sglang0.4.5.post1/python/sglang/srt/reasoning_parser.py

155 lines
5.5 KiB
Python

import re
from typing import Dict, Tuple
class StreamingParseResult:
"""Result of streaming incremental parsing."""
def __init__(self, normal_text: str = "", reasoning_text: str = ""):
self.normal_text = normal_text
self.reasoning_text = reasoning_text
class BaseReasoningFormatDetector:
"""Base class providing two sets of interfaces: one-time and streaming incremental."""
def __init__(
self,
think_start_token: str,
think_end_token: str,
force_reasoning: bool = False,
stream_reasoning: bool = True,
):
self.think_start_token = think_start_token
self.think_end_token = think_end_token
self._in_reasoning = force_reasoning
self.stream_reasoning = stream_reasoning
self._buffer = ""
self.stripped_think_start = False
def detect_and_parse(self, text: str) -> StreamingParseResult:
"""
One-time parsing: Detects and parses reasoning sections in the provided text.
Returns both reasoning content and normal text separately.
"""
text = text.replace(self.think_start_token, "").strip()
if self.think_end_token not in text:
# Assume reasoning was truncated before `</think>` token
return StreamingParseResult(reasoning_text=text)
# Extract reasoning content
splits = text.split(self.think_end_token, maxsplit=1)
reasoning_text = splits[0]
text = splits[1].strip()
return StreamingParseResult(normal_text=text, reasoning_text=reasoning_text)
def parse_streaming_increment(self, new_text: str) -> StreamingParseResult:
"""
Streaming incremental parsing for reasoning content.
Handles partial reasoning tags and content.
If stream_reasoning is False:
Accumulates reasoning content until the end tag is found
If stream_reasoning is True:
Streams reasoning content as it arrives
"""
self._buffer += new_text
current_text = self._buffer
# Strip `<think>` token if present
if not self.stripped_think_start and self.think_start_token in current_text:
current_text = current_text.replace(self.think_start_token, "")
self.stripped_think_start = True
# Handle end of reasoning block
if self._in_reasoning and self.think_end_token in current_text:
end_idx = current_text.find(self.think_end_token)
reasoning_text = current_text[:end_idx]
self._buffer = ""
self._in_reasoning = False
normal_text = current_text[end_idx + len(self.think_end_token) :]
return StreamingParseResult(
normal_text=normal_text, reasoning_text=reasoning_text.rstrip()
)
# Continue with reasoning content
if self._in_reasoning:
if self.stream_reasoning:
# Stream the content immediately
self._buffer = ""
return StreamingParseResult(reasoning_text=current_text)
else:
return StreamingParseResult()
# If we're not in a reasoning block return as normal text
if not self._in_reasoning:
self._buffer = ""
return StreamingParseResult(normal_text=new_text)
return StreamingParseResult()
class DeepSeekR1Detector(BaseReasoningFormatDetector):
"""
Detector for DeepSeek-R1 model.
Assumes reasoning format:
(<think>)*(.*)</think>
Returns all the text before the </think> tag as `reasoning_text`
and the rest of the text as `normal_text`.
Args:
stream_reasoning (bool): If False, accumulates reasoning content until the end tag.
If True, streams reasoning content as it arrives.
"""
def __init__(self, stream_reasoning: bool = True):
# DeepSeek-R1 is assumed to be reasoning until `</think>` token
super().__init__(
"<think>",
"</think>",
force_reasoning=True,
stream_reasoning=stream_reasoning,
)
# https://github.com/sgl-project/sglang/pull/3202#discussion_r1950153599
class ReasoningParser:
"""
Parser that handles both streaming and non-streaming scenarios for extracting
reasoning content from model outputs.
Args:
model_type (str): Type of model to parse reasoning from
stream_reasoning (bool): If Flase, accumulates reasoning content until complete.
If True, streams reasoning content as it arrives.
"""
DetectorMap: Dict[str, BaseReasoningFormatDetector] = {
"deepseek-r1": DeepSeekR1Detector
}
def __init__(self, model_type: str = None, stream_reasoning: bool = True):
if not model_type:
raise ValueError("Model type must be specified")
detector_class = self.DetectorMap.get(model_type.lower())
if not detector_class:
raise ValueError(f"Unsupported model type: {model_type}")
self.detector = detector_class(stream_reasoning=stream_reasoning)
def parse_non_stream(self, full_text: str) -> Tuple[str, str]:
"""Non-streaming call: one-time parsing"""
ret = self.detector.detect_and_parse(full_text)
return ret.reasoning_text, ret.normal_text
def parse_stream_chunk(self, chunk_text: str) -> Tuple[str, str]:
"""Streaming call: incremental parsing"""
ret = self.detector.parse_streaming_increment(chunk_text)
return ret.reasoning_text, ret.normal_text