mirror of
https://github.com/hpcaitech/ColossalAI.git
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125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
# Copyright Unakar
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# Modified from https://github.com/Unakar/Logic-RL/blob/086373176ac198c97277ff50f4b6e7e1bfe669d3/verl/utils/reward_score/kk.py#L99
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Dict, Optional, Tuple
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def validate_response_structure(processed_str: str, tags: Dict = None) -> bool:
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"""Performs comprehensive validation of response structure.
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Args:
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processed_str: Processed response string from the model
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Returns:
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Boolean indicating whether all formatting requirements are met
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"""
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validation_passed = True
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# Check required tags
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if tags is None:
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tags = {
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"think_start": {"text": "<think>", "num_occur": 1},
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"think_end": {"text": "</think>", "num_occur": 1},
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"answer_start": {"text": "<answer>", "num_occur": 1},
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"answer_end": {"text": "</answer>", "num_occur": 1},
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}
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positions = {}
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for tag_name, tag_info in tags.items():
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tag_str = tag_info["text"]
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expected_count = tag_info["num_occur"]
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count = processed_str.count(tag_str)
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positions[tag_name] = pos = processed_str.find(tag_str)
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if count != expected_count:
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validation_passed = False
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# Verify tag order
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if (
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positions["think_start"] > positions["think_end"]
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or positions["think_end"] > positions["answer_start"]
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or positions["answer_start"] > positions["answer_end"]
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):
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validation_passed = False
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if len(processed_str) - positions["answer_end"] != len(tags["answer_end"]["text"]):
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validation_passed = False
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return validation_passed
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def extract_solution(solution_str: str) -> Tuple[Optional[str], str]:
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"""Extracts the final answer from the model's response string.
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Args:
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solution_str: Raw response string from the language model
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Returns:
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Tuple containing (extracted_answer, processed_string)
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"""
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# Extract final answer using XML-style tags
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answer_pattern = r"<answer>(.*?)</answer>"
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matches = list(re.finditer(answer_pattern, solution_str, re.DOTALL))
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if not matches:
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return None, solution_str
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final_answer = matches[-1].group(1).strip()
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return final_answer, solution_str
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def extract_boxed_solution(text: str) -> Optional[str]:
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"""
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Modified from: https://gist.github.com/lewtun/9c2ce1937b741404090a3dc4c7c022b3
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Retrieves the content from the last occurrence of `\boxed{}` in a LaTeX-like string.
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Args:
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text (str): A string potentially containing LaTeX-style boxed expressions.
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Returns:
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Optional[str]: The text inside the final `\boxed{}` if successfully extracted;
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returns `None` if no properly closed box is found.
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Examples:
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>>> extract_boxed_solution("The answer is \\boxed{42}.")
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'42'
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>>> extract_boxed_solution("Here is an unmatched \\boxed{42")
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None
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"""
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try:
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# Find the last occurrence of "\boxed{"
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start_idx = text.rindex("\\boxed{")
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# Move past "\boxed{" to find the start of the content
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content_start = start_idx + len("\\boxed{")
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open_braces = 1
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pos = content_start
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# Traverse the string to find the matching closing brace
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while open_braces > 0 and pos < len(text):
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if text[pos] == "{":
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open_braces += 1
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elif text[pos] == "}":
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open_braces -= 1
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pos += 1
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# If all braces are matched, extract and return the content
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if open_braces == 0:
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return text[content_start : pos - 1].strip()
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else:
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return None
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except ValueError:
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# "\boxed{" not found
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return None
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except Exception:
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# Any other unexpected error
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return None
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