import asyncio import time from typing import Dict, List, Optional from cacheflow.logger import init_logger from cacheflow.outputs import RequestOutput from cacheflow.sampling_params import SamplingParams from cacheflow.server.arg_utils import AsyncServerArgs from cacheflow.server.llm_server import LLMEngine from cacheflow.server.ray_utils import ray, initialize_cluster logger = init_logger(__name__) TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds class AsyncLLMEngine: """An asynchronous wrapper for LLMEngine. This class is used to wrap the LLMEngine class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The LLMEngine is kicked by the generate method when there are requests in the waiting queue. The generate method yields the outputs from the LLMEngine to the caller. NOTE: For the comprehensive list of arguments, see `LLMEngine`. Args: worker_use_ray: Whether to use Ray for model workers. Required for distributed execution. Should be the same as `parallel_config.worker_use_ray`. server_use_ray: Whether to make LLMEngine a Ray actor. If so, the async frontend will be executed in a separate process as the model workers. log_requests: Whether to log the requests. *args, *kwargs: Arguments for LLMEngine. """ def __init__(self, worker_use_ray: bool, server_use_ray: bool, log_requests: bool = True, *args, **kwargs) -> None: self.worker_use_ray = worker_use_ray self.server_use_ray = server_use_ray self.log_requests = log_requests if not self.server_use_ray: server_class = LLMEngine elif self.worker_use_ray: server_class = ray.remote(num_cpus=0)(LLMEngine).remote else: server_class = ray.remote(num_gpus=1)(LLMEngine).remote self.server = server_class(*args, **kwargs) # Request id -> request output. self.request_outputs: Dict[str, RequestOutput] = {} # Request id -> event to notify that there is new output. self.request_events: Dict[str, asyncio.Event] = {} self.is_server_running = False self.kicking_request_id: Optional[str] = None async def server_step(self, kicking_request_id: Optional[str] = None): """Kick the server to process the waiting requests.""" self.is_server_running = True self.kicking_request_id = kicking_request_id if self.server_use_ray: request_outputs = await self.server.step.remote() else: # Yield to the event loop to allow other coroutines to run # while is_server_running is True. This let the server to add new # requests into the queue. await asyncio.sleep(0) request_outputs = self.server.step() self.is_server_running = False self.kicking_request_id = None # Notify the waiting coroutines that there are new outputs ready. for request_output in request_outputs: request_id = request_output.request_id self.request_outputs[request_id] = request_output self.request_events[request_id].set() async def generate( self, prompt: Optional[str], sampling_params: SamplingParams, request_id: str, prompt_token_ids: Optional[List[int]] = None ) -> RequestOutput: """Generate outputs for a request. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs from the LLMEngine to the caller. Args: prompt: The prompt string. Can be None if prompt_token_ids is provided. sampling_params: The sampling parameters of the request. request_id: The unique id of the request. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. Yields: The output `RequestOutput` objects from the LLMEngine for the request. """ # Preprocess the request. arrival_time = time.time() # Create an event to notify us that there is new output from the # cacheflow server. request_event = asyncio.Event() self.request_events[request_id] = request_event if self.log_requests: logger.info(f"Received request {request_id}: " f"prompt: {prompt!r}, " f"sampling params: {sampling_params}, " f"prompt token ids: {prompt_token_ids}.") # Add the request into the cacheflow server's waiting queue. if self.server_use_ray: await self.server.add_request.remote( request_id, prompt, sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time) else: self.server.add_request( request_id, prompt, sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time) # The cacheflow server does not have a background loop that keeps # processing incoming requests. Therefore, we need to keep kicking # the server to process the requests. while True: if request_id not in self.request_events: # The request has been aborted. return # Kick the server if the server is not running. if not self.is_server_running: await self.server_step(request_id) # Wait for new output. The group_event will be set in server_step # when there is new output available for the sequence group. # Added a timeout to prevent deadlock. try: await asyncio.wait_for(request_event.wait(), timeout=TIMEOUT_TO_PREVENT_DEADLOCK) except asyncio.TimeoutError: continue # Reset the event to wait for the next output. request_event.clear() # Decode and return new outputs. request_output = self.request_outputs[request_id] yield request_output # Once finished, release the resources of the sequence group. if request_output.finished(): if self.log_requests: logger.info(f"Finished request {request_id}.") del self.request_outputs[request_id] del self.request_events[request_id] # Kick the server if the server is not running. This is to # prevent that there are still requests in server's waiting # queue to be executed. if not self.is_server_running: await self.server_step() break async def abort(self, request_id: str) -> None: """Abort a request. Abort a submitted request. If the request is finished or not found, this method will be a no-op. Args: request_id: The unique id of the request. """ if request_id not in self.request_events: # The request has already finished or been aborted. return if self.log_requests: logger.info(f"Aborted request {request_id}.") if self.server_use_ray: await self.server.abort_request.remote(request_id) else: self.server.abort_request(request_id) if request_id in self.request_events: del self.request_events[request_id] if request_id in self.request_outputs: del self.request_outputs[request_id] # To prevent deadlock when a request is aborted while the server is # running. if self.kicking_request_id == request_id: self.is_server_running = False self.kicking_request_id = None @classmethod def from_server_args(cls, server_args: AsyncServerArgs) -> "AsyncLLMEngine": """Creates an async LLM server from the server arguments.""" # Create the server configs. server_configs = server_args.create_server_configs() parallel_config = server_configs[2] # Initialize the cluster. distributed_init_method, devices = initialize_cluster( parallel_config, server_args.server_use_ray) # Create the LLM server. server = cls(server_args.worker_use_ray, server_args.server_use_ray, not server_args.disable_log_requests, *server_configs, distributed_init_method, devices, log_stats=not server_args.disable_log_stats) return server