forked from EngineX-Cambricon/enginex-mlu370-vllm
142 lines
3.9 KiB
Python
142 lines
3.9 KiB
Python
import enum
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import random
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from typing import NamedTuple, Optional, Tuple, Union
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import numpy as np
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import torch
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class PlatformEnum(enum.Enum):
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CUDA = enum.auto()
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ROCM = enum.auto()
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TPU = enum.auto()
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HPU = enum.auto()
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XPU = enum.auto()
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CPU = enum.auto()
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NEURON = enum.auto()
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OPENVINO = enum.auto()
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MLU = enum.auto()
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UNSPECIFIED = enum.auto()
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class DeviceCapability(NamedTuple):
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major: int
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minor: int
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def as_version_str(self) -> str:
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return f"{self.major}.{self.minor}"
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def to_int(self) -> int:
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"""
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Express device capability as an integer ``<major><minor>``.
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It is assumed that the minor version is always a single digit.
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"""
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assert 0 <= self.minor < 10
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return self.major * 10 + self.minor
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class Platform:
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_enum: PlatformEnum
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def is_cuda(self) -> bool:
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return self._enum == PlatformEnum.CUDA
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def is_rocm(self) -> bool:
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return self._enum == PlatformEnum.ROCM
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def is_tpu(self) -> bool:
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return self._enum == PlatformEnum.TPU
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def is_hpu(self) -> bool:
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return self._enum == PlatformEnum.HPU
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def is_xpu(self) -> bool:
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return self._enum == PlatformEnum.XPU
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def is_cpu(self) -> bool:
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return self._enum == PlatformEnum.CPU
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def is_neuron(self) -> bool:
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return self._enum == PlatformEnum.NEURON
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def is_openvino(self) -> bool:
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return self._enum == PlatformEnum.OPENVINO
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def is_mlu(self) -> bool:
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return self._enum == PlatformEnum.MLU
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def is_cuda_alike(self) -> bool:
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"""Stateless version of :func:`torch.cuda.is_available`."""
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return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM)
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@classmethod
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def get_device_capability(
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cls,
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device_id: int = 0,
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) -> Optional[DeviceCapability]:
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"""Stateless version of :func:`torch.cuda.get_device_capability`."""
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@staticmethod
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def get_device_capability(device_id: int = 0) -> Optional[Tuple[int, int]]:
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return None
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@classmethod
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def has_device_capability(
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cls,
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capability: Union[Tuple[int, int], int],
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device_id: int = 0,
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) -> bool:
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"""
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Test whether this platform is compatible with a device capability.
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The ``capability`` argument can either be:
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- A tuple ``(major, minor)``.
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- An integer ``<major><minor>``. (See :meth:`DeviceCapability.to_int`)
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"""
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current_capability = cls.get_device_capability(device_id=device_id)
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if current_capability is None:
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return False
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if isinstance(capability, tuple):
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return current_capability >= capability
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return current_capability.to_int() >= capability
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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"""Get the name of a device."""
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raise NotImplementedError
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@classmethod
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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"""Get the total memory of a device in bytes."""
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raise NotImplementedError
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@classmethod
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def inference_mode(cls):
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"""A device-specific wrapper of `torch.inference_mode`.
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This wrapper is recommended because some hardware backends such as TPU
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do not support `torch.inference_mode`. In such a case, they will fall
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back to `torch.no_grad` by overriding this method.
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"""
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return torch.inference_mode(mode=True)
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@classmethod
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def seed_everything(cls, seed: int) -> None:
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"""
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Set the seed of each random module.
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`torch.manual_seed` will set seed on all devices.
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Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
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"""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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class UnspecifiedPlatform(Platform):
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_enum = PlatformEnum.UNSPECIFIED
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