interface.py

import enum
import platform
import random
from platform import uname
from typing import TYPE_CHECKING, NamedTuple, Optional, Tuple, Union

import numpy as np
import torch

from vllm.logger import init_logger

if TYPE_CHECKING:
    from vllm.config import VllmConfig
else:
    VllmConfig = None

logger = init_logger(__name__)


def in_wsl() -> bool:
    # Reference: https://github.com/microsoft/WSL/issues/4071
    return "microsoft" in " ".join(uname()).lower()


class _Backend(enum.Enum):
    FLASH_ATTN = enum.auto()
    FLASH_ATTN_VLLM_V1 = enum.auto()
    XFORMERS = enum.auto()
    ROCM_FLASH = enum.auto()
    TORCH_SDPA = enum.auto()
    OPENVINO = enum.auto()
    FLASHINFER = enum.auto()
    TRITON_MLA = enum.auto()
    HPU_ATTN = enum.auto()
    PALLAS = enum.auto()
    IPEX = enum.auto()
    BLOCK_SPARSE_FLASH_ATTN = enum.auto()
    NO_ATTENTION = enum.auto()


class PlatformEnum(enum.Enum):
    CUDA = enum.auto()
    ROCM = enum.auto()
    TPU = enum.auto()
    HPU = enum.auto()
    XPU = enum.auto()
    CPU = enum.auto()
    NEURON = enum.auto()
    OPENVINO = enum.auto()
    OOT = enum.auto()
    UNSPECIFIED = enum.auto()


class CpuArchEnum(enum.Enum):
    X86 = enum.auto()
    ARM = enum.auto()
    POWERPC = enum.auto()
    OTHER = enum.auto()
    UNKNOWN = enum.auto()


class DeviceCapability(NamedTuple):
    major: int
    minor: int

    def as_version_str(self) -> str:
        return f"{self.major}.{self.minor}"

    def to_int(self) -> int:
        """
        Express device capability as an integer ``<major><minor>``.

        It is assumed that the minor version is always a single digit.
        """
        assert 0 <= self.minor < 10
        return self.major * 10 + self.minor


class Platform:
    _enum: PlatformEnum
    device_name: str
    device_type: str

    # available dispatch keys:
    # check https://github.com/pytorch/pytorch/blob/313dac6c1ca0fa0cde32477509cce32089f8532a/torchgen/model.py#L134 # noqa
    # use "CPU" as a fallback for platforms not registered in PyTorch
    dispatch_key: str = "CPU"

    # available ray device keys:
    # https://github.com/ray-project/ray/blob/10ba5adadcc49c60af2c358a33bb943fb491a171/python/ray/_private/ray_constants.py#L438 # noqa
    # empty string means the device does not support ray
    ray_device_key: str = ""

    # platform-agnostic way to specify the device control environment variable,
    # .e.g. CUDA_VISIBLE_DEVICES for CUDA.
    # hint: search for "get_visible_accelerator_ids_env_var" in
    # https://github.com/ray-project/ray/tree/master/python/ray/_private/accelerators # noqa
    device_control_env_var: str = "VLLM_DEVICE_CONTROL_ENV_VAR_PLACEHOLDER"

    # The torch.compile backend for compiling simple and
    # standalone functions. The default value is "inductor" to keep
    # the same behavior as PyTorch.
    # NOTE: for the forward part of the model, vLLM has another separate
    # compilation strategy.
    simple_compile_backend: str = "inductor"

    supported_quantization: list[str] = []

    def is_cuda(self) -> bool:
        return self._enum == PlatformEnum.CUDA

    def is_rocm(self) -> bool:
        return self._enum == PlatformEnum.ROCM

    def is_tpu(self) -> bool:
        return self._enum == PlatformEnum.TPU

    def is_hpu(self) -> bool:
        return self._enum == PlatformEnum.HPU

    def is_xpu(self) -> bool:
        return self._enum == PlatformEnum.XPU

    def is_cpu(self) -> bool:
        return self._enum == PlatformEnum.CPU

    def is_neuron(self) -> bool:
        return self._enum == PlatformEnum.NEURON

    def is_openvino(self) -> bool:
        return self._enum == PlatformEnum.OPENVINO

    def is_out_of_tree(self) -> bool:
        return self._enum == PlatformEnum.OOT

    def is_cuda_alike(self) -> bool:
        """Stateless version of :func:`torch.cuda.is_available`."""
        return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM)

    @classmethod
    def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
                             dtype: torch.dtype, kv_cache_dtype: Optional[str],
                             block_size: int, use_v1: bool,
                             use_mla: bool) -> str:
        """Get the attention backend class of a device."""
        return ""

    @classmethod
    def get_device_capability(
        cls,
        device_id: int = 0,
    ) -> Optional[DeviceCapability]:
        """Stateless version of :func:`torch.cuda.get_device_capability`."""
        return None

    @classmethod
    def has_device_capability(
        cls,
        capability: Union[Tuple[int, int], int],
        device_id: int = 0,
    ) -> bool:
        """
        Test whether this platform is compatible with a device capability.

        The ``capability`` argument can either be:

        - A tuple ``(major, minor)``.
        - An integer ``<major><minor>``. (See :meth:`DeviceCapability.to_int`)
        """
        current_capability = cls.get_device_capability(device_id=device_id)
        if current_capability is None:
            return False

        if isinstance(capability, tuple):
            return current_capability >= capability

        return current_capability.to_int() >= capability

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        """Get the name of a device."""
        raise NotImplementedError

    @classmethod
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        """Get the total memory of a device in bytes."""
        raise NotImplementedError

    @classmethod
    def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
        """
        Check if the current platform supports async output.
        """
        raise NotImplementedError

    @classmethod
    def inference_mode(cls):
        """A device-specific wrapper of `torch.inference_mode`.

        This wrapper is recommended because some hardware backends such as TPU
        do not support `torch.inference_mode`. In such a case, they will fall
        back to `torch.no_grad` by overriding this method.
        """
        return torch.inference_mode(mode=True)

    @classmethod
    def seed_everything(cls, seed: int) -> None:
        """
        Set the seed of each random module.
        `torch.manual_seed` will set seed on all devices.

        Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
        """
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)

    @classmethod
    def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
        """
        Check and update the configuration for the current platform.

        It can raise an exception if the configuration is not compatible with
        the current platform, or it can update the configuration to make it
        compatible with the current platform.

        The config is passed by reference, so it can be modified in place.
        """
        pass

    @classmethod
    def verify_model_arch(cls, model_arch: str) -> None:
        """
        Verify whether the current platform supports the specified model
        architecture.

        - This will raise an Error or Warning based on the model support on
        the current platform.
        - By default all models are considered supported.
        """
        pass

    @classmethod
    def verify_quantization(cls, quant: str) -> None:
        """
        Verify whether the quantization is supported by the current platform.
        """
        if cls.supported_quantization and \
            quant not in cls.supported_quantization:
            raise ValueError(
                f"{quant} quantization is currently not supported in "
                f"{cls.device_name}.")

    @classmethod
    def get_cpu_architecture(cls) -> CpuArchEnum:
        """
        Determine the CPU architecture of the current system.
        Returns CpuArchEnum indicating the architecture type.
        """
        machine = platform.machine().lower()

        if machine in ("x86_64", "amd64", "i386", "i686"):
            return CpuArchEnum.X86
        elif machine.startswith("arm") or machine.startswith("aarch"):
            return CpuArchEnum.ARM
        elif machine.startswith("ppc"):
            return CpuArchEnum.POWERPC

        return CpuArchEnum.OTHER if machine else CpuArchEnum.UNKNOWN

    @classmethod
    def is_pin_memory_available(cls) -> bool:
        """Checks whether pin memory is available on the current platform."""
        if in_wsl():
            # Pinning memory in WSL is not supported.
            # https://docs.nvidia.com/cuda/wsl-user-guide/index.html#known-limitations-for-linux-cuda-applications
            logger.warning("Using 'pin_memory=False' as WSL is detected. "
                           "This may slow down the performance.")
            return False
        return True

    @classmethod
    def get_current_memory_usage(cls,
                                 device: Optional[torch.types.Device] = None
                                 ) -> float:
        """
        Return the memory usage in bytes.
        """
        raise NotImplementedError

    @classmethod
    def get_punica_wrapper(cls) -> str:
        """
        Return the punica wrapper for current platform.
        """
        raise NotImplementedError


class UnspecifiedPlatform(Platform):
    _enum = PlatformEnum.UNSPECIFIED
    device_type = ""