greenmind_14b_r1_reasoning_parser.py

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

import regex as re
from transformers import PreTrainedTokenizerBase

from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaMessage)
from vllm.logger import init_logger
from vllm.reasoning import ReasoningParser, ReasoningParserManager

logger = init_logger(__name__)


@ReasoningParserManager.register_module("greenmind_14b_r1")
class GreenMind14bR1ReasoningParser(ReasoningParser):
    """
    Reasoning parser for GreenMind-14B-R1 model.

    The GreenMind-14B-R1 model uses </think> token to denote the end of reasoning 
    text. This parser extracts all content before </think> as reasoning content.

    think start: "<think>\n": [13708, 766, 397]
    think ends: "\n</think>\n<answer>\n": [198, 522, 26865, 397, 27, 9217, 397]
    response ends: "</answer>": [198, 522, 9217, 29]
    """

    def __init__(self, tokenizer: PreTrainedTokenizerBase):
        super().__init__(tokenizer)
        self.think_start_expr = r"<think>\n"
        self.think_end_expr = r"\n</think>\n"

        self.response_start_expr = r"\n</think>\n<answer>"
        self.response_end_expr = r"</answer>"

        self.full_match_reasoning_regex = re.compile(
            rf"(?:{self.think_start_expr}(.*?){self.response_start_expr})?(.*?){self.response_end_expr}",
            re.DOTALL)

        self.half_match_reasoning_regex = re.compile(
            rf"{self.think_start_expr}(.*?){self.response_start_expr}(.*)",
            re.DOTALL)

        self.think_start_ids = [13708, 766, 397] # <think>\n
        self.think_start_ids_fast = [27, 26865, 397] # < think >\n
        self.response_start_ids = [522, 26865, 397, 27, 9217, 397] # </think>\n<answer>\n
        self.response_start_ids_fast = [522, 26865, 397, 27, 9217, 29] # </think>\n<answer>
        self.response_end_ids = [522, 9217, 29]
        self.fast_think_ids = [
            13708, 766, 1339, 522, 26865, 397, 27, 9217, 397
        ]

        # when state change, send out all the buffered text in last state
        self.buffered_text = []
        self.buffered_ids = []

        self.current_state = "reasoning"
        self.all_states = ["reasoning", "response"]

        self.current_state = "idle"
        self.expected_sequence = self.think_start_ids
        # this sequence only for the think start, it has two way to start.
        self.expected_sequence_side = self.think_start_ids_fast
        self.sequence_index = 0
        self.token_buffer = []
        self.text_buffer = ""

    def is_reasoning_end(self, input_ids: list[int]) -> bool:
        return self.current_state == "response"

    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
        # for hunyuan streaming reason parsing, the stream parse
        # will call first, and the same token will be called in
        # is_reasoning_end and extract_content_ids
        # this id is not part of content, so just return [] here.
        return []

    def extract_reasoning_content(
            self, model_output: str, request: ChatCompletionRequest
    ) -> tuple[Optional[str], Optional[str]]:
        """Extract the reasoning content & content sections, respectively.
        If the sequence doesn't match what we expect, i.e., the model generates
        something else, all content is considered non-reasoning content.

        Args:
            model_output (str): Output of the model to be parsed.
            request (ChatCompletionRequest): Request being processed.

        Returns:
            tuple[Optional[str], Optional[str]]: Tuple pair containing the
            reasoning content and non-reasoning content.
        """

        re_match = self.full_match_reasoning_regex.findall(model_output)
        if re_match:
            reasoning_content, response_content = re_match[0]
            if len(reasoning_content) == 0:
                reasoning_content = None
            if len(response_content) == 0:
                response_content = None
            return reasoning_content, response_content

        fallback_regex = self.half_match_reasoning_regex
        fallback_match = fallback_regex.findall(model_output)
        if fallback_match:
            reasoning_content, response_content = fallback_match[0]

            if response_content.endswith(self.response_end_expr):
                response_content = response_content[:-len(self.
                                                          response_end_expr)]

            if len(reasoning_content) == 0:
                reasoning_content = None
            if len(response_content) == 0:
                response_content = None

            return reasoning_content, response_content

        return None, model_output

    def _is_strict_increasing_subsequence(self, subsequence: Sequence[int],
                                          sequence: Sequence[int]) -> bool:
        if not subsequence:
            return False

        sub_idx = 0
        for num in sequence:
            if sub_idx < len(subsequence) and num == subsequence[sub_idx]:
                sub_idx += 1
        return sub_idx == len(subsequence)

    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 content using token ID sequence state machine"""
        # Define sequences
        think_start_sequence = self.think_start_ids
        response_start_sequence = self.response_start_ids
        response_end_sequence = self.response_end_ids

        assert (len(delta_token_ids) == 1)
        # Process each token in the delta
        token = delta_token_ids[0]

        def check_token_with_sequence(token):
            if self.current_state == "idle" or self.current_state == "think":
                return (token == self.expected_sequence[self.sequence_index]
                         or token ==  \
                         self.expected_sequence_side[self.sequence_index])
            else:
                return token == self.expected_sequence[self.sequence_index]

        def check_last_token(token):
            if self.current_state == "idle" or self.current_state == "think":
                # only return true if it's judge using a side sequence.
                if (self.sequence_index - 1 < len(self.expected_sequence_side)
                        and token
                        == self.expected_sequence_side[self.sequence_index -
                                                       1]):
                    return self.sequence_index == len(
                        self.expected_sequence_side)
                else:
                    return self.sequence_index == len(self.expected_sequence)
            else:
                return self.sequence_index == len(self.expected_sequence)

        # Check if token matches expected sequence
        token_in_state_seq = check_token_with_sequence(token)

        if token_in_state_seq:
            # Store matching token
            self.token_buffer.append(token)
            self.text_buffer += delta_text
            self.sequence_index += 1
            ## state change from idle->think->response->idle

            # Check if sequence fully matched
            if check_last_token(token):
                # State transition
                if self.current_state == "idle":
                    self.current_state = "think"
                    self.expected_sequence = response_start_sequence
                    self.expected_sequence_side = self.response_start_ids_fast
                elif self.current_state == "think":
                    self.current_state = "response"
                    self.expected_sequence = response_end_sequence
                elif self.current_state == "response":
                    self.current_state = "idle"
                    self.expected_sequence = think_start_sequence
                    self.expected_sequence_side = self.think_start_ids_fast

                # Reset matching state
                self.sequence_index = 0
                self.token_buffer = []
                self.text_buffer = ""
                # Do not send content for state transition texts.
        else:
            # Sequence broken - handle buffered content
            if self.token_buffer and len(self.token_buffer) > 0:
                # Send buffered tokens
                buffered_content = self.text_buffer + delta_text
                # Reset matching state
                self.sequence_index = 0
                self.token_buffer = []
                self.text_buffer = ""

                # Return content based on current state
                if self.current_state == "think":
                    return DeltaMessage(reasoning_content=buffered_content,
                                        content=None)
                else:
                    return DeltaMessage(reasoning_content=None,
                                        content=buffered_content)
            else:
                # No buffered content, send normally
                if self.current_state == "think":
                    return DeltaMessage(reasoning_content=delta_text,
                                        content=None)
                else:
                    return DeltaMessage(reasoning_content=None,
                                        content=delta_text)

        # If no content to send in this delta
        return None