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[Bugfix] add qwen3 reasoning-parser fix content is None when disable … (#17369)
Signed-off-by: mofanke <mofanke@gmail.com>
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@ -15,6 +15,7 @@ vLLM currently supports the following reasoning models:
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| [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) | `deepseek_r1` | `guided_json`, `guided_regex` | ❌ |
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| [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | `deepseek_r1` | `guided_json`, `guided_regex` | ✅ |
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| [IBM Granite 3.2 language models](https://huggingface.co/collections/ibm-granite/granite-32-language-models-67b3bc8c13508f6d064cff9a) | `granite` | ❌ | ❌ |
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| [Qwen3 series](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) | `qwen3` | `guided_json`, `guided_regex` | ✅ |
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- IBM Granite 3.2 reasoning is disabled by default; to enable it, you must also pass `thinking=True` in your `chat_template_kwargs`.
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141
tests/reasoning/test_qwen3_reasoning_parser.py
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141
tests/reasoning/test_qwen3_reasoning_parser.py
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@ -0,0 +1,141 @@
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from transformers import AutoTokenizer
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from tests.reasoning.utils import run_reasoning_extraction
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from vllm.reasoning import ReasoningParser, ReasoningParserManager
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parser_name = "qwen3"
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start_token = "<think>"
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end_token = "</think>"
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REASONING_MODEL_NAME = "Qwen/Qwen3-0.6B"
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@pytest.fixture(scope="module")
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def qwen3_tokenizer():
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return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
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# 带 <think></think>，非stream
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WITH_THINK = {
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"output": "<think>This is a reasoning section</think>This is the rest",
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"reasoning_content": "This is a reasoning section",
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"content": "This is the rest",
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}
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# 带 <think></think>，stream
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WITH_THINK_STREAM = {
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"output": "<think>This is a reasoning section</think>This is the rest",
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"reasoning_content": "This is a reasoning section",
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"content": "This is the rest",
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}
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# 不带 <think></think>，非stream
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WITHOUT_THINK = {
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"output": "This is the rest",
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"reasoning_content": None,
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"content": "This is the rest",
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}
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# 不带 <think></think>，stream
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WITHOUT_THINK_STREAM = {
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"output": "This is the rest",
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"reasoning_content": None,
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"content": "This is the rest",
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}
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COMPLETE_REASONING = {
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"output": "<think>This is a reasoning section</think>",
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"reasoning_content": "This is a reasoning section",
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"content": None,
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}
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MULTILINE_REASONING = {
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"output":
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"<think>This is a reasoning\nsection</think>This is the rest\nThat",
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"reasoning_content": "This is a reasoning\nsection",
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"content": "This is the rest\nThat",
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}
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ONLY_OPEN_TAG = {
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"output": "<think>This is a reasoning section",
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"reasoning_content": None,
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"content": "<think>This is a reasoning section",
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}
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ONLY_OPEN_TAG_STREAM = {
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"output": "<think>This is a reasoning section",
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"reasoning_content": "This is a reasoning section",
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"content": None,
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}
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TEST_CASES = [
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pytest.param(
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False,
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WITH_THINK,
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id="with_think",
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),
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pytest.param(
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True,
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WITH_THINK_STREAM,
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id="with_think_stream",
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),
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pytest.param(
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False,
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WITHOUT_THINK,
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id="without_think",
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),
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pytest.param(
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True,
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WITHOUT_THINK_STREAM,
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id="without_think_stream",
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),
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pytest.param(
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False,
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COMPLETE_REASONING,
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id="complete_reasoning",
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),
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pytest.param(
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True,
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COMPLETE_REASONING,
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id="complete_reasoning_stream",
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),
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pytest.param(
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False,
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MULTILINE_REASONING,
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id="multiline_reasoning",
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),
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pytest.param(
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True,
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MULTILINE_REASONING,
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id="multiline_reasoning_stream",
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),
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pytest.param(
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False,
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ONLY_OPEN_TAG,
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id="only_open_tag",
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),
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pytest.param(
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True,
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ONLY_OPEN_TAG_STREAM,
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id="only_open_tag_stream",
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),
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]
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@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
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def test_reasoning(
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streaming: bool,
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param_dict: dict,
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qwen3_tokenizer,
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):
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output = qwen3_tokenizer.tokenize(param_dict["output"])
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output_tokens: list[str] = [
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qwen3_tokenizer.convert_tokens_to_string([token]) for token in output
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]
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parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
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parser_name)(qwen3_tokenizer)
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reasoning, content = run_reasoning_extraction(parser,
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output_tokens,
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streaming=streaming)
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assert reasoning == param_dict["reasoning_content"]
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assert content == param_dict["content"]
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@ -3,10 +3,12 @@
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from .abs_reasoning_parsers import ReasoningParser, ReasoningParserManager
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from .deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
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from .granite_reasoning_parser import GraniteReasoningParser
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from .qwen3_reasoning_parser import Qwen3ReasoningParser
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__all__ = [
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"ReasoningParser",
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"ReasoningParserManager",
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"DeepSeekR1ReasoningParser",
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"GraniteReasoningParser",
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"Qwen3ReasoningParser",
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]
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138
vllm/reasoning/qwen3_reasoning_parser.py
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138
vllm/reasoning/qwen3_reasoning_parser.py
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@ -0,0 +1,138 @@
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# SPDX-License-Identifier: Apache-2.0
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import re
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from collections.abc import Sequence
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from typing import Optional, Union
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from transformers import PreTrainedTokenizerBase
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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DeltaMessage)
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from vllm.logger import init_logger
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from vllm.reasoning import ReasoningParser, ReasoningParserManager
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logger = init_logger(__name__)
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@ReasoningParserManager.register_module("qwen3")
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class Qwen3ReasoningParser(ReasoningParser):
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"""
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Reasoning parser for the Qwen3 model.
