106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocess the QA dataset to parquet format
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"""
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import re
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import os
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import datasets
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from verl.utils.hdfs_io import copy, makedirs
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import argparse
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def make_prefix(dp, template_type):
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question = dp['question']
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# NOTE: also need to change reward_score/countdown.py
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if template_type == 'base':
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"""This works for any base model"""
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prefix = f"""Answer the given question. \
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You must conduct reasoning inside <think> and </think> first every time you get new information. \
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After reasoning, if you find you lack some knowledge, you can call a search engine by <search> query </search> and it will return the top searched results between <information> and </information>. \
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You can search as many times as your want. \
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If you find no further external knowledge needed, you can directly provide the answer inside <answer> and </answer>, without detailed illustrations. For example, <answer> Beijing </answer>. Question: {question}\n"""
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else:
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raise NotImplementedError
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return prefix
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--local_dir', default='./data/nq_search')
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parser.add_argument('--hdfs_dir', default=None)
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parser.add_argument('--template_type', type=str, default='base')
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parser.add_argument('--data_sources', default='nq')
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args = parser.parse_args()
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# data_source = 'nq'
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data_sources = args.data_sources.split(',')
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all_dataset = []
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for data_source in data_sources:
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dataset = datasets.load_dataset('RUC-NLPIR/FlashRAG_datasets', data_source)
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train_dataset = dataset['train']
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# add a row to each data item that represents a unique id
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def make_map_fn(split):
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def process_fn(example, idx):
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example['question'] = example['question'].strip()
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if example['question'][-1] != '?':
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example['question'] += '?'
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question = make_prefix(example, template_type=args.template_type)
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solution = {
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"target": example['golden_answers'],
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}
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data = {
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"data_source": data_source,
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"prompt": [{
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"role": "user",
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"content": question,
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}],
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"ability": "fact-reasoning",
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"reward_model": {
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"style": "rule",
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"ground_truth": solution
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},
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"extra_info": {
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'split': split,
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'index': idx,
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}
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}
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return data
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return process_fn
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train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True)
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all_dataset.append(train_dataset)
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local_dir = args.local_dir
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hdfs_dir = args.hdfs_dir
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all_train_dataset = datasets.concatenate_datasets(all_dataset)
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all_train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
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if hdfs_dir is not None:
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makedirs(hdfs_dir)
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copy(src=local_dir, dst=hdfs_dir)
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