# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio from collections.abc import Iterable from dataclasses import dataclass from typing import Any, cast import torch from vllm.outputs import ( CompletionOutput, PoolingOutput, PoolingRequestOutput, RequestOutput, ) from vllm.sampling_params import RequestOutputKind from vllm.tracing import SpanAttributes, SpanKind, Tracer, extract_trace_context from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils import length_from_prompt_token_ids_or_embeds 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.engine.parallel_sampling import ParentRequest from vllm.v1.metrics.stats import ( IterationStats, LoRARequestStates, RequestStateStats, SchedulerStats, ) class RequestOutputCollector: """ Collects streamed RequestOutputs per individual request, for hand-off to the consuming asyncio generate task. When streaming deltas, RequestOutputs are merged if the producer gets ahead of the consumer. """ def __init__(self, output_kind: RequestOutputKind): self.aggregate = output_kind == RequestOutputKind.DELTA self.output: RequestOutput | PoolingRequestOutput | Exception | None = None self.ready = asyncio.Event() def put(self, output: RequestOutput | PoolingRequestOutput | Exception) -> None: """Non-blocking put operation.""" if self.output is None or isinstance(output, Exception): self.output = output self.ready.set() elif isinstance(self.output, RequestOutput) and isinstance( output, RequestOutput ): # This ensures that request outputs with different request indexes # (if n > 1) do not override each other. self.output.add(output, aggregate=self.aggregate) elif isinstance(self.output, PoolingRequestOutput) and isinstance( output, PoolingRequestOutput ): self.output = output async def get(self) -> RequestOutput | PoolingRequestOutput: """Get operation blocks on put event.""" while (output := self.output) is None: await self.ready.wait() self.output = None self.ready.clear() if isinstance(output, Exception): raise output return output def get_nowait(self) -> RequestOutput | PoolingRequestOutput | None: """Non-blocking get operation.""" output = self.output if output is not None: self.output = None self.ready.clear() if isinstance(output, Exception): raise output return output @dataclass class OutputProcessorOutput: request_outputs: list[RequestOutput | PoolingRequestOutput] reqs_to_abort: list[str] class RequestState: def __init__( self, request_id: str, parent_req: ParentRequest | None, request_index: int, lora_name: str | None, output_kind: RequestOutputKind, prompt: str | None, prompt_token_ids: list[int] | None, prompt_embeds: torch.Tensor | None, logprobs_processor: LogprobsProcessor | None, detokenizer: IncrementalDetokenizer | None, max_tokens_param: int | None, arrival_time: float, queue: RequestOutputCollector | None, log_stats: bool, stream_interval: int, top_p: float | None = None, n: int | None = None, temperature: float | None = None, ): self.request_id = request_id self.parent_req = parent_req self.request_index = request_index self.lora_name = lora_name self.output_kind = output_kind self.prompt = prompt self.prompt_token_ids = prompt_token_ids self.prompt_embeds = prompt_embeds self.prompt_len = length_from_prompt_token_ids_or_embeds( self.prompt_token_ids, self.prompt_embeds ) self.logprobs_processor = logprobs_processor self.detokenizer = detokenizer self.max_tokens_param = max_tokens_param self.top_p = top_p self.n = n self.temperature = temperature self.is_prefilling = True self.queue = queue self.num_cached_tokens = 0 self.stats = RequestStateStats(arrival_time=arrival_time) if log_stats else None # Stream Interval self.stream_interval = stream_interval self.sent_tokens_offset = 0 # Offset of sent tokens @classmethod def from_new_request( cls, tokenizer: AnyTokenizer, request: EngineCoreRequest, prompt: str | None, parent_req: ParentRequest | None, request_index: int, queue: RequestOutputCollector | None, log_stats: bool, stream_interval: int, ) -> "RequestState": if sampling_params := request.sampling_params: if not sampling_params.detokenize: tokenizer = None output_kind = sampling_params.output_kind