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1faccd4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | # 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 historical AIME dataset to parquet format.
"""
import argparse
import json
import os
import datasets
from verl.utils.hdfs_io import copy, makedirs
DATA_SOURCE = "gneubig/aime-1983-2024"
INSTRUCTION_FOLLOWING = "Let's think step by step and output the final answer within \\boxed{}."
KEY_CANDIDATES = {
"year": ["Year", "year"],
"problem_number": ["Problem Number", "Problem_Number", "problem_number"],
"question": ["Problem", "Question", "problem", "question"],
"answer": ["Answer", "answer", "solution"],
}
def find_key(column_names, key_name):
for candidate in KEY_CANDIDATES[key_name]:
if candidate in column_names:
return candidate
raise KeyError(f"Could not find {key_name} column in dataset columns: {column_names}")
def load_raw_dataset(local_dataset_path=None):
if local_dataset_path is None:
dataset = datasets.load_dataset(DATA_SOURCE)
elif os.path.isdir(local_dataset_path):
dataset = datasets.load_dataset(local_dataset_path)
elif local_dataset_path.endswith(".csv"):
dataset = datasets.load_dataset("csv", data_files=local_dataset_path)
elif local_dataset_path.endswith(".json") or local_dataset_path.endswith(".jsonl"):
dataset = datasets.load_dataset("json", data_files=local_dataset_path)
else:
dataset = datasets.load_dataset(local_dataset_path)
if isinstance(dataset, datasets.DatasetDict):
if "train" in dataset:
return dataset["train"]
return next(iter(dataset.values()))
return dataset
def build_dataset(raw_dataset, split_name, year_min=None, year_max=None, data_source="aime_history"):
year_key = find_key(raw_dataset.column_names, "year")
problem_number_key = find_key(raw_dataset.column_names, "problem_number")
question_key = find_key(raw_dataset.column_names, "question")
answer_key = find_key(raw_dataset.column_names, "answer")
def year_in_range(example):
year = int(example[year_key])
if year_min is not None and year < year_min:
return False
if year_max is not None and year > year_max:
return False
return True
filtered_dataset = raw_dataset.filter(year_in_range)
def process_fn(example, idx):
year = int(example[year_key])
problem_number = int(example[problem_number_key])
question = str(example[question_key]).strip()
ground_truth = str(example[answer_key]).strip()
prompt = f"{question} {INSTRUCTION_FOLLOWING}"
return {
"data_source": data_source,
"prompt": [{"role": "user", "content": prompt}],
"ability": "math",
"reward_model": {"style": "rule", "ground_truth": ground_truth},
"extra_info": {
"split": split_name,
"index": idx,
"year": year,
"problem_number": problem_number,
"question": question,
},
}
return filtered_dataset.map(process_fn, with_indices=True, remove_columns=filtered_dataset.column_names)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--local_dir", default=None)
parser.add_argument("--hdfs_dir", default=None)
parser.add_argument("--local_dataset_path", default=None)
parser.add_argument(
"--local_save_dir",
default="~/data/aime",
help="The save directory for the preprocessed dataset.",
)
args = parser.parse_args()
local_save_dir = args.local_dir
if local_save_dir is not None:
print("Warning: Argument 'local_dir' is deprecated. Please use 'local_save_dir' instead.")
else:
local_save_dir = args.local_save_dir
local_dir = os.path.expanduser(local_save_dir)
os.makedirs(local_dir, exist_ok=True)
print(f"Loading the {DATA_SOURCE} dataset from huggingface...", flush=True)
raw_dataset = load_raw_dataset(args.local_dataset_path)
dataset_all = build_dataset(raw_dataset, split_name="train", year_min=1983, year_max=2024)
dataset_until_2022 = build_dataset(raw_dataset, split_name="train", year_min=1983, year_max=2022)
dataset_until_2023 = build_dataset(raw_dataset, split_name="train", year_min=1983, year_max=2023)
dataset_2023 = build_dataset(raw_dataset, split_name="test", year_min=2023, year_max=2023, data_source="aime23")
dataset_2024 = build_dataset(raw_dataset, split_name="test", year_min=2024, year_max=2024, data_source="aime24")
outputs = [
("aime-history-1983-2024.parquet", "aime-history-1983-2024.example.json", dataset_all),
("aime-history-1983-2022.parquet", "aime-history-1983-2022.example.json", dataset_until_2022),
("aime-history-1983-2023.parquet", "aime-history-1983-2023.example.json", dataset_until_2023),
("aime-history-2023.parquet", "aime-history-2023.example.json", dataset_2023),
("aime-history-2024.parquet", "aime-history-2024.example.json", dataset_2024),
]
for parquet_name, example_name, dataset in outputs:
dataset.to_parquet(os.path.join(local_dir, parquet_name))
if len(dataset) > 0:
with open(os.path.join(local_dir, example_name), "w") as f:
json.dump(dataset[0], f, indent=2)
if args.hdfs_dir is not None:
makedirs(args.hdfs_dir)
copy(src=local_dir, dst=args.hdfs_dir)
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