# What does this PR do?
Fixes #22312.
After internal discussions, it appears that adding the possibility to pad with `-1`  to `tokenizers` is not really feasible ( nor is it desirable). 
However, what we can do is by default resize the embedding layer to the nearest size that is optimal for the dtype of the model [following this](https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc).

Motivations:
- the `_get_resized_embeddings` is not exposed, and thus making this automatic can be a big silent win. 
- if properly documented, should not really have issues. 

Cons:
- it is not backward compatible, so some kind of `config.optimise_resize` might be needed? 
- it is hidden and people might not really get why tokenizer.vocab_size will be different than the model's embedding dimension. 
[`resize_embedding`] Introduce `pad_to_multiple_of` and guidance. # What does this PR do?
Fixes #22312.
After internal discussions, it appears that adding the possibility to pad with `-1`  to `tokenizers` is not really feasible ( nor is it desirable). 
However, what we can do is by default resize the embedding layer to the nearest size that is optimal for the dtype of the model [following this](https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc).

Motivations:
- the `_get_resized_embeddings` is not exposed, and thus making this automatic can be a big silent win. 
- if properly documented, should not really have issues. 

Cons:
- it is not backward compatible, so some kind of `config.optimise_resize` might be needed? 
- it is hidden and people might not really get why tokenizer.vocab_size will be different than the model's embedding dimension. 
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