(GPT2) Running out of GPU memory(24G) on WSL2 but not on native linux.
## Environment info
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- `transformers` version: 
- Platform: WSL2 Debian 
- Python version: 3.7
- PyTorch version (GPU?): 1.3.1
- Tensorflow version (GPU?): 
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help
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 albert, bert, GPT2, XLM: @LysandreJik 
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 TextGeneration: @TevenLeScao 
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## Information

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

The problem arises when using:
the official example scripts: (give details below)
I'm running the run_language_modeling.py trying to finetune GPT-2

----
On WSL2 i Run out of memory:

```RuntimeError: CUDA out of memory. Tried to allocate 384.00 MiB (GPU 0; 24.00 GiB total capacity; 22.01 GiB already allocated; 342.71 MiB free; 65.09 MiB cached)```

but if i boot a live ubuntu and run the exact same script it works fine.

I'm using all default settings just as in the example doc.

not sure what is it due to and how to fix it?

I can train the bert-base-multilingual-cased model and its taking almost all my memory (21109MiB / 24576MiB) on WSL2 meanwhile only taking about 8G on native linux..

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