# SPDX-License-Identifier: Apache-2.0 import asyncio from dataclasses import dataclass from typing import Dict, List, Optional, Union from vllm.outputs import RequestOutput from vllm.sampling_params import RequestOutputKind from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest, FinishReason from vllm.v1.engine.detokenizer import IncrementalDetokenizer from vllm.v1.engine.logprobs import LogprobsProcessor from vllm.v1.metrics.stats import (IterationStats, LoRARequestStates, RequestStateStats) @dataclass class OutputProcessorOutput: request_outputs: List[RequestOutput] reqs_to_abort: List[str] class RequestState: def __init__( self, request_id: str, lora_name: Optional[str], output_kind: RequestOutputKind, prompt: Optional[str], prompt_token_ids: List[int], logprobs_processor: LogprobsProcessor, detokenizer: IncrementalDetokenizer, arrival_time: float, queue: Optional[asyncio.Queue[RequestOutput]], log_stats: bool, ): self.request_id = request_id self.lora_name = lora_name self.output_kind = output_kind self.prompt = prompt self.prompt_token_ids = prompt_token_ids self.prompt_len = len(prompt_token_ids) self.logprobs_processor = logprobs_processor self.detokenizer = detokenizer self.is_prefilling = True self.queue = queue self.stats = RequestStateStats( arrival_time=arrival_time) if log_stats else None @classmethod def from_new_request( cls, tokenizer: AnyTokenizer, request: EngineCoreRequest, queue: Optional[asyncio.Queue[RequestOutput]], log_stats: bool, ) -> "RequestState": return cls( request_id=request.request_id, lora_name=(request.lora_request.name if request.lora_request is not None else None), output_kind=request.sampling_params.output_kind, prompt=request.prompt, prompt_token_ids=request.prompt_token_ids, logprobs_processor=LogprobsProcessor.from_new_request( tokenizer=tokenizer, request=request, ), detokenizer=IncrementalDetokenizer.from_new_request( tokenizer=tokenizer, request=request, ), arrival_time=request.arrival_time, queue=queue, log_stats=log_stats, ) class OutputProcessor: """Process EngineCoreOutputs into RequestOutputs.""" def __init__( self, tokenizer: BaseTokenizerGroup, log_stats: bool, ): self.log_stats = log_stats self.tokenizer = tokenizer self.request_states: Dict[str, RequestState] = {} self.lora_states = LoRARequestStates() def is_request_active(self, request_id: str) -> bool: return request_id in self.request_states def get_num_unfinished_requests(self): return len(self.request_states) def has_unfinished_requests(self) -> bool: return len(self.request_states) > 0 def abort_requests( self, request_ids: List[str], ) -> None: for request_id in request_ids: req_state = self.request_states.pop(request_id, None) if req_state is not None: self.lora_states.abort_request(req_state) def add_request( self, request: EngineCoreRequest, queue: Optional[asyncio.Queue[RequestOutput]] = None, ) -> None: request_id = request.request_id if request_id in self.request_states: raise ValueError(f"Request id {request_id} already running.") req_state = RequestState.from_new_request( tokenizer=self.tokenizer.get_lora_tokenizer(request.lora_request), request=request, queue=queue, log_stats=self.log_stats) self.request_states[request_id] = req_state self.lora_states.add_request(req_state) def process_outputs( self, engine_core_outputs: List[EngineCoreOutput], engine_core_timestamp: Optional[float] = None, iteration_stats: Optional[IterationStats] = None, ) -> OutputProcessorOutput: """ Process the EngineCoreOutputs: 1) Compute stats for logging 2) Detokenize 3) Create and handle RequestOutput objects: * If there is a queue (for usage with AsyncLLM), put the RequestOutput objects into the queue for handling by the per-request generate() tasks. * If there is no queue (for usage with LLMEngine), return a list of RequestOutput objects. ****************** NOTE FOR DEVELOPERS ****************** VLLM V1 minimizes the number of python loops over the full batch to ensure system overheads are minimized. This is the only function that should loop over EngineCoreOutputs. If you need to touch every element of the batch, do it from within the loop below. ********************************************************** """ request_outputs: List[RequestOutput] = [] reqs_to_abort: List[str] = [] for engine_core_output in engine_core_outputs: req_id = engine_core_output.request_id req_state = self.request_states.get(req_id) if req_state is None: # Ignore output for already-aborted request. continue # 1) Compute stats for this