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90 lines
2.7 KiB
Python
90 lines
2.7 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 GSM8k dataset to parquet format
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"""
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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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from verl.utils.reward_score.math import remove_boxed, last_boxed_only_string
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def extract_solution(solution_str):
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return remove_boxed(last_boxed_only_string(solution_str))
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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/math')
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parser.add_argument('--hdfs_dir', default=None)
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args = parser.parse_args()
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data_source = 'lighteval/MATH'
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dataset = datasets.load_dataset(data_source, trust_remote_code=True)
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train_dataset = dataset['train']
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test_dataset = dataset['test']
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instruction_following = "Let's think step by step and output the final answer within \\boxed{}."
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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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question = example.pop('problem')
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question = question + ' ' + instruction_following
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answer = example.pop('solution')
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solution = extract_solution(answer)
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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": "math",
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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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test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True)
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local_dir = args.local_dir
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hdfs_dir = args.hdfs_dir
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train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
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test_dataset.to_parquet(os.path.join(local_dir, 'test.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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