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import json |
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import os |
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import random |
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import re |
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from pathlib import Path |
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import tiktoken |
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from datasets import Dataset |
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from opencompass.datasets.base import BaseDataset |
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from opencompass.openicl import BaseEvaluator |
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from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS |
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def get_random_line_by_language(file_path, language): |
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with open(file_path, 'r', encoding='utf-8') as file: |
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lines = [ |
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json.loads(line.strip()) for line in file |
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if json.loads(line.strip())['language'] == language |
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] |
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if lines: |
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random_line = random.choice(lines) |
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return { |
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'needle': random_line['needle'], |
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'retrieval_question': random_line['retrieval_question'], |
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'keyword': random_line['arg2'] |
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} |
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else: |
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return None |
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@LOAD_DATASET.register_module() |
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class NeedleBenchOriginDataset(BaseDataset): |
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@staticmethod |
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def load( |
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path: str, |
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length: int, |
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depth: int, |
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tokenizer_model: str, |
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file_list: list[str], |
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num_repeats_per_file: int, |
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length_buffer: int, |
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guide: bool, |
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language: str, |
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needle_file_name: str, |
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): |
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data = {'prompt': [], 'answer': []} |
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tokenizer = tiktoken.encoding_for_model(tokenizer_model) |
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def _generate_context(tokens_context, depth_percent, needle): |
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tokens_needle = _get_tokens_from_context(needle) |
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insertion_point = int(len(tokens_context) * (depth_percent / 100)) |
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tokens_context = (tokens_context[:insertion_point] + |
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tokens_needle + tokens_context[insertion_point:]) |
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new_context = _decode_tokens(tokens_context) |
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return new_context |
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def _get_tokens_from_context(context): |
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return tokenizer.encode(context) |
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def _decode_tokens(tokens): |
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return tokenizer.decode(tokens) |
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def _modify_retrieval_question(retrieval_question): |
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if language == 'Chinese': |
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parts = retrieval_question.split('请按照') |
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guide_retrieval_question = (parts[0] + '在回答之前，请思考文档中与此问题' |
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'最相关的内容是什么。请按照' + parts[1]) |
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return guide_retrieval_question |
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elif language == 'English': |
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parts = retrieval_question.split('Please answer in the format') |
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guide_retrieval_question = ( |
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parts[0] + 'Before answering, please consider' |
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' what in the document is most relevant to this question.' |
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' Please answer in the format' + parts[1]) |
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return guide_retrieval_question |
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else: |
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raise ValueError(f"Language '{language}' is not supported.") |
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def _generate_prompt(context, retrieval_question): |
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if guide: |
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retrieval_question = _modify_retrieval_question( |
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retrieval_question) |
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if language == 'Chinese': |
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prompt = ('你是一个善于回答用户问题的智能AI助手\n' |
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'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' |
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'，或重复你的回答\n' |
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f'用户现在给你的文档是{context}\n\n' |
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f'现在请问：{retrieval_question}') |
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elif language == 'English': |
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prompt = ('You are an intelligent AI assistant skilled in ' |
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'answering user questions.\n' |
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'Please keep your answers concise and clear. Do not' |
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' talk about irrelevant topics or repeat your ' |
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'answers.\n' |
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f'The document given to you by the user is {context}' |
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f'\n\nNow, the question is: {retrieval_question}') |
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else: |
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raise ValueError(f"Language '{language}' is not supported.") |
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return prompt |
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files = Path(path).glob('*.jsonl') |
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for file in files: |
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if file.name not in file_list: |
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continue |
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with open(file, 'r', encoding='utf-8') as f: |
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lines_bak = [json.loads(line.strip()) for line in f] |
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lines = lines_bak.copy() |
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for counter in range(num_repeats_per_file): |
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random.seed(counter) |
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random.shuffle(lines) |
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needle_file_path = os.path.join(path, needle_file_name) |
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random_needle = get_random_line_by_language( |
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needle_file_path, language) |
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needle = '\n' + random_needle['needle'] + '\n' |
