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#!/usr/bin/env python | |
# Copyright 2020 The HuggingFace Team. All rights reserved. | |
# | |
# 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. | |
import fire | |
from torch.utils.data import DataLoader | |
from tqdm import tqdm | |
from transformers import AutoTokenizer | |
from utils import Seq2SeqDataset, pickle_save | |
def save_len_file( | |
tokenizer_name, data_dir, max_source_length=1024, max_target_length=1024, consider_target=False, **kwargs | |
): | |
"""Save max(src_len, tgt_len) for each example to allow dynamic batching.""" | |
tok = AutoTokenizer.from_pretrained(tokenizer_name) | |
train_ds = Seq2SeqDataset(tok, data_dir, max_source_length, max_target_length, type_path="train", **kwargs) | |
pad = tok.pad_token_id | |
def get_lens(ds): | |
dl = tqdm( | |
DataLoader(ds, batch_size=512, num_workers=8, shuffle=False, collate_fn=ds.collate_fn), | |
desc=str(ds.len_file), | |
) | |
max_lens = [] | |
for batch in dl: | |
src_lens = batch["input_ids"].ne(pad).sum(1).tolist() | |
tgt_lens = batch["labels"].ne(pad).sum(1).tolist() | |
if consider_target: | |
for src, tgt in zip(src_lens, tgt_lens): | |
max_lens.append(max(src, tgt)) | |
else: | |
max_lens.extend(src_lens) | |
return max_lens | |
train_lens = get_lens(train_ds) | |
val_ds = Seq2SeqDataset(tok, data_dir, max_source_length, max_target_length, type_path="val", **kwargs) | |
val_lens = get_lens(val_ds) | |
pickle_save(train_lens, train_ds.len_file) | |
pickle_save(val_lens, val_ds.len_file) | |
if __name__ == "__main__": | |
fire.Fire(save_len_file) | |