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| # coding=utf-8 | |
| # Copyright 2020 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 Seq2Seq TF Hub checkpoint.""" | |
| import argparse | |
| from . import ( | |
| BertConfig, | |
| BertGenerationConfig, | |
| BertGenerationDecoder, | |
| BertGenerationEncoder, | |
| load_tf_weights_in_bert_generation, | |
| logging, | |
| ) | |
| logging.set_verbosity_info() | |
| def convert_tf_checkpoint_to_pytorch(tf_hub_path, pytorch_dump_path, is_encoder_named_decoder, vocab_size, is_encoder): | |
| # Initialise PyTorch model | |
| bert_config = BertConfig.from_pretrained( | |
| "bert-large-cased", | |
| vocab_size=vocab_size, | |
| max_position_embeddings=512, | |
| is_decoder=True, | |
| add_cross_attention=True, | |
| ) | |
| bert_config_dict = bert_config.to_dict() | |
| del bert_config_dict["type_vocab_size"] | |
| config = BertGenerationConfig(**bert_config_dict) | |
| if is_encoder: | |
| model = BertGenerationEncoder(config) | |
| else: | |
| model = BertGenerationDecoder(config) | |
| print(f"Building PyTorch model from configuration: {config}") | |
| # Load weights from tf checkpoint | |
| load_tf_weights_in_bert_generation( | |
| model, | |
| tf_hub_path, | |
| model_class="bert", | |
| is_encoder_named_decoder=is_encoder_named_decoder, | |
| is_encoder=is_encoder, | |
| ) | |
| # Save pytorch-model | |
| print(f"Save PyTorch model and config to {pytorch_dump_path}") | |
| model.save_pretrained(pytorch_dump_path) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # Required parameters | |
| parser.add_argument( | |
| "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." | |
| ) | |
| parser.add_argument( | |
| "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." | |
| ) | |
| parser.add_argument( | |
| "--is_encoder_named_decoder", | |
| action="store_true", | |
| help="If decoder has to be renamed to encoder in PyTorch model.", | |
| ) | |
| parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.") | |
| parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model") | |
| args = parser.parse_args() | |
| convert_tf_checkpoint_to_pytorch( | |
| args.tf_hub_path, | |
| args.pytorch_dump_path, | |
| args.is_encoder_named_decoder, | |
| args.vocab_size, | |
| is_encoder=args.is_encoder, | |
| ) | |