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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

* 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

* 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

* 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

* 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>

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

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

* precise skip, only if agent has only cpu

* [feat] Add AdaScaleWrapper

- This enables a different API for wrapping an optimizer with AdaScale.
- This also enables AdaScale to be wrapped by OSS.
- However, OSS wrapping AdaScale results in different optimization,
  which future research will be needed to study its effects.

testing: add unit tests.

* addressed comment: typo

* adding the .to(device) support + unit testing
* doc update

* adding a test to prove the inter operability with upstream pytorch
* updating the changelog
* eager state pruning
* pytorch 1.5 compat

* no idea about the root issue, but it proved to be fairly narrowed (gloo+cpu+python3.8+no cuda installed) so I guess that's out of scope for fairscale

* [test]: test with py39 + torch 1.8 nightly

* version fix

* more fix

* fix version function for nightly version

* fix torch_pg build

* invalidate cache

* separate benchmark requirements

* comment

* fixed mypy

* fixed a test

* [test]: test adascale with oss

* minor fix

* add a small comment

* refactor: moved find_tensor_by_shape

* refactor: move test golden data into its own module

* refactor: simplied the train function

* refactor: added comments as suggested

* Add AMPnet implementation (clean version)

* Move ampnet to experimental

* Move stuff around pipeline

* Address review comments and fix pre-commit errors

* Refactor and modify delegate functionality

* Modify header in pipe.py

* working around broken mypy

* minor, but ease of life, one less papercut

* tentatively fixing the cpu version of circleci jobs, now pipe tests are the last ones standing
* fixing oss backcompat, trying to fix rpc in old pytorch also
* fixing the file based init in torch 1.5

* adding a parity unit test
* code review, better testing, use torch defaults and check for the loss, log world size

* add additional unit test
* support model parallelism in oss

* adding the pytest timeout plugin to properly root out hanging tests
* removing redundant code, slightly more reasonable timeout, works on single cuda
* finding the root bug for some of the cpu hangs, rpc init
* propagating all the rpc init test changes to the pipe and model parallel tests

* [feat] sync adