Recommended Adafactor settings for T5 cause error
## Environment info
- `transformers` version: 3.3.1
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@sshleifer   (from activity on Adafactor PRs)

## Information

Model I am using (Bert, XLNet ...): T5

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

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

## To reproduce

The Adafactor docs recommend the following for T5 : `Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=True)`

However, the init code then has:
```
        if lr is not None and relative_step:
            raise ValueError("Cannot combine manual lr and relative_step options")
        if warmup_init and not relative_step:
            raise ValueError("warmup_init requires relative_step=True")
```
which makes this setting impossible (as well as just changing to `relative_step=True`). So something seems to be missing either in the recommendations or in the implementation.

Thanks!


I think the doc should recommend
```python
Adafactor(model.parameters(), relative_step=True, warmup_init=True, lr=None)
```
want to fix it?<|||||>I think what corresponds to the original T5 training code is `Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=False)`, however that didn't work great for me so far (much slower than Adam, and giving me NaN's even in FP32).<|||||>Hello @OyvindTafjord, have you been able to fine-tune T5 with Adafactor? Thanks, Sonali<|||||>No, I haven't investigated further regarding the slowness and NaN's I was getting.<|||||>This issue persists (i.e. the suggested defaults still produce the error).

I can confirm that `Adafactor(lr=1e-3, relative_step=False, warmup_init=False)` seems to break training (i.e. I observe no learning over 4 epochs, whereas `Adafactor(model.parameters(), relative_step=True, warmup_init=True, lr=None)` works well (much better than adam)