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165
reasoning/ernie45_reasoning_parser.py
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165
reasoning/ernie45_reasoning_parser.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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from transformers import PreTrainedTokenizerBase
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from vllm.entrypoints.openai.protocol import ChatCompletionRequest, DeltaMessage
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from vllm.logger import init_logger
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from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
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logger = init_logger(__name__)
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class Ernie45ReasoningParser(BaseThinkingReasoningParser):
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"""
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Reasoning parser for Ernie45 thinking model.
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The Ernie45 thinking model ouput format is
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abc\n</think>\n\n<response>\ndef\n</response>\n
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or abc\n</think>\ndef
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"""
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response_start_token: str = "<response>"
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response_end_token: str = "</response>"
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newline_token: str = "<0x0A>"
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@property
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def start_token(self) -> str:
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"""The token that starts reasoning content."""
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return "<think>"
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@property
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def end_token(self) -> str:
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"""The token that ends reasoning content."""
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return "</think>"
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def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
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super().__init__(tokenizer, *args, **kwargs)
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if not self.model_tokenizer:
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raise ValueError(
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"The model tokenizer must be passed to the ReasoningParser "
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"constructor during construction."
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)
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self.start_token_id = self.vocab.get(self.start_token)
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self.end_token_id = self.vocab.get(self.end_token)
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self.response_start_token_id = self.vocab.get(self.response_start_token)
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self.response_end_token_id = self.vocab.get(self.response_end_token)
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self.newline_token_id = self.vocab.get(self.newline_token)
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self.parser_token_ids = [self.end_token_id, self.response_end_token_id]
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if self.start_token_id is None or self.end_token_id is None:
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raise RuntimeError(
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"Ernie45 reasoning parser could not locate think start/end "
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"tokens in the tokenizer!"
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)
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def extract_reasoning_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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) -> DeltaMessage | None:
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"""
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Extract reasoning content from a delta message.
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Handles streaming output where previous + delta = current.
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Uses token IDs for faster processing.
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The Ernie45 thinking model ouput format is
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abc\n</think>\n\n<response>\ndef\n</response>\n
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or abc\n</think>\ndef
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- 'abc' goes to reasoning
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- 'def' goes to content
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"""
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# Skip single special tokens
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if len(delta_token_ids) == 1 and (
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delta_token_ids[0]
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in [
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self.start_token_id,
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self.end_token_id,
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self.response_start_token_id,
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self.response_end_token_id,
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]
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):
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return None
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# No <think> in previous or delta, also need to check for </think>.
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# Because the model may have generated </think> without <think>
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if self.end_token_id in delta_token_ids:
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# </think> in delta with more tokens,
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# extract reasoning content and content
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think_end_index = delta_text.find(self.end_token)
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reasoning = delta_text[:think_end_index]
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content = delta_text[think_end_index + len(self.end_token) :]
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content = content.lstrip("\n")
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response_start_idx = content.find(self.response_start_token)
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response_end_idx = content.rfind(self.response_end_token)
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if response_start_idx != -1:
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content = content[response_start_idx + len(self.response_start_token) :]
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if response_end_idx != -1:
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content = content[:response_end_idx]
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return DeltaMessage(
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reasoning=reasoning,
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content=content if content else None,
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)
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elif self.end_token_id in previous_token_ids:
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# </think> in previous, thinking content ends
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content = delta_text
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if self.response_start_token_id in delta_token_ids:
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content = content.lstrip("\n")
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response_start_idx = content.find(self.response_start_token)
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content = content[response_start_idx + len(self.response_start_token) :]
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# if have </response>, remove it
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response_end_idx = content.rfind(self.response_end_token)
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if response_end_idx != -1:
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content = content[:response_end_idx]
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elif self.response_end_token_id in delta_token_ids:
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response_end_idx = content.rfind(self.response_end_token)
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content = content[:response_end_idx]
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# remove \n after </think> or </response>
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if previous_token_ids[-1] in self.parser_token_ids and (
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len(delta_token_ids) > 0 and delta_token_ids[0] == self.newline_token_id
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):
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content = content.lstrip("\n")
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# remove \n after </think>\n
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if (
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len(previous_token_ids) > 1
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and previous_token_ids[-2] == self.end_token_id
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) and (
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len(delta_token_ids) > 0 and delta_token_ids[0] == self.newline_token_id
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):
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content = content.lstrip("\n")
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return DeltaMessage(content=content if content else None)
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else:
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# no </think> in previous or delta, reasoning content continues
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return DeltaMessage(reasoning=delta_text)
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def extract_reasoning(
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self, model_output: str, request: ChatCompletionRequest
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) -> tuple[str | None, str | None]:
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"""
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Extract reasoning content from the model output.
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The Ernie45 thinking model ouput format is
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abc\n</think>\n\n\n<response>\ndef\n</response>\n
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or abc\n</think>\ndef
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- 'abc' goes to reasoning
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- 'def' goes to content
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Returns:
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tuple[Optional[str], Optional[str]]: reasoning content and content
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"""
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reasoning, content = super().extract_reasoning(model_output, request)
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if content:
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start_idx = content.find(self.response_start_token)
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end_idx = content.rfind(self.response_end_token)
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# Simultaneously existing and in the correct order
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if start_idx != -1 and end_idx != -1 and start_idx < end_idx:
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content = content[start_idx + len(self.response_start_token) : end_idx]
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final_content = content or None
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return reasoning, final_content
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