File size: 3,381 Bytes
f2de183 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocess the Geometry3k dataset to parquet format
"""
import os
import datasets
from verl.utils.hdfs_io import copy, makedirs
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default='visual', choices=['visual', 'text'])
parser.add_argument('--local_dir', default='~/data/')
parser.add_argument('--hdfs_dir', default=None)
parser.add_argument('--train_data_size', default=256, type=int)
parser.add_argument('--val_data_size', default=256, type=int)
args = parser.parse_args()
print(f"processing data for mode: {args.mode}")
args.local_dir = os.path.join(args.local_dir, args.mode)
data_source = 'hiyouga/geometry3k'
dataset = datasets.load_dataset(data_source)
train_dataset = dataset['train'].select(range(args.train_data_size))
test_dataset = dataset['test'].select(range(args.val_data_size))
instruction_following = {
"visual": "<image>",
"text": "",
}
# add a row to each data item that represents a unique id
def make_map_fn(split):
def process_fn(example, idx):
problem = example.pop('problem')
prompt = instruction_following[args.mode]
# answer = example.pop('answer')
images = example.pop('images')
if args.mode == 'visual':
data = {
"data_source": args.mode,
"prompt": [{
"role": "user",
"content": prompt,
}],
"images": images,
"ability": "agent",
"extra_info": {
'split': split,
'index': idx,
}
}
else:
data = {
"data_source": args.mode,
"prompt": [{
"role": "user",
"content": prompt,
}],
"ability": "agent",
"extra_info": {
'split': split,
'index': idx,
}
}
return data
return process_fn
train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True, num_proc=8)
test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True, num_proc=8)
local_dir = args.local_dir
hdfs_dir = args.hdfs_dir
train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet'))
if hdfs_dir is not None:
makedirs(hdfs_dir)
copy(src=local_dir, dst=hdfs_dir)
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