Optimize Token Classification models for TPU
As per the XLA [documentation](https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md#known-performance-caveats) XLA cannot handle masked indexing well. So token classification
models for BERT and others use an implementation based on `torch.where`. This implementation
works well on TPU. 

ALBERT, ELECTRA and LayoutLM token classification model uses the masked indexing which 
causes performance issues on TPU. 

This PR fixes this issue by following the BERT implementation.

Relevant code in [BERT](https://github.com/huggingface/transformers/blob/c4e1586db8ef6b4102016fc5cb038940fde45325/src/transformers/models/bert/modeling_bert.py#L1741) 

# What does this PR do?

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Fixes # (issue)


## Before submitting
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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Hello, thank you for your PR! Would you happen to have performance results we can take a look at to see the improvement your PR offers?<|||||>Sure. I found this issue while running experiments for our paper. I cannot make that code public yet. I will make a colab notebook illustrating the issue.<|||||>Hi. Sorry for being so late. I have prepared a colab notebook showing the speedup [here](https://colab.research.google.com/drive/1Y9f3BWkTeQS7lFJSGXjcCULURYAJ1NZw?usp=sharing).  With this PR we can improve ALBERT Token Classification model training time from 27.5 minutes to 3minutes.<|||||>Thank you for sharing, I have requested access to the doc<|||||>I have updated the permission. <|||||>Thank you, this looks good! @sgugger, @mfuntowicz, I think you're the most experienced with torch operations - do you have some feedback for this PR?<|||||>Sure. I will run the code on GPU too :)<|||||>Here is the [colab notebook](https://colab.research.google.com/drive/1OTBfHNt-ZGqFlz1kFvFEq-oCBRYFEuLw?usp=sharing) comparing the execution of the original implementation and the patched implementation on GPU. As expected, there is no performance degradation on GPU.<|||||>Thanks for checking! The failing test has been fixed on master so this is good to merge for me.<|||||>Thanks for your work @ibraheem-moosa, super nice addition!