logprobs_processor = LogprobsProcessor.from_new_request( tokenizer=tokenizer, request=request, ) detokenizer = IncrementalDetokenizer.from_new_request( tokenizer=tokenizer, request=request, ) max_tokens_param = sampling_params.max_tokens top_p = sampling_params.top_p n = sampling_params.n temperature = sampling_params.temperature else: logprobs_processor = None detokenizer = None max_tokens_param = None top_p = None n = None temperature = None assert request.pooling_params is not None output_kind = request.pooling_params.output_kind return cls( request_id=request.request_id, parent_req=parent_req, request_index=request_index, lora_name=( request.lora_request.name if request.lora_request is not None else None ), output_kind=output_kind, prompt=prompt, prompt_token_ids=request.prompt_token_ids, prompt_embeds=request.prompt_embeds, logprobs_processor=logprobs_processor, detokenizer=detokenizer, max_tokens_param=max_tokens_param, top_p=top_p, n=n, temperature=temperature, arrival_time=request.arrival_time, queue=queue, log_stats=log_stats, stream_interval=stream_interval, ) def make_request_output( self, new_token_ids: list[int], pooling_output: torch.Tensor | None, finish_reason: FinishReason | None, stop_reason: int | str | None, kv_transfer_params: dict[str, Any] | None = None, ) -> RequestOutput | PoolingRequestOutput | None: finished = finish_reason is not None final_only = self.output_kind == RequestOutputKind.FINAL_ONLY if not finished and final_only: # Only the final output is required in FINAL_ONLY mode. return None if self.stream_interval > 1: assert self.detokenizer is not None # Send output request only when # 1. It has finished, or # 2. It is the first token, or # 3. It has reached the stream interval number of tokens if not ( finished or self.sent_tokens_offset == 0 or len(self.detokenizer.output_token_ids) - self.sent_tokens_offset >= self.stream_interval ): return None if self.output_kind == RequestOutputKind.DELTA: # Send tokens from the offset in DELTA mode, otherwise all # tokens are sent. new_token_ids = self.detokenizer.output_token_ids[ self.sent_tokens_offset : ] self.sent_tokens_offset = len(self.detokenizer.output_token_ids) request_id = self.request_id if pooling_output is not None: return self._new_request_output( request_id, [self._new_pooling_output(pooling_output)], finished ) output = self._new_completion_output(new_token_ids, finish_reason, stop_reason) if self.parent_req is None: outputs = [output] else: request_id, outputs, finished = self.parent_req.get_outputs( request_id, output ) if not outputs: return None return self._new_request_output( request_id, outputs, finished, kv_transfer_params ) def _new_request_output( self, request_id: str, outputs: list[CompletionOutput] | list[PoolingOutput], finished: bool, kv_transfer_params: dict[str, Any] | None = None, ) -> RequestOutput | PoolingRequestOutput: first_output = outputs[0] if isinstance(first_output, PoolingOutput): assert len(outputs) == 1 # Prompt embeddings are currently not supported by pooling requests. assert self.prompt_token_ids is not None return PoolingRequestOutput( request_id=request_id, outputs=first_output, num_cached_tokens=self.num_cached_tokens, prompt_token_ids=self.prompt_token_ids, finished=finished, ) assert self.logprobs_processor is not None if self.output_kind == RequestOutputKind.DELTA: # Side effect: logprobs processor forgets prompt logprobs prompt_logprobs = self.logprobs_processor.pop_prompt_logprobs() else: prompt_logprobs = self.logprobs_processor.prompt_logprobs # If prompt embeds were used, put placeholder prompt token ids prompt_token_ids = self.prompt_token_ids if prompt_token_ids is None and self.prompt_embeds is not None: prompt_token_ids = [0] * len(self.prompt_embeds) return RequestOutput( request_id=request_id, prompt=self.prompt, prompt_token_ids=prompt_token_ids, prompt_logprobs=prompt_logprobs, outputs=cast(list[CompletionOutput], outputs), finished=finished, kv_transfer_params=kv_transfer_params, num_cached_tokens=self.num_cached_tokens, metrics=self.stats, ) def _new_completion_output( self, token_ids: list[int], finish_reason: FinishReason | None, stop_reason: int | str | None, ) -> CompletionOutput: assert self.detokenizer is not None assert self.logprobs_processor is not None finished = finish_reason is not None delta = self.output_kind == RequestOutputKind.DELTA # Prepare text and token_ids, based on delta mode text = self.detokenizer.get_next_output_text(finished, delta) if not delta: token_ids = self.detokenizer.output_token_ids # Prepare logprobs, based on delta mode logprobs = self.logprobs_processor.logprobs if delta and logprobs: logprobs = logprobs[-len(token_ids) :] return CompletionOutput( index=self.request_index, text=text, token_ids=token_ids, logprobs=logprobs, cumulative_logprob=self.logprobs_processor.cumulative_logprob, finish_reason=str(finish_reason) if finished else None, stop_reason=stop_reason if finished else None, ) def _new_pooling_output( self, pooling_output: torch.Tensor, ) -> PoolingOutput: return PoolingOutput(data=pooling_output) class OutputProcessor: """Process EngineCoreOutputs into RequestOutputs.""" def __init__( self, tokenizer: AnyTokenizer, log_stats: bool, stream_interval: int = 1 ): self.log_stats = log_stats self.tokenizer = tokenizer self.stream_interval = stream_interval self.request_states: dict[str, RequestState] = {} self.parent_requests: dict[str, ParentRequest] = {} self.lora_states = LoRARequestStates(log_stats) self.tracer: Tracer | None = None def get_num_unfinished_requests(self): return len(self.request_states) def has_unfinished_requests(self) -> bool: return len(self.request_states) > 0 def propagate_error(self, e: Exception): """Propagate error to all generate() tasks.""" for _, state in self.request_states.items(): assert state.queue is not None state.queue.put(e) def abort_requests( self, request_ids: Iterable[str], ) -> list[str]: request_ids_to_abort = [] for request_id in request_ids: req_state = self.request_states.pop(request_id, None) if req_state is not None: self.lora_states.request_finished(request_id, req_state.lora_name) request_ids_to_abort.append(request_id) # Produce final abort output. if req_state.queue is not None and ( request_output := req_state.make_request_output( new_token_ids=[], # Set pooling_output is not None to # correctly enter the abort pooling branch pooling_output=torch.randn(0, device="cpu") if req_state.detokenizer is None else None, finish_reason=FinishReason.ABORT, stop_reason=None, kv_transfer_params=None, ) ): req_state.queue.put(request_output) elif parent := self.parent_requests.get(request_id): # Abort children prior to removing the parent. if parent.child_requests: child_reqs = list(parent.child_requests) child_reqs = self.abort_requests(child_reqs) request_ids_to_abort.extend(child_reqs) self.parent_requests.pop(request_id, None) return request_ids_to_abort def add_request( self, request: EngineCoreRequest, prompt: str | None, parent_req: ParentRequest | None = None, request_index: int = 0, queue: RequestOutputCollector | None = 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, request=request, prompt=prompt, parent_req=parent_req, request_index=request_index, queue=queue, log_stats=self.log_stats, stream_interval=self.stream_interval, ) self.request_states[request_id] = req_state if parent_req: self.parent_requests[parent_req.request_id] = parent_req def process_outputs( self, engine_core_outputs: list[EngineCoreOutput], engine_core_timestamp: float | None = None, iteration_stats: IterationStats | None = 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 | PoolingRequestOutput] = [] 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 pooling_output = engine_core_output.pooling_output finish_reason = engine_core_output.finish_reason stop_reason = engine_core_output.stop_reason kv_transfer_params = engine_core_output.kv_transfer_params req_state.num_cached_tokens = engine_core_output.num_cached_tokens req_state.is_prefilling = False if pooling_output is None: assert req_state.detokenizer is not None assert req_state.logprobs_processor is not None # 2) Detokenize the token ids into text and perform stop checks. stop_string = req_state.detokenizer.update( new_token_ids, finish_reason == FinishReason.STOP ) if stop_string: 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 := req_state.make_request_output( new_token_ids, pooling_output, finish_reason, stop_reason, kv_transfer_params, ): if req_state.queue is not None: # AsyncLLM: put into queue for handling by generate(). req_state.queue.put(request_output) else: # LLMEngine: return list of RequestOutputs. request_outputs.append(request_output) # Free completed requests. if finish_reason is not None: self.request_states.pop(req_id) # Remove parent request if applicable. parent_req = req_state.parent_req if parent_req and not parent_req.child_requests: self.parent_requests.pop(parent_req.request_id, None) 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, finish_reason, iteration_stats ) if self.tracer: self.do_tracing(engine_core_output, req_state, iteration_stats) return OutputProcessorOutput( request_outputs=request_outputs, reqs_to_abort=reqs_to_abort, ) def update_scheduler_stats(self, scheduler_stats: SchedulerStats | None): self.lora_states.update_scheduler_stats(scheduler_stats) def do_tracing( self, engine_core_output: EngineCoreOutput, req_state: RequestState, iteration_stats: IterationStats | None, ) -> None: assert req_state.stats is not None assert iteration_stats is not None assert self.tracer is not None arrival_time_nano_seconds = int(req_state.stats.arrival_time * 1e9) trace_context = extract_trace_context(engine_core_output.trace_headers) prompt_length = length_from_prompt_token_ids_or_embeds( req_state.prompt_token_ids, req_state.prompt_embeds ) with self.tracer.start_as_current_span( "llm_request", kind=SpanKind.SERVER, context=trace_context, start_time=arrival_time_nano_seconds, ) as span: metrics = req_state.stats e2e_time = iteration_stats.iteration_timestamp - metrics.arrival_time queued_time = metrics.scheduled_ts - metrics.queued_ts prefill_time = metrics.first_token_ts - metrics.scheduled_ts decode_time = metrics.last_token_ts - metrics.first_token_ts inference_time = metrics.last_token_ts - metrics.scheduled_ts span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, metrics.first_token_latency, ) span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E, e2e_time) span.set_attribute(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE, queued_time) span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS, prompt_length) span.set_attribute( SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS, metrics.num_generation_tokens, ) span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_PREFILL, prefill_time ) span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_DECODE, decode_time ) span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_INFERENCE, inference_time ) # meta span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID, req_state.request_id) if req_state.top_p: span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P, req_state.top_p) if req_state.max_tokens_param: span.set_attribute( SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS, req_state.max_tokens_param ) if req_state.temperature: span.set_attribute( SpanAttributes.GEN_AI_REQUEST_TEMPERATURE, req_state.temperature ) if req_state.n: span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N, req_state.n) def _update_stats_from_output( self, req_state: RequestState, engine_core_output: EngineCoreOutput, engine_core_timestamp: float | None, iteration_stats: IterationStats | None, ): if iteration_stats is None: return 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, self.lora_states, req_state.lora_name, ) def _update_stats_from_finished( self, req_state: RequestState, finish_reason: FinishReason | None, iteration_stats: IterationStats | None, ): 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=finish_reason, num_prompt_tokens=length_from_prompt_token_ids_or_embeds( req_state.prompt_token_ids, req_state.prompt_embeds ), max_tokens_param=req_state.max_tokens_param, req_stats=req_state.stats, ) self.lora_states.request_finished(req_state.request_id, req_state.lora_name) ParentRequest.observe_finished_request( req_state.parent_req, iteration_stats, req_state.stats.num_generation_tokens )