Possible bug in `train_batch_size`
## Environment info
- `transformers` version: 4.1.1
- Platform: Linux-4.4.0-62-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

### Who can help
 Trainer: @sgugger 

## Information

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

BERT 

The problem arises when using:
* [X ] my own modified scripts: (give details below)

I'm running a model on a toy dataset with only 2 examples and a batch size of 2. In trainer, `num_examples` is 2, but `total_train_batch_size` is 12 even though I do not have the `model_parallel` flag set to `True` (Note I do have 6 GPUs available on the machine). This doesn't seem to impact my code because `train_dataset_is_sized=True`, but it seems strange. 

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ X] my own task or dataset: (give details below)

toy classification jsonl dataset with 2 examples

## To reproduce

I think that [this line](https://github.com/huggingface/transformers/blob/64103fb6beac8cc865945d3956266fd80b44f18f/src/transformers/training_args.py#L454) has an unnecessary `not`. Should this be `if self.model_parallel` instead of `if not self.model_parallel`? Thanks!

Think @sgugger can best answer here when he's back from holiday :-) <|||||>You misunderstand the flag `model_parallel`, it's not there to enable the use of several GPUs as this is done automatically by the `Trainer` (you have to set `CUDA_VISIBLE_DEVICES` to just one GPU if you don't want the Trainer to use them all). That flag is there to split the model layers on the various GPUs available (only available for a few models).<|||||>Got it, I didn't realize that the Trainer automatically uses multiple GPUs if visible. Thanks! 