vllm/vllm/v1/engine/output_processor.py
Harry Mellor 3b352a2f92
Correct capitalisation: VLLM -> vLLM (#14562)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
2025-03-10 16:36:21 +00:00

370 lines
14 KiB
Python

# SPDX-License-Identifier: Apache-2.0
import asyncio
from dataclasses import dataclass
from typing import Optional, Union
from vllm.outputs import CompletionOutput, 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.engine.parallel_sampling import ParentRequest
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,
parent_req: Optional[ParentRequest],
request_index: int,
lora_name: Optional[str],
output_kind: RequestOutputKind,
prompt: Optional[str],
prompt_token_ids: list[int],
logprobs_processor: LogprobsProcessor,
detokenizer: IncrementalDetokenizer,
max_tokens_param: Optional[int],
arrival_time: float,
queue: Optional[asyncio.Queue[RequestOutput]],
log_stats: bool,
):
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_len = len(prompt_token_ids)
self.logprobs_processor = logprobs_processor
self.detokenizer = detokenizer
self.max_tokens_param = max_tokens_param
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,
parent_req: Optional[ParentRequest],
request_index: int,
queue: Optional[asyncio.Queue[RequestOutput]],
log_stats: bool,
) -> "RequestState":
if not request.sampling_params.detokenize:
tokenizer = None
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=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,
),
max_tokens_param=(request.sampling_params.max_tokens if
request.sampling_params is not None else None),
arrival_time=request.arrival_time,
queue=queue,
log_stats=log_stats,
)
def make_request_output(
self,
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 = self.output_kind
final_only = output_kind == RequestOutputKind.FINAL_ONLY
# In follow up, we will switch to invariant where EngineCore
# does not stream partial prefills.
if not finished and (self.is_prefilling or final_only):
# Only the final output is required in FINAL_ONLY mode.
return None
def new_request_output(request_id: str) -> RequestOutput:
return self._new_request_output(request_id, finished)
completion_output = self._new_completion_output(
new_token_ids, finish_reason, stop_reason)
if self.parent_req is not None:
return self.parent_req.make_request_output(final_only,
completion_output,
new_request_output)
request_output = new_request_output(self.request_id)
request_output.outputs.append(completion_output)
return request_output
def _new_request_output(
self,
request_id: str,
finished: bool,
) -> RequestOutput:
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
return RequestOutput(
request_id=request_id,
prompt=self.prompt,
prompt_token_ids=self.prompt_token_ids,
prompt_logprobs=prompt_logprobs,
outputs=[],
finished=finished,
)
def _new_completion_output(
self,
token_ids: list[int],
finish_reason: Optional[FinishReason],
stop_reason: Union[int, str, None],
) -> CompletionOutput:
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)
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 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)
if req_state.parent_req is not None:
req_state.parent_req.finish_child_request(request_id)
def add_request(
self,
request: EngineCoreRequest,
parent_req: Optional[ParentRequest] = None,
request_index: int = 0,
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,
parent_req=parent_req,
request_index=request_index,
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 := req_state.make_request_output(
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 finish_reason is not None:
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)
if req_state.parent_req is not None:
req_state.parent_req.finish_child_request(req_id)
# Track per-request stats
self._update_stats_from_finished(req_state, 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,
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=finish_reason,
num_prompt_tokens=len(req_state.prompt_token_ids),
max_tokens_param=req_state.max_tokens_param,
req_stats=req_state.stats)
self.lora_states.finish_request(req_state)
ParentRequest.observe_finished_request(
req_state.parent_req, iteration_stats,
req_state.stats.num_generation_tokens)