fixing various parts of model conversion, loading and weights sharing

examples/eval_transfo_xl.py

@@ -42,7 +42,7 @@ parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model
 # parser.add_argument('--data', type=str, default='../data/wikitext-103',
 #                     help='location of the data corpus')
 parser.add_argument('--model_name', type=str, default='transfo-xl-wt103',
-                    choices=['transfo-xl-wt103'], #, 'lm1b', 'enwik8', 'text8'],
+                    # choices=['transfo-xl-wt103'], #, 'lm1b', 'enwik8', 'text8'],
                     help='pretrained model name')
 parser.add_argument('--split', type=str, default='test',
                     choices=['all', 'valid', 'test'],

pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -116,7 +116,8 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
         # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
         pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_NAME
         print("Save vocabulary to {}".format(pytorch_vocab_dump_path))
-        torch.save(corpus.vocab.__dict__, pytorch_vocab_dump_path)
+        corpus_vocab_dict = corpus.vocab.__dict__
+        torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)
 
         corpus_dict_no_vocab = corpus.__dict__
         corpus_dict_no_vocab.pop('vocab', None)
@@ -139,7 +140,7 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
         model = TransfoXLModel(config)
 
         # Build TF to PyTorch weights loading map
-        tf_to_pt_map = build_tf_to_pytorch_map(model.transformer, config)
+        tf_to_pt_map = build_tf_to_pytorch_map(model, config)
 
         # Load weights from TF model
         init_vars = tf.train.list_variables(tf_path)

pytorch_pretrained_bert/tokenization_transfo_xl.py

@@ -444,6 +444,12 @@ class TransfoXLCorpus(object):
         for key, value in corpus_dict.items():
             corpus.__dict__[key] = value
         corpus.vocab = vocab
+        if corpus.train is not None:
+            corpus.train = torch.tensor(corpus.train, dtype=torch.long)
+        if corpus.valid is not None:
+            corpus.valid = torch.tensor(corpus.valid, dtype=torch.long)
+        if corpus.test is not None:
+            corpus.test = torch.tensor(corpus.test, dtype=torch.long)
         return corpus
 
     def __init__(self, *args, **kwargs):