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106 lines
3.6 KiB
Python
106 lines
3.6 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 argparse
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import os
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import re
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import datasets
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from verl.utils.hdfs_io import copy, makedirs
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def extract_solution(solution_str):
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solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str)
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assert solution is not None
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final_solution = solution.group(0)
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final_solution = final_solution.split("#### ")[1].replace(",", "")
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return final_solution
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--local_dir", default=None, help="The save directory for the preprocessed dataset.")
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parser.add_argument("--hdfs_dir", default=None)
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parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.")
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parser.add_argument(
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"--local_save_dir", default="~/data/gsm8k", help="The save directory for the preprocessed dataset."
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)
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args = parser.parse_args()
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local_dataset_path = args.local_dataset_path
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data_source = "openai/gsm8k"
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if local_dataset_path is not None:
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dataset = datasets.load_dataset(local_dataset_path, "main")
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else:
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dataset = datasets.load_dataset(data_source, "main")
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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 after "####".'
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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_raw = example.pop("question")
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question = question_raw + " " + instruction_following
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answer_raw = example.pop("answer")
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solution = extract_solution(answer_raw)
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data = {
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"data_source": data_source,
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"prompt": [
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{
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"role": "user",
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"content": question,
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}
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],
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"ability": "math",
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"reward_model": {"style": "rule", "ground_truth": solution},
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"extra_info": {
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"split": split,
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"index": idx,
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"answer": answer_raw,
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"question": question_raw,
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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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hdfs_dir = args.hdfs_dir
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local_save_dir = args.local_dir
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if local_save_dir is not None:
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print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.")
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else:
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local_save_dir = args.local_save_dir
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train_dataset.to_parquet(os.path.join(local_save_dir, "train.parquet"))
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test_dataset.to_parquet(os.path.join(local_save_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_save_dir, dst=hdfs_dir)
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