Fix `beam_scores` shape when token scores shape changes after `logits_processor`
Author: BakerBunker
Pull request: yes

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When token scores shape changes after `logits_processor`, `next_token_scores_processed ` has different shape with `beam_scores[:, None].expand_as(next_token_scores)`, this PR fixes this issue.


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Comments:
cc @gante 

Hi @BakerBunker 👋 

You wrote "When token scores shape changes after `logits_processor`" as the cause for the proposed changes -- this situation should not happen 🤔 

Would you be able to share an example? 

Sure, I trained a model with different sizes of input and output embeddings, because the output vocab of the model is much smaller compared to the input vocab. And because the input and output embeddings make up a large percentage of the parameters in the model, this saves a lot of GPU memory during training. However, during the generation process, I need to align the input and output `input_ids` to call the `generate()` interface properly. Here is my code for the align process:

```python
class TokenAlignProcessor(LogitsProcessor):
    def __call__(self, input_ids, scores):
        new_score = torch.empty(scores.shape[0], len(tokenizer), device=DEVICE).fill_(
            -torch.inf
        )
        new_score[:, -OVERLAP_TOKEN_NUMS :] = scores
        return new_score
```

Thank you for the contribution, @BakerBunker 💛