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86647d1cd0
Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai> Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
350 lines
13 KiB
Python
350 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import ABC, abstractmethod
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from typing import Optional
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import tokenizers
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from packaging import version
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from tokenizers import Tokenizer
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from tokenizers.decoders import DecodeStream
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from transformers import PreTrainedTokenizerFast
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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.utils import length_from_prompt_token_ids_or_embeds
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from vllm.v1.engine import EngineCoreRequest
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logger = init_logger(__name__)
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# Only tokenizers >= 0.21.1 supports DecodeStream used for
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# FastIncrementalDetokenizer.
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USE_FAST_DETOKENIZER = version.parse(
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tokenizers.__version__) >= version.parse("0.21.1")
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# Error string from https://github.com/huggingface/tokenizers/blob/909fdde2a4ffedd9295206f705eb612be2a91b12/tokenizers/src/tokenizer/mod.rs#L1042
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INVALID_PREFIX_ERR_MSG = "Invalid prefix encountered"
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class IncrementalDetokenizer:
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def __init__(self):
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self.token_ids: list[int] = []
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@property
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def output_token_ids(self) -> list[int]:
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return self.token_ids
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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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self.token_ids.extend(new_token_ids)
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return None
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def get_next_output_text(self, finished: bool, delta: bool) -> str:
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return ""
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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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assert request.sampling_params is not None
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if tokenizer is None:
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# No tokenizer => skipping detokenization.
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return IncrementalDetokenizer()
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if USE_FAST_DETOKENIZER and isinstance(tokenizer,
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PreTrainedTokenizerFast):
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# Fast tokenizer => use tokenizers library DecodeStream.
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return FastIncrementalDetokenizer(tokenizer, request)
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# Fall back to slow python-based incremental detokenization.
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return SlowIncrementalDetokenizer(tokenizer, request)
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class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):
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def __init__(self, request: EngineCoreRequest):
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super().__init__()
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# Stop strings
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params = request.sampling_params
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assert params is not None
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self.stop = stop = params.stop
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self.min_tokens = params.min_tokens
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self.include_stop_str_in_output = params.include_stop_str_in_output
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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 stop and not self.include_stop_str_in_output:
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self.stop_buffer_length = max(len(s) for s in stop) - 1
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else:
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self.stop_buffer_length = 0
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self._last_output_text_offset: int = 0
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# Generation data
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self.output_text = ""
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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 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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stop_check_offset = len(self.output_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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self.output_text += self.decode_next(new_token_id)
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# Support min_tokens, see https://github.com/vllm-project/vllm/pull/22014
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if self.min_tokens and len(
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self.output_token_ids) <= self.min_tokens:
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stop_check_offset = len(self.output_text)
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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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# 2) Evaluate stop strings.
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stop_string = None
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if self.stop and len(self.output_token_ids) > self.min_tokens:
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stop = check_stop_strings(
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output_text=self.output_text,
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new_char_count=len(self.output_text) - stop_check_offset,
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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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@abstractmethod
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def decode_next(self, next_token_id: int) -> str:
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raise NotImplementedError
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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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class FastIncrementalDetokenizer(BaseIncrementalDetokenizer):
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def __init__(self, tokenizer: PreTrainedTokenizerFast,
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request: EngineCoreRequest):
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super().__init__(request)
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sampling_params = request.sampling_params
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assert sampling_params is not None
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self.request_id = request.request_id
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self.skip_special_tokens = sampling_params.skip_special_tokens
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self.stream = DecodeStream(
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skip_special_tokens=self.skip_special_tokens)
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self.tokenizer: Tokenizer = tokenizer._tokenizer
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# Find a safe place to start.
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prompt_token_ids = request.prompt_token_ids or []
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prompt_suffix = prompt_token_ids
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prompt_len = len(prompt_suffix)
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if prompt_len > 4:
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for i in range(4, min(prompt_len + 1, 24)):
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suffix = prompt_token_ids[-i:]
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if '<EFBFBD>' not in self.tokenizer.decode(suffix):
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prompt_suffix = suffix
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break
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# Prime the stream.
