Repository: huggingface/transformers
Pull request: Drop unnecessary tokens in GPT2Model generation

# What does this PR do?

In the current GPT2 implementation, the LMHead module processes all tokens during prefill, even though only the final token’s output is used for generation. This PR aligns the behavior with LlamaModel by computing the LMHead output only for the last token, reducing unnecessary computation during prefill.

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Fixes #38977 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case. #38977 
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


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Changed files:
filename: src/transformers/models/gpt2/modeling_gpt2.py
patch: @@ -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,