[Pretrained Model] Add resize_position_embeddings
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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This PR adds a `resize_position_embeddings` function similar to `resize_token_embeddings`. In contrast to `resize_token_embeddings` the function will be added to the `PreTrainedModel` class **only** as an abstract function and should be implemented directly in the model specific class. It will raise a `NotImplementedError(...)` if not overwitten in the model-specific class.

 The reason behind is this code design is that it's easier to read (all logic in the same modeling file) and allows for more flexibility which is necessary as there are quite some different kinds of position embeddings. For now the method is only implemented for Distilbert and Pegasus, mostly to enable use-cases as described in https://github.com/huggingface/transformers/issues/11344


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Models:

- albert, bert, xlm: @LysandreJik
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Comments:
> I agree with the solution here, very clean! But from the first look, it seems it adds a bit of complexity.
> 
> Also,
> 
> * Should we always resize pos embeds in seq2seq scripts? IMO it's fine if we are using sinusoidal or non-learned pos embeds, but for learned position embeds the user should be made aware of this, as some embeddings will be randomly initialized and some will be trained, which might/mightnot affect training, so maybe add a flag?
> * I'm not sure if it's a good idea to reduce the size of learned embeddings, that essentially means throwing away learned weights. And, pos embeds don't need much memory compared to the rest of the model so why reduce the size?

1. See: https://github.com/huggingface/transformers/pull/13559#discussion_r708308465 , think I'm happy to go with both solutions -> let's also see what @stas00 thinks!

2. Note that the summarization script never reduces the position embeddings but only adds tokens if necessary. Think it doesn't hurt to have the possibility to reduce the position embeddings in general.

Any ideas for implementing it for commonly used models like gpt series、bert and so on？

Any updates? Especially for CLIP?