04a3ae0aca
Signed-off-by: YiSheng5 <syhm@mail.ustc.edu.cn> Signed-off-by: yan ma <yan.ma@intel.com> Co-authored-by: YiSheng5 <syhm@mail.ustc.edu.cn>
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
from typing import Callable, List, Optional, Tuple, Type, Union
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import torch
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from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
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ModelConfig, ObservabilityConfig, ParallelConfig,
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PromptAdapterConfig, SchedulerConfig,
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SpeculativeConfig)
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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__(
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self,
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model_config: ModelConfig,
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cache_config: CacheConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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device_config: DeviceConfig,
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load_config: LoadConfig,
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lora_config: Optional[LoRAConfig],
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prompt_adapter_config: Optional[PromptAdapterConfig],
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speculative_config: Optional[SpeculativeConfig],
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observability_config: Optional[ObservabilityConfig],
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) -> None:
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assert device_config.device_type == "xpu"
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assert (not speculative_config
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), "Speculative decoding not yet supported for XPU backend"
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model_config = _verify_and_get_model_config(model_config)
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self.model_config = model_config
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self.cache_config = cache_config
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self.load_config = load_config
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self.lora_config = lora_config
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self.parallel_config = _verify_and_get_parallel_config(parallel_config)
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self.scheduler_config = scheduler_config
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self.device_config = device_config
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self.prompt_adapter_config = prompt_adapter_config
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self.speculative_config = None
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self.observability_config = observability_config
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# Instantiate the worker and load the model to GPU.
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self._init_executor()
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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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def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig:
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if config.dtype == torch.bfloat16:
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logger.warning(
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"bfloat16 is not fully supported on XPU, casting to float16.")
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config.dtype = torch.float16
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if not config.enforce_eager:
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logger.warning(
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"CUDA graph is not supported on XPU, fallback to the eager "
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"mode.")
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config.enforce_eager = True
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return config
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def _verify_and_get_parallel_config(config: ParallelConfig) -> ParallelConfig:
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if (config.distributed_executor_backend is not None
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and config.distributed_executor_backend != "ray"):
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logger.warning(
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"%s is not supported on XPU, fallback to ray distributed executor "
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"backend.", config.distributed_executor_backend)
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config.distributed_executor_backend = "ray"
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return config
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