basic_parsers.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from abc import abstractmethod
from collections.abc import Sequence
from typing import Optional, Union

from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaMessage, ResponsesRequest)
from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
from vllm.transformers_utils.tokenizer import AnyTokenizer


class BaseThinkingReasoningParser(ReasoningParser):
    """
    Base class for reasoning parsers that use thinking tokens.
    
    This class provides common functionality for parsers that use start and end
    tokens to delimit reasoning content (
        e.g., <think>...</think>, <seed:think>...</seed:think>).
    
    Subclasses must implement the start and end tokens via abstract
    properties.
    """

    @property
    @abstractmethod
    def start_token(self) -> str:
        """The token that starts reasoning content."""
        raise NotImplementedError

    @property
    @abstractmethod
    def end_token(self) -> str:
        """The token that ends reasoning content."""
        raise NotImplementedError

    def __init__(self, tokenizer: AnyTokenizer, *args, **kwargs):
        super().__init__(tokenizer, *args, **kwargs)

        if not self.model_tokenizer:
            raise ValueError(
                "The model tokenizer must be passed to the ReasoningParser "
                "constructor during construction.")

        if not self.start_token or not self.end_token:
            raise ValueError(
                "start_token and end_token must be defined in subclasses")

        self.start_token_id = self.vocab.get(self.start_token)
        self.end_token_id = self.vocab.get(self.end_token)
        if self.start_token_id is None or self.end_token_id is None:
            raise RuntimeError(
                f"{self.__class__.__name__} reasoning parser could not locate "
                "think start/end tokens in the tokenizer!")

    def is_reasoning_end(self, input_ids: list[int]) -> bool:
        return self.end_token_id in input_ids

    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
        """
        Extract the content after the end tokens
        """
        if self.end_token_id not in input_ids[:-1]:
            return []
        else:
            return input_ids[input_ids.index(self.end_token_id) + 1:]

    def extract_reasoning_content_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
    ) -> Union[DeltaMessage, None]:
        """
        Extract reasoning content from a delta message.
        Handles streaming output where previous + delta = current.
        Uses token IDs for faster processing.
        """
        # Skip single special tokens
        if len(delta_token_ids) == 1 and (delta_token_ids[0] in [
                self.start_token_id, self.end_token_id
        ]):
            return None

        # Check if start token is present in previous or delta.
        # Keep compatibility with models that don't generate start tokens.
        if self.start_token_id in previous_token_ids:
            if self.end_token_id in delta_token_ids:
                # start token in previous, end token in delta,
                # extract reasoning content
                end_index = delta_text.find(self.end_token)
                reasoning_content = delta_text[:end_index]
                content = delta_text[end_index + len(self.end_token):]
                return DeltaMessage(
                    reasoning_content=reasoning_content,
                    content=content if content else None,
                )
            elif self.end_token_id in previous_token_ids:
                # start token in previous, end token in previous,
                # reasoning content continues
                return DeltaMessage(content=delta_text)
            else:
                # start token in previous, no end token in previous or delta,
                # reasoning content continues
                return DeltaMessage(reasoning_content=delta_text)
        elif self.start_token_id in delta_token_ids:
            if self.end_token_id in delta_token_ids:
                # start token in delta, end token in delta,
                # extract reasoning content
                start_index = delta_text.find(self.start_token)
                end_index = delta_text.find(self.end_token)
                reasoning_content = delta_text[start_index +
                                               len(self.start_token):end_index]
                content = delta_text[end_index + len(self.end_token):]
                return DeltaMessage(
                    reasoning_content=reasoning_content,
                    content=content if content else None,
                )
            else:
                # start token in delta, no end token in delta,
                # reasoning content continues
                return DeltaMessage(reasoning_content=delta_text)
        else:
            # not find thinking start token
            return DeltaMessage(content=delta_text)

    def extract_reasoning_content(
        self, model_output: str, request: Union[ChatCompletionRequest,
                                                ResponsesRequest]
    ) -> tuple[Optional[str], Optional[str]]:
        """
        Extract reasoning content from the model output.
        
        This is the base implementation that works for most models.
        Subclasses can override this method for specific behavior.
        """
        # Check if the start token is present in the model output, remove it
        # if it is present.
        model_output_parts = model_output.partition(self.start_token)
        model_output = model_output_parts[2] if model_output_parts[
            1] else model_output_parts[0]

        # For models that may not generate start token,
        # assume the reasoning content is always at the start.
        if self.end_token not in model_output:
            return model_output, None
        else:
            reasoning_content, _, content = model_output.partition(
                self.end_token)
            # If generation stops right after end-of-think, return null content
            final_content = content or None
            return reasoning_content, final_content