Generate can return cross-attention weights too
Author: Mehrad0711
Pull request: yes

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Fixes #10335 


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Comments:
Hi, I tried to keep the code changes to a minimum. Thus, I avoided adding another argument for returning cross-attention weights and used `output_attentions` to check instead. 
Also in docstrings, for `decoder_attentions` the shape is mentioned as `(batch_size*num_return_sequences, num_heads, generated_length,
            sequence_length)`, although it seems that the shape is always `(batch_size*num_return_sequences, num_heads, 1, 1)`. `generated_length` is 1 as the code returns tuples per generated token and `sequence_length` is 1 since it's decoder self-attentions.
Similarly for `cross_attentions` the shape should be `(batch_size, num_heads, 1, input_sequence_length)`. Are there any examples where this is not True?

Please let me know what you think. Thanks!

> Hi, I tried to keep the code changes to a minimum. Thus, I avoided adding another argument for returning cross-attention weights and used `output_attentions` to check instead.
> Also in docstrings, for `decoder_attentions` the shape is mentioned as `(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)`, although it seems that the shape is always `(batch_size*num_return_sequences, num_heads, 1, 1)`. `generated_length` is 1 as the code returns tuples per generated token and `sequence_length` is 1 since it's decoder self-attentions.
> Similarly for `cross_attentions` the shape should be `(batch_size, num_heads, 1, input_sequence_length)`. Are there any examples where this is not True?
> 
> Please let me know what you think. Thanks!

Regarding the `decoder_attentions` shape -> it's just the first attentions that is of shape `batch_size, num_heads, 1, 1`. Then it goes up to `..., 2, 2`, `..., 3, 3` etc. However if `use_cache` is enabled, then the shape is `...1, 1`, `...1, 2`, `....1, 3`.

Check this example:

```python
from transformers import BartForConditionalGeneration
import torch

bart = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
bart.config.max_length = 5 
bart.config.num_beams = 1

outputs_use_cache = bart.generate(torch.tensor([ 10 * [0]]), return_dict_in_generate=True, output_attentions=True)
outputs_no_cache = bart.generate(torch.tensor([ 10 * [0]]), return_dict_in_generate=True, output_attentions=True, use_cache=False)

# outputs_no_cache.decoder_attentions[1][0].shape gives (1, 16, 2, 2)
# outputs_use_cache.decoder_attentions[1][0].shape gives (1, 16, 1, 2)
```

> Regarding the decoder_attentions shape -> it's just the first attentions that is of shape batch_size, num_heads, 1, 1. Then it goes up to ..., 2, 2, ..., 3, 3 etc. However if use_cache is enabled, then the shape is ...1, 1, ...1, 2, ....1, 3.

Thanks for the example! It makes sense now.

Thanks for the great work. But I am still a bit confused about getting the cross-attention or encode-decoder attention if I am not mistaken the cross attention. Any hint would be great. Maybe it would be like 

```
# outputs_no_cache.cross_attentions[1][0]
# outputs_use_cache.cross_attentions[1][0]
```

Thanks! @Mehrad0711 

hi @xwuShirley 

Yes, you are right. You can access the cross attentions using 
`outputs. cross_attentions`