setup.py 23 KB
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import ctypes
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import importlib.util
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import logging
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import os
import re
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import subprocess
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import sys
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from pathlib import Path
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from shutil import which
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from typing import Dict, List
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import torch
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from packaging.version import Version, parse
from setuptools import Extension, find_packages, setup
from setuptools.command.build_ext import build_ext
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from setuptools_scm import get_version
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from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
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def load_module_from_path(module_name, path):
    spec = importlib.util.spec_from_file_location(module_name, path)
    module = importlib.util.module_from_spec(spec)
    sys.modules[module_name] = module
    spec.loader.exec_module(module)
    return module


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ROOT_DIR = os.path.dirname(__file__)
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logger = logging.getLogger(__name__)
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# cannot import envs directly because it depends on vllm,
#  which is not installed yet
envs = load_module_from_path('envs', os.path.join(ROOT_DIR, 'vllm', 'envs.py'))

VLLM_TARGET_DEVICE = envs.VLLM_TARGET_DEVICE
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if sys.platform.startswith("darwin") and VLLM_TARGET_DEVICE != "cpu":
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    logger.warning(
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        "VLLM_TARGET_DEVICE automatically set to `cpu` due to macOS")
    VLLM_TARGET_DEVICE = "cpu"
elif not (sys.platform.startswith("linux")
          or sys.platform.startswith("darwin")):
    logger.warning(
        "vLLM only supports Linux platform (including WSL) and MacOS."
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        "Building on %s, "
        "so vLLM may not be able to run correctly", sys.platform)
    VLLM_TARGET_DEVICE = "empty"
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MAIN_CUDA_VERSION = "12.1"

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def is_sccache_available() -> bool:
    return which("sccache") is not None


def is_ccache_available() -> bool:
    return which("ccache") is not None


def is_ninja_available() -> bool:
    return which("ninja") is not None


class CMakeExtension(Extension):

    def __init__(self, name: str, cmake_lists_dir: str = '.', **kwa) -> None:
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        super().__init__(name, sources=[], py_limited_api=True, **kwa)
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        self.cmake_lists_dir = os.path.abspath(cmake_lists_dir)


class cmake_build_ext(build_ext):
    # A dict of extension directories that have been configured.
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    did_config: Dict[str, bool] = {}
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    #
    # Determine number of compilation jobs and optionally nvcc compile threads.
    #
    def compute_num_jobs(self):
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        # `num_jobs` is either the value of the MAX_JOBS environment variable
        # (if defined) or the number of CPUs available.
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        num_jobs = envs.MAX_JOBS
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        if num_jobs is not None:
            num_jobs = int(num_jobs)
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            logger.info("Using MAX_JOBS=%d as the number of jobs.", num_jobs)
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        else:
            try:
                # os.sched_getaffinity() isn't universally available, so fall
                #  back to os.cpu_count() if we get an error here.
                num_jobs = len(os.sched_getaffinity(0))
            except AttributeError:
                num_jobs = os.cpu_count()
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        nvcc_threads = None
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        if _is_cuda() and get_nvcc_cuda_version() >= Version("11.2"):
            # `nvcc_threads` is either the value of the NVCC_THREADS
            # environment variable (if defined) or 1.
            # when it is set, we reduce `num_jobs` to avoid
            # overloading the system.
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            nvcc_threads = envs.NVCC_THREADS
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            if nvcc_threads is not None:
                nvcc_threads = int(nvcc_threads)
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                logger.info(
                    "Using NVCC_THREADS=%d as the number of nvcc threads.",
                    nvcc_threads)
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            else:
                nvcc_threads = 1
            num_jobs = max(1, num_jobs // nvcc_threads)
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        return num_jobs, nvcc_threads

