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#
# 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 dataset to parquet format
"""
import argparse
import os
from functools import partial
from datasets import concatenate_datasets, load_dataset
from verl.utils.hdfs_io import copy, makedirs
def example_map_fn(example, idx, process_fn, data_source, ability, split):
question, prompt, ground_truth = process_fn(example)
data = {
"data_source": data_source,
"prompt": [{"role": "user", "content": prompt}],
"ability": ability,
"reward_model": {"style": "rule", "ground_truth": ground_truth},
"extra_info": {"split": split, "index": idx, "question": question},
}
return data
def build_aime2024_dataset():
def process_aime2024(example):
question, ground_truth = example["Problem"], str(example["Answer"])
prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}."
return question, prompt, ground_truth
data_source = "Maxwell-Jia/AIME_2024"
print(f"Loading the {data_source} dataset from huggingface...", flush=True)
dataset = load_dataset(data_source, split="train")
map_fn = partial(example_map_fn, process_fn=process_aime2024, data_source="aime24", ability="Math", split="test")
dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names)
return dataset
def build_aime2025_dataset():
def process_aime2025(example):
question, ground_truth = example["problem"], str(example["solution"])
prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}."
return question, prompt, ground_truth
data_source = "yentinglin/aime_2025"
print(f"Loading the {data_source} dataset from huggingface...", flush=True)
dataset = load_dataset(data_source, split="train")
map_fn = partial(example_map_fn, process_fn=process_aime2025, data_source="aime25", ability="Math", split="test")
dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names)
return dataset
def build_gpqa_diamond_dataset():
import random
GPQA_QUERY_TEMPLATE = (
"{Question}\n"
"A. {A}\nB. {B}\nC. {C}\nD. {D}\n\n"
"Please reason step by step, and put your final answer (only the choice letter) within \\boxed{{}}."
)
def process_gpqa_diamond(example):
choices = [
example["Incorrect Answer 1"].strip(),
example["Incorrect Answer 2"].strip(),
example["Incorrect Answer 3"].strip(),
]
random.shuffle(choices)
gold_index = random.randint(0, 3)
choices.insert(gold_index, example["Correct Answer"].strip())
question = example["Question"]
query_prompt = GPQA_QUERY_TEMPLATE.format(
A=choices[0],
B=choices[1],
C=choices[2],
D=choices[3],
Question=question,
)
gold_choice = "ABCD"[gold_index]
return question, query_prompt, gold_choice
data_source = "Idavidrein/gpqa"
print(f"Loading the {data_source} dataset from huggingface...", flush=True)
dataset = load_dataset(data_source, "gpqa_diamond", split="train")
map_fn = partial(
example_map_fn, process_fn=process_gpqa_diamond, data_source="gpqa-diamond", ability="General", split="test"
)
dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names)
return dataset
def build_dapo_train_dataset():
def process_dapo(example):
question, ground_truth = example["prompt"], example["solution"]
prompt = question.strip() + "\n\n" + "Please reason step by step, and put your final answer within \\boxed{}."
return question, prompt, ground_truth
data_source = "open-r1/DAPO-Math-17k-Processed"
print(f"Loading the {data_source} dataset from huggingface...", flush=True)
dataset = load_dataset(data_source, "all", split="train")
map_fn = partial(example_map_fn, process_fn=process_dapo, data_source="math-dapo", ability="Math", split="train")
dataset = dataset.map(map_fn, with_indices=True, remove_columns=dataset.column_names)
return dataset
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--local_dir", default="~/data/genrm")
parser.add_argument("--hdfs_dir", default=None)
parser.add_argument("--tasks", default="all")
args = parser.parse_args()
train_dataset = build_dapo_train_dataset()
train_dataset = concatenate_datasets([train_dataset for _ in range(20)])
test_datasets = []
# AIME 2024
aime24_dataset = build_aime2024_dataset()
test_datasets.extend([aime24_dataset for _ in range(32)])
# AIME 2025
aime25_dataset = build_aime2025_dataset()
test_datasets.extend([aime25_dataset for _ in range(32)])
# GPQA Diamond
gpqa_dataset = build_gpqa_diamond_dataset()
test_datasets.extend([gpqa_dataset for _ in range(4)])
test_dataset = concatenate_datasets(test_datasets)
local_dir = args.local_dir
hdfs_dir = args.hdfs_dir
train_dataset.to_parquet(os.path.join(local_dir, "fapo-train-boxed.parquet"))
test_dataset.to_parquet(os.path.join(local_dir, "fapo-test-full-boxed.parquet"))
if hdfs_dir is not None:
makedirs(hdfs_dir)
copy(src=local_dir, dst=hdfs_dir)
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