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import json
import os
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
def get_random_line_by_language(file_path, language):
with open(file_path, 'r', encoding='utf-8') as file:
lines = [
json.loads(line.strip()) for line in file
if json.loads(line.strip())['language'] == language
]
if lines:
random_line = random.choice(lines)
return {
'needle': random_line['needle'],
'retrieval_question': random_line['retrieval_question'],
'keyword': random_line['arg2']
}
else:
return None
@LOAD_DATASET.register_module()
class NeedleBenchOriginDataset(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,
needle_file_name: str,
):
data = {'prompt': [], 'answer': []}
tokenizer = tiktoken.encoding_for_model(tokenizer_model)
def _generate_context(tokens_context, depth_percent, needle):
tokens_needle = _get_tokens_from_context(needle)
insertion_point = int(len(tokens_context) * (depth_percent / 100))
tokens_context = (tokens_context[:insertion_point] +
tokens_needle + tokens_context[insertion_point:])
new_context = _decode_tokens(tokens_context)
return new_context
def _get_tokens_from_context(context):
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)
needle_file_path = os.path.join(path, needle_file_name)
random_needle = get_random_line_by_language(
needle_file_path, language)
needle = '\n' + random_needle['needle'] + '\n'
retrieval_question = random_needle['retrieval_question']
keyword = random_needle['keyword']
context_length = length - length_buffer
target_length_per_record = context_length - len(
_get_tokens_from_context(needle))
target_length_per_record = max(target_length_per_record, 0)
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,
needle)
processed_prompt = _generate_prompt(processed_text,
retrieval_question)
data['prompt'].append(processed_prompt)
data['answer'].append(needle + '*' + keyword)
dataset = Dataset.from_dict({
'prompt': data['prompt'],
'answer': data['answer'],
})
return dataset
class NeedleBenchOriginEvaluator(BaseEvaluator):
def __init__(self, use_trim=False):
self.use_trim = use_trim
@staticmethod
def _trim_prediction(prediction, reference):
"""Trims the prediction string based on the length of the reference
string.
Args:
prediction (str): The prediction string.
reference (str): The reference string.
Returns:
str: The trimmed prediction string.
"""
l08 = int(0.8 * len(reference))
l12 = int(1.2 * len(reference))
trimmed_prediction = prediction[:l12]
if len(trimmed_prediction) > l08 and \
reference[-1] in trimmed_prediction[l08:]:
end_pos = l08 + trimmed_prediction[l08:].index(reference[-1]) + 1
trimmed_prediction = trimmed_prediction[:end_pos]
return trimmed_prediction
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, gold):
if len(predictions) != len(gold):
return {'error': 'predictions and gold have different lengths'}
total_score = 0
details = []
for prediction, reference in zip(predictions, gold):
keyword = reference.split('*')[1]
reference = reference.split('*')[0]
raw_prediction = prediction
prediction = re.sub(r'\s+', '', prediction)
reference = re.sub(r'\s+', '', reference)
if self.use_trim:
prediction = NeedleBenchOriginEvaluator._trim_prediction(
prediction, 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 raw_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('needlebench')
def needlebench_postprocess(text: str) -> str:
return text
@TEXT_POSTPROCESSORS.register_module('needlebench_dataset')
def needlebench_dataset_postprocess(text: str) -> str:
return text
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