    #
    # Perform cmake configuration for a single extension.
    #
    def configure(self, ext: CMakeExtension) -> None:
        # If we've already configured using the CMakeLists.txt for
        # this extension, exit early.
        if ext.cmake_lists_dir in cmake_build_ext.did_config:
            return

        cmake_build_ext.did_config[ext.cmake_lists_dir] = True

        # Select the build type.
        # Note: optimization level + debug info are set by the build type
        default_cfg = "Debug" if self.debug else "RelWithDebInfo"
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        cfg = envs.CMAKE_BUILD_TYPE or default_cfg
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        cmake_args = [
            '-DCMAKE_BUILD_TYPE={}'.format(cfg),
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            '-DVLLM_TARGET_DEVICE={}'.format(VLLM_TARGET_DEVICE),
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        ]

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        verbose = envs.VERBOSE
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        if verbose:
            cmake_args += ['-DCMAKE_VERBOSE_MAKEFILE=ON']

        if is_sccache_available():
            cmake_args += [
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                '-DCMAKE_C_COMPILER_LAUNCHER=sccache',
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                '-DCMAKE_CXX_COMPILER_LAUNCHER=sccache',
                '-DCMAKE_CUDA_COMPILER_LAUNCHER=sccache',
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                '-DCMAKE_HIP_COMPILER_LAUNCHER=sccache',
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            ]
        elif is_ccache_available():
            cmake_args += [
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                '-DCMAKE_C_COMPILER_LAUNCHER=ccache',
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                '-DCMAKE_CXX_COMPILER_LAUNCHER=ccache',
                '-DCMAKE_CUDA_COMPILER_LAUNCHER=ccache',
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                '-DCMAKE_HIP_COMPILER_LAUNCHER=ccache',
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            ]

        # Pass the python executable to cmake so it can find an exact
        # match.
        cmake_args += ['-DVLLM_PYTHON_EXECUTABLE={}'.format(sys.executable)]

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        # Pass the python path to cmake so it can reuse the build dependencies
        # on subsequent calls to python.
        cmake_args += ['-DVLLM_PYTHON_PATH={}'.format(":".join(sys.path))]

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        # Override the base directory for FetchContent downloads to $ROOT/.deps
        # This allows sharing dependencies between profiles,
        # and plays more nicely with sccache.
        # To override this, set the FETCHCONTENT_BASE_DIR environment variable.
        fc_base_dir = os.path.join(ROOT_DIR, ".deps")
        fc_base_dir = os.environ.get("FETCHCONTENT_BASE_DIR", fc_base_dir)
        cmake_args += ['-DFETCHCONTENT_BASE_DIR={}'.format(fc_base_dir)]

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        #
        # Setup parallelism and build tool
        #
        num_jobs, nvcc_threads = self.compute_num_jobs()

        if nvcc_threads:
            cmake_args += ['-DNVCC_THREADS={}'.format(nvcc_threads)]

        if is_ninja_available():
            build_tool = ['-G', 'Ninja']
            cmake_args += [
                '-DCMAKE_JOB_POOL_COMPILE:STRING=compile',
                '-DCMAKE_JOB_POOLS:STRING=compile={}'.format(num_jobs),
            ]
        else:
            # Default build tool to whatever cmake picks.
            build_tool = []
        subprocess.check_call(
            ['cmake', ext.cmake_lists_dir, *build_tool, *cmake_args],
            cwd=self.build_temp)

    def build_extensions(self) -> None:
        # Ensure that CMake is present and working
        try:
            subprocess.check_output(['cmake', '--version'])
        except OSError as e:
            raise RuntimeError('Cannot find CMake executable') from e

        # Create build directory if it does not exist.
        if not os.path.exists(self.build_temp):
            os.makedirs(self.build_temp)

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        targets = []
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        def target_name(s: str) -> str:
            return s.removeprefix("vllm.").removeprefix("vllm_flash_attn.")

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        # Build all the extensions
        for ext in self.extensions:
            self.configure(ext)
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            targets.append(target_name(ext.name))
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        num_jobs, _ = self.compute_num_jobs()
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        build_args = [
            "--build",
            ".",
            f"-j={num_jobs}",
            *[f"--target={name}" for name in targets],
        ]
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        subprocess.check_call(["cmake", *build_args], cwd=self.build_temp)
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        # Install the libraries
        for ext in self.extensions:
            # Install the extension into the proper location
            outdir = Path(self.get_ext_fullpath(ext.name)).parent.absolute()

            # Skip if the install directory is the same as the build directory
            if outdir == self.build_temp:
                continue

            # CMake appends the extension prefix to the install path,
            # and outdir already contains that prefix, so we need to remove it.
            prefix = outdir
            for i in range(ext.name.count('.')):
                prefix = prefix.parent

            # prefix here should actually be the same for all components
            install_args = [
                "cmake", "--install", ".", "--prefix", prefix, "--component",
                target_name(ext.name)
            ]
            subprocess.check_call(install_args, cwd=self.build_temp)

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    def run(self):
        # First, run the standard build_ext command to compile the extensions
        super().run()

        # copy vllm/vllm_flash_attn/*.py from self.build_lib to current
        # directory so that they can be included in the editable build
        import glob
        files = glob.glob(
            os.path.join(self.build_lib, "vllm", "vllm_flash_attn", "*.py"))
        for file in files:
            dst_file = os.path.join("vllm/vllm_flash_attn",
                                    os.path.basename(file))
            print(f"Copying {file} to {dst_file}")
            self.copy_file(file, dst_file)

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class repackage_wheel(build_ext):
    """Extracts libraries and other files from an existing wheel."""
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    default_wheel = "https://wheels.vllm.ai/nightly/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl"
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    def run(self) -> None:
        wheel_location = os.getenv("VLLM_PRECOMPILED_WHEEL_LOCATION",
                                   self.default_wheel)

        assert _is_cuda(
        ), "VLLM_USE_PRECOMPILED is only supported for CUDA builds"

        import zipfile

        if os.path.isfile(wheel_location):
            wheel_path = wheel_location
            print(f"Using existing wheel={wheel_path}")
        else:
            # Download the wheel from a given URL, assume
            # the filename is the last part of the URL
            wheel_filename = wheel_location.split("/")[-1]

            import tempfile

            # create a temporary directory to store the wheel
            temp_dir = tempfile.mkdtemp(prefix="vllm-wheels")
            wheel_path = os.path.join(temp_dir, wheel_filename)

            print(f"Downloading wheel from {wheel_location} to {wheel_path}")

            from urllib.request import urlretrieve

            try:
                urlretrieve(wheel_location, filename=wheel_path)
            except Exception as e:
                from setuptools.errors import SetupError

                raise SetupError(
                    f"Failed to get vLLM wheel from {wheel_location}") from e

        with zipfile.ZipFile(wheel_path) as wheel:
            files_to_copy = [
                "vllm/_C.abi3.so",
                "vllm/_moe_C.abi3.so",
                "vllm/vllm_flash_attn/vllm_flash_attn_c.abi3.so",
                "vllm/vllm_flash_attn/flash_attn_interface.py",
                "vllm/vllm_flash_attn/__init__.py",
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                "vllm/cumem_allocator.abi3.so",
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                # "vllm/_version.py", # not available in nightly wheels yet
            ]
            file_members = filter(lambda x: x.filename in files_to_copy,
                                  wheel.filelist)

            for file in file_members:
                print(f"Extracting and including {file.filename} "
                      "from existing wheel")
                package_name = os.path.dirname(file.filename).replace("/", ".")
                file_name = os.path.basename(file.filename)

                if package_name not in package_data:
                    package_data[package_name] = []

                wheel.extract(file)
                if file_name.endswith(".py"):
                    # python files shouldn't be added to package_data
                    continue

                package_data[package_name].append(file_name)


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def _is_hpu() -> bool:
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    # if VLLM_TARGET_DEVICE env var was set explicitly, skip HPU autodetection
    if os.getenv("VLLM_TARGET_DEVICE", None) == VLLM_TARGET_DEVICE:
        return VLLM_TARGET_DEVICE == "hpu"

