add conversion script, rename conversion scripts

pytorch_transformers/convert_bert_pytorch_checkpoint_to_tf.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 BERT checkpoint."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import tensorflow as tf
+
+from pytorch_transformers import BertConfig, TFBertForPreTraining, load_pt_weights_in_bert
+
+import logging
+logging.basicConfig(level=logging.INFO)
+
+def convert_bert_checkpoint_to_tf(pytorch_checkpoint_path, bert_config_file, tf_dump_path):
+    # Initialise TF model
+    config = BertConfig.from_json_file(bert_config_file)
+    print("Building TensorFlow model from configuration: {}".format(str(config)))
+    model = TFBertForPreTraining(config)
+
+    # Load weights from tf checkpoint
+    model = load_pt_weights_in_bert(model, config, pytorch_checkpoint_path)
+
+    # Save pytorch-model
+    print("Save TensorFlow model to {}".format(tf_dump_path))
+    model.save_weights(tf_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ## Required parameters
+    parser.add_argument("--pytorch_checkpoint_path",
+                        default = None,
+                        type = str,
+                        required = True,
+                        help = "Path to the PyTorch 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("--tf_dump_path",
+                        default = None,
+                        type = str,
+                        required = True,
+                        help = "Path to the output Tensorflow dump file.")
+    args = parser.parse_args()
+    convert_bert_checkpoint_to_tf(args.pytorch_checkpoint_path,
+                                     args.bert_config_file,
+                                     args.tf_dump_path)