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# coding=utf-8
# 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
from collections import defaultdict
from typing import Optional
import fire
from tqdm import tqdm
from llmtuner.data import get_dataset
from llmtuner.hparams import get_train_args
from llmtuner.model import load_model_and_tokenizer
def length_cdf(
model_name_or_path: str,
dataset: Optional[str] = "alpaca_en",
dataset_dir: Optional[str] = "data",
template: Optional[str] = "default",
interval: Optional[int] = 1000,
):
model_args, data_args, training_args, finetuning_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,
)
)
_, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, add_valuehead=False)
trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
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)
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