Add common properties input_embeddings and output_embeddings

templates/adding_a_new_model/modeling_xxx.py

@@ -280,12 +280,14 @@ class XxxModel(XxxPreTrainedModel):
 
         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}
@@ -376,17 +378,13 @@ class XxxForMaskedLM(XxxPreTrainedModel):
         super(XxxForMaskedLM, self).__init__(config)
 
         self.transformer = XxxModel(config)
-        self.cls = XxxOnlyMLMHead(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
 
         self.init_weights()
-        self.tie_weights()
 
-    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.cls.predictions.decoder,
-                                   self.transformer.embeddings.word_embeddings)
+    @property
+    def output_embeddings(self):
+        return self.lm_head
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
                 masked_lm_labels=None):