Beam search fails when using model parallelism
## Environment info
- `transformers` version: 4.1.1
- Platform: Linux-4.4.0-194-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes, two GTX 1080, on a single node
- Using distributed or parallel set-up in script?: Using model parallelism through `model.parallelize()`

### Who can help
@LysandreJik 
@alexorona 

## Information

Model I am using (Bert, XLNet ...): GPT2

The problem arises when using:
* [ ] the official example scripts:
* [x] my own modified scripts:

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: 
* [ ] my own task or dataset:

## To reproduce

The recent (and awesome!) model `parallelize()` doesn't seem to work with beam search decoding at the moment. The behavior can be reproduced on the official `huggingface/transformers-pytorch-gpu:4.1.1` docker image by running the following (on a machine with multiple GPUs):

```python
import transformers

tokenizer = transformers.GPT2Tokenizer.from_pretrained("gpt2")
model = transformers.GPT2LMHeadModel.from_pretrained("gpt2")
model.parallelize()

input_ids = tokenizer.encode("This is a test", return_tensors="pt").to("cuda:0")
model.generate(input_ids, num_beams=2)
```
This raises the following error:
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/generation_utils.py", line 612, in generate
    **model_kwargs,
  File "/usr/local/lib/python3.6/dist-packages/transformers/generation_utils.py", line 1088, in beam_search
    model_kwargs["past"] = self._reorder_cache(model_kwargs["past"], beam_idx)
  File "/usr/local/lib/python3.6/dist-packages/transformers/generation_utils.py", line 229, in _reorder_cache
    return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)
  File "/usr/local/lib/python3.6/dist-packages/transformers/generation_utils.py", line 229, in <genexpr>
    return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)
RuntimeError: Input, output and indices must be on the current device
```

## Expected behavior

The expected behavior is to not raise an error, but instead correctly return the beam search decoding.

As the trace suggests, the error seem to come from the `_reorder_cache` method in `generation_utils.py`. Since the model is parallelized among multiple devices, it fails since the device of `beam_idx` and `layer_past` don't match for all layers.

I just tried to modify line 229 in `generation_utils.py` to:
```
return tuple(layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in past)
```
which seems to work. 
I'm happy to file a PR with this change if you approve. Please let me know if there is anything I should be aware of, or pay extra attention to.<|||||>FWIW, this fix doesn't currently work for T5, as the fix to `_reorder_cache` is not reflected in the `modeling_t5.py` file. Following the above, changing [this line](https://github.com/huggingface/transformers/blob/fa84540e98a6af309c3007f64def5011db775a70/src/transformers/models/t5/modeling_t5.py#L1679) to `layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),` appears to fix it.

@patrickvonplaten <|||||>@OyvindTafjord - would you mind opening a new PR for it? :-)