remove final_logits_bias (#10606)

src/transformers/models/m2m_100/modeling_m2m_100.py

@@ -1153,7 +1153,6 @@ class M2M100Model(M2M100PreTrainedModel):
 class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = [
-        r"final_logits_bias",
         r"encoder\.version",
         r"decoder\.version",
         r"lm_head\.weight",
@@ -1168,7 +1167,6 @@ class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
     def __init__(self, config: M2M100Config):
         super().__init__(config)
         self.model = M2M100Model(config)
-        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
         self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
 
         self.init_weights()
@@ -1181,18 +1179,8 @@ class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
 
     def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
         new_embeddings = super().resize_token_embeddings(new_num_tokens)
-        self._resize_final_logits_bias(new_num_tokens)
         return new_embeddings
 
-    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
-        old_num_tokens = self.final_logits_bias.shape[-1]
-        if new_num_tokens <= old_num_tokens:
-            new_bias = self.final_logits_bias[:, :new_num_tokens]
-        else:
-            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
-            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
-        self.register_buffer("final_logits_bias", new_bias)
-
     def get_output_embeddings(self):
         return self.lm_head
 
@@ -1266,7 +1254,7 @@ class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
-        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+        lm_logits = self.lm_head(outputs[0])
 
         masked_lm_loss = None
         if labels is not None: