Converting script

transformers/__init__.py

@@ -107,7 +107,7 @@ if is_torch_available():
                                 CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
 
-    from .modeling_albert import (AlbertModel, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
+    from .modeling_albert import (AlbertModel, AlbertForMaskedLM, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
     # Optimization
     from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup,

transformers/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."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
+import argparse
+import torch
 
-from transformers import AlbertConfig, BertForPreTraining, load_tf_weights_in_bert
-
+from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert
 
+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)
+    config = AlbertConfig.from_json_file(bert_config_file)
     print("Building PyTorch model from configuration: {}".format(str(config)))
-    model = BertForPreTraining(config)
+    model = AlbertForMaskedLM(config)
 
     # Load weights from tf checkpoint
-    load_tf_weights_in_bert(model, config, tf_checkpoint_path)
+    load_tf_weights_in_albert(model, config, tf_checkpoint_path)
 
     # Save pytorch-model
     print("Save PyTorch model to {}".format(pytorch_dump_path))
@@ -31,7 +52,7 @@ if __name__ == "__main__":
                         default = None,
                         type = str,
                         required = True,
-                        help = "The config json file corresponding to the pre-trained BERT model. \n"
+                        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,
@@ -40,5 +61,6 @@ if __name__ == "__main__":
                         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.albert_config_file,
                                      args.pytorch_dump_path)
+ 
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