Add head_mask/decoder_head_mask for BART
Description:
This PR adds `head_mask` and `decoder_head_mask` for BART PyTorch implementation according to BERT implementation.

Motivation:
According to HuggingFace's websites "There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call “BERTology”)." This PR enables to mask attention heads in encoder and decoder models exactly like for BERT. This PR thus creates an opportunity to study the importance of attention heads in encoder-decoder BERT-like model.

Reviewer: @patrickvonplaten

Dear @patrickvonplaten and the rest of HuggingFace group.

I implemented the concept of `head_mask` from BERT into BART so that the internal of decoder-encoder-like models can be studied as well. However, as this is my very first attempt to contribute to such a large-scale open-source project, I have been a bit struggling to pass the tests. Would you be, please, able to guide me what everything needs to be done in this case in order to achieve a valid pull request?

Thank you very much for all your time in advance. I really do appreciate it.<|||||>Hi @stancld - thanks a lot for pinging me! I'm happy to help you here :-) I think you're PR is a nice addition. Sadly, we did many changes to Bart recently (see https://github.com/huggingface/transformers/pull/9343) so that you'll probably have to rebase your PR to the current version of master. <|||||>After that I'm happy to get the tests passing together!<|||||>Hi @patrickvonplaten, the model should be rebased according to the commit #9343 at this moment. :) I'll be more than happy to finish this PR with you. Thanks a lot in advance :) <|||||>@stancld, please do let me know if you're stuck and need help or if your PR is ready for review, just ping me here :-)<|||||>Hi @patrickvonplaten, I would like to bring an update after the weekend off.

First of all, I would like to apologise for a bit of messy PR, as I was initially struggling with on my local (I'll do better next time).
Regarding this PR: To pass all the tests, `head_mask` and `decoder_head_mask` is now implemented for the following PyTorch BART-based models:

- **BART**,
- **MBart**,
- **Blenderbot**,
- **BlenderbotSmall**,
- **Marian**,
- **Pegasus**.

Besides, I think some additional tests for head_mask for these models might be desired to implement, but I leave this decision up to you. In any case, please, let me know what it needs to do to complete this PR.
<|||||>@patrickvonplaten I think this PR is ready for review. I've currently resolved one conflict arose last night after a commit to `master` and now I've been tracking changes on my local and everything still seems to be working.<|||||>Hey @stancld,

This is a super nice PR. It's very clean and that without any help - awesome!

I think there are 3 things we should change/add:

1) I think we should change the order of the forward args of all `...Model` and `...ForConditionalGeneration` as explained above. This a) means that there is no breaking change in the way Bart is used with torchscript and it's the better option IMO as well since the first 4 args should always be `input_ids, attention_mask, decoder_input_ids, decoder_attention_mask` for EncDec models

2) Let's try to remove all "hard-coded" model names in the common tests. I've commented above. We don't really need to test torchscript with head_mask and for the signature it'd be better to change it according to 1)

3) It would be awesome if you could a `if model.config.is_encoder_decoder` part to the `test_headmasking` test in `test_modeling_common.py` that tests headmasking correctly for Seq2Seq models. To enable this test for all Bart-like models you'll have to set `test_head_masking` to True in `BartModelTest` and others. One thing we'll have to adapt in the test is we should change the line:
```
attentions = outputs[-1]
```

to 
```python
attentions = outputs.attetions
```

for the `model.config.is_encoder_decoder is False` case and to 

```python
encoder_attentions = outputs.encoder_attentions
decoder_attentions = outputs.decoder_attentions
```

for the other case.

I can also help you with 3) in case you're stuck.
Really impressed by how clean the PR is! Think there is not much left to do. 1) and 2) are very easy changes and 3) will require a bit more time, but should be fine as well.<|||||>Hey @patrickvonplaten, thanks a lot for your thorough feedback. I believe to come back later today with a new commit fixing the listed issues :)<|||||>Hey @patrickvonplaten, this PR is again ready for review after making some changes according to your notes above. The one problem at this moment is that BART-like models do not satisfy one condition in `test_headmasking`:
```
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0).
```

I am not sure whether the formula for masking attention heads (in BART-like models) is implemented correctly. Now, if `head_mask` in the test case is specified as
```
head_mask = torch.ones(
    self.model_tester.num_hidden_layers,
    self.model_tester.num_attention_heads,
    device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
```
then `outputs.encoder_attentions[1][..., :, :, :]` or `outputs.decoder_attentions[1][..., :, :, :]` equals tensor of `0.0` for all examples over all heads but the last one. This is not the case, however, for **non**-encoder-decoder models with `attentions[1][..., :, :, :]`. Do you have any idea where the problem can be?

Anyway, I hope we will solve this issue and merge this PR. :) <|||||>I made some mistakes during updating my branch, which resulted in the problem with tracking files not edited actually by myself. I find this quite inconvenient and I have failed to repair this issue so far. Therefore, I've created a new (clean) branch, which might be found here https://github.com/stancld/transformers/tree/head_mask_for_bart_new.

If you, @patrickvonplaten, were okay with that, I would close this PR (after resolving those rather minor issues raised in our discussion above) and create a new one from the new branch referenced above to make everything nice and clean before an eventual merge. <|||||>@stancld absolutely! Feel free to close this PR and open a new one :-) This happens to me all the time as well <|||||>We can just link this closed PR to the new PR to have a reference to the discussion we had<|||||>@patrickvonplaten - Great, you can find a newly open PR at #9569 :) 