    # if VLLM_TARGET_DEVICE was not set explicitly, check if hl-smi succeeds,
    # and if it doesn't, check if habanalabs driver is loaded
    is_hpu_available = False
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    try:
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        out = subprocess.run(["hl-smi"], capture_output=True, check=True)
        is_hpu_available = out.returncode == 0
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    except (FileNotFoundError, PermissionError, subprocess.CalledProcessError):
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        if sys.platform.startswith("linux"):
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            try:
                output = subprocess.check_output(
                    'lsmod | grep habanalabs | wc -l', shell=True)
                is_hpu_available = int(output) > 0
            except (ValueError, FileNotFoundError, PermissionError,
                    subprocess.CalledProcessError):
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                pass
    return is_hpu_available
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def _no_device() -> bool:
    return VLLM_TARGET_DEVICE == "empty"


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def _is_cuda() -> bool:
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    has_cuda = torch.version.cuda is not None
    return (VLLM_TARGET_DEVICE == "cuda" and has_cuda
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            and not (_is_neuron() or _is_tpu() or _is_hpu()))
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def _is_hip() -> bool:
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    return (VLLM_TARGET_DEVICE == "cuda"
            or VLLM_TARGET_DEVICE == "rocm") and torch.version.hip is not None
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def _is_neuron() -> bool:
    torch_neuronx_installed = True
    try:
        subprocess.run(["neuron-ls"], capture_output=True, check=True)
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    except (FileNotFoundError, PermissionError, subprocess.CalledProcessError):
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        torch_neuronx_installed = False
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    return torch_neuronx_installed or VLLM_TARGET_DEVICE == "neuron"
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def _is_tpu() -> bool:
    return VLLM_TARGET_DEVICE == "tpu"


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def _is_cpu() -> bool:
    return VLLM_TARGET_DEVICE == "cpu"


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def _is_openvino() -> bool:
    return VLLM_TARGET_DEVICE == "openvino"


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def _is_xpu() -> bool:
    return VLLM_TARGET_DEVICE == "xpu"


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def _build_custom_ops() -> bool:
    return _is_cuda() or _is_hip() or _is_cpu()


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def get_rocm_version():
    # Get the Rocm version from the ROCM_HOME/bin/librocm-core.so
    # see https://github.com/ROCm/rocm-core/blob/d11f5c20d500f729c393680a01fa902ebf92094b/rocm_version.cpp#L21
    try:
        librocm_core_file = Path(ROCM_HOME) / "lib" / "librocm-core.so"
        if not librocm_core_file.is_file():
            return None
        librocm_core = ctypes.CDLL(librocm_core_file)
        VerErrors = ctypes.c_uint32
        get_rocm_core_version = librocm_core.getROCmVersion
        get_rocm_core_version.restype = VerErrors
        get_rocm_core_version.argtypes = [
            ctypes.POINTER(ctypes.c_uint32),
            ctypes.POINTER(ctypes.c_uint32),
            ctypes.POINTER(ctypes.c_uint32),
        ]
        major = ctypes.c_uint32()
        minor = ctypes.c_uint32()
        patch = ctypes.c_uint32()
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        if (get_rocm_core_version(ctypes.byref(major), ctypes.byref(minor),
                                  ctypes.byref(patch)) == 0):
            return "%d.%d.%d" % (major.value, minor.value, patch.value)
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        return None
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    except Exception:
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        return None
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def get_neuronxcc_version():
    import sysconfig
    site_dir = sysconfig.get_paths()["purelib"]
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    version_file = os.path.join(site_dir, "neuronxcc", "version",
                                "__init__.py")
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    # Check if the command was executed successfully
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    with open(version_file) as fp:
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        content = fp.read()

    # Extract the version using a regular expression
    match = re.search(r"__version__ = '(\S+)'", content)
    if match:
        # Return the version string
        return match.group(1)
    else:
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        raise RuntimeError("Could not find Neuron version in the output")
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def get_nvcc_cuda_version() -> Version:
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    """Get the CUDA version from nvcc.

