cuda_archs(): cc_flag.append("-gencode") cc_flag.append("arch=compute_100,code=sm_100") if bare_metal_version >= Version("12.8") and "120" in cuda_archs(): cc_flag.append("-gencode") cc_flag.append("arch=compute_120,code=sm_120") # 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_hdim256_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim256_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim32_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim32_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim64_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim64_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim96_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim96_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim128_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim128_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim160_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim160_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim192_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim192_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim256_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_hdim256_bf16_causal_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_hdim256_fp16_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim32_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim32_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim64_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim64_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim96_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim96_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim128_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim128_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim160_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim160_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim192_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim192_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim256_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_bwd_hdim256_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim128_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim128_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim160_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim160_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim192_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim192_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim32_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim32_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim64_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim64_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim96_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim96_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim128_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim128_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim160_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim160_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim192_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim192_bf16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim256_fp16_causal_sm80.cu", "csrc/flash_attn/src/flash_fwd_split_hdim256_bf16_causal_sm80.cu", ], extra_compile_args={ "cxx": ["-O3", "-std=c++17"], "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", # "-DFLASHATTENTION_DISABLE_BACKWARD", # "-DFLASHATTENTION_DISABLE_DROPOUT", # "-DFLASHATTENTION_DISABLE_ALIBI", # "-DFLASHATTENTION_DISABLE_SOFTCAP", # "-DFLASHATTENTION_DISABLE_UNEVEN_K", # "-DFLASHATTENTION_DISABLE_LOCAL", ] + cc_flag ), }, include_dirs=[ Path(this_dir) / "csrc" / "flash_attn", Path(this_dir) / "csrc" / "flash_attn" / "src", Path(this_dir) / "csrc" / "cutlass" / "include", ], ) ) elif not SKIP_CUDA_BUILD and IS_ROCM: print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__)) TORCH_MAJOR = int(torch.__version__.split(".")[0]) TORCH_MINOR = int(torch.__version__.split(".")[1]) if USE_TRITON_ROCM: # Skip C++ extension compilation if using Triton Backend pass else: ck_dir = "csrc/composable_kernel" #use codegen get code dispatch if not os.path.exists("./build"): os.makedirs("build") subprocess.run([sys.executable, f"{ck_dir}/example/ck_tile/01_fmha/generate.py", "-d", "fwd", "--output_dir", "build", "--receipt", "2"], check=True) subprocess.run([sys.executable, f"{ck_dir}/example/ck_tile/01_fmha/generate.py", "-d", "fwd_appendkv", "--output_dir", "build", "--receipt", "2"], check=True) subprocess.run([sys.executable, f"{ck_dir}/example/ck_tile/01_fmha/generate.py", "-d", "fwd_splitkv", "--output_dir", "build", "--receipt", "2"], check=True) subprocess.run([sys.executable, f"{ck_dir}/example/ck_tile/01_fmha/generate.py", "-d", "bwd", "--output_dir", "build", "--receipt", "2"], check=True) # 