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Copy pathoutput_processor.py
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836 lines (738 loc) · 31.7 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
from collections import defaultdict, deque
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any, cast
import numpy as np
import torch
from vllm.lora.request import LoRARequest
from vllm.outputs import (
STREAM_FINISHED,
CompletionOutput,
PoolingOutput,
PoolingRequestOutput,
RequestOutput,
)
from vllm.sampling_params import RequestOutputKind
from vllm.tokenizers import TokenizerLike
from vllm.tracing import (
SpanAttributes,
SpanKind,
extract_trace_context,
instrument_manual,
)
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,
)
# shared empty CPU tensor used as a placeholder pooling output
EMPTY_CPU_TENSOR = torch.empty(0, device="cpu")
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, request_id: str):
self.aggregate = output_kind == RequestOutputKind.DELTA
self.request_id = request_id
self.output: RequestOutput | PoolingRequestOutput | Exception | None = None
self.ready = asyncio.Event()
self._input_stream_task: asyncio.Task | None = None
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
def close(self):
if self._input_stream_task is not None:
self._input_stream_task.cancel()
self._input_stream_task = None
def __del__(self):
if (task := self._input_stream_task) is not None:
task.get_loop().call_soon_threadsafe(task.cancel)
self._input_stream_task = None
@dataclass
class OutputProcessorOutput:
request_outputs: list[RequestOutput | PoolingRequestOutput]
reqs_to_abort: list[str]
@dataclass
class StreamingUpdate:
"""Streaming input update data for output processor.
Contains the incremental prompt data to be applied to a request state
when the current sub-request completes.
"""
prompt: str | None
prompt_token_ids: list[int] | None
arrival_time: float
final: bool = False
class RequestState:
def __init__(
self,
request_id: str,
external_req_id: str,
parent_req: ParentRequest | None,
request_index: int,
lora_request: LoRARequest | 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,
stream_input: bool = False,
):
self.request_id = request_id
self.external_req_id = external_req_id
self.parent_req = parent_req
self.request_index = request_index
self.lora_request = lora_request
self.lora_name = lora_request.lora_name if lora_request is not None else None
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.num_cache_creation_tokens = 0
self.stats = RequestStateStats(arrival_time=arrival_time) if log_stats else None
# Routed experts accumulation (prompt + sample chunks)
self.routed_experts_chunks: list[np.ndarray] = []
# Stream Interval
self.stream_interval = stream_interval
self.sent_tokens_offset = 0 # Offset of sent tokens
# Streaming input queue
self.streaming_input = stream_input
self.input_chunk_queue: deque[StreamingUpdate] | None = (
deque() if stream_input else None
)
def apply_streaming_update(self, update: StreamingUpdate) -> None:
# Apply the update to the request state.
self.streaming_input = not update.final
# TODO also include relevant output tokens in new prompt here
# (match scheduler behavior).
if update.prompt:
self.prompt = (
(self.prompt + update.prompt) if self.prompt else update.prompt
)
if self.prompt_token_ids:
self.prompt_token_ids.extend(update.prompt_token_ids or ())
else:
self.prompt_token_ids = update.prompt_token_ids or []
assert self.prompt_token_ids is not None
self.prompt_len = len(self.prompt_token_ids)
if self.stats is not None:
self.stats.arrival_time = update.arrival_time
self.is_prefilling = True
@classmethod
def from_new_request(
cls,
tokenizer: TokenizerLike | None,
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
if sampling_params.stream_interval is not None:
# clamp to the engine-level stream interval.
stream_interval = max(sampling_params.stream_interval, stream_interval)
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
assert request.external_req_id is not None
return cls(
request_id=request.request_id,
external_req_id=request.external_req_id,
parent_req=parent_req,
request_index=request_index,
lora_request=request.lora_request,
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,
stream_input=request.resumable,
)
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,
ec_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 self.detokenizer.num_output_tokens() - 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 = self.detokenizer.num_output_tokens()
external_req_id = self.external_req_id
if pooling_output is not None:
return self._new_request_output(
external_req_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:
outputs, finished = self.parent_req.get_outputs(self.request_id, output)
if not outputs:
return None
external_req_id = self.parent_req.external_req_id
return self._new_request_output(
external_req_id,
outputs,
finished,
kv_transfer_params,
ec_transfer_params,
)
def _new_request_output(
self,
external_req_id: str,
outputs: list[CompletionOutput] | list[PoolingOutput],
finished: bool,
kv_transfer_params: dict[str, Any] | None = None,
ec_transfer_params: dict[str, Any] | None = None,
) -> RequestOutput | PoolingRequestOutput:
# 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)
assert prompt_token_ids is not None
first_output = outputs[0]
if isinstance(first_output, PoolingOutput):
assert len(outputs) == 1
return PoolingRequestOutput(
request_id=external_req_id,
outputs=first_output,
num_cached_tokens=self.num_cached_tokens,
prompt_token_ids=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
return RequestOutput(
request_id=external_req_id, # request_id is what was provided externally
lora_request=self.lora_request,
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,
ec_transfer_params=ec_transfer_params,
num_cached_tokens=self.num_cached_tokens,
num_cache_creation_tokens=self.num_cache_creation_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) :]
# Concatenate routed experts on finish
routed_experts = None
if finished and self.routed_experts_chunks:
routed_experts = np.concatenate(self.routed_experts_chunks, axis=0)
return CompletionOutput(
index=self.request_index,
text=text,
token_ids=token_ids,
routed_experts=routed_experts,
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: TokenizerLike | None,
*,
log_stats: bool,
stream_interval: int = 1,
tracing_enabled: bool = False,
):
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.external_req_ids: defaultdict[str, list[str]] = defaultdict(list)
self.lora_states = LoRARequestStates(log_stats)
self.tracing_enabled = tracing_enabled
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], internal: bool) -> list[str]:
"""Abort a list of requests.