    Adapted from https://github.com/NVIDIA/apex/blob/8b7a1ff183741dd8f9b87e7bafd04cfde99cea28/setup.py
    """
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    assert CUDA_HOME is not None, "CUDA_HOME is not set"
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    nvcc_output = subprocess.check_output([CUDA_HOME + "/bin/nvcc", "-V"],
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                                          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


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def get_path(*filepath) -> str:
    return os.path.join(ROOT_DIR, *filepath)


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def get_gaudi_sw_version():
    """
    Returns the driver version.
    """
    # Enable console printing for `hl-smi` check
    output = subprocess.run("hl-smi",
                            shell=True,
                            text=True,
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                            capture_output=True,
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                            env={"ENABLE_CONSOLE": "true"})
    if output.returncode == 0 and output.stdout:
        return output.stdout.split("\n")[2].replace(
            " ", "").split(":")[1][:-1].split("-")[0]
    return "0.0.0"  # when hl-smi is not available


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def get_vllm_version() -> str:
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    version = get_version(
        write_to="vllm/_version.py",  # TODO: move this to pyproject.toml
    )
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    sep = "+" if "+" not in version else "."  # dev versions might contain +
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    if _no_device():
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        if envs.VLLM_TARGET_DEVICE == "empty":
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            version += f"{sep}empty"
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    elif _is_cuda():
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        if envs.VLLM_USE_PRECOMPILED:
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            version += f"{sep}precompiled"
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        else:
            cuda_version = str(get_nvcc_cuda_version())
            if cuda_version != MAIN_CUDA_VERSION:
                cuda_version_str = cuda_version.replace(".", "")[:3]
                # skip this for source tarball, required for pypi
                if "sdist" not in sys.argv:
                    version += f"{sep}cu{cuda_version_str}"
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    elif _is_hip():
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        # Get the Rocm Version
        rocm_version = get_rocm_version() or torch.version.hip
        if rocm_version and rocm_version != MAIN_CUDA_VERSION:
            version += f"{sep}rocm{rocm_version.replace('.', '')[:3]}"
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    elif _is_neuron():
        # Get the Neuron version
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        neuron_version = str(get_neuronxcc_version())
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        if neuron_version != MAIN_CUDA_VERSION:
            neuron_version_str = neuron_version.replace(".", "")[:3]
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            version += f"{sep}neuron{neuron_version_str}"
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    elif _is_hpu():
        # Get the Intel Gaudi Software Suite version
        gaudi_sw_version = str(get_gaudi_sw_version())
        if gaudi_sw_version != MAIN_CUDA_VERSION:
            gaudi_sw_version = gaudi_sw_version.replace(".", "")[:3]
            version += f"{sep}gaudi{gaudi_sw_version}"
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    elif _is_openvino():
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        version += f"{sep}openvino"
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    elif _is_tpu():
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        version += f"{sep}tpu"
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    elif _is_cpu():
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        version += f"{sep}cpu"
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    elif _is_xpu():
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        version += f"{sep}xpu"
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    else:
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        raise RuntimeError("Unknown runtime environment")
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    return version


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def read_readme() -> str:
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    """Read the README file if present."""
    p = get_path("README.md")
    if os.path.isfile(p):
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        with open(get_path("README.md"), encoding="utf-8") as f:
            return f.read()
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    else:
        return ""
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def get_requirements() -> List[str]:
    """Get Python package dependencies from requirements.txt."""
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    def _read_requirements(filename: str) -> List[str]:
        with open(get_path(filename)) as f:
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            requirements = f.read().strip().split("\n")
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        resolved_requirements = []
        for line in requirements:
            if line.startswith("-r "):
                resolved_requirements += _read_requirements(line.split()[1])
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            elif line.startswith("--"):
                continue
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            else:
                resolved_requirements.append(line)
        return resolved_requirements

