transformers/examples/legacy/seq2seq/save_randomly_initialized_model.py

#!/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 transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer


def save_randomly_initialized_version(config_name: str, save_dir: str, **config_kwargs):
    """Save a randomly initialized version of a model using a pretrained config.
    Args:
        config_name: which config to use
        save_dir: where to save the resulting model and tokenizer
        config_kwargs: Passed to AutoConfig

    Usage::
        save_randomly_initialized_version("facebook/bart-large-cnn", "distilbart_random_cnn_6_3", encoder_layers=6, decoder_layers=3, num_beams=3)
    """
    cfg = AutoConfig.from_pretrained(config_name, **config_kwargs)
    model = AutoModelForSeq2SeqLM.from_config(cfg)
    model.save_pretrained(save_dir)
    AutoTokenizer.from_pretrained(config_name).save_pretrained(save_dir)
    return model


if __name__ == "__main__":
    fire.Fire(save_randomly_initialized_version)