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# 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 GSM8k dataset to parquet format
"""
import os
import datasets
from verl.utils.hdfs_io import copy, makedirs
import argparse
from verl.utils.reward_score.math import remove_boxed, last_boxed_only_string
def extract_solution(solution_str):
return remove_boxed(last_boxed_only_string(solution_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--local_dir', default='~/data/math')
parser.add_argument('--hdfs_dir', default=None)
args = parser.parse_args()
# 'lighteval/MATH' is no longer available on huggingface.
# Use mirror repo: DigitalLearningGmbH/MATH-lighteval
data_source = 'DigitalLearningGmbH/MATH-lighteval'
print(f"Loading the {data_source} dataset from huggingface...", flush=True)
dataset = datasets.load_dataset(data_source, trust_remote_code=True)
train_dataset = dataset['train']
test_dataset = dataset['test']
instruction_following = "Let's think step by step and output the final answer within \\boxed{}."
# add a row to each data item that represents a unique id
def make_map_fn(split):
def process_fn(example, idx):
question = example.pop('problem')
question = question + ' ' + instruction_following
answer = example.pop('solution')
solution = extract_solution(answer)
data = {
"data_source": data_source,
"prompt": [{
"role": "user",
"content": question
}],
"ability": "math",
"reward_model": {
"style": "rule",
"ground_truth": solution
},
"extra_info": {
'split': split,
'index': idx
}
}
return data
return process_fn
train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True)
test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True)
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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