The request_ids may be either external request IDs (those passed to
InputProcessor.process_inputs()) or internal request IDs (those randomly
generated when creating the EngineCoreRequest).
If an external request ID is provided, and that external request ID
was used for multiple requests, all requests associated with that external
request ID are aborted.
In the case of parallel sampling, a request ID may be used to identify
a parent request, in which case the associated child requests are aborted
also.
"""
internal_req_ids = []
for request_id in request_ids:
if internal:
# Internal ID - this may be a parent request
internal_req_ids.append(request_id)
# Remove internal ID from the external->internal mapping
if req_state := self.request_states.get(request_id):
external_req_id = req_state.external_req_id
internal_ids = self.external_req_ids[external_req_id]
internal_ids.remove(request_id)
if not internal_ids:
del self.external_req_ids[external_req_id]
elif internal_ids := self.external_req_ids.pop(request_id, []):
# External ID - abort all requests in the external->internal mapping
internal_req_ids.extend(internal_ids)
request_ids_to_abort = []
for request_id in internal_req_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=EMPTY_CPU_TENSOR
if req_state.detokenizer is None
else None,
finish_reason=FinishReason.ABORT,
stop_reason=None,
kv_transfer_params=None,
ec_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, internal=True)
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
req_state = self.request_states.get(request_id)
if req_state is not None:
self._update_streaming_request_state(req_state, request, prompt)
return
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
# Track the external_req_id -> [internal_req_id, ...] mapping
self.external_req_ids[req_state.external_req_id].append(request_id)
def _update_streaming_request_state(
self, req_state: RequestState, request: EngineCoreRequest, prompt: str | None
) -> None:
"""Queue a streaming update instead of immediately applying it."""
if not request.resumable:
# Final request - just mark completion, don't add its dummy tokens.
if req_state.input_chunk_queue is None:
# Engine already finished - emit final output and clean up.
self._finish_request(req_state)
if req_state.queue is not None:
# Emit a final output with finished=True
# to unblock the generate() loop.
req_state.queue.put(STREAM_FINISHED)
elif req_state.input_chunk_queue:
req_state.input_chunk_queue[-1].final = True
else:
req_state.streaming_input = False
return
update = StreamingUpdate(
prompt=prompt,
prompt_token_ids=request.prompt_token_ids,
arrival_time=request.arrival_time,
)
# Apply request updates now if the last input already completed.
if req_state.input_chunk_queue is None:
req_state.apply_streaming_update(update)
req_state.input_chunk_queue = deque()
else:
# Queue the streaming update otherwise.
req_state.input_chunk_queue.append(update)
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
ec_transfer_params = engine_core_output.ec_transfer_params
if engine_core_output.routed_experts is not None:
req_state.routed_experts_chunks.append(
engine_core_output.routed_experts
)
if req_state.is_prefilling:
if engine_core_output.prefill_stats is not None:
req_state.num_cached_tokens = (
engine_core_output.prefill_stats.num_cached_tokens
)
req_state.num_cache_creation_tokens = (
engine_core_output.prefill_stats.num_cache_creation_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,
ec_transfer_params,
):
if req_state.streaming_input:
request_output.finished = False
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:
if req_state.streaming_input:
if req_state.input_chunk_queue:
update = req_state.input_chunk_queue.popleft()
req_state.apply_streaming_update(update)
else:
req_state.input_chunk_queue = None
else:
self._finish_request(req_state)
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.tracing_enabled:
self.do_tracing(engine_core_output, req_state, iteration_stats)
return OutputProcessorOutput(
request_outputs=request_outputs,
reqs_to_abort=reqs_to_abort,
)
def _finish_request(self, req_state: RequestState) -> None:
req_id = req_state.request_id
self.request_states.pop(req_id)
internal_ids = self.external_req_ids[req_state.external_req_id]
internal_ids.remove(req_id)
if not internal_ids:
del self.external_req_ids[req_state.external_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)
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
metrics = req_state.stats
arrival_time_ns = int(metrics.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
)
# Calculate timing metrics
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
# Build attributes dict
attributes: dict[str, Any] = {
SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN: (
metrics.first_token_latency
),
SpanAttributes.GEN_AI_LATENCY_E2E: e2e_time,
SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE: queued_time,
SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS: prompt_length,
SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS: (
metrics.num_generation_tokens
),
SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_PREFILL: prefill_time,
SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_DECODE: decode_time,
SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_INFERENCE: inference_time,
SpanAttributes.GEN_AI_REQUEST_ID: req_state.external_req_id,
}
# Add optional request parameters
if req_state.top_p:
attributes[SpanAttributes.GEN_AI_REQUEST_TOP_P] = req_state.top_p
if req_state.max_tokens_param:
attributes[SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS] = (
req_state.max_tokens_param
)
if req_state.temperature:
attributes[SpanAttributes.GEN_AI_REQUEST_TEMPERATURE] = (
req_state.temperature
)
if req_state.n:
attributes[SpanAttributes.GEN_AI_REQUEST_N] = req_state.n
instrument_manual(
span_name="llm_request",
start_time=arrival_time_ns,
attributes=attributes,
context=trace_context,
kind=SpanKind.SERVER,
)
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.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,
request_id=req_state.external_req_id,
num_prompt_tokens=req_state.prompt_len,
max_tokens_param=req_state.max_tokens_param,
req_stats=req_state.stats,
num_cached_tokens=req_state.num_cached_tokens,
)
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
)