add transformer xl compatibility for torchhub

hubconfs/transformer_xl_hubconf.py

+from pytorch_pretrained_bert.tokenization_transfo_xl import TransfoXLTokenizer
+from pytorch_pretrained_bert.modeling_transfo_xl import (
+	TransfoXLModel,
+	TransfoXLLMHeadModel
+)
+
+# A lot of models share the same param doc. Use a decorator
+# to save typing
+transformer_xl_docstring = """
+    Transformer XL use a relative positioning (with sinusiodal patterns) and adaptive softmax inputs which means that:
+    - you don't need to specify positioning embeddings indices
+    - the tokens in the vocabulary have to be sorted to decreasing frequency.
+
+	Params:
+		pretrained_model_name_or_path: either:
+			- a str with the name of a pre-trained model to load selected in the list of:
+				. `transfo-xl-wt103`
+			- a path or url to a pretrained model archive containing:
+				. `transfo_xl_config.json` a configuration file for the model
+				. `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
+			- a path or url to a pretrained model archive containing:
+				. `transfo_xl_config.json` a configuration file for the model
+				. `model.chkpt` a TensorFlow checkpoint
+		from_tf: should we load the weights from a locally saved TensorFlow checkpoint
+		cache_dir: an optional path to a folder in which the pre-trained models will be cached.
+		state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
+		*inputs, **kwargs: additional input for the specific TransformerXL class
+"""
+
+
+def _append_from_pretrained_docstring(docstr):
+    def docstring_decorator(fn):
+        fn.__doc__ = fn.__doc__ + docstr
+        return fn
+    return docstring_decorator
+
+
+def transformerXLTokenizer(*args, **kwargs):
+    """
+    Instantiate a Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl
+
+    Args:
+    pretrained_model_name_or_path: Path to pretrained model archive
+                                   or one of pre-trained vocab configs below.
+                                       * transfo-xl-wt103
+
+    Example:
+		>>> import torch
+        >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103')
+		
+		>>> text = "Who was Jim Henson ?"
+        >>> tokenized_text = tokenizer.tokenize(tokenized_text)
+		>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
+    """
+    tokenizer = TransfoXLTokenizer.from_pretrained(*args, **kwargs)
+    return tokenizer
+
+
+@_append_from_pretrained_docstring(transformer_xl_docstring)
+def transformerXLModel(*args, **kwargs):
+    """
+    gpt2Model is the basic OpenAI GPT-2 Transformer model based on
+	identical stacked masked self-attention blocks and pre-trained
+	on large scale dataset using language modeling signal.
+
+    Example:
+        # Load the tokenizer
+		>>> import torch
+        >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103')
+
+        #  Prepare tokenized input
+        >>> text_1 = "Who was Jim Henson ?"
+		>>> text_2 = "Jim Henson was a puppeteer"
+		>>> tokenized_text_1 = tokenizer.tokenize(text_1)
+		>>> tokenized_text_2 = tokenizer.tokenize(text_2)
+        >>> indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
+        >>> indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
+		>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
+		>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
+
+        # Load transformerXLModel
+        >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLModel', 'transfo-xl-wt103')
+        >>> model.eval()
+
+        # Predict hidden states features for each layer
+		# We can re-use the memory cells in a subsequent call to attend a longer context
+        >>> with torch.no_grad():
+                hidden_states_1, mems_1 = model(tokens_tensor_1)
+				hidden_states_2, past = model(tokens_tensor_2, past=past)
+    """
+    model = TransfoXLModel.from_pretrained(*args, **kwargs)
+    return model
+
+
+@_append_from_pretrained_docstring(transformer_xl_docstring)
+def transformerXLLMHeadModel(*args, **kwargs):
+    """
+    gpt2LMHeadModel is the OpenAI GPT-2 Transformer model with the
+	tied (pre-trained) language modeling head on top.
+
+	Example:
+        # Load the tokenizer
+		>>> import torch
+        >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLTokenizer', 'transfo-xl-wt103')
+
+        #  Prepare tokenized input
+        >>> text_1 = "Who was Jim Henson ?"
+		>>> text_2 = "Jim Henson was a puppeteer"
+		>>> tokenized_text_1 = tokenizer.tokenize(text_1)
+		>>> tokenized_text_2 = tokenizer.tokenize(text_2)
+        >>> indexed_tokens_1 = tokenizer.convert_tokens_to_ids(tokenized_text_1)
+        >>> indexed_tokens_2 = tokenizer.convert_tokens_to_ids(tokenized_text_2)
+		>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
+		>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
+
+        # Load transformerXLLMHeadModel
+        >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'transformerXLLMHeadModel', 'transfo-xl-wt103')
+        >>> model.eval()
+
+        # Predict hidden states features for each layer
+		# We can re-use the memory cells in a subsequent call to attend a longer context
+        >>> with torch.no_grad():
+                predictions_1, mems_1 = model(tokens_tensor_1)
+				predictions_2, past = model(tokens_tensor_2, past=past)
+
+		# Get the predicted last token
+		>>> predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
+		>>> predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
+		>>> assert predicted_token == 'who'
+    """
+    model = TransfoXLLMHeadModel.from_pretrained(*args, **kwargs)
+    return model

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -888,8 +888,7 @@ class TransfoXLPreTrainedModel(nn.Module):
         pass
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
-                        from_tf=False, *inputs, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
         """
         Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
         Download and cache the pre-trained model file if needed.
@@ -897,19 +896,25 @@ class TransfoXLPreTrainedModel(nn.Module):
         Params:
             pretrained_model_name_or_path: either:
                 - a str with the name of a pre-trained model to load selected in the list of:
-                    . `transfo-xl`
+                    . `transfo-xl-wt103`
                 - a path or url to a pretrained model archive containing:
                     . `transfo_xl_config.json` a configuration file for the model
                     . `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
                 - a path or url to a pretrained model archive containing:
-                    . `bert_config.json` a configuration file for the model
+                    . `transfo_xl_config.json` a configuration file for the model
                     . `model.chkpt` a TensorFlow checkpoint
             from_tf: should we load the weights from a locally saved TensorFlow checkpoint
             cache_dir: an optional path to a folder in which the pre-trained models will be cached.
             state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
-            *inputs, **kwargs: additional input for the specific Bert class
-                (ex: num_labels for BertForSequenceClassification)
+            *inputs, **kwargs: additional input for the specific TransformerXL class
         """
+        state_dict = kwargs.get('state_dict', None)
+        kwargs.pop('state_dict', None)
+        cache_dir = kwargs.get('cache_dir', None)
+        kwargs.pop('cache_dir', None)
+        from_tf = kwargs.get('from_tf', False)
+        kwargs.pop('from_tf', None)
+
         if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
             archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
             config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]