detokenizer.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from typing import Optional

import tokenizers
from packaging import version
from tokenizers import Tokenizer
from tokenizers.decoders import DecodeStream
from transformers import PreTrainedTokenizerFast

from vllm.logger import init_logger
from vllm.transformers_utils.detokenizer_utils import (
    AnyTokenizer, convert_prompt_ids_to_tokens, detokenize_incrementally)
from vllm.utils import length_from_prompt_token_ids_or_embeds
from vllm.v1.engine import EngineCoreRequest

logger = init_logger(__name__)

# Only tokenizers >= 0.21.1 supports DecodeStream used for
# FastIncrementalDetokenizer.
USE_FAST_DETOKENIZER = version.parse(
    tokenizers.__version__) >= version.parse("0.21.1")

# Error string from https://github.com/huggingface/tokenizers/blob/909fdde2a4ffedd9295206f705eb612be2a91b12/tokenizers/src/tokenizer/mod.rs#L1042
INVALID_PREFIX_ERR_MSG = "Invalid prefix encountered"


class IncrementalDetokenizer:

    def __init__(self):
        self.token_ids: list[int] = []

    @property
    def output_token_ids(self) -> list[int]:
        return self.token_ids

    def update(self, new_token_ids: list[int],
               stop_terminated: bool) -> Optional[str]:
        self.token_ids.extend(new_token_ids)
        return None

    def get_next_output_text(self, finished: bool, delta: bool) -> str:
        return ""

    @classmethod
    def from_new_request(
        cls,
        tokenizer: Optional[AnyTokenizer],
        request: EngineCoreRequest,
    ) -> "IncrementalDetokenizer":

        assert request.sampling_params is not None

        if tokenizer is None:
            # No tokenizer => skipping detokenization.
            return IncrementalDetokenizer()

        if USE_FAST_DETOKENIZER and isinstance(tokenizer,
                                               PreTrainedTokenizerFast):
            # Fast tokenizer => use tokenizers library DecodeStream.
            return FastIncrementalDetokenizer(tokenizer, request)

        # Fall back to slow python-based incremental detokenization.
        return SlowIncrementalDetokenizer(tokenizer, request)


class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):

    def __init__(self, request: EngineCoreRequest):
        super().__init__()

        # Stop strings
        params = request.sampling_params
        assert params is not None
        self.stop = stop = params.stop
        self.min_tokens = params.min_tokens
        self.include_stop_str_in_output = params.include_stop_str_in_output

        # Number of chars to hold back when stop strings are to be excluded
        # from streamed output.
        if stop and not self.include_stop_str_in_output:
            self.stop_buffer_length = max(len(s) for s in stop) - 1
        else:
            self.stop_buffer_length = 0
        self._last_output_text_offset: int = 0

        # Generation data
        self.output_text = ""

    def update(self, new_token_ids: list[int],
               stop_terminated: bool) -> Optional[str]:
        """
        Update RequestState for the request_id by:
            1) Detokenize the new token ids incrementally.
            2) Evaluate stop criteria.

        Return matched stop string or None.
        """
        if not new_token_ids:
            # Skip detokenization if no new token ids.
            return None

        if stop_terminated and not self.include_stop_str_in_output:
            # If stop-terminated, exclude last token from detokenization
            # based on include_stop_str_in_output parameter.
            skipped_stop_token_id = new_token_ids[-1]
            new_token_ids = new_token_ids[:-1]
        else:
            skipped_stop_token_id = None

        # 1) Detokenize the new token ids incrementally.
        # TODO(woosuk): This method becomes very inefficient when the number of
        # new_token_ids is more than 1. We need to optimize this.
        stop_check_offset = len(self.output_text)
        for new_token_id in new_token_ids:
            self.token_ids.append(new_token_id)
            self.output_text += self.decode_next(new_token_id)
            # Support min_tokens, see https://github.com/vllm-project/vllm/pull/22014
            if self.min_tokens and len(
                    self.output_token_ids) <= self.min_tokens:
                stop_check_offset = len(self.output_text)

        if skipped_stop_token_id is not None:
            # Cleanup after skipping detokenization.
            self.token_ids.append(skipped_stop_token_id)

        # 2) Evaluate stop strings.
        stop_string = None
        if self.stop and len(self.output_token_ids) > self.min_tokens:
            stop = check_stop_strings(
                output_text=self.output_text,
                new_char_count=len(self.output_text) - stop_check_offset,
                stop=self.stop,
                include_in_output=self.include_stop_str_in_output,
            )
            if stop is not None:
                stop_string, truncate_to = stop
                if truncate_to != -1:
                    self.output_text = self.output_text[:truncate_to]

        return stop_string

    @abstractmethod
    def decode_next(self, next_token_id: int) -> str:
        raise NotImplementedError

    def get_next_output_text(self, finished: bool, delta: bool) -> str:
        """If delta is True, only new text since the last call to
        this method is returned"""

        # We return the full output text if the sequence is finished.
        buffer_length = 0 if finished else self.stop_buffer_length
        if not delta:
            return self.output_text[:-buffer_length] if buffer_length else (
                self.output_text)
        length = len(self.output_text) - buffer_length
        last_offset = self._last_output_text_offset
        if last_offset < length:
            self._last_output_text_offset = length
            return self.output_text[last_offset:length]
        return ""


class FastIncrementalDetokenizer(BaseIncrementalDetokenizer):

    def __init__(self, tokenizer: PreTrainedTokenizerFast,
                 request: EngineCoreRequest):
        super().__init__(request)

        sampling_params = request.sampling_params
        assert sampling_params is not None

        self.request_id = request.request_id
        self.skip_special_tokens = sampling_params.skip_special_tokens
        self.stream = DecodeStream(
            skip_special_tokens=self.skip_special_tokens)

        self.tokenizer: Tokenizer = tokenizer._tokenizer

        # Find a safe place to start.
        prompt_token_ids = request.prompt_token_ids or []
        prompt_suffix = prompt_token_ids
        prompt_len = len(prompt_suffix)
        if prompt_len > 4:
            for i in range(4, min(prompt_len + 1, 24)):
                suffix = prompt_token_ids[-i:]
                if '�' not in self.tokenizer.decode(suffix):
                    prompt_suffix = suffix
                    break

        # Prime the stream.
        for tid in prompt_suffix:
            self._protected_step(tid)

        self.spaces_between_special_tokens = (
            sampling_params.skip_special_tokens
            or sampling_params.spaces_between_special_tokens)

        if not self.spaces_between_special_tokens:
            # Store dict of added token ids so that we can suppress
            # the spaces between them.
            if (added_token_ids := getattr(self.tokenizer, "added_token_ids",
                                           None)) is None:
                self.tokenizer.added_token_ids = added_token_ids = {
                    tid: tok.content
                    for tid, tok in
                    self.tokenizer.get_added_tokens_decoder().items()
                }

            if added_token_ids:
                self.last_special = False
                self.added_token_ids = added_token_ids
            else:
                # No added tokens.
                self.spaces_between_special_tokens = True

    def decode_next(self, next_token_id: int) -> str:
        token = self._protected_step(next_token_id)

        if not self.spaces_between_special_tokens:
            special_token = self.added_token_ids.get(next_token_id)
            is_special = special_token is not None
            if is_special and self.last_special:
                # Return raw token string without any prefixed spaces.
                token = special_token
            self.last_special = is_special

        return token or ""

    def _protected_step(self, next_token_id: int) -> Optional[str]:
        try:
            token = self.stream.step(self.tokenizer, next_token_id)
        except OverflowError:
            # Handle rare observed overflow, still to be diagnosed.
            # See https://github.com/vllm-project/vllm/issues/21951.
            logger.exception("Encountered invalid token id: %d", next_token_id)
            token = None
        except Exception as e:
            if not str(e).startswith(INVALID_PREFIX_ERR_MSG):
                raise e
            # Recover from edge case where tokenizer can produce non-monotonic,
            # invalid UTF-8 output, which breaks the internal state of
            # tokenizers' DecodeStream.
            # See https://github.com/vllm-project/vllm/issues/17448.
            logger.warning(
                "Encountered invalid prefix detokenization error"
                " for request %s, resetting decode stream.", self.request_id)
            self.stream = DecodeStream(
                skip_special_tokens=self.skip_special_tokens)
            token = self.stream.step(self.tokenizer, next_token_id)
        return token


class SlowIncrementalDetokenizer(BaseIncrementalDetokenizer):

    def __init__(self, tokenizer: AnyTokenizer, request: EngineCoreRequest):
        super().__init__(request)

        self.tokenizer = tokenizer
        params = request.sampling_params
        assert params is not None

        self.prompt_len = length_from_prompt_token_ids_or_embeds(
            request.prompt_token_ids, request.prompt_embeds)

        # Metadata for incremental detokenization.
        if request.prompt_token_ids is not None:
            self.tokens, self.prefix_offset, self.read_offset = (
                convert_prompt_ids_to_tokens(
                    tokenizer=tokenizer,
                    prompt_ids=request.prompt_token_ids,
                    skip_special_tokens=params.skip_special_tokens,
                ))
        else:
            # Prompt embedding requests cannot be detokenized, in general.
            self.tokens = [""] * self.prompt_len
            self.prefix_offset = 0
            self.read_offest = 0

        self.token_ids.extend(request.prompt_token_ids
                              or [0] * self.prompt_len)

        self.skip_special_tokens = params.skip_special_tokens
        self.spaces_between_special_tokens = (
            params.spaces_between_special_tokens)

    @property
    def output_token_ids(self) -> list[int]:
        return self.token_ids if not self.prompt_len else (
            self.token_ids[self.prompt_len:])

    def decode_next(self, next_token_id: int) -> str:
        new_tokens, decoded_text, prefix_offset, read_offset = (
            detokenize_incrementally(
                tokenizer=self.tokenizer,
                all_input_ids=self.token_ids,
                prev_tokens=self.tokens,
                prefix_offset=self.prefix_offset,
                read_offset=self.read_offset,
                skip_special_tokens=self.skip_special_tokens,
                spaces_between_special_tokens=self.
                spaces_between_special_tokens,
            ))

        self.tokens.extend(new_tokens)
        self.prefix_offset = prefix_offset
        self.read_offset = read_offset

        return decoded_text


def check_stop_strings(
    output_text: str,
    new_char_count: int,
    stop: list[str],
    include_in_output: bool,
) -> Optional[tuple[str, int]]:
    """Check if any stop strings are matched and truncate sequence
    output text accordingly.

    Returns tuple (stop_string, offset) if matched or else None.

    Where stop_string is the matched stop string and offset is the
    length to which output_text should be truncated, or -1 for no
    truncation.
    """
    if not new_char_count or not stop:
        return None

    for stop_str in stop:
        stop_string_len = len(stop_str)
        # Avoid searching already-searched text.
        stop_index = output_text.find(stop_str,
                                      1 - new_char_count - stop_string_len)
        if stop_index == -1:
            continue

        if include_in_output:
            # Truncate to end of stop string.
            stop_index += stop_string_len
            if stop_index >= len(output_text):
                # No truncation required.
                return stop_str, -1

        # Truncate the output text to either the beginning
        # or end of the stop string.
        return stop_str, stop_index
    return None