rscd/models/backbones/lib_mamba/kernels/selective_scan/setup.py

# Modified by $@#Anonymous#@$ #20240123
# Copyright (c) 2023, Albert Gu, Tri Dao.
import sys
import warnings
import os
import re
import ast
from pathlib import Path
from packaging.version import parse, Version
import platform
import shutil

from setuptools import setup, find_packages
import subprocess
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

import torch
from torch.utils.cpp_extension import (
    BuildExtension,
    CppExtension,
    CUDAExtension,
    CUDA_HOME,
)

# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))
# 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("FORCE_CXX11_ABI", "FALSE") == "TRUE"

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

MODES = ["oflex"]
# MODES = ["core", "ndstate", "oflex"]
# MODES = ["core", "ndstate", "oflex", "nrow"]

def get_ext():
    cc_flag = []

    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
    print("\n\nCUDA_HOME = {}\n\n".format(CUDA_HOME))

    # Check, if CUDA11 is installed for compute capability 8.0
    multi_threads = True
    gencode_sm90 = False
    if CUDA_HOME is not None:
        _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
        print("CUDA version: ", bare_metal_version, flush=True)
        if bare_metal_version >= Version("11.8"):
            gencode_sm90 = True
        if bare_metal_version < Version("11.6"):
            warnings.warn("CUDA version ealier than 11.6 may leads to performance mismatch.")
        if bare_metal_version < Version("11.2"):
            multi_threads = False
            
    cc_flag.extend(["-gencode", "arch=compute_70,code=sm_70"])
    cc_flag.extend(["-gencode", "arch=compute_80,code=sm_80"])
    if gencode_sm90:
        cc_flag.extend(["-gencode", "arch=compute_90,code=sm_90"])
    if multi_threads:
        cc_flag.extend(["--threads", "4"])

    # 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 = dict(
        core=[
            "csrc/selective_scan/cus/selective_scan.cpp",
            "csrc/selective_scan/cus/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cus/selective_scan_core_bwd.cu",
        ],
        nrow=[
            "csrc/selective_scan/cusnrow/selective_scan_nrow.cpp",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd2.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd3.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_fwd4.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd2.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd3.cu",
            "csrc/selective_scan/cusnrow/selective_scan_core_bwd4.cu",
        ],
        ndstate=[
            "csrc/selective_scan/cusndstate/selective_scan_ndstate.cpp",
            "csrc/selective_scan/cusndstate/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusndstate/selective_scan_core_bwd.cu",
        ],
        oflex=[
            "csrc/selective_scan/cusoflex/selective_scan_oflex.cpp",
            "csrc/selective_scan/cusoflex/selective_scan_core_fwd.cu",
            "csrc/selective_scan/cusoflex/selective_scan_core_bwd.cu",
        ],
    )

    names = dict(
        core="selective_scan_cuda_core",
        nrow="selective_scan_cuda_nrow",
        ndstate="selective_scan_cuda_ndstate",
        oflex="selective_scan_cuda_oflex",
    )

    ext_modules = [
        CUDAExtension(
            name=names.get(MODE, None),
            sources=sources.get(MODE, None),
            extra_compile_args={
                "cxx": ["-O3", "-std=c++17"],
                "nvcc": [
                            "-O3",
                            "-std=c++17",
                            "-U__CUDA_NO_HALF_OPERATORS__",
                            "-U__CUDA_NO_HALF_CONVERSIONS__",
                            "-U__CUDA_NO_BFLOAT16_OPERATORS__",
                            "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
                            "-U__CUDA_NO_BFLOAT162_OPERATORS__",
                            "-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
                            "--expt-relaxed-constexpr",
                            "--expt-extended-lambda",
                            "--use_fast_math",
                            "--ptxas-options=-v",
                            "-lineinfo",
                        ]
                        + cc_flag
            },
            include_dirs=[Path(this_dir) / "csrc" / "selective_scan"],
        )
        for MODE in MODES
    ]

    return ext_modules

ext_modules = get_ext()
setup(
    name="selective_scan",
    version="0.0.2",
    packages=[],
    author="Tri Dao, Albert Gu, $@#Anonymous#@$ ",
    author_email="tri@tridao.me, agu@cs.cmu.edu, $@#Anonymous#EMAIL@$",
    description="selective scan",
    long_description="",
    long_description_content_type="text/markdown",
    url="https://github.com/state-spaces/mamba",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: BSD License",
        "Operating System :: Unix",
    ],
    ext_modules=ext_modules,
    cmdclass={"bdist_wheel": _bdist_wheel, "build_ext": BuildExtension} if ext_modules else {"bdist_wheel": _bdist_wheel,},
    python_requires=">=3.7",
    install_requires=[
        "torch",
        "packaging",
        "ninja",
        "einops",
    ],
)