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The Qwen3 model uses <think>...</think> tokens to denote reasoning text
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within its output. The model provides a strict switch to disable reasoning
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output via the 'enable_thinking=False' parameter. This parser extracts the
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reasoning content enclosed by <think> and </think> tokens from the model's
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output.
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"""
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def __init__(self, tokenizer: PreTrainedTokenizerBase):
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super().__init__(tokenizer)
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self.think_start_token = "<think>"
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self.think_end_token = "</think>"
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self.reasoning_regex = re.compile(
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rf"{self.think_start_token}(.*?){self.think_end_token}", re.DOTALL)
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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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self.think_start_token_id = self.vocab.get(self.think_start_token)
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self.think_end_token_id = self.vocab.get(self.think_end_token)
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if (self.think_start_token_id is None
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or self.think_end_token_id is None):
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raise RuntimeError(
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"Qwen3 reasoning parser could not locate think start/end "
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"tokens in the tokenizer!")
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def extract_reasoning_content_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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) -> Union[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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For text <think>abc</think>xyz:
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- 'abc' goes to reasoning_content
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- 'xyz' 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 (delta_token_ids[0] in [
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self.think_start_token_id, self.think_end_token_id
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]):
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return None
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if self.think_start_token_id in previous_token_ids:
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if self.think_end_token_id in delta_token_ids:
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# <think> in previous, </think> in delta,
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# extract reasoning content
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end_index = delta_text.find(self.think_end_token)
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reasoning_content = delta_text[:end_index]
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content = delta_text[end_index + len(self.think_end_token):]
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return DeltaMessage(reasoning_content=reasoning_content,
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content=content if content else None)
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elif self.think_end_token_id in previous_token_ids:
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# <think> in previous, </think> in previous,
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# reasoning content continues
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return DeltaMessage(content=delta_text)
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else:
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# <think> in previous, no </think> in previous or delta,
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# reasoning content continues
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return DeltaMessage(reasoning_content=delta_text)
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elif self.think_start_token_id in delta_token_ids:
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logger.info(delta_text)
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if self.think_end_token_id in delta_token_ids:
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# <think> in delta, </think> in delta, extract reasoning content
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start_index = delta_text.find(self.think_start_token)
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end_index = delta_text.find(self.think_end_token)
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reasoning_content = delta_text[start_index +
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len(self.think_start_token
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):end_index]
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content = delta_text[end_index + len(self.think_end_token):]
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return DeltaMessage(reasoning_content=reasoning_content,
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content=content if content else None)
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else:
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# <think> in delta, no </think> in delta,
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# reasoning content continues
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return DeltaMessage(reasoning_content=delta_text)
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else:
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# thinking is disabled, just content
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return DeltaMessage(content=delta_text)
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def extract_reasoning_content(
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self, model_output: str, request: ChatCompletionRequest
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) -> tuple[Optional[str], Optional[str]]:
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# Check if the model output contains the <think> tokens.
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if (self.think_start_token not in model_output
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or self.think_end_token not in model_output):
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return None, model_output
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else:
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# Use a regex to find the reasoning content
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reasoning_content = self.reasoning_regex.findall(model_output)[0]
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# Remove the reasoning content from the model output
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# Although <think> token is always at the
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# beginning of the line, we cannot guarantee that the
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# other models will follow this convention.
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# Therefore, we need to add :start_index.
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start_index = model_output.find(self.think_start_token)
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if start_index != -1:
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end_index = start_index + len(
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f"{self.think_start_token}{reasoning_content}{self.think_end_token}"
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)
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model_output = model_output[:start_index] + \
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model_output[end_index:]
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if len(model_output) == 0:
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return reasoning_content, None
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return reasoning_content, model_output
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