add GPT2 torchhub compatibility

hubconfs/gpt2_hubconf.py

@@ -0,0 +1,165 @@
+from pytorch_pretrained_bert.tokenization_gpt2 import GPT2Tokenizer
+from pytorch_pretrained_bert.modeling_openai import (
+	GPT2Model,
+	GPT2LMHeadModel,
+	GPT2DoubleHeadsModel
+)
+
+# A lot of models share the same param doc. Use a decorator
+# to save typing
+gpt2_docstring = """
+	Params:
+		pretrained_model_name_or_path: either:
+			- a str with the name of a pre-trained model to load selected in the list of:
+				. `gpt2`
+			- a path or url to a pretrained model archive containing:
+				. `gpt2_config.json` a configuration file for the model
+				. `pytorch_model.bin` a PyTorch dump of a GPT2Model instance
+			- a path or url to a pretrained model archive containing:
+				. `gpt2_config.json` a configuration file for the model
+				. a TensorFlow checkpoint with trained weights
+		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 dictionary (collections.OrderedDict object) to use instead of pre-trained models
+		*inputs, **kwargs: additional input for the specific GPT-2 class
+"""
+
+
+def _append_from_pretrained_docstring(docstr):
+    def docstring_decorator(fn):
+        fn.__doc__ = fn.__doc__ + docstr
+        return fn
+    return docstring_decorator
+
+
+def gpt2Tokenizer(*args, **kwargs):
+    """
+    Instantiate a GPT-2 BPE tokenizer for OpenAI GPT-2 from a pre-trained/customized vocab file.
+	Peculiarities:
+        - Byte-level BPE
+
+    Args:
+    pretrained_model_name_or_path: Path to pretrained model archive
+                                   or one of pre-trained vocab configs below.
+                                       * openai-gpt
+    Keyword args:
+	special_tokens: Special tokens in vocabulary that are not pretrained ([SEP], [CLS]...)
+					Default: None
+	max_len: An artificial maximum length to truncate tokenized sequences to;
+        	 Effective maximum length is always the minimum of this
+             value (if specified) and the underlying BERT model's
+             sequence length.
+			 Default: None
+
+    Example:
+		>>> import torch
+        >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2')
+		
+		>>> text = "Who was Jim Henson ?"
+        >>> indexed_tokens = tokenizer.encode(tokenized_text)
+    """
+    tokenizer = GPT2Tokenizer.from_pretrained(*args, **kwargs)
+    return tokenizer
+
+
+@_append_from_pretrained_docstring(gpt2_docstring)
+def gpt2Model(*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', 'gpt2Tokenizer', 'gpt2')
+
+        #  Prepare tokenized input
+        >>> text_1 = "Who was Jim Henson ?"
+		>>> text_2 = "Jim Henson was a puppeteer"
+        >>> indexed_tokens_1 = tokenizer.encode(text_1)
+        >>> indexed_tokens_2 = tokenizer.encode(text_2)
+		>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
+		>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
+
+        # Load gpt2Model
+        >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Model', 'gpt2')
+        >>> model.eval()
+
+        # Predict hidden states features for each layer
+		# past can be used to reuse precomputed hidden state in a subsequent predictions
+        >>> with torch.no_grad():
+                hidden_states_1, past = model(tokens_tensor_1)
+				hidden_states_2, past = model(tokens_tensor_2, past=past)
+				
+    """
+    model = GPT2Model.from_pretrained(*args, **kwargs)
+    return model
+
+
+@_append_from_pretrained_docstring(gpt2_docstring)
+def gpt2LMHeadModel(*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', 'gpt2Tokenizer', 'gpt2')
+
+        #  Prepare tokenized input
+        >>> text_1 = "Who was Jim Henson ?"
+		>>> text_2 = "Jim Henson was a puppeteer"
+        >>> indexed_tokens_1 = tokenizer.encode(text_1)
+        >>> indexed_tokens_2 = tokenizer.encode(text_2)
+		>>> tokens_tensor_1 = torch.tensor([indexed_tokens_1])
+		>>> tokens_tensor_2 = torch.tensor([indexed_tokens_2])
+
+        # Load gpt2LMHeadModel
+        >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2LMHeadModel', 'gpt2')
+        >>> model.eval()
+
+        # Predict hidden states features for each layer
+		# past can be used to reuse precomputed hidden state in a subsequent predictions
+        >>> with torch.no_grad():
+				predictions_1, past = 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.decode([predicted_index])
+        >>> assert predicted_token == ' who'
+    """
+    model = OpenAIGPTLMHeadModel.from_pretrained(*args, **kwargs)
+    return model
+
+
+@_append_from_pretrained_docstring(gpt2_docstring)
+def gpt2DoubleHeadsModel(*args, **kwargs):
+    """
+    gpt2DoubleHeadsModel is the OpenAI GPT-2 Transformer model with the
+	tied (pre-trained) language modeling head and a multiple choice
+	classification head (only initialized, not pre-trained).
+
+	Example:
+        # Load the tokenizer
+		>>> import torch
+        >>> tokenizer = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2Tokenizer', 'gpt2')
+
+        #  Prepare tokenized input
+		>>> text = "Who was Jim Henson ?"
+        >>> indexed_tokens = tokenizer.encode(tokenized_text)
+        >>> tokens_tensor = torch.tensor([indexed_tokens])
+		>>> mc_token_ids = torch.LongTensor([ [len(tokenized_text)] ])
+
+        # Load openAIGPTDoubleHeadsModel
+        >>> model = torch.hub.load('huggingface/pytorch-pretrained-BERT', 'gpt2DoubleHeadsModel', 'gpt2')
+        >>> model.eval()
+
+        # Predict hidden states features for each layer
+        >>> with torch.no_grad():
+                lm_logits, multiple_choice_logits, presents = model(tokens_tensor, mc_token_ids)
+    """
+    model = GPT2DoubleHeadsModel.from_pretrained(*args, **kwargs)
+    return model

pytorch_pretrained_bert/modeling_gpt2.py

@@ -362,9 +362,7 @@ class GPT2PreTrainedModel(nn.Module):
             module.bias.data.zero_()
 
     @classmethod
-    def from_pretrained(
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
-        cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs
-    ):
         """
         Instantiate a GPT2PreTrainedModel from a pre-trained model file or a pytorch state dict.
         Download and cache the pre-trained model file if needed.
@@ -382,8 +380,15 @@ class GPT2PreTrainedModel(nn.Module):
             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 dictionary (collections.OrderedDict object) to use instead of pre-trained models
-            *inputs, **kwargs: additional input for the specific GPT class
+            *inputs, **kwargs: additional input for the specific GPT2 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]

pytorch_pretrained_bert/tokenization_gpt2.py

@@ -91,7 +91,7 @@ class GPT2Tokenizer(object):
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
         """
-        Instantiate a PreTrainedBertModel from a pre-trained model file.
+        Instantiate a GPT2Tokenizer from a pre-trained model file.
         Download and cache the pre-trained model file if needed.
         """
         if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: