RobertaForMaskedLM loss calculated wrong(?)
## Environment info
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- `transformers` version: 4.8.1 (or github main as of 2021.8.5)
- Platform: MacOS (or any)
- Python version: 3.9 (or any 3.x)
- PyTorch version (GPU?): 1.9.0 cpu
- Tensorflow version (GPU?):
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
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## Information

Model I am using (Bert, XLNet ...): XLM-R/Roberta

The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

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

## To reproduce

Steps to reproduce the behavior:

1. [Tutorial example here](https://huggingface.co/transformers/model_doc/xlmroberta.html#transformers.XLMRobertaForMaskedLM)
2. [Source code here](https://github.com/huggingface/transformers/blob/60e448c87eff29b166bf2821f5389056a72343e3/src/transformers/models/roberta/modeling_roberta.py#L1105)
3.

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## Expected behavior
The logic of calculating MLM seems wrong. Shouldn't CrossEntropyLoss only run on **masked tokens** rather than all tokens? I see no operation here.
<!-- A clear and concise description of what you would expect to happen. -->

The calculation is correct. However, it's the responsibility of the user to prepare the labels for the model, so you need to make sure you set the labels to -100 for positions where you don't want to incur a loss (as -100 is the `ignore_index` of PyTorch's `CrossEntropyLoss`).<|||||>Ah, that makes sense, thanks!. I think you should modify the tutorial page in documentation page? Or put what you said somewhere in the doc pagr?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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