180 lines
6.4 KiB
Python
180 lines
6.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from dataclasses import dataclass, field
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from typing import Optional
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from vllm.engine.output_processor.stop_checker import StopChecker
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from vllm.logger import init_logger
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from vllm.transformers_utils.detokenizer_utils import (
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AnyTokenizer, convert_prompt_ids_to_tokens, detokenize_incrementally)
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from vllm.v1.engine import EngineCoreRequest
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logger = init_logger(__name__)
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@dataclass
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class IncrementalDetokenizer:
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# Generation data
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token_ids: list[int]
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output_text: str = ""
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tokens: list[str] = field(default_factory=list)
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prompt_len: int = 0
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# Stop strings
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stop: list[str] = field(default_factory=list)
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include_stop_str_in_output: bool = False
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# Metadata for incremental detokenization
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prefix_offset: int = 0
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read_offset: int = 0
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# Parameters for detokenization
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skip_special_tokens: bool = True
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spaces_between_special_tokens: bool = True
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# Tokenizer for this request,
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# None if detokenization is disabled.
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tokenizer: Optional[AnyTokenizer] = None
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# Accounting for stop string buffering
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stop_buffer_length: int = 0
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_last_output_text_offset: int = 0
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@property
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def output_token_ids(self) -> list[int]:
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return self.token_ids if not self.prompt_len else (
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self.token_ids[self.prompt_len:])
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@classmethod
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def from_new_request(
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cls,
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tokenizer: Optional[AnyTokenizer],
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request: EngineCoreRequest,
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) -> "IncrementalDetokenizer":
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if tokenizer is None:
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return cls(token_ids=[])
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tokens, prefix_offset, read_offset = convert_prompt_ids_to_tokens(
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tokenizer=tokenizer,
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prompt_ids=request.prompt_token_ids,
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skip_special_tokens=request.sampling_params.skip_special_tokens,
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)
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stops = request.sampling_params.stop
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# Number of chars to hold back when stop strings are to be excluded
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# from streamed output.
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if stops and not request.sampling_params.include_stop_str_in_output:
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stop_buffer_length = max(len(s) for s in stops) - 1
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else:
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stop_buffer_length = 0
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return cls(
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tokens=tokens,
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# Detokenizer mutates this list, so need a unique copy.
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# NOTE(Nick): could we take ownership of it though?
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token_ids=request.prompt_token_ids.copy(),
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stop=stops,
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include_stop_str_in_output=request.sampling_params.
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include_stop_str_in_output,
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prefix_offset=prefix_offset,
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read_offset=read_offset,
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skip_special_tokens=request.sampling_params.skip_special_tokens,
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spaces_between_special_tokens=request.sampling_params.
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spaces_between_special_tokens,
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prompt_len=len(request.prompt_token_ids),
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tokenizer=tokenizer,
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stop_buffer_length=stop_buffer_length,
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)
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def update(self, new_token_ids: list[int],
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stop_terminated: bool) -> Optional[str]:
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"""
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Update RequestState for the request_id by:
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1) Detokenize the new token ids incrementally.
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2) Evaluate stop criteria.
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Return matched stop string or None.
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"""
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if not new_token_ids:
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# Skip detokenization if no new token ids
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return None
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if self.tokenizer is None:
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# Skip detokenization if no tokenizer
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self.token_ids.extend(new_token_ids)
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return None
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if stop_terminated and not self.include_stop_str_in_output:
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# If stop-terminated, exclude last token from detokenization
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# based on include_stop_str_in_output parameter.
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skipped_stop_token_id = new_token_ids[-1]
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new_token_ids = new_token_ids[:-1]
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else:
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skipped_stop_token_id = None
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# 1) Detokenize the new token ids incrementally.
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# TODO(woosuk): This method becomes very inefficient when the number of
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# new_token_ids is more than 1. We need to optimize this.
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decoded_text = ""
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for new_token_id in new_token_ids:
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self.token_ids.append(new_token_id)
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(new_tokens, new_decoded_token_text, prefix_offset,
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read_offset) = detokenize_incrementally(
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tokenizer=self.tokenizer,
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all_input_ids=self.token_ids,
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prev_tokens=self.tokens,
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prefix_offset=self.prefix_offset,
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read_offset=self.read_offset,
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skip_special_tokens=self.skip_special_tokens,
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spaces_between_special_tokens=self.
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spaces_between_special_tokens,
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)
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self.tokens.extend(new_tokens)
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self.prefix_offset = prefix_offset
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self.read_offset = read_offset
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decoded_text += new_decoded_token_text
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self.output_text += decoded_text
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if stop_terminated:
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if skipped_stop_token_id is not None:
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# Cleanup after skipping detokenization
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self.token_ids.append(skipped_stop_token_id)
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# Stop token triggered; skip stop string check
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return None
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# 2) Evaluate stop strings.
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stop_string = None
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if self.stop:
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stop = StopChecker.check_stop_strings(
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output_text=self.output_text,
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new_char_count=len(decoded_text),
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stop=self.stop,
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include_in_output=self.include_stop_str_in_output,
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)
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if stop is not None:
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stop_string, truncate_to = stop
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if truncate_to != -1:
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self.output_text = self.output_text[:truncate_to]
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return stop_string
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def get_next_output_text(self, finished: bool, delta: bool) -> str:
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"""If delta is True, only new text since the last call to
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this method is returned"""
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# We return the full output text if the sequence is finished.
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buffer_length = 0 if finished else self.stop_buffer_length
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if not delta:
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return self.output_text[:-buffer_length] if buffer_length else (
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self.output_text)
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length = len(self.output_text) - buffer_length
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last_offset = self._last_output_text_offset
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if last_offset < length:
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self._last_output_text_offset = length
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return self.output_text[last_offset:length]
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return ""
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