starting

pytorch_pretrained_bert/convert_xlnet_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 BERT checkpoint."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import torch
+
+from pytorch_pretrained_bert.modeling_xlnet import XLNetConfig, XLNetRunConfig, XLNetModel, load_tf_weights_in_xlnet
+
+def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
+    # Initialise PyTorch model
+    config = XLNetConfig.from_json_file(bert_config_file)
+    print("Building PyTorch model from configuration: {}".format(str(config)))
+    model = XLNetModel(config)
+
+    # Load weights from tf checkpoint
+    load_tf_weights_in_xlnet(model, 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 the TensorFlow checkpoint path.")
+    parser.add_argument("--xlnet_config_file",
+                        default = None,
+                        type = str,
+                        required = True,
+                        help = "The config json file corresponding to the pre-trained XLNet 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_xlnet_checkpoint_to_pytorch(args.tf_checkpoint_path,
+                                     args.xlnet_config_file,
+                                     args.pytorch_dump_path)

pytorch_pretrained_bert/modeling.py

@@ -718,7 +718,7 @@ class BertPreTrainedModel(nn.Module):
             state_dict = torch.load(resolved_archive_file, map_location='cpu')
         if from_tf:
             # Directly load from a TensorFlow checkpoint
-            return load_tf_weights_in_bert(model, weights_path)
+            return load_tf_weights_in_bert(model, resolved_archive_file)
         # Load from a PyTorch state_dict
         old_keys = []
         new_keys = []

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -236,7 +236,7 @@ class TransfoXLConfig(object):
             dropout: The dropout probabilitiy for all fully connected
                 layers in the embeddings, encoder, and pooler.
             dropatt: The dropout ratio for the attention probabilities.
-            untie_r: untie relative position biases           
+            untie_r: untie relative position biases
             embd_pdrop: The dropout ratio for the embeddings.
             init: parameter initializer to use
             init_range: parameters initialized by U(-init_range, init_range).