setup.py

"""
Copyright (c) 2024 by SageAttention team.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import os
import subprocess
from packaging.version import parse, Version
from typing import List, Set
import warnings

from setuptools import setup, find_packages
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME

HAS_SM80 = False
HAS_SM86 = False
HAS_SM89 = False
HAS_SM90 = False

# Supported NVIDIA GPU architectures.
SUPPORTED_ARCHS = {"8.0", "8.6", "8.9", "9.0"}

# Compiler flags.
CXX_FLAGS = ["-g", "-O3", "-fopenmp", "-lgomp", "-std=c++17", "-DENABLE_BF16"]
NVCC_FLAGS = [
    "-O3",
    "-std=c++17",
    "-U__CUDA_NO_HALF_OPERATORS__",
    "-U__CUDA_NO_HALF_CONVERSIONS__",
    "--use_fast_math",
    "--threads=8",
    "-Xptxas=-v",
    "-diag-suppress=174", # suppress the specific warning
]

ABI = 1 if torch._C._GLIBCXX_USE_CXX11_ABI else 0
CXX_FLAGS += [f"-D_GLIBCXX_USE_CXX11_ABI={ABI}"]
NVCC_FLAGS += [f"-D_GLIBCXX_USE_CXX11_ABI={ABI}"]

if CUDA_HOME is None:
    raise RuntimeError(
        "Cannot find CUDA_HOME. CUDA must be available to build the package.")

def get_nvcc_cuda_version(cuda_dir: str) -> Version:
    """Get the CUDA version from nvcc.

    Adapted from https://github.com/NVIDIA/apex/blob/8b7a1ff183741dd8f9b87e7bafd04cfde99cea28/setup.py
    """
    nvcc_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
                                          universal_newlines=True)
    output = nvcc_output.split()
    release_idx = output.index("release") + 1
    nvcc_cuda_version = parse(output[release_idx].split(",")[0])
    return nvcc_cuda_version

def get_torch_arch_list() -> Set[str]:
    # TORCH_CUDA_ARCH_LIST can have one or more architectures,
    # e.g. "8.0" or "7.5,8.0,8.6+PTX". Here, the "8.6+PTX" option asks the
    # compiler to additionally include PTX code that can be runtime-compiled
    # and executed on the 8.6 or newer architectures. While the PTX code will
    # not give the best performance on the newer architectures, it provides
    # forward compatibility.
    env_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
    if env_arch_list is None:
        return set()

    # List are separated by ; or space.
    torch_arch_list = set(env_arch_list.replace(" ", ";").split(";"))
    if not torch_arch_list:
        return set()

    # Filter out the invalid architectures and print a warning.
    valid_archs = SUPPORTED_ARCHS.union({s + "+PTX" for s in SUPPORTED_ARCHS})
    arch_list = torch_arch_list.intersection(valid_archs)
    # If none of the specified architectures are valid, raise an error.
    if not arch_list:
        raise RuntimeError(
            "None of the CUDA architectures in `TORCH_CUDA_ARCH_LIST` env "
            f"variable ({env_arch_list}) is supported. "
            f"Supported CUDA architectures are: {valid_archs}.")
    invalid_arch_list = torch_arch_list - valid_archs
    if invalid_arch_list:
        warnings.warn(
            f"Unsupported CUDA architectures ({invalid_arch_list}) are "
            "excluded from the `TORCH_CUDA_ARCH_LIST` env variable "
            f"({env_arch_list}). Supported CUDA architectures are: "
            f"{valid_archs}.")
    return arch_list

# First, check the TORCH_CUDA_ARCH_LIST environment variable.
compute_capabilities = get_torch_arch_list()
if not compute_capabilities:
    # If TORCH_CUDA_ARCH_LIST is not defined or empty, target all available
    # GPUs on the current machine.
    device_count = torch.cuda.device_count()
    for i in range(device_count):
        major, minor = torch.cuda.get_device_capability(i)
        if major < 8:
            raise RuntimeError(
                "GPUs with compute capability below 8.0 are not supported.")
        compute_capabilities.add(f"{major}.{minor}")

nvcc_cuda_version = get_nvcc_cuda_version(CUDA_HOME)
if not compute_capabilities:
    raise RuntimeError("No GPUs found. Please specify the target GPU architectures or build on a machine with GPUs.")

# Validate the NVCC CUDA version.
if nvcc_cuda_version < Version("12.0"):
    raise RuntimeError("CUDA 12.0 or higher is required to build the package.")
if nvcc_cuda_version < Version("12.4") and any(cc.startswith("8.9") for cc in compute_capabilities):
    raise RuntimeError(
        "CUDA 12.4 or higher is required for compute capability 8.9.")
if nvcc_cuda_version < Version("12.3") and any(cc.startswith("9.0") for cc in compute_capabilities):
    if any(cc.startswith("9.0") for cc in compute_capabilities):
        raise RuntimeError(
            "CUDA 12.3 or higher is required for compute capability 9.0.")

# Add target compute capabilities to NVCC flags.
for capability in compute_capabilities:
    num = capability[0] + capability[2]
    if num == "80":
        HAS_SM80 = True
    elif num == "86":
        HAS_SM86 = True
    elif num == "89":
        HAS_SM89 = True
    elif num == "90":
        HAS_SM90 = True
        num = num + "a" # convert sm90 to sm9a
    NVCC_FLAGS += ["-gencode", f"arch=compute_{num},code=sm_{num}"]
    if capability.endswith("+PTX"):
        NVCC_FLAGS += ["-gencode", f"arch=compute_{num},code=compute_{num}"]

ext_modules = []

if HAS_SM80 or HAS_SM86 or HAS_SM89 or HAS_SM90:
    qattn_extension = CUDAExtension(
        name="sageattention._qattn_sm80",
        sources=[
            "csrc/qattn/pybind_sm80.cpp",
            "csrc/qattn/qk_int_sv_f16_cuda_sm80.cu",
        ],
        extra_compile_args={
            "cxx": CXX_FLAGS,
            "nvcc": NVCC_FLAGS,
        },
    )
    ext_modules.append(qattn_extension)

if HAS_SM89:
    qattn_extension = CUDAExtension(
        name="sageattention._qattn_sm89",
        sources=[
            "csrc/qattn/pybind_sm89.cpp",
            "csrc/qattn/qk_int_sv_f8_cuda_sm89.cu",
        ],
        extra_compile_args={
            "cxx": CXX_FLAGS,
            "nvcc": NVCC_FLAGS,
        },
    )
    ext_modules.append(qattn_extension)

if HAS_SM90:
    qattn_extension = CUDAExtension(
        name="sageattention._qattn_sm90",
        sources=[
            "csrc/qattn/pybind_sm90.cpp",
            "csrc/qattn/qk_int_sv_f8_cuda_sm90.cu",
        ],
        extra_compile_args={
            "cxx": CXX_FLAGS,
            "nvcc": NVCC_FLAGS,
        },
        extra_link_args=['-lcuda'],
    )
    ext_modules.append(qattn_extension)

# Fused kernels.
fused_extension = CUDAExtension(
    name="sageattention._fused",
    sources=["csrc/fused/pybind.cpp", "csrc/fused/fused.cu"],
    extra_compile_args={
        "cxx": CXX_FLAGS,
        "nvcc": NVCC_FLAGS,
    },
)
ext_modules.append(fused_extension)

setup(
    name='sageattention', 
    version='2.1.0',  
    author='SageAttention team',
    license='Apache 2.0 License',  
    description='Accurate and efficient plug-and-play low-bit attention.',  
    long_description=open('README.md').read(),  
    long_description_content_type='text/markdown', 
    url='https://github.com/thu-ml/SageAttention', 
    packages=find_packages(),
    python_requires='>=3.9',
    ext_modules=ext_modules,
    cmdclass={"build_ext": BuildExtension},
)