import json import random import re from pathlib import Path import tiktoken from datasets import Dataset from opencompass.datasets.base import BaseDataset from opencompass.openicl import BaseEvaluator from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS @LOAD_DATASET.register_module() class CDMEDataset(BaseDataset): @staticmethod def load( path: str, length: int, depth: int, tokenizer_model: str, file_list: 'list[str]', num_repeats_per_file: int, length_buffer: int, guide: bool, language: str, needles: 'list[str]', diff: int, retrieval_question: str, answer: str, keyword: str, ): data = {'prompt': [], 'answer': []} tokenizer = tiktoken.encoding_for_model(tokenizer_model) def _generate_context(tokens_context, depth_percent, needles): tokens_needle = [ _get_tokens_from_context(needle) for needle in needles ] insertion_points = [] total_length = len(tokens_context) for i, needle_tokens in enumerate(tokens_needle): if i == 0: insertion_point = int(total_length * (depth_percent / 100)) else: insertion_point = int(insertion_points[i - 1] + len(tokens_needle[i - 1]) + total_length * (diff / 100)) insertion_point = min( insertion_point, total_length + sum(len(tn) for tn in tokens_needle[:i])) insertion_points.append(insertion_point) for i, needle_tokens in enumerate(tokens_needle): tokens_context = tokens_context[:insertion_points[i]] \ + needle_tokens + tokens_context[insertion_points[i]:] for j in range(i + 1, len(insertion_points)): insertion_points[j] += len(needle_tokens) new_context = _decode_tokens(tokens_context) return new_context def _get_tokens_from_context(context): if isinstance(context, list): return [tokenizer.encode(item) for item in context] else: return tokenizer.encode(context) def _decode_tokens(tokens): return tokenizer.decode(tokens) def _modify_retrieval_question(retrieval_question): if language == 'Chinese': parts = retrieval_question.split('请按照') guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题' '最相关的内容是什么。请按照' + parts[1]) return guide_retrieval_question elif language == 'English': parts = retrieval_question.split('Please answer in the format') guide_retrieval_question = ( parts[0] + 'Before answering, please consider' ' what in the document is most relevant to this question.' ' Please answer in the format' + parts[1]) return guide_retrieval_question else: raise ValueError(f"Language '{language}' is not supported.") def _generate_prompt(context, retrieval_question): if guide: retrieval_question = _modify_retrieval_question( retrieval_question) if language == 'Chinese': prompt = ('你是一个善于回答用户问题的智能AI助手\n' '请保持你的回答简洁清楚。不要说和下面文档中的无关的话' '，或重复你的回答\n' f'用户现在给你的文档是{context}\n\n' f'现在请问：{retrieval_question}') elif language == 'English': prompt = ('You are an intelligent AI assistant skilled in ' 'answering user questions.\n' 'Please keep your answers concise and clear. Do not' ' talk about irrelevant topics or repeat your ' 'answers.\n' f'The document given to you by the user is {context}' f'\n\nNow, the question is: {retrieval_question}') else: raise ValueError(f"Language '{language}' is not supported.") return prompt files = Path(path).glob('*.jsonl') for file in files: if file.name not in file_list: continue with open(file, 'r', encoding='utf-8') as f: lines_bak = [json.loads(line.strip()) for line in f] lines = lines_bak.copy() for counter in range(num_repeats_per_file): random.seed(counter) random.shuffle(lines) context_length = length - length_buffer target_length_per_record = context_length - \ sum(len(tokens) for tokens in _get_tokens_from_context(needles)) accumulated_tokens = [] for line in lines: tokens_current_line = _get_tokens_from_context( line['text']) accumulated_tokens.extend(tokens_current_line) if len(accumulated_tokens) >= target_length_per_record: break processed_text = _generate_context( accumulated_tokens[:target_length_per_record], depth, needles) processed_prompt = _generate_prompt(processed_text, retrieval_question) data['prompt'].append(processed_prompt) data['answer'].append(answer + '*' + keyword) dataset = Dataset.from_dict({ 'prompt': data['prompt'], 'answer': data['answer'], }) return dataset class CDMEEvaluator(BaseEvaluator): def levenshtein_distance(self, s1, s2): if len(s1) < len(s2): return self.levenshtein_distance(s2, s1) if len(s2) == 0: return len(s1) previous_row = range(len(s2) + 1) for i, c1 in enumerate(s1): current_row = [i + 1] for j, c2 in enumerate(s2): insertions = previous_row[j + 1] + 1 deletions = current_row[j] + 1 substitutions = previous_row[j] + (c1 != c2) current_row.append(min(insertions, deletions, substitutions)) previous_row = current_row return previous_row[-1] def score(self, predictions, references): if len(predictions) != len(references): return { 'error': 'predictions and references have different lengths' } total_score = 0 details = [] for prediction, reference in zip(predictions, references): keyword = reference.split('*')[1] reference = reference.split('*')[0] prediction = re.sub(r'\s+', '', prediction) reference = re.sub(r'\s+', '', reference) edit_distance = self.levenshtein_distance(prediction, reference) max_len = max(len(prediction), len(reference)) score = 100 * (1 - edit_distance / max_len) if max_len != 0 else 100 if keyword in prediction: print(f'{keyword} is in {prediction}') score = 100 else: print(f'{keyword} is not in {prediction}') score = 0.2 * score detail = { 'pred': prediction, 'answer': reference, 'edit_distance': edit_distance, 'score': score } total_score += score details.append(detail) average_score = total_score / len(predictions) if predictions else 0 result = {'score': average_score, 'details': details} return result @TEXT_POSTPROCESSORS.register_module('cdme') def cdme_postprocess(text: str) -> str: return text @TEXT_POSTPROCESSORS.register_module('cdme_dataset') def cdme_dataset_postprocess(text: str) -> str: return text