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vllm/reasoning/basic_parsers.py

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+# 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