[All Seq2Seq model + CLM models that can be used with EncoderDecoder] Add cross-attention weights to outputs
Author: ysgit
Pull request: yes

# What does this PR do?

This PR causes models that support cross-attention to output the cross-attention tensors as well as the self-attention tensors when output_attentions is set

(from @patrickvonplaten)
This PR adds cross-attention outptus to all Seq2Seq models and to CLM models compatible with the `EncoderDecoderModel` framework.

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Comments:
Hey @ysgit - I like the idea! However, I think we should actually create a new output tuple for the cross-attention (called `cross_attention` when `return_dict`=True). For this we will have to create new ModelOutputs for Bert, RoBERTa, etc...


@patrickvonplaten that makes sense, I guess I suspected that might be the reaction, I'll see if that's something I can manage although I'm a little hampered by not being able to get the test suite to successfully run locally

@patrickvonplaten I have done as you suggested and separated cross attentions into a new variable in the output. please take a look and let me know what you think. many thanks!

In a future PR or a "Good First Issue", we could add this functionality to the TFSeq2Seq models as well, but I'm a bit hesitant given the current problems with the `output_attentions` flag in TF.
What do you think @sgugger @LysandreJik @jplu ?

This should be easier to integrate in TF in the next release :)

Thanks everyone!