Add common properties input_embeddings and output_embeddings

transformers/modeling_distilbert.py

@@ -334,9 +334,6 @@ class DistilBertPreTrainedModel(PreTrainedModel):
     load_tf_weights = None
     base_model_prefix = "distilbert"
 
-    def __init__(self, *inputs, **kwargs):
-        super(DistilBertPreTrainedModel, self).__init__(*inputs, **kwargs)
-    
     def _init_weights(self, module):
         """ Initialize the weights.
         """
@@ -424,12 +421,14 @@ class DistilBertModel(DistilBertPreTrainedModel):
 
         self.init_weights()
 
-    def _resize_token_embeddings(self, new_num_tokens):
-        old_embeddings = self.embeddings.word_embeddings
-        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
-        self.embeddings.word_embeddings = new_embeddings
+    @property
+    def input_embeddings(self):
         return self.embeddings.word_embeddings
 
+    @input_embeddings.setter
+    def input_embeddings(self, new_embeddings):
+        self.embeddings.word_embeddings = new_embeddings
+
     def _prune_heads(self, heads_to_prune):
         """ Prunes heads of the model.
             heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
@@ -511,16 +510,12 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
         self.vocab_projector = nn.Linear(config.dim, config.vocab_size)
 
         self.init_weights()
-        self.tie_weights()
 
         self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
 
-    def tie_weights(self):
-        """ Make sure we are sharing the input and output embeddings.
-            Export to TorchScript can't handle parameter sharing so we are cloning them instead.
-        """
-        self._tie_or_clone_weights(self.vocab_projector,
-                                   self.distilbert.embeddings.word_embeddings)
+    @property
+    def output_embeddings(self):
+        return self.vocab_projector
 
     def forward(self, input_ids, attention_mask=None, head_mask=None, masked_lm_labels=None):
         dlbrt_output = self.distilbert(input_ids=input_ids,