52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from typing import Callable, List, Optional, Tuple, Type, Union
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from vllm.executor.executor_base import ExecutorAsyncBase
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from vllm.executor.gpu_executor import GPUExecutor
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from vllm.logger import init_logger
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.sequence import ExecuteModelRequest, PoolerOutput
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from vllm.utils import make_async
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from vllm.worker.worker_base import WorkerBase
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logger = init_logger(__name__)
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class XPUExecutor(GPUExecutor):
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uses_ray: bool = False
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def _init_executor(self) -> None:
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assert self.device_config.device_type == "xpu"
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assert self.speculative_config is None, (
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"Speculative decoding not yet supported for XPU backend")
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GPUExecutor._init_executor(self)
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def _get_worker_module_and_class(
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self) -> Tuple[str, str, Optional[Callable[[], Type[WorkerBase]]]]:
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worker_class_fn = None
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if self.speculative_config is not None:
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raise NotImplementedError(
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"XPU does not support speculative decoding")
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else:
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worker_module_name = "vllm.worker.xpu_worker"
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worker_class_name = "XPUWorker"
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return (worker_module_name, worker_class_name, worker_class_fn)
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def execute_model(
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self, execute_model_req: ExecuteModelRequest
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) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]:
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output = self.driver_worker.execute_model(execute_model_req)
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return output
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class XPUExecutorAsync(XPUExecutor, ExecutorAsyncBase):
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async def execute_model_async(
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self,
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execute_model_req: ExecuteModelRequest,
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) -> List[SamplerOutput]:
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output = await make_async(self.driver_worker.execute_model
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)(execute_model_req=execute_model_req)
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return output
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