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    if _no_device():
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        requirements = _read_requirements("requirements-cpu.txt")
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    elif _is_cuda():
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        requirements = _read_requirements("requirements-cuda.txt")
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        cuda_major, cuda_minor = torch.version.cuda.split(".")
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        modified_requirements = []
        for req in requirements:
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            if ("vllm-flash-attn" in req
                    and not (cuda_major == "12" and cuda_minor == "1")):
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                # vllm-flash-attn is built only for CUDA 12.1.
                # Skip for other versions.
                continue
            modified_requirements.append(req)
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        requirements = modified_requirements
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    elif _is_hip():
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        requirements = _read_requirements("requirements-rocm.txt")
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    elif _is_neuron():
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        requirements = _read_requirements("requirements-neuron.txt")
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    elif _is_hpu():
        requirements = _read_requirements("requirements-hpu.txt")
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    elif _is_openvino():
        requirements = _read_requirements("requirements-openvino.txt")
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    elif _is_tpu():
        requirements = _read_requirements("requirements-tpu.txt")
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    elif _is_cpu():
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        requirements = _read_requirements("requirements-cpu.txt")
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    elif _is_xpu():
        requirements = _read_requirements("requirements-xpu.txt")
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    else:
        raise ValueError(
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            "Unsupported platform, please use CUDA, ROCm, Neuron, HPU, "
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            "OpenVINO, or CPU.")
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    return requirements


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ext_modules = []

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if _is_cuda() or _is_hip():
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    ext_modules.append(CMakeExtension(name="vllm._moe_C"))

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if _is_hip():
    ext_modules.append(CMakeExtension(name="vllm._rocm_C"))

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if _is_cuda():
    ext_modules.append(
        CMakeExtension(name="vllm.vllm_flash_attn.vllm_flash_attn_c"))
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    ext_modules.append(CMakeExtension(name="vllm.cumem_allocator"))
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if _build_custom_ops():
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    ext_modules.append(CMakeExtension(name="vllm._C"))

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package_data = {
    "vllm": ["py.typed", "model_executor/layers/fused_moe/configs/*.json"]
}
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if _no_device():
    ext_modules = []

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if not ext_modules:
    cmdclass = {}
else:
    cmdclass = {
        "build_ext":
        repackage_wheel if envs.VLLM_USE_PRECOMPILED else cmake_build_ext
    }

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setup(
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    name="vllm",
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    version=get_vllm_version(),
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    author="vLLM Team",
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    license="Apache 2.0",
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    description=("A high-throughput and memory-efficient inference and "
                 "serving engine for LLMs"),
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    long_description=read_readme(),
    long_description_content_type="text/markdown",
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    url="https://github.com/vllm-project/vllm",
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    project_urls={
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        "Homepage": "https://github.com/vllm-project/vllm",
        "Documentation": "https://vllm.readthedocs.io/en/latest/",
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    },
    classifiers=[
        "Programming Language :: Python :: 3.9",
        "Programming Language :: Python :: 3.10",
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        "Programming Language :: Python :: 3.11",
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        "Programming Language :: Python :: 3.12",
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        "License :: OSI Approved :: Apache Software License",
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        "Intended Audience :: Developers",
        "Intended Audience :: Information Technology",
        "Intended Audience :: Science/Research",
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        "Topic :: Scientific/Engineering :: Artificial Intelligence",
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        "Topic :: Scientific/Engineering :: Information Analysis",
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    ],
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    packages=find_packages(exclude=("benchmarks", "csrc", "docs", "examples",
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                                    "tests*")),
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    python_requires=">=3.9",
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    install_requires=get_requirements(),
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    ext_modules=ext_modules,
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    extras_require={
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        "tensorizer": ["tensorizer>=2.9.0"],
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        "runai": ["runai-model-streamer", "runai-model-streamer-s3", "boto3"],
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        "audio": ["librosa", "soundfile"],  # Required for audio processing
        "video": ["decord"]  # Required for video processing
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    },
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    cmdclass=cmdclass,
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    package_data=package_data,
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    entry_points={
        "console_scripts": [
            "vllm=vllm.scripts:main",
        ],
    },
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)