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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 data and split the training set into 75% for training RM and 25% for validting RM.
- All the training data is used to train SFT and RL.
- Both chosen and rejected is used to train SFT
"""
import argparse
import os
import pandas as pd
from datasets import load_dataset
from tqdm.auto import tqdm
from verl.utils.fs import copy, makedirs
def generate_sft_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlh/sft"):
dataset = load_dataset("Dahoas/full-hh-rlhf")
output = {"prompt": [], "response": []}
for data in tqdm(dataset["train"]):
# add chosen
output["prompt"].append(data["prompt"])
output["response"].append(data["chosen"])
# add rejection
output["prompt"].append(data["prompt"])
output["response"].append(data["rejected"])
df = pd.DataFrame(output)
local_dir = os.path.expanduser(local_dir)
os.makedirs(local_dir, exist_ok=True)
local_path = os.path.join(local_dir, "train.parquet")
df.to_parquet(path=local_path)
if target_hdfs_path_dir is not None:
hdfs_dir = target_hdfs_path_dir + "/" + "train.parquet"
makedirs(hdfs_dir)
copy(local_path, hdfs_dir)
def generate_rm_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlh/rm"):
train_dataset = load_dataset("Dahoas/full-hh-rlhf", split="train[:75%]")
test_dataset = load_dataset("Dahoas/full-hh-rlhf", split="train[-25%:]")
local_dir = os.path.expanduser(local_dir)
os.makedirs(local_dir, exist_ok=True)
for dataset, name in zip([train_dataset, test_dataset], ["train", "test"]):
output = {"prompt": [], "chosen": [], "rejected": []}
for data in tqdm(dataset):
# add chosen
output["prompt"].append(data["prompt"])
output["chosen"].append(data["chosen"])
output["rejected"].append(data["rejected"])
df = pd.DataFrame(output)
local_path = os.path.join(local_dir, name + ".parquet")
df.to_parquet(path=local_path)
if target_hdfs_path_dir is not None:
hdfs_dir = target_hdfs_path_dir + "/" + name + ".parquet"
makedirs(hdfs_dir)
copy(local_path, hdfs_dir)
def generate_rl_dataset(target_hdfs_path_dir, local_dir="~/data/full_hh_rlhf/rl"):
dataset = load_dataset("Dahoas/full-hh-rlhf")
train_dataset = dataset["train"]
data_source = "Dahoas/full-hh-rlhf"
# add a row to each data item that represents a unique id
def make_map_fn(split):
def process_fn(example, idx):
prompt = example.pop("prompt")
response = example.pop("response")
data = {
"data_source": data_source,
"prompt": [{"role": "user", "content": prompt}],
"ability": "alignment",
"reward_model": {
"style": "model",
"ground_truth": response, # should not be used
},
"extra_info": {"split": split, "index": idx},
}
return data
return process_fn
train_dataset = train_dataset.map(function=make_map_fn("train"), with_indices=True)
local_dir = os.path.expanduser(local_dir)
local_path = os.path.join(local_dir, "train.parquet")
train_dataset.to_parquet(local_path)
if target_hdfs_path_dir is not None:
hdfs_dir = target_hdfs_path_dir + "/" + "train.parquet"
makedirs(hdfs_dir)
copy(local_path, hdfs_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--split", type=str, choices=["sft", "rm", "rl"], required=True)
parser.add_argument("--local_dir", type=str, default="~/data/full_hh_rlhf")
parser.add_argument("--hdfs_dir", type=str, required=False, default=None)
args = parser.parse_args()
if args.split == "sft":
generate_sft_dataset(args.hdfs_dir, os.path.join(args.local_dir, args.split))
elif args.split == "rm":
generate_rm_dataset(args.hdfs_dir, os.path.join(args.local_dir, args.split))
elif args.split == "rl":
generate_rl_dataset(args.hdfs_dir, os.path.join(args.local_dir, args.split))
else:
raise NotImplementedError
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