Mengqing Cao d5b28447e0
[Platforms] Refactor xpu code (#10468)
Signed-off-by: MengqingCao <cmq0113@163.com>
2024-11-19 22:52:13 -08:00

58 lines
1.8 KiB
Python

from typing import TYPE_CHECKING
import torch
from vllm.logger import init_logger
from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
if TYPE_CHECKING:
from vllm.config import VllmConfig
else:
VllmConfig = None
logger = init_logger(__name__)
class XPUPlatform(Platform):
_enum = PlatformEnum.XPU
@classmethod
def get_default_attn_backend(cls, selected_backend: _Backend) -> _Backend:
if selected_backend != _Backend.IPEX:
logger.info("Cannot use %s backend on XPU.", selected_backend)
return _Backend.IPEX
@staticmethod
def get_device_capability(device_id: int = 0) -> DeviceCapability:
major, minor, *_ = torch.xpu.get_device_capability(
device_id)['version'].split('.')
return DeviceCapability(major=int(major), minor=int(minor))
@staticmethod
def get_device_name(device_id: int = 0) -> str:
return torch.xpu.get_device_name(device_id)
@classmethod
def get_device_total_memory(cls, device_id: int = 0) -> int:
device_props = torch.xpu.get_device_properties(device_id)
return device_props.total_memory
@staticmethod
def inference_mode():
return torch.no_grad()
@classmethod
def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
# check and update model config
model_config = vllm_config.model_config
if model_config.dtype == torch.bfloat16:
logger.warning(
"bfloat16 is not fully supported on XPU, casting to float16.")
model_config.dtype = torch.float16
if not model_config.enforce_eager:
logger.warning(
"CUDA graph is not supported on XPU, fallback to the eager "
"mode.")
model_config.enforce_eager = True