iteration. self._update_stats_from_output(req_state, engine_core_output, engine_core_timestamp, iteration_stats) new_token_ids = engine_core_output.new_token_ids finish_reason = engine_core_output.finish_reason stop_reason = engine_core_output.stop_reason # TODO(andy): prompt logprobs + chunked prefill can # result in engine core returning an output for a # partial prefill (in order to send back partial # prompt logprobs.) This breaks the invariant that # process_outputs is only operating on engine core # outputs associated with non-partial completions. # Currently this is handled by having `is_prefilling` # check for new decoded tokens, indicating that # the completion is not partial. # # Follow up will aggregate partial prompt logprobs # in the EngineCore. req_state.is_prefilling = not new_token_ids # 2) Detokenize the token ids into text and check for stop # strings. stop_string = req_state.detokenizer.update(new_token_ids) if stop_string and finish_reason != FinishReason.STOP: finish_reason = FinishReason.STOP stop_reason = stop_string # 3) Compute sample and prompt logprobs for request, # if required. req_state.logprobs_processor.update_from_output(engine_core_output) # 4) Create and handle RequestOutput objects. if request_output := self._make_request_output( req_state, new_token_ids, finish_reason, stop_reason): if req_state.queue is not None: # AsyncLLM: put into queue for handling by generate(). req_state.queue.put_nowait(request_output) else: # LLMEngine: return list of RequestOutputs. request_outputs.append(request_output) # Free completed requests. if request_output.finished: self.request_states.pop(req_id) if not engine_core_output.finished: # If req not finished in EngineCore, but Detokenizer # detected stop string, abort needed in EngineCore. reqs_to_abort.append(req_id) # Track per-request stats self._update_stats_from_finished(req_state, request_output, finish_reason, iteration_stats) self.lora_states.update_iteration_stats(iteration_stats) return OutputProcessorOutput( request_outputs=request_outputs, reqs_to_abort=reqs_to_abort, ) def _update_stats_from_output(self, req_state: RequestState, engine_core_output: EngineCoreOutput, engine_core_timestamp: Optional[float], iteration_stats: Optional[IterationStats]): if iteration_stats is None: return lora_stats = self.lora_states.get_stats(req_state) assert engine_core_timestamp is not None assert req_state.stats is not None iteration_stats.update_from_output(engine_core_output, engine_core_timestamp, req_state.is_prefilling, req_state.prompt_len, req_state.stats, lora_stats) def _update_stats_from_finished(self, req_state: RequestState, request_output: RequestOutput, finish_reason: Optional[FinishReason], iteration_stats: Optional[IterationStats]): if iteration_stats is None: return assert finish_reason is not None assert req_state.stats is not None iteration_stats.update_from_finished_request(finish_reason, request_output, req_state.stats) self.lora_states.finish_request(req_state) @staticmethod def _make_request_output( request_state: RequestState, new_token_ids: List[int], finish_reason: Optional[FinishReason], stop_reason: Union[int, str, None], ) -> Optional[RequestOutput]: finished = finish_reason is not None output_kind = request_state.output_kind # In follow up, we will switch to invariant where EngineCore # does not stream partial prefills. if not finished and (request_state.is_prefilling or output_kind == RequestOutputKind.FINAL_ONLY): # Only the final output is required in FINAL_ONLY mode. return None detokenizer = request_state.detokenizer logprobs_processor = request_state.logprobs_processor delta = output_kind == RequestOutputKind.DELTA logprobs = logprobs_processor.logprobs if delta: if logprobs: logprobs = logprobs[-len(new_token_ids):] # Side effect: logprobs processor forgets prompt logprobs prompt_logprobs = logprobs_processor.pop_prompt_logprobs() else: prompt_logprobs = logprobs_processor.prompt_logprobs request_output = RequestOutput.new( request_id=request_state.request_id, prompt=request_state.prompt, prompt_token_ids=request_state.prompt_token_ids, text=detokenizer.get_next_output_text(finished, delta), token_ids=new_token_ids if delta else detokenizer.output_token_ids, logprobs=logprobs, prompt_logprobs=prompt_logprobs, cumulative_logprob=logprobs_processor.cumulative_logprob, finished=finished, ) if finished: completion_output = request_output.outputs[0] completion_output.finish_reason = str(finish_reason) completion_output.stop_reason = stop_reason return request_output