BART - Fix attention mask device issue on copied models
# What does this PR do?

This PR fixes a small issue when combining `device_map=auto` and OPT. When running the script below (tested it on my VM + Google Colab) (`pip install accelerate && pip install transformers`)

```
from transformers import AutoModelForCausalLM, AutoTokenizer

MAX_NEW_TOKENS = 128
model_name = "facebook/opt-2.7b"
text = "Hello my name is"

tokenizer = AutoTokenizer.from_pretrained(model_name)
input_ids = tokenizer(text, return_tensors="pt").input_ids

model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto')
generated_ids = model.generate(input_ids, max_length=MAX_NEW_TOKENS)
print(model.hf_device_map)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```

We are getting: 
```
8 frames
[/usr/local/lib/python3.7/dist-packages/transformers/models/opt/modeling_opt.py](https://localhost:8080/#) in _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length)
    533             expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
    534             combined_attention_mask = (
--> 535                 expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
    536             )
    537 

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
```

This is because `_expand_mask` creates the mask on the cpu whereas `combined_attention_mask` is always created on the same device as `inputs_embeds`. This PR fixes this issue

Thanks @ArthurZucker !
cc @sgugger 

All OPT slow tests are passing with this fix!
_The documentation is not available anymore as the PR was closed or merged._<|||||>I guess the reason we did not see it yet for other models using the same attention mask pre-processing function is that those models does not support `device_map=auto` yet (tried it with PegasusForCausalLM only) <|||||>BART slow tests are passing! Merging now