apply_chunking_to_forward should only be the same in the chunking dimension
Author: pedrocolon93
Pull request: no

## Environment info
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- `transformers` version: 3.4.0
- Platform: All
- Python version:
- PyTorch version (GPU?):
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
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## Information

Model I am using (Bert, XLNet ...): XLNet

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Simply send in 2 tensor to the apply_chunking_to_forward that have the same batch length, same batch size, but different dimensionality and it will pop up with an exception


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## Expected behavior
Should only chunk if they are the same in the chunk dimension

```
 assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors)
    tensor_shape = input_tensors[0].shape
    assert all(
        input_tensor.shape == tensor_shape for input_tensor in input_tensors
    ), "All input tenors have to be of the same shape"
```
Should be:
```
    tensor_shape = input_tensors[0].shape[chunk_dim]
    assert all(
        input_tensor.shape[chunk_dim] == tensor_shape for input_tensor in input_tensors
    ), "All input tenors have to be of the same shape"
```

In here if there are 2 input tensors with the shapes:
[512,2,768] and [512,2,300] the method throws an exception when it should only chunk based on the chunk dimension (in this case 2).



Comments:
Great catch @pedrocolon93 ! Do you feel like opening a PR to fix it? :-) Feel free to tag me and I'll help you!

Sounds good! I'll get it in and link it later today!