Remove unused code and dependencies from setup.py

Author: LoserCheems

setup.py

@@ -1,341 +1,3 @@
-# Copyright (c) 2025, Jingze Shi.
-
-import sys
-import functools
-import warnings
-import os
-import re
-import ast
-import glob
-from pathlib import Path
-from packaging.version import parse, Version
-import platform
-from typing import Optional
-
 from setuptools import setup
-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,
-    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 = "flash_sparse_attn"
-
-BASE_WHEEL_URL = (
-    "https://github.com/flash-algo/flash-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
-# Also useful when user only wants Triton/Flex backends without CUDA compilation
-FORCE_BUILD = os.getenv("FLASH_SPARSE_ATTENTION_FORCE_BUILD", "FALSE") == "TRUE"
-SKIP_CUDA_BUILD = os.getenv("FLASH_SPARSE_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_SPARSE_ATTENTION_FORCE_CXX11_ABI", "FALSE") == "TRUE"
-
-# Auto-detect if user wants only Triton/Flex backends based on pip install command
-# This helps avoid unnecessary CUDA compilation when user only wants Python backends
-def should_skip_cuda_build():
-    """Determine if CUDA build should be skipped based on installation context."""
-    
-    if SKIP_CUDA_BUILD:
-        return True
-    
-    if FORCE_BUILD:
-        return False  # User explicitly wants to build, respect that
-    
-    # Check command line arguments for installation hints
-    if len(sys.argv) > 1:
-        install_args = ' '.join(sys.argv)
-        
-        # Check if Triton or Flex extras are requested
-        has_triton_or_flex = 'triton' in install_args or 'flex' in install_args
-        has_all_or_dev = 'all' in install_args or 'dev' in install_args
-
-        if has_triton_or_flex and not has_all_or_dev:
-            print("Detected Triton/Flex-only installation. Skipping CUDA compilation.")
-            print("Set FLASH_SPARSE_ATTENTION_FORCE_BUILD=TRUE to force CUDA compilation.")
-            return True
-    
-    return False
-
-# Update SKIP_CUDA_BUILD based on auto-detection
-SKIP_CUDA_BUILD = should_skip_cuda_build()
-
-@functools.lru_cache(maxsize=None)
-def cuda_archs():
-    return os.getenv("FLASH_SPARSE_ATTENTION_CUDA_ARCHS", "80;90;100").split(";")
-
-
-def detect_preferred_sm_arch() -> Optional[str]:
-    """Detect the preferred SM arch from the current CUDA device.
-    Returns None if CUDA is unavailable or detection fails.
-    """
-    try:
-        if torch.cuda.is_available():
-            idx = torch.cuda.current_device()
-            major, minor = torch.cuda.get_device_capability(idx)
-            return f"{major}{minor}"
-    except Exception:
-        pass
-    return None
-
-
-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 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.
-if os.path.isdir(".git"):
-    subprocess.run(["git", "submodule", "update", "--init", "csrc/cutlass"], check=True)
-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:
-    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_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(
-                "Flash Sparse Attention 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 "86" in cuda_archs():
-            cc_flag.append("-gencode")
-            cc_flag.append("arch=compute_86,code=sm_86")
-        if bare_metal_version >= Version("11.8") and "89" in cuda_archs():
-            cc_flag.append("-gencode")
-            cc_flag.append("arch=compute_89,code=sm_89")
-        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
-
-    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_SOFTCAP",
-    # "-DFLASHATTENTION_DISABLE_UNEVEN_K",
-    ]
-
-    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="flash_sparse_attn_cuda",
-            sources=(
-                [
-                    "csrc/flash_sparse_attn/flash_api.cpp",
-                ]
-                + sorted(glob.glob("csrc/flash_sparse_attn/src/instantiations/flash_*.cu"))
-            ),
-            extra_compile_args={
-                "cxx": compiler_c17_flag,
-                "nvcc": append_nvcc_threads(nvcc_flags + cc_flag),
-            },
-            include_dirs=[
-                Path(this_dir) / "csrc" / "flash_sparse_attn",
-                Path(this_dir) / "csrc" / "flash_sparse_attn" / "src",
-                Path(this_dir) / "csrc" / "cutlass" / "include",
-            ],
-        )
-    )
-
-
-def get_package_version():
-    with open(Path(this_dir) / "flash_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_SPARSE_ATTENTION_LOCAL_VERSION")
-    if local_version:
-        return f"{public_version}+{local_version}"
-    else:
-        return str(public_version)
-
-
-def get_wheel_url():
-    sm_arch = detect_preferred_sm_arch()
-    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}+sm{sm_arch}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() or 1) // 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(
-    ext_modules=ext_modules,
-    cmdclass={"bdist_wheel": CachedWheelsCommand, "build_ext": NinjaBuildExtension}
-    if ext_modules
-    else {
-        "bdist_wheel": CachedWheelsCommand,
-    },
-)
+setup()