ColossalAI/applications/ColossalChat/coati/distributed/reward/reward_utils.py
2025-04-28 17:53:20 +08:00

125 lines
4.2 KiB
Python

# Copyright Unakar
# Modified from https://github.com/Unakar/Logic-RL/blob/086373176ac198c97277ff50f4b6e7e1bfe669d3/verl/utils/reward_score/kk.py#L99
#
# 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.
import re
from typing import Dict, Optional, Tuple
def validate_response_structure(processed_str: str, tags: Dict = None) -> bool:
"""Performs comprehensive validation of response structure.
Args:
processed_str: Processed response string from the model
Returns:
Boolean indicating whether all formatting requirements are met
"""
validation_passed = True
# Check required tags
if tags is None:
tags = {
"think_start": {"text": "<think>", "num_occur": 1},
"think_end": {"text": "</think>", "num_occur": 1},
"answer_start": {"text": "<answer>", "num_occur": 1},
"answer_end": {"text": "</answer>", "num_occur": 1},
}
positions = {}
for tag_name, tag_info in tags.items():
tag_str = tag_info["text"]
expected_count = tag_info["num_occur"]
count = processed_str.count(tag_str)
positions[tag_name] = pos = processed_str.find(tag_str)
if count != expected_count:
validation_passed = False
# Verify tag order
if (
positions["think_start"] > positions["think_end"]
or positions["think_end"] > positions["answer_start"]
or positions["answer_start"] > positions["answer_end"]
):
validation_passed = False
if len(processed_str) - positions["answer_end"] != len(tags["answer_end"]["text"]):
validation_passed = False
return validation_passed
def extract_solution(solution_str: str) -> Tuple[Optional[str], str]:
"""Extracts the final answer from the model's response string.
Args:
solution_str: Raw response string from the language model
Returns:
Tuple containing (extracted_answer, processed_string)
"""
# Extract final answer using XML-style tags
answer_pattern = r"<answer>(.*?)</answer>"
matches = list(re.finditer(answer_pattern, solution_str, re.DOTALL))
if not matches:
return None, solution_str
final_answer = matches[-1].group(1).strip()
return final_answer, solution_str
def extract_boxed_solution(text: str) -> Optional[str]:
"""
Modified from: https://gist.github.com/lewtun/9c2ce1937b741404090a3dc4c7c022b3
Retrieves the content from the last occurrence of `\boxed{}` in a LaTeX-like string.
Args:
text (str): A string potentially containing LaTeX-style boxed expressions.
Returns:
Optional[str]: The text inside the final `\boxed{}` if successfully extracted;
returns `None` if no properly closed box is found.
Examples:
>>> extract_boxed_solution("The answer is \\boxed{42}.")
'42'
>>> extract_boxed_solution("Here is an unmatched \\boxed{42")
None
"""
try:
# Find the last occurrence of "\boxed{"
start_idx = text.rindex("\\boxed{")
# Move past "\boxed{" to find the start of the content
content_start = start_idx + len("\\boxed{")
open_braces = 1
pos = content_start
# Traverse the string to find the matching closing brace
while open_braces > 0 and pos < len(text):
if text[pos] == "{":
open_braces += 1
elif text[pos] == "}":
open_braces -= 1
pos += 1
# If all braces are matched, extract and return the content
if open_braces == 0:
return text[content_start : pos - 1].strip()
else:
return None
except ValueError:
# "\boxed{" not found
return None
except Exception:
# Any other unexpected error
return None