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python split_data_by_length.py \
--input_path train_data \
--output_dir train_data_split \
--cache_dir .cache \
--log_name .split_log \
--length_list 0 500 1000 2000 3000 4000 5000 6000 7000 \
--model_name_or_path BAAI/bge-m3 \
--num_proc 16 \
--overwrite False
"""
import os
import json
import math
import time
import argparse
import datasets
from tqdm import tqdm
from pprint import pprint
from transformers import AutoTokenizer
from datasets import load_dataset, Features, Value, Sequence
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input_path', type=str, required=True, help='the path of input datas')
parser.add_argument('--output_dir', type=str, required=True, help='the dir of output datas')
parser.add_argument('--cache_dir', type=str, default=None, help='the cache dir')
parser.add_argument('--log_name', type=str, default='.split_log', help='the name of log file, default: `.split_log`, which will be saved to `output_dir`')
parser.add_argument('--length_list', type=int, default=[0, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000], nargs='+', help='the length list to split')
parser.add_argument('--model_name_or_path', type=str, default='BAAI/bge-m3', help='the model name or path of the tokenizer')
parser.add_argument('--num_proc', type=int, default=16, help='the number of process, default: 16')
parser.add_argument('--overwrite', action='store_true', default=False, help='whether to overwrite the output file, default: False')
args = parser.parse_args()
return args
class SplitByLengthHandler:
def __init__(self,
model_name_or_path: str,
cache_dir: str=None,
num_proc: int=16,
length_list: list=[0, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000],
overwrite: bool=False):
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
self.cache_dir = cache_dir
self.num_proc = num_proc
self.length_ranges_list = self._get_length_ranges_list(length_list)
self.overwrite = overwrite
pprint(self.length_ranges_list)
def _map_func(examples):
results = {}
results['idx'] = []
results['max_length'] = []
for i in range(len(examples['query'])):
idx = examples['idx'][i]
query = examples['query'][i]
pos, neg = examples['pos'][i], examples['neg'][i]
all_texts = [query] + pos + neg
max_len = 0
for x in all_texts:
tokenized_x = self.tokenizer(x)['input_ids']
if len(tokenized_x) > max_len:
max_len = len(tokenized_x)
results['idx'].append(idx)
results['max_length'].append(max_len)
return results
self._map_func = _map_func
@staticmethod
def _get_length_ranges_list(length_list: list):
length_ranges_list = []
length_list = sorted(length_list)
for i in range(len(length_list)):
length_l = length_list[i]
if i == len(length_list) - 1:
length_r = math.inf
else:
length_r = length_list[i + 1]
assert 0 <= length_l < length_r
length_ranges_list.append((length_l, length_r))
return length_ranges_list
def _process_dir(self, dir_path: str, output_dir: str):
assert os.path.isdir(dir_path)
log_info_list = []
for file in tqdm(os.listdir(dir_path), desc=f'processing {dir_path}'):
file_path = os.path.join(dir_path, file)
if not file_path.endswith('.jsonl'):
print(f"skip {file_path} ...")
continue
output_path = os.path.join(output_dir, '.'.join(file.split('.')[:-1]))
log_info = self._process_file(file_path, output_path)
log_info_list.append(log_info)
return log_info_list
def _process_file(self, file_path: str, output_path: str):
assert not os.path.isdir(file_path)
start_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
features = Features({
'query': Value('string'),
'pos': Sequence(Value('string')),
'neg': Sequence(Value('string'))
})
kd_features = Features({
'query': Value('string'),
'pos': Sequence(Value('string')),
'neg': Sequence(Value('string')),
'pos_scores': Sequence(Value('float')),
'neg_scores': Sequence(Value('float'))
})
try:
dataset = load_dataset('json', data_files=file_path, cache_dir=self.cache_dir, features=features)['train']
except:
dataset = load_dataset('json', data_files=file_path, cache_dir=self.cache_dir, features=kd_features)['train']
dataset_with_idx_list = []
for i, data in enumerate(dataset):
data['idx'] = i
dataset_with_idx_list.append(data)
dataset_with_idx = datasets.Dataset.from_list(dataset_with_idx_list)
mapped_dataset = dataset_with_idx.map(self._map_func, batched=True, num_proc=self.num_proc)
split_info_dict = {}
for length_l, length_r in self.length_ranges_list:
save_path = output_path + f'_len-{length_l}-{length_r}.jsonl'
if os.path.exists(save_path) and not self.overwrite:
print(f'{save_path} exists, skip')
continue
idxs = mapped_dataset.filter(lambda x: length_l <= x['max_length'] < length_r, num_proc=self.num_proc)
split_dataset = dataset_with_idx.select(idxs['idx'])
split_dataset = split_dataset.remove_columns('idx')
split_info_dict[f'len-{length_l}-{length_r}'] = len(split_dataset)
if len(split_dataset) > 0:
split_dataset.to_json(save_path, force_ascii=False)
end_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
size = len(dataset)
avg_length = sum(mapped_dataset['max_length']) / size
log_info = {
'file_name': os.path.basename(file_path),
'size': size,
'avg_length': avg_length,
'file_path': file_path,
'start_time': start_time,
'end_time': end_time,
'split_info': split_info_dict
}
return log_info
def run(self, input_path: str, output_dir: str, log_name: str=None):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if log_name is None:
log_path = os.path.join(output_dir, '.split_log')
else:
log_path = os.path.join(output_dir, log_name)
log_info_list = []
if os.path.isdir(input_path):
log_info_list = self._process_dir(input_path, output_dir)
else:
file_name = os.path.basename(input_path)
output_path = os.path.join(output_dir, '.'.join(file_name.split('.')[:-1]))
log_info = self._process_file(input_path, output_path)
log_info_list.append(log_info)
with open(log_path, 'a', encoding='utf-8') as f:
for log_info in log_info_list:
json.dump(log_info, f, ensure_ascii=False)
f.write('\n')
def main(args):
input_path = args.input_path
output_dir = args.output_dir
log_name = args.log_name
handler = SplitByLengthHandler(
model_name_or_path=args.model_name_or_path,
cache_dir=args.cache_dir,
num_proc=args.num_proc,
length_list=args.length_list if isinstance(args.length_list, list) else [args.length_list],
overwrite=args.overwrite
)
handler.run(
input_path=input_path,
output_dir=output_dir,
log_name=log_name
)
print('\nDONE!')
if __name__ == "__main__":
args = get_args()
main(args)
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