"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
from collections.abc import Sequence
from typing import Optional, Union
from fastdeploy.entrypoints.openai.protocol import (ChatCompletionRequest,
DeltaMessage)
from fastdeploy.reasoning import ReasoningParser, ReasoningParserManager
@ReasoningParserManager.register_module("qwen3")
class Qwen3ReasoningParser(ReasoningParser):
"""
Reasoning parser for ernir_vl model.
The ernie_vl model uses ...... tokens to denote reasoning text
within its output. The model provides a strict switch to disable reasoning
output via the 'enable_thinking=False' parameter. This parser extracts the
reasoning content enclosed by and tokens from the model's
output.
"""
def __init__(self, tokenizer):
super().__init__(tokenizer)
self.think_start_token = ""
self.think_end_token = ""
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ReasoningParser "
"constructor during construction.")
self.think_start_token_id = self.vocab.get(self.think_start_token)
self.think_end_token_id = self.vocab.get(self.think_end_token)
if self.think_end_token_id is None:
raise RuntimeError(
"Qwen3 reasoning parser could not locate think end "
"tokens in the tokenizer!")
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.
For text abcxyz:
- 'abc' goes to reasoning_content
- 'xyz' goes to content
"""
# Skip single special tokens
if len(delta_token_ids) == 1 and (delta_token_ids[0] in [
self.think_start_token_id, self.think_end_token_id
]):
return "", ""
if self.think_start_token_id in previous_token_ids:
if self.think_end_token_id in delta_token_ids:
# in previous, in delta,
# extract reasoning content
end_index = delta_text.find(self.think_end_token)
reasoning_content = delta_text[:end_index]
content = delta_text[end_index + len(self.think_end_token):]
content = content if content else None
return reasoning_content, content
elif self.think_end_token_id in previous_token_ids:
# in previous, in previous,
# reasoning content continues
return "", delta_text
else:
# in previous, no in previous or delta,
# reasoning content continues
return delta_text, ""
elif self.think_start_token_id in delta_token_ids:
if self.think_end_token_id in delta_token_ids:
# in delta, in delta, extract reasoning content
start_index = delta_text.find(self.think_start_token)
end_index = delta_text.find(self.think_end_token)
reasoning_content = delta_text[start_index +
len(self.think_start_token
):end_index]
content = delta_text[end_index + len(self.think_end_token):]
content = content if content else None
return reasoning_content, content
else:
# in delta, no in delta,
# reasoning content continues
return delta_text, ""
else:
# thinking is disabled, just content
return "", delta_text
def extract_reasoning_content(
self, model_output: str, request: ChatCompletionRequest
) -> tuple[Optional[str], Optional[str]]:
"""
Extract reasoning content from the model output.
For text abcxyz:
- 'abc' goes to reasoning_content
- 'xyz' goes to content
Returns:
tuple[Optional[str], Optional[str]]: reasoning content and content
"""
# Check if the model output contains the and tokens.
if (self.think_start_token not in model_output
or self.think_end_token not in model_output):
return None, model_output
# Check if the is present in the model output, remove it
# if it is present.
model_output_parts = model_output.partition(self.think_start_token)
model_output = model_output_parts[2] if model_output_parts[
1] else model_output_parts[0]
# Check if the model output contains the tokens.
# If the end token is not found, return the model output as is.
if self.think_end_token not in model_output:
return None, model_output
# Extract reasoning content from the model output.
reasoning_content, _, content = model_output.partition(
self.think_end_token)
final_content = content or None
return reasoning_content, final_content