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"] check_if_rocm_home_none("flash_attn") archs = os.getenv("GPU_ARCHS", "native").split(";") validate_and_update_archs(archs) cc_flag = [f"--offload-arch={arch}" for arch in archs] # 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 sources = ["csrc/flash_attn_ck/flash_api.cpp", "csrc/flash_attn_ck/flash_common.cpp", "csrc/flash_attn_ck/mha_bwd.cpp", "csrc/flash_attn_ck/mha_fwd_kvcache.cpp", "csrc/flash_attn_ck/mha_fwd.cpp", "csrc/flash_attn_ck/mha_varlen_bwd.cpp", "csrc/flash_attn_ck/mha_varlen_fwd.cpp"] + glob.glob( f"build/fmha_*wd*.cpp" ) rename_cpp_to_cu(sources) renamed_sources = ["csrc/flash_attn_ck/flash_api.cu", "csrc/flash_attn_ck/flash_common.cu", "csrc/flash_attn_ck/mha_bwd.cu", "csrc/flash_attn_ck/mha_fwd_kvcache.cu", "csrc/flash_attn_ck/mha_fwd.cu", "csrc/flash_attn_ck/mha_varlen_bwd.cu", "csrc/flash_attn_ck/mha_varlen_fwd.cu"] + glob.glob(f"build/fmha_*wd*.cu") cc_flag += ["-O3","-std=c++17", "-DCK_TILE_FMHA_FWD_FAST_EXP2=1", "-fgpu-flush-denormals-to-zero", "-DCK_ENABLE_BF16", "-DCK_ENABLE_BF8", "-DCK_ENABLE_FP16", "-DCK_ENABLE_FP32", "-DCK_ENABLE_FP64", "-DCK_ENABLE_FP8", "-DCK_ENABLE_INT8", "-DCK_USE_XDL", "-DUSE_PROF_API=1", # "-DFLASHATTENTION_DISABLE_BACKWARD", "-D__HIP_PLATFORM_HCC__=1"] cc_flag += [f"-DCK_TILE_FLOAT_TO_BFLOAT16_DEFAULT={os.environ.get('CK_TILE_FLOAT_TO_BFLOAT16_DEFAULT', 3)}"] # Imitate https://github.com/ROCm/composable_kernel/blob/c8b6b64240e840a7decf76dfaa13c37da5294c4a/CMakeLists.txt#L190-L214 hip_version = get_hip_version() if hip_version > Version('5.7.23302'): cc_flag += ["-fno-offload-uniform-block"] if hip_version > Version('6.1.40090'): cc_flag += ["-mllvm", "-enable-post-misched=0"] if hip_version > Version('6.2.41132'): cc_flag += ["-mllvm", "-amdgpu-early-inline-all=true", "-mllvm", "-amdgpu-function-calls=false"] if hip_version > Version('6.2.41133') and hip_version < Version('6.3.00000'): cc_flag += ["-mllvm", "-amdgpu-coerce-illegal-types=1"] extra_compile_args = { "cxx": ["-O3", "-std=c++17"] + generator_flag, "nvcc": cc_flag + generator_flag, } include_dirs = [ Path(this_dir) / "csrc" / "composable_kernel" / "include", Path(this_dir) / "csrc" / "composable_kernel" / "library" / "include", Path(this_dir) / "csrc" / "composable_kernel" / "example" / "ck_tile" / "01_fmha", ] ext_modules.append( CUDAExtension( name="flash_attn_2_cuda", sources=renamed_sources, extra_compile_args=extra_compile_args, include_dirs=include_dirs, ) ) 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) def get_wheel_url(): 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() torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}" cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper() if IS_ROCM: torch_hip_version = get_hip_version() hip_version = f"{torch_hip_version.major}{torch_hip_version.minor}" wheel_filename = f"{PACKAGE_NAME}-{flash_version}+rocm{hip_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl" else: # 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) # For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.3 # to save CI time. Minor versions should be compatible. torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.3") # cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}" cuda_version = f"{torch_cuda_version.major}" # 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) return wheel_url, wheel_filename 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() wheel_url, wheel_filename = get_wheel_url() 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, urllib.error.URLError): print("Precompiled wheel not found. Building from source...") # If the wheel could not be downloaded, build from source super().run() class NinjaBuildExtension(BuildExtension): def __init__(self, *args, **kwargs) -> None: # do not override env MAX_JOBS if already exists if not os.environ.get("MAX_JOBS"): import psutil # calculate the maximum allowed NUM_JOBS based on cores max_num_jobs_cores = max(1, os.cpu_count() // 2) # calculate the maximum allowed NUM_JOBS based on free memory free_memory_gb = psutil.virtual_memory().available / (1024 ** 3) # free memory in GB max_num_jobs_memory = int(free_memory_gb / 9) # each JOB peak memory cost is ~8-9GB when threads = 4 # pick lower value of jobs based on cores vs memory metric to minimize oom and swap usage during compilation max_jobs = max(1, min(max_num_jobs_cores, max_num_jobs_memory)) os.environ["MAX_JOBS"] = str(max_jobs) super().__init__(*args, **kwargs) 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="tri@tridao.me", 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": NinjaBuildExtension} if ext_modules else { "bdist_wheel": CachedWheelsCommand, }, python_requires=">=3.9", install_requires=[ "torch", "einops", ], setup_requires=[ "packaging", "psutil", "ninja", ], )