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for tid in prompt_suffix:
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self._protected_step(tid)
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self.spaces_between_special_tokens = (
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sampling_params.skip_special_tokens
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or sampling_params.spaces_between_special_tokens)
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if not self.spaces_between_special_tokens:
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# Store dict of added token ids so that we can suppress
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# the spaces between them.
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if (added_token_ids := getattr(self.tokenizer, "added_token_ids",
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None)) is None:
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self.tokenizer.added_token_ids = added_token_ids = {
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tid: tok.content
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for tid, tok in
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self.tokenizer.get_added_tokens_decoder().items()
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}
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if added_token_ids:
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self.last_special = False
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self.added_token_ids = added_token_ids
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else:
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# No added tokens.
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self.spaces_between_special_tokens = True
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def decode_next(self, next_token_id: int) -> str:
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token = self._protected_step(next_token_id)
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if not self.spaces_between_special_tokens:
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special_token = self.added_token_ids.get(next_token_id)
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is_special = special_token is not None
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if is_special and self.last_special:
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# Return raw token string without any prefixed spaces.
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token = special_token
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self.last_special = is_special
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return token or ""
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def _protected_step(self, next_token_id: int) -> Optional[str]:
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try:
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token = self.stream.step(self.tokenizer, next_token_id)
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except OverflowError:
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# Handle rare observed overflow, still to be diagnosed.
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# See https://github.com/vllm-project/vllm/issues/21951.
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logger.exception("Encountered invalid token id: %d", next_token_id)
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token = None
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except Exception as e:
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if not str(e).startswith(INVALID_PREFIX_ERR_MSG):
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raise e
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# Recover from edge case where tokenizer can produce non-monotonic,
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# invalid UTF-8 output, which breaks the internal state of
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# tokenizers' DecodeStream.
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# See https://github.com/vllm-project/vllm/issues/17448.
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logger.warning(
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"Encountered invalid prefix detokenization error"
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" for request %s, resetting decode stream.", self.request_id)
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self.stream = DecodeStream(
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skip_special_tokens=self.skip_special_tokens)
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token = self.stream.step(self.tokenizer, next_token_id)
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return token
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class SlowIncrementalDetokenizer(BaseIncrementalDetokenizer):
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def __init__(self, tokenizer: AnyTokenizer, request: EngineCoreRequest):
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super().__init__(request)
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self.tokenizer = tokenizer
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params = request.sampling_params
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assert params is not None
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self.prompt_len = length_from_prompt_token_ids_or_embeds(
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request.prompt_token_ids, request.prompt_embeds)
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# Metadata for incremental detokenization.
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if request.prompt_token_ids is not None:
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self.tokens, self.prefix_offset, self.read_offset = (
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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=params.skip_special_tokens,
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))
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else:
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# Prompt embedding requests cannot be detokenized, in general.
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self.tokens = [""] * self.prompt_len
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self.prefix_offset = 0
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self.read_offest = 0
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self.token_ids.extend(request.prompt_token_ids
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or [0] * self.prompt_len)
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self.skip_special_tokens = params.skip_special_tokens
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self.spaces_between_special_tokens = (
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params.spaces_between_special_tokens)
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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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def decode_next(self, next_token_id: int) -> str:
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new_tokens, decoded_text, prefix_offset, read_offset = (
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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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return decoded_text
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def check_stop_strings(
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output_text: str,
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new_char_count: int,
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stop: list[str],
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include_in_output: bool,
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) -> Optional[tuple[str, int]]:
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"""Check if any stop strings are matched and truncate sequence
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output text accordingly.
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Returns tuple (stop_string, offset) if matched or else None.
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Where stop_string is the matched stop string and offset is the
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length to which output_text should be truncated, or -1 for no
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truncation.
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"""
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if not new_char_count or not stop:
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return None
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for stop_str in stop:
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stop_string_len = len(stop_str)
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# Avoid searching already-searched text.
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stop_index = output_text.find(stop_str,
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1 - new_char_count - stop_string_len)
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if stop_index == -1:
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continue
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if include_in_output:
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# Truncate to end of stop string.
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stop_index += stop_string_len
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if stop_index >= len(output_text):
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# No truncation required.
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return stop_str, -1
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# Truncate the output text to either the beginning
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# or end of the stop string.
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return stop_str, stop_index
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return None
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