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