GitLab

* [test]: add peak mem in checkpoint test

* more debugging

* new test

* more fix

* better collection of debug in case of future failures

* update the comment

* typo

* comment

* clarify

* better wording

* v0.3.0 it is, celebration time

* move experimental to the fairscale repo

* lint error fixes

* modify test imports

* lint error fixes

* lint errors
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* POC, testing against the DDP comm hook when available
* docs, adding a reference to DDP's compress hook
* updating changelog, prep for v0.1.8 release

* [bug]: not all CUDA memory is freed when model is deleted

* fixed memory leak

- without this, peak memory will be high when more than one model
  is trained (i.e. first model leave staff around pushing up the
  peak memory when the second model runs)

* addressed comments

* fix

* changelog

Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and [Google](https://arxiv.org/abs/2004.13336

) has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper, which is a drop-in replacement for PyTorch's `DistributedDataParallel` (DDP) wrapper.

Compared to PyTorch DDP:
* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs
* FSDP with `reshard_after_forward=False` has the same communication cost as PyTorch DDP and is similar to ZeRO-2
* FSDP with `reshard_after_forward=True` increases total communication by 50% and is similar to ZeRO-3:
    * all-gather parameters at start of forward pass and start of backward pass
    * reduce-scatter grads at end of backward pass
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* adding an assert + corresponding unit test
* updated changelog
* adjusting the adascale tests

Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>

* [docs]: add checkpoint_wrapper and many small fixes

* update copyright year

* test with and without buckets for all the shardedDDP unit tests
* parametrize all the things
* refactoring, adding even more  combinations at times
* handle hosts not having cuda

* [fix] expose checkpoint_wrapper

* fix formatting

* Adding multiple groups support to ShardedDDP + unit test
* adding gloo to the backends tested for multiple groups

* [fix] ShardedDDP train/eval modes
* Update CHANGELOG.md

* initial implementation, with unit test and assert
* added changelog and better debug string

* WIP, needs to be fixed !

* should be a fix, many thanks Weiyi Zheng

* slightly better unit test, sorting the states on the way out

* reproducing the issue from Weiyi in a unit test, and finally properly fixing

* fixing unit test on pytorch1.5 - original loss diff 26.404895782470703 - 26.404342651367188

This reverts commit 8be9d930.

* many thanks Weiyi Zheng

* Better unit testing
* Make it possible to refresh the DDP assumptions when the model has changed. Make it optional so that you save some time
* Enabling accumulation tests

* super minor, opportunistic micro optim

* v0.1.6

* Add fairscale.utils.containers
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>

* Add fairscale.nn.misc.checkpoint_activations
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* flat params all along, way simpler
* updating the docstring

fix a broken earlier commit, only worked for the first step

* minor

* minor

cache this iterator, easy speed up

* Adding a proper ddp parity / AMP unit test, overdue
* catch non-AMP pytorch

It is not currently being used so we can simplify the interface
by removing it.

* restoring the regression test, adding a test of the for_each optims
* fix the regression test on circleci
* removing unused flags