# Copyright (c) 2023, Tri Dao. # Adapted from https://github.com/Dao-AILab/flash-attention/blob/main/setup.py import sys import functools 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 = "block_sparse_attn" BASE_WHEEL_URL = ( "https://github.com/mit-han-lab/Block-Sparse-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("BLOCK_SPARSE_ATTN_FORCE_BUILD", "FALSE") == "TRUE" SKIP_CUDA_BUILD = os.getenv("BLOCK_SPARSE_ATTN_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("BLOCK_SPARSE_ATTN_FORCE_CXX11_ABI", "FALSE") == "TRUE" @functools.lru_cache(maxsize=None) def cuda_archs() -> str: # 默认仅编译 sm_89 (RTX 4080 Super) return os.getenv("BLOCK_SPARSE_ATTN_CUDA_ARCHS", "89").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 add_cuda_gencodes(cc_flag, archs, bare_metal_version): """ Adds -gencode flags based on nvcc capabilities: - sm_80/89/90 (regular) - sm_100/120 on CUDA >= 12.8 - Use 100f on CUDA >= 12.9 (Blackwell family-specific) - Map requested 110 -> 101 if CUDA < 13.0 (Thor rename) - Embed PTX for newest arch for forward compatibility """ # sm_80 if "80" in archs: cc_flag += ["-gencode", "arch=compute_80,code=sm_80"] # sm_89 (Ada Lovelace, e.g., RTX 4080 Super) - needs CUDA >= 11.8 if "89" in archs: cc_flag += ["-gencode", "arch=compute_89,code=sm_89"] # Hopper 9.0 needs >= 11.8 if bare_metal_version >= Version("11.8") and "90" in archs: cc_flag += ["-gencode", "arch=compute_90,code=sm_90"] # Blackwell 10.x requires >= 12.8 if bare_metal_version >= Version("12.8"): if "100" in archs: # CUDA 12.9 introduced "family-specific" for Blackwell (100f) if bare_metal_version >= Version("12.9"): cc_flag += ["-gencode", "arch=compute_100f,code=sm_100"] else: cc_flag += ["-gencode", "arch=compute_100,code=sm_100"] if "120" in archs: # sm_120 is supported in CUDA 12.8/12.9+ toolkits if bare_metal_version >= Version("12.9"): cc_flag += ["-gencode", "arch=compute_120f,code=sm_120"] else: cc_flag += ["-gencode", "arch=compute_120,code=sm_120"] # Thor rename: 12.9 uses sm_101; 13.0+ uses sm_110 if "110" in archs: if bare_metal_version >= Version("13.0"): cc_flag += ["-gencode", "arch=compute_110f,code=sm_110"] else: # Provide Thor support for CUDA 12.9 via sm_101 if bare_metal_version >= Version("12.8"): cc_flag += ["-gencode", "arch=compute_101,code=sm_101"] # else: no Thor support in older toolkits # PTX for newest requested arch (forward-compat) numeric = [a for a in archs if a.isdigit()] if numeric: newest = max(numeric, key=int) cc_flag += ["-gencode", f"arch=compute_{newest},code=compute_{newest}"] return cc_flag 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 append_nvcc_threads(nvcc_extra_args): nvcc_threads = os.getenv("NVCC_THREADS") or "4" return nvcc_extra_args + ["--threads", nvcc_threads] 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_cuda_home_none("block_sparse_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( "Block Sparse Attention is only supported on CUDA 11.7 and above. " "Note: make sure nvcc has a supported version by running nvcc -V." ) # Build -gencode (regular + PTX + family-specific 'f' when available) add_cuda_gencodes(cc_flag, set(cuda_archs()), bare_metal_version) else: # No nvcc present; warnings already emitted above pass # 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 nvcc_flags = [ "-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", ] compiler_c17_flag=["-O3", "-std=c++17"] # Add Windows-specific flags if sys.platform == "win32" and os.getenv('DISTUTILS_USE_SDK') == '1': nvcc_flags.extend(["-Xcompiler", "/Zc:__cplusplus"]) compiler_c17_flag=["-O2", "/std:c++17", "/Zc:__cplusplus"] ext_modules.append( CUDAExtension( name="block_sparse_attn_cuda", sources=[ "csrc/block_sparse_attn/flash_api.cpp", # add by JXGuo "csrc/block_sparse_attn/src/flash_fwd_block_hdim32_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim32_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim32_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim32_bf16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim64_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim64_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim64_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim64_bf16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim128_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim128_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim128_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_fwd_block_hdim128_bf16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim32_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim32_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim32_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim32_bf16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim64_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim64_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim64_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim64_bf16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim128_fp16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim128_fp16_causal_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim128_bf16_sm80.cu", "csrc/block_sparse_attn/src/flash_bwd_block_hdim128_bf16_causal_sm80.cu", ], extra_compile_args={ "cxx": compiler_c17_flag, "nvcc": append_nvcc_threads(nvcc_flags + cc_flag), }, include_dirs=[ Path(this_dir) / "csrc" / "block_sparse_attn", Path(this_dir) / "csrc" / "block_sparse_attn" / "src", Path(this_dir) / "csrc" / "cutlass" / "include", ], ) ) def get_package_version(): with open(Path(this_dir) / "block_sparse_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() # 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", "block_sparse_attn.egg-info", ) ), author="Junxian Guo", author_email="junxian@mit.edu", description="Block Sparse Attention", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/mit-han-lab/Block-Sparse-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", ], )