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/
transformers_4_35_0
/models
/albert
/convert_albert_original_tf_checkpoint_to_pytorch.py
| # coding=utf-8 | |
| # Copyright 2018 The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """Convert ALBERT checkpoint.""" | |
| import argparse | |
| import torch | |
| from ...utils import logging | |
| from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert | |
| logging.set_verbosity_info() | |
| def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): | |
| # Initialise PyTorch model | |
| config = AlbertConfig.from_json_file(albert_config_file) | |
| print(f"Building PyTorch model from configuration: {config}") | |
| model = AlbertForPreTraining(config) | |
| # Load weights from tf checkpoint | |
| load_tf_weights_in_albert(model, config, tf_checkpoint_path) | |
| # Save pytorch-model | |
| print(f"Save PyTorch model to {pytorch_dump_path}") | |
| torch.save(model.state_dict(), pytorch_dump_path) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # Required parameters | |
| parser.add_argument( | |
| "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." | |
| ) | |
| parser.add_argument( | |
| "--albert_config_file", | |
| default=None, | |
| type=str, | |
| required=True, | |
| help=( | |
| "The config json file corresponding to the pre-trained ALBERT model. \n" | |
| "This specifies the model architecture." | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." | |
| ) | |
| args = parser.parse_args() | |
| convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path) | |