Files
2026-01-09 15:09:53 +08:00

77 lines
3.1 KiB
Python

from typing import Optional
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.lora.request import LoRARequest
from vllm.sampling_params import SamplingParams
from vllm.sequence import SequenceStatus
from vllm.zero_overhead.sequence import ZeroOverheadSequence
class ZeroOverheadStopChecker(StopChecker):
def __init__(self, max_model_len, get_tokenizer_for_seq):
super().__init__(max_model_len, get_tokenizer_for_seq)
def maybe_stop_sequence(
self,
seq: ZeroOverheadSequence,
new_char_count: int,
sampling_params: SamplingParams,
lora_req: Optional[LoRARequest] = None,
) -> None:
"""Stop the finished sequences.
new_char_count is the number of chars added to the
sequence's output text for the newly generated token
"""
# Check if the minimum number of tokens has been generated yet;
# skip the stop string/token checks if not
if seq.zero_overhead_get_output_len() < sampling_params.min_tokens:
return
# Check if the sequence has generated the EOS token.
if ((not sampling_params.ignore_eos)
and seq.zero_overhead_get_last_token_id() == seq.eos_token_id):
# Remove the last EOS token unless explicitly specified
# This prevents unintended exposure of the EOS token
if new_char_count and (
not sampling_params.include_stop_str_in_output):
seq.output_text = seq.output_text[:-new_char_count]
seq.status = SequenceStatus.FINISHED_STOPPED
return
# Check if a stop token was encountered.
# This assumes a single token produced per step.
last_token_id = seq.zero_overhead_get_last_token_id()
if last_token_id in (sampling_params.stop_token_ids or ()):
if new_char_count and (
not sampling_params.include_stop_str_in_output):
# Remove last token
seq.output_text = seq.output_text[:-new_char_count]
seq.status = SequenceStatus.FINISHED_STOPPED
seq.stop_reason = last_token_id
return
# Check if any stop strings are matched.
stop = self.check_stop_strings(
seq.output_text, new_char_count, sampling_params.stop,
sampling_params.include_stop_str_in_output)
if stop is not None:
stop_str, truncate_to = stop
if truncate_to != -1:
seq.output_text = seq.output_text[:truncate_to]
seq.status = SequenceStatus.FINISHED_STOPPED
seq.stop_reason = stop_str
return
# Check if the sequence has reached max_model_len.
if seq.zero_overhead_get_len() > self._get_max_model_len(lora_req):
seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
return
# Check if the sequence has reached max_tokens.
if seq.zero_overhead_get_output_len() == sampling_params.max_tokens:
seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
return