Drop unnecessary tokens in GPT2Model generation

src/transformers/models/gpt2/modeling_gpt2.py

@@ -1163,6 +1163,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
     ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
         r"""
@@ -1208,25 +1209,26 @@ def forward(
             torch.cuda.set_device(self.transformer.first_device)
             hidden_states = hidden_states.to(self.lm_head.weight.device)
 
-        lm_logits = self.lm_head(hidden_states)
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
         if labels is not None:
             # Flatten the tokens
             loss = self.loss_function(
-                lm_logits,
+                logits,
                 labels,
                 vocab_size=self.config.vocab_size,
                 **kwargs,
             )
 
         if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
+            output = (logits,) + transformer_outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
         return CausalLMOutputWithCrossAttentions(
             loss=loss,
-            logits=lm_logits,
+            logits=logits,
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,