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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | # 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", local_dataset_path=None):
if local_dataset_path is not None:
dataset = load_dataset(local_dataset_path)
else:
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", local_dataset_path=None):
if local_dataset_path is not None:
train_dataset = load_dataset(local_dataset_path, split="train[:75%]")
test_dataset = load_dataset(local_dataset_path, split="train[-25%:]")
else:
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"], strict=True):
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", local_dataset_path=None):
if local_dataset_path is not None:
dataset = load_dataset(local_dataset_path)
else:
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", default=None, help="The save directory for the preprocessed dataset.")
parser.add_argument("--hdfs_dir", type=str, required=False, default=None)
parser.add_argument("--local_dataset_path", default=None, help="The local path to the raw dataset, if it exists.")
parser.add_argument(
"--local_save_dir",
type=str,
default="~/data/full_hh_rlhf",
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
if args.split == "sft":
generate_sft_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path)
elif args.split == "rm":
generate_rm_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path)
elif args.split == "rl":
generate_rl_dataset(args.hdfs_dir, os.path.join(local_save_dir, args.split), args.local_dataset_path)
else:
raise NotImplementedError
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