# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from typing import Optional, Union
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("glm45")
class Glm4MoeModelReasoningParser(ReasoningParser):
"""
Reasoning parser for the Glm4MoeModel model.
The Glm4MoeModel 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: PreTrainedTokenizerBase):
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_start_token_id is None
or self.think_end_token_id is None):
raise RuntimeError(
"Glm4MoeModel reasoning parser could not locate "
"think start/end tokens in the tokenizer!")
def is_reasoning_end(self, input_ids: list[int]) -> bool:
return self.think_end_token_id in input_ids
def extract_content_ids(self, input_ids: list[int]) -> list[int]:
"""
Extract the content after the end tokens
"""
if self.think_end_token_id not in input_ids[:-1]:
return []
else:
return input_ids[input_ids.index(self.think_end_token_id) + 1:]
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 None
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):]
return DeltaMessage(reasoning_content=reasoning_content,
content=content if content else None)
elif self.think_end_token_id in previous_token_ids:
# in previous, in previous,
# reasoning content continues
return DeltaMessage(content=delta_text)
else:
# in previous, no in previous or delta,
# reasoning content continues
return DeltaMessage(reasoning_content=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):]
return DeltaMessage(reasoning_content=reasoning_content,
content=content if content else None)
else:
# in delta, no in delta,
# reasoning content continues
return DeltaMessage(reasoning_content=delta_text)
else:
# thinking is disabled, just content
return DeltaMessage(content=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