Fix and improve REALM fine-tuning
# What does this PR do?

This PR 
1. adds `block_embedding_to` function to `RealmForOpenQA`, which allows users to send `block_emb` variable to a specific device. 
3. adds an input argument `block_mask` for reader that replaces previous used `token_type_ids` as the candidate mask. As a result, the reader logits at the second `[SEP]` position of the sequence will be masked out, which would prevent `[SEP]` from occurring on the predicted answer spans and matches the original TF implementation.

Their difference:
```
sentence: "[CLS] Hello How are you? [SEP] Good Job [SEP]"
token_type_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1] # the last 1 is the second [SEP] token
block_mask     = [0, 0, 0, 0, 0, 0, 0, 1, 1, 0]
```

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Fixes # (issue)


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Models:

- albert, bert, xlm: @LysandreJik
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_15297). All of your documentation changes will be reflected on that endpoint.<|||||>@patrickvonplaten - The inference time of NQ evaluation dataset is `8:25` when `block_emb` is on GPU, while the inference time is `21:07` when `block_emb` is on CPU, which is roughly 3 times slower than on GPU. The test was performed on single RTX 2080Ti GPU.

Do you think we should provide an option to let users decide whether to send `block_emb` to GPU? Considering that much slower inference speed...<|||||>> evaluation

That is indeed a pretty big slow down...given such inference times I think we should have `block_emb` by default on the same device than the other parameters (so we should not overwrite the `.to(...)` method I think). I would be happy with adding a `def block_embedding_to(device)` method that lets someone easily move only `block_emb` to CPU/GPU - what do you think?<|||||>Considering most users would expect the model can be fine-tuned on single gpu with 12GiB memory as the paper states, I'm a bit worried that if we by default have `block_emb` on the same device (let's say a 2080Ti) as other parameters, when users call `block_embedding_to` to send it back to CPU, the cuda memory occupied by `block_emb` might not be able to be released as expected. 

To my knowledge, `torch.Tensor.to` will have the original tensor untouched and create a new tensor on the new device (so I think it's more like a copy rather than a transfer), and the way I know to release the memory occupied by the original tensor is delete the tensor until it is cleared by Python GC or by manually calling `torch.cuda.empty_cache()`. However, because I'm not very familiar with how PyTorch cuda context works, and I've seen in some circumstances that even though the tensor object was deleted and manually cleared, occupied spaces are still not be freed up, I don't have 100% confidence that the model can be trained on the GPU with 12GiB memory normally.

So to sum up, I think the most assured approach would be overriding `model.to` and adding a `def block_embedding_to(device)` as you suggest, this way users can choose whether they want to move `block_emb` to the GPU if they have one with big enough memory.

Does this make sense to you?<|||||>@patrickvonplaten - I noticed that the `block_records.npy` file has not been uploaded to model repos having an `openqa` suffix. Could you help with this so that people are able to fine-tune the model? Thanks.<|||||>> 

Hey @qqaatw,

Is it ok if we first move the repos to the official Google org and then add the indices? This should be a bit easier :-)

<|||||>@patrickvonplaten Sure, but can I keep these checkpoints also on my namespace for testing (I can set private for them)? I will not be able to update checkpoints within Google org namespace right?<|||||>Hi @patrickvonplaten, do the above comments make sense to you? or I can just follow your guide. Sorry for the late update.