[RAG, Bart] Align RAG, Bart cache with T5 and other models of transformers
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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In Transformers, the cache should always have the same structure. This becomes especially important for composite models like `RAG` and `EncoderDecoder` that expect all models to have the same cache.

Bart and T5 had different caches with Bart being most different from the standard cache of the library.
This PR aligns the `past_key_values` cache of Bart/Rag with all other models in the library. In general, the philosophy should be:

the past_key_value should have exactly one level for each layer, no matter whether the model is a decoder-only a.k.a. GPT2 or BART. This was not correctly refactored in BART (it should have been implemented 1-to-1 as in T5). No breaking changes here though.

- `past_key_value` tuple for each layer should always be a tuple of tensors, **not** a tuple of a tuple
- for decodre-only models (GPT2), the tuple for each layer contains 2 tensors: key and value states
- for seq2seq (BART/T5), the tuple for each layer contains 4 tensors: key and value states of uni-directional self-attention, saved key and value states for cross-attention

This doesn't break any backward compatibility and should fix some RAG problems (@ratthachat). All RAG, Bart slow tests are passing and changes correspond just to the tuple structure.

PR is blocking me for TFBart refactor -> will merge already.

cc @LysandreJik, @sgugger, @patil-suraj for info.

## Before submitting
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## Who can review?

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 albert, bert, XLM: @LysandreJik
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