 T5 Gradient Checkpointing
# What does this PR do?

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Partially fixes #6564. This is inspired by @xFinal's workaround. However, instead of modifying PyTorch's implementation of gradient checkpointing, I only modify `T5Block` here and replace the None value with a dummy tensor that requires a gradient.

~Gradient checkpointing is only enabled for the encoder. Some of the outputs of the decoder don't require gradients and will cause problems. PyTorch 1.8.0 has fixed this (see note 1 below).~

Additional notes:

1. `require_grad = True` for the dummy tensor is no longer required since PyTorch 1.8.0 ([From this PR](https://github.com/pytorch/pytorch/pull/45934)).
2. None as a return value is allowed since [this PyTorch PR](https://github.com/pytorch/pytorch/pull/52422). It has not been released yet (the latest release is 1.8.1 at the time of writing). We won't even need the dummy tensor after that.
3. I tested this code locally with PyTorch 1.7.1.
4. I did not write any additional test because it seems that [test_training_gradient_checkpointing](https://github.com/huggingface/transformers/blob/81009b7a5c5cb183a9275c15bf347bdc988b02c4/tests/test_modeling_common.py#L242) in `ModelTesterMixin` already covers it.

~EDIT: I forgot this PR only covers the encoder, not the decoder. The description has been updated to reflect this fact.~
EDIT: This PR covers the decoder now.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation). (Updated docstring for T5Config.)
- [ ] Did you write any new necessary tests? (Already covered by existing tests.)


## Who can review?

@patrickvonplaten (T5)
@sgugger (Documentation)

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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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