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- # 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 BERT checkpoint."""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import argparse
- import torch
-
- from pytorch_transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
-
- import logging
- logging.basicConfig(level=logging.INFO)
-
- def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
- # Initialise PyTorch model
- config = BertConfig.from_json_file(bert_config_file)
- print("Building PyTorch model from configuration: {}".format(str(config)))
- model = BertForPreTraining(config)
-
- # Load weights from tf checkpoint
- load_tf_weights_in_bert(model, config, tf_checkpoint_path)
-
- # Save pytorch-model
- print("Save PyTorch model to {}".format(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("--bert_config_file",
- default = None,
- type = str,
- required = True,
- help = "The config json file corresponding to the pre-trained BERT 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.bert_config_file,
- args.pytorch_dump_path)
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