Spaces:
Paused
Paused
| #!/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) | |