Add minimal vLLM 0.16.1 build repo for BI-V150
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vllm/reasoning/qwen3_reasoning_parser.py
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133
vllm/reasoning/qwen3_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 vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionRequest,
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)
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from vllm.entrypoints.openai.engine.protocol import DeltaMessage
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from vllm.entrypoints.openai.responses.protocol import (
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ResponsesRequest,
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)
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from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
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class Qwen3ReasoningParser(BaseThinkingReasoningParser):
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"""
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Reasoning parser for the Qwen3/Qwen3.5 model family.
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The Qwen3 model family uses <think>...</think> tokens to denote reasoning
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text. Starting with Qwen3.5, the chat template places <think> in the
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prompt so only </think> appears in the generated output. The model
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provides a strict switch to disable reasoning output via the
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'enable_thinking=False' parameter.
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When thinking is disabled, the template places <think>\\n\\n</think>\\n\\n
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in the prompt. The serving layer detects this via prompt_is_reasoning_end
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and routes deltas as content without calling the streaming parser.
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NOTE: Models up to the 2507 release (e.g., Qwen/Qwen3-235B-A22B-Instruct-2507)
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use an older chat template where the model generates <think> itself.
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This parser handles both styles: if <think> appears in the generated output
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it is stripped before extraction (non-streaming) or skipped (streaming).
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"""
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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 extract_reasoning(
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self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
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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 <think> token is placed in the prompt by the chat template,
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so typically only </think> appears in the generated output.
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If <think> is present (e.g. from a different template), it is
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stripped before extraction.
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When thinking is disabled (no </think> in output), returns
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(None, model_output) to indicate all output is 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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# Strip <think> if present in the generated output.
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model_output_parts = model_output.partition(self.start_token)
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model_output = (
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model_output_parts[2] if model_output_parts[1] else model_output_parts[0]
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)
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if self.end_token not in model_output:
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# No end token means thinking is disabled or the model
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# did not produce reasoning. Treat everything as content.
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return None, model_output
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# Extract reasoning content from the model output.
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reasoning, _, content = model_output.partition(self.end_token)
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final_content = content or None
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return reasoning, final_content
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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 streaming delta.
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Since <think> is placed in the prompt by the chat template, all
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generated tokens before </think> are reasoning and tokens after
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are content.
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NOTE: When thinking is disabled, no think tokens appear in the
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generated output. The serving layer detects this via
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prompt_is_reasoning_end and routes deltas as content without
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calling this method.
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"""
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# Strip <think> from delta if present (old template / edge case
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# where the model generates <think> itself).
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if self.start_token_id in delta_token_ids:
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start_idx = delta_text.find(self.start_token)
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if start_idx >= 0:
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delta_text = delta_text[start_idx + len(self.start_token) :]
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if self.end_token_id in delta_token_ids:
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# End token in this delta: split reasoning from content.
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end_index = delta_text.find(self.end_token)
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if end_index >= 0:
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reasoning = delta_text[:end_index]
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content = delta_text[end_index + len(self.end_token) :]
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if not reasoning and not content:
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return None
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return DeltaMessage(
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reasoning=reasoning if reasoning else None,
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content=content if content else None,
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)
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# end_token_id in IDs but not in text (already stripped)
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return None
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# No end token in this delta.
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if not delta_text:
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# Nothing left after stripping start token.
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return None
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elif self.end_token_id in previous_token_ids:
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# End token already passed: everything is content now.
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return DeltaMessage(content=delta_text)
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else:
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# No end token yet: still in reasoning phase.
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return DeltaMessage(reasoning=delta_text)
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