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retrieval_question = random_needle['retrieval_question'] |
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keyword = random_needle['keyword'] |
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context_length = length - length_buffer |
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target_length_per_record = context_length - len( |
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_get_tokens_from_context(needle)) |
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target_length_per_record = max(target_length_per_record, 0) |
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accumulated_tokens = [] |
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for line in lines: |
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tokens_current_line = _get_tokens_from_context( |
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line['text']) |
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accumulated_tokens.extend(tokens_current_line) |
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if len(accumulated_tokens) >= target_length_per_record: |
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break |
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processed_text = _generate_context( |
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accumulated_tokens[:target_length_per_record], depth, |
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needle) |
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processed_prompt = _generate_prompt(processed_text, |
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retrieval_question) |
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data['prompt'].append(processed_prompt) |
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data['answer'].append(needle + '*' + keyword) |
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dataset = Dataset.from_dict({ |
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'prompt': data['prompt'], |
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'answer': data['answer'], |
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}) |
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return dataset |
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class NeedleBenchOriginEvaluator(BaseEvaluator): |
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def __init__(self, use_trim=False): |
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self.use_trim = use_trim |
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@staticmethod |
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def _trim_prediction(prediction, reference): |
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"""Trims the prediction string based on the length of the reference |
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string. |
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Args: |
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prediction (str): The prediction string. |
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reference (str): The reference string. |
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Returns: |
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str: The trimmed prediction string. |
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""" |
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l08 = int(0.8 * len(reference)) |
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l12 = int(1.2 * len(reference)) |
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trimmed_prediction = prediction[:l12] |
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if len(trimmed_prediction) > l08 and \ |
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reference[-1] in trimmed_prediction[l08:]: |
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end_pos = l08 + trimmed_prediction[l08:].index(reference[-1]) + 1 |
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trimmed_prediction = trimmed_prediction[:end_pos] |
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return trimmed_prediction |
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def levenshtein_distance(self, s1, s2): |
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if len(s1) < len(s2): |
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return self.levenshtein_distance(s2, s1) |
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if len(s2) == 0: |
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return len(s1) |
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previous_row = range(len(s2) + 1) |
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for i, c1 in enumerate(s1): |
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current_row = [i + 1] |
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for j, c2 in enumerate(s2): |
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insertions = previous_row[j + 1] + 1 |
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deletions = current_row[j] + 1 |
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substitutions = previous_row[j] + (c1 != c2) |
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current_row.append(min(insertions, deletions, substitutions)) |
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previous_row = current_row |
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return previous_row[-1] |
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def score(self, predictions, gold): |
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if len(predictions) != len(gold): |
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return {'error': 'predictions and gold have different lengths'} |
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total_score = 0 |
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details = [] |
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for prediction, reference in zip(predictions, gold): |
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keyword = reference.split('*')[1] |
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reference = reference.split('*')[0] |
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raw_prediction = prediction |
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prediction = re.sub(r'\s+', '', prediction) |
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reference = re.sub(r'\s+', '', reference) |
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if self.use_trim: |
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prediction = NeedleBenchOriginEvaluator._trim_prediction( |
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prediction, reference) |
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edit_distance = self.levenshtein_distance(prediction, reference) |
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max_len = max(len(prediction), len(reference)) |
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score = 100 * (1 - |
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edit_distance / max_len) if max_len != 0 else 100 |
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if keyword in raw_prediction: |
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print(f'{keyword} is in {prediction}') |
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score = 100 |
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else: |
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print(f'{keyword} is not in {prediction}') |
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score = 0.2 * score |
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detail = { |
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'pred': prediction, |
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'answer': reference, |
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'edit_distance': edit_distance, |
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'score': score |
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} |
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total_score += score |
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details.append(detail) |
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average_score = total_score / len(predictions) if predictions else 0 |
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result = {'score': average_score, 'details': details} |
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return result |
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@TEXT_POSTPROCESSORS.register_module('needlebench') |
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def needlebench_postprocess(text: str) -> str: |
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return text |
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@TEXT_POSTPROCESSORS.register_module('needlebench_dataset') |
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def needlebench_dataset_postprocess(text: str) -> str: |
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return text |
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