from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sequence import ExecuteModelRequest, PoolerOutput from vllm.utils import (get_distributed_init_method, get_ip, get_open_port, make_async) from vllm.worker.worker_base import WorkerBase, WorkerWrapperBase logger = init_logger(__name__) def create_worker(worker_module_name: str, worker_class_name: str, worker_class_fn: Optional[Callable[[], Type[WorkerBase]]], **kwargs): wrapper = WorkerWrapperBase( worker_module_name=worker_module_name, worker_class_name=worker_class_name, worker_class_fn=worker_class_fn, ) wrapper.init_worker(**kwargs) return wrapper.worker class GPUExecutor(ExecutorBase): uses_ray: bool = False def _init_executor(self) -> None: """Initialize the worker and load the model. """ assert self.parallel_config.world_size == 1, ( "GPUExecutor only supports single GPU.") self.driver_worker = self._create_worker() self.driver_worker.init_device() self.driver_worker.load_model() def _get_worker_kwargs( self, local_rank: int = 0, rank: int = 0, distributed_init_method: Optional[str] = None) -> Dict[str, Any]: """Return worker init args for a given rank.""" if distributed_init_method is None: distributed_init_method = get_distributed_init_method( get_ip(), get_open_port()) return dict( model_config=self.model_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config, device_config=self.device_config, cache_config=self.cache_config, load_config=self.load_config, local_rank=local_rank, rank=rank, distributed_init_method=distributed_init_method, lora_config=self.lora_config, speculative_config=self.speculative_config, prompt_adapter_config=self.prompt_adapter_config, is_driver_worker=(not self.parallel_config) or (rank % self.parallel_config.tensor_parallel_size == 0), observability_config=self.observability_config, ) def _get_worker_module_and_class( self) -> Tuple[str, str, Optional[Callable[[], Type[WorkerBase]]]]: worker_class_fn = None if self.scheduler_config.is_multi_step: worker_module_name = "vllm.worker.multi_step_worker" worker_class_name = "MultiStepWorker" elif self.speculative_config: worker_module_name = "vllm.spec_decode.spec_decode_worker" worker_class_name = "create_spec_worker" else: worker_module_name = "vllm.worker.worker" worker_class_name = "Worker" return (worker_module_name, worker_class_name, worker_class_fn) def _get_create_worker_kwargs( self, local_rank: int = 0, rank: int = 0, distributed_init_method: Optional[str] = None) -> Dict: worker_kwargs = self._get_worker_kwargs(local_rank, rank, distributed_init_method) (worker_module_name, worker_class_name, worker_class_fn) = self._get_worker_module_and_class() worker_kwargs.update( worker_module_name=worker_module_name, worker_class_name=worker_class_name, worker_class_fn=worker_class_fn, ) return worker_kwargs def _create_worker(self, local_rank: int = 0, rank: int = 0, distributed_init_method: Optional[str] = None): return create_worker(**self._get_create_worker_kwargs( local_rank=local_rank, rank=rank, distributed_init_method=distributed_init_method)) def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available KV blocks by invoking the underlying worker. """ return self.driver_worker.determine_num_available_blocks() def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None: """Initialize the KV cache by invoking the underlying worker. """ # NOTE: This is logged in the executor because there can be >1 worker # with other executors. We could log in the engine level, but work # remains to abstract away the device for non-GPU configurations. logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks, num_cpu_blocks) self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks) def execute_model( self, execute_model_req: ExecuteModelRequest ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]: output = self.driver_worker.execute_model(execute_model_req) return output def add_lora(self, lora_request: LoRARequest) -> bool: assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." return self.driver_worker.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.driver_worker.remove_lora(lora_id) def pin_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.driver_worker.pin_lora(lora_id) def list_loras(self) -> Set[int]: return self.driver_worker.list_loras() def add_prompt_adapter( self, prompt_adapter_request: PromptAdapterRequest) -> bool: assert prompt_adapter_request.prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.driver_worker.add_prompt_adapter(prompt_adapter_request) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.driver_worker.remove_prompt_adapter(prompt_adapter_id) def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.driver_worker.pin_prompt_adapter(prompt_adapter_id) def list_prompt_adapters(self) -> Set[int]: return self.driver_worker.list_prompt_adapters() def check_health(self) -> None: # GPUExecutor will always be healthy as long as # it's running. return def start_profile(self) -> None: self.driver_worker.start_profile() def stop_profile(self) -> None: self.driver_worker.stop_profile() class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase): async def execute_model_async( self, execute_model_req: ExecuteModelRequest, ) -> List[Union[SamplerOutput, PoolerOutput]]: output = await make_async(self.driver_worker.execute_model )(execute_model_req=execute_model_req) return output