BartLearnedPositionalEmbedding's forward method signature obstructs private (Opacus) training of BART
### System Info

-`transformers` version: 4.20.1
-Platform: Linux-5.4.0-1086-azure-x86_64-with-glibc2.17
-Python version: 3.8.13
-Huggingface_hub version: 0.8.1
-PyTorch version (GPU?): 1.9.1+cu102 (False)
-Tensorflow version (GPU?): not installed (NA)
-Flax version (CPU?/GPU?/TPU?): not installed (NA)
-Jax version: not installed
-JaxLib version: not installed
-Using GPU in script?: yes (NA)
-Using distributed or parallel set-up in script?: no (NA)

### Who can help?

Tagging @patil-suraj as BART model owner.

Details:
The signature of `BartLearnedPositionalEmbedding`'s forward method takes an input of type `torch.Size`, which breaks in Opacus. The reason is that Opacus makes a (reasonable) assumption that all layers take input of type `torch.Tensor`. 

In particular, opacus/grad_sample/grad_sample_module.py line 190 (the `capture_activations_hook` method) tries to detach the input from device via:

`module.activations.append(forward_input[0].detach())`

If we pass the tensor instead, this will allow fine-tuning BART-type summarization models with differential privacy.

Only a few lines of code need to be changed in `modeling_bart.py`. In particular, the forward signature of `BartLearnedPositionalEmbedding.forward()` and references to this method.

I already have a change implemented with BART-related tests passing. More than happy to create a PR which I can tag you in @patil-suraj.

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```
import torch

from transformers.models.bart.modeling_bart import  BartLearnedPositionalEmbedding


from opacus.tests.grad_samples.common import GradSampleHooks_test
class TestPositionalEmbedding(GradSampleHooks_test):
    def test_grad_sample(self):
        """
        Verify that our custom implementation of the grad sample for huggingface's
        BartLearnedPositionalEmbedding layer works. Built on the test routines in opacus's library.
        """
        register_grad_sampler()
        batch_size = 1
        max_pos_embs = 10
        embed_dim = 3
    
        x = torch.randint(0, max_pos_embs - 1, (batch_size, embed_dim))
        layer = BartLearnedPositionalEmbedding(max_pos_embs, embed_dim)
        self.run_test(x, layer, batch_first=True)
```
where a custom `register_grad_sampler()` method is called for `BartLearnedPositionalEmbedding` layer.

### Expected behavior

Test above should pass.
Tagging @sgugger as you have previously shown support for private training of HF models via Opacus.<|||||>Happy to look at a PR that fixes the issue!<|||||>Fantastic, I'll create it now 👍<|||||>@donebydan Hi, have you generated the fine-tuned BART with OPACUS? I'm working on it and changed the code to a merged one. But the model generation is weird like repeating  `the the..`<|||||>Hi @SeolhwaLee, we are integrating a BART with Opacus example in our [`dp-transformers`](https://github.com/microsoft/dp-transformers) library. It is [this PR](https://github.com/microsoft/dp-transformers/pull/5), but it is pending some updates to newer Opacus (1.13) and HF versions right now.