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* small refactor, getting rid of the while loop

- Aurick noticed this bug and I ran into it yesterday
- after the fix, our cifar training shows same gain values from
  different replics now:

```
20-Oct-20 16:00:19 - DEBUG - rank1 - scale 2, gain ratio 1.3512124098087777
20-Oct-20 16:00:19 - DEBUG - rank0 - scale 2, gain ratio 1.3512124098087777
20-Oct-20 16:00:19 - DEBUG - rank1 - timing: data 0:00:00.000600 fwd 0:00:00.003678 loss 0:00:00.000086 bwd 0:00:00.314158 update 0:00:00.002132 rest 0:00:00.000399
20-Oct-20 16:00:19 - DEBUG - rank0 - timing: data 0:00:00.000643 fwd 0:00:00.003460 loss 0:00:00.000084 bwd 0:00:00.314678 update 0:00:00.002001 rest 0:00:00.000408
20-Oct-20 16:00:19 - DEBUG - rank1 - scale 2, gain ratio 1.3514997779980324
20-Oct-20 16:00:19 - DEBUG - rank0 - scale 2, gain ratio 1.3514997779980324
20-Oct-20 16:00:19 - DEBUG - rank1 - timing: data 0:00:00.000732 fwd 0:00:00.003689 loss 0:00:00.000086 bwd 0:00:00.314176 update 0:00:00.002146 rest 0:00:00.000397
20-Oct-20 16:00:19 - DEBUG - rank0 - timing: data 0:00:00.000646 fwd 0:00:00.003542 loss 0:00:00.000089 bwd 0:00:00.314549 update 0:00:00.001956 rest 0:00:00.000392
20-Oct-20 16:00:19 - DEBUG - rank1 - scale 2, gain ratio 1.352149646693932
20-Oct-20 16:00:19 - DEBUG - rank0 - scale 2, gain ratio 1.352149646693932
```

- fixed typing
- make it run less often to reduce CI time

testing: run it in a loop make sure it is run in the right frequency.

- close #143

Adds support for distributing pipeline stages across multiple processes (and therefore multiple machines)
* Adds a style argument to the Pipe constructor, defaulting to PipelineStyle.SingleProcess, but also supporting PipelineStyle.MultiProcess
* Added support for lazy construction of modules (see lazy_construction for an example)
* Added two implementations of inter-process communication: one based on rpc with globally visible queues, one based on send/recv
* Copied all the relevant tests from tests/pipe to tests/pipe_process and modified them to exercise PipelineStyle.MultiProcess

Add GradScaler to Fairscale, subclassing PyTorch's GradScaler. Use GradScaler in the pipe benchmark; though it is not needed in this case, it is a good example of how to use gradient scaling for larger models that do require gradient scaling in order to converge.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Workaround PyTorch bug that casts state (pytorch/pytorch#43706).

Copied from https://github.com/pytorch/fairseq/blob/v0.9.0/fairseq/optim/fp16_optimizer.py#L251-L268

authored-by: Mandeep Singh Baines <msb@fb.com>

Co-authored-by: Jun Ru Anderson <andersonic@fb.com>