update decoder_vocab_size when resizing embeds (#16700)

src/transformers/models/marian/modeling_marian.py

@@ -1280,11 +1280,9 @@ class MarianMTModel(MarianPreTrainedModel):
         super().__init__(config)
         self.model = MarianModel(config)
 
-        self.target_vocab_size = (
-            config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
-        )
-        self.register_buffer("final_logits_bias", torch.zeros((1, self.target_vocab_size)))
-        self.lm_head = nn.Linear(config.d_model, self.target_vocab_size, bias=False)
+        target_vocab_size = config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
+        self.register_buffer("final_logits_bias", torch.zeros((1, target_vocab_size)))
+        self.lm_head = nn.Linear(config.d_model, target_vocab_size, bias=False)
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -1306,6 +1304,10 @@ class MarianMTModel(MarianPreTrainedModel):
         new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
         self.set_input_embeddings(new_embeddings)
 
+        # update config.decoder_vocab_size if embeddings are tied
+        if self.config.share_encoder_decoder_embeddings:
+            self.config.decoder_vocab_size = new_num_tokens
+
         # if word embeddings are not tied, make sure that lm head is resized as well
         if (
             self.config.share_encoder_decoder_embeddings
@@ -1451,7 +1453,7 @@ class MarianMTModel(MarianPreTrainedModel):
         masked_lm_loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(lm_logits.view(-1, self.target_vocab_size), labels.view(-1))
+            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.decoder_vocab_size), labels.view(-1))
 
         if not return_dict:
             output = (lm_logits,) + outputs[1:]