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Fixes #190

* fallback on internal pytorch numbering

Fixing the relative positions of the html docs.

* make the basic example usable out of the box
* clarify

* testing median and MAD

* synchronize on kernels to make sure that we're measuring the actual completion time

* adjusting the circleci threshold, not that the speed has regressed but because we measure proper cuda execution time

* rewrite using autograd and Variable execution queue to make the reduce automatic
* share buckets with OSS to remove duplication
* some speed still likely on the table since the speed vs. bucketing does not match expectations, could be a follow up

Fixes #190

* reverting a change which slipped in #188

* Add CPU support for pipe.py benchmarks, CUDA-free

* adding a shard-aware GradScaler wrap, credits to Sean Naren for the idea
* adding stubs & explanations in the documentation

- removed experimental warning as we have validated it on cifar and
imagenet, transformer is looking good so far too.
- fixed API doc formatting
- make it consistent with the other code in the repo
- tested by making the doc locally and inspect the results

add a clip gradients util, equivalent to torch's but aware of the sharded states. Add a corresponding unit test

* now works on a machine without cuda, easier to debug and quick test

Adds support for:
* Reused layers (e.g. for weight sharing)
* Lazily-constructed layers
* Single-process control via PipeRPCWrapper
* PipelineStyle.AsyncScheudle, which lays the foundation for asynchronous pipeline work by introducing an event loop for each rank/worker to process either activations or gradients as they arrive

Also added examples for multi-process and PipeRPCWrapper

* oss benchmark: add an --amp option
* add a circleCI test

* Some ease of use in the benchmark tool, add a debug option

* small refactor, getting rid of the while loop

fix broken link