68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 the LlamaFactory team.
|
|
#
|
|
# 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.
|
|
|
|
from collections import defaultdict
|
|
|
|
import fire
|
|
from tqdm import tqdm
|
|
|
|
from llamafactory.data import get_dataset
|
|
from llamafactory.hparams import get_train_args
|
|
from llamafactory.model import load_tokenizer
|
|
|
|
|
|
def length_cdf(
|
|
model_name_or_path: str,
|
|
dataset: str = "alpaca_en",
|
|
dataset_dir: str = "data",
|
|
template: str = "default",
|
|
interval: int = 1000,
|
|
):
|
|
r"""
|
|
Calculates the distribution of the input lengths in the dataset.
|
|
Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
|
|
"""
|
|
model_args, data_args, training_args, _, _ = get_train_args(
|
|
dict(
|
|
stage="sft",
|
|
model_name_or_path=model_name_or_path,
|
|
dataset=dataset,
|
|
dataset_dir=dataset_dir,
|
|
template=template,
|
|
cutoff_len=1_000_000,
|
|
output_dir="dummy_dir",
|
|
overwrite_cache=True,
|
|
do_train=True,
|
|
)
|
|
)
|
|
tokenizer_module = load_tokenizer(model_args)
|
|
trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)["train_dataset"]
|
|
total_num = len(trainset)
|
|
length_dict = defaultdict(int)
|
|
for sample in tqdm(trainset["input_ids"]):
|
|
length_dict[len(sample) // interval * interval] += 1
|
|
|
|
length_tuples = list(length_dict.items())
|
|
length_tuples.sort()
|
|
count_accu, prob_accu = 0, 0
|
|
for length, count in length_tuples:
|
|
count_accu += count
|
|
prob_accu += count / total_num * 100
|
|
print("{:d} ({:.2f}%) samples have length < {}.".format(count_accu, prob_accu, length + interval))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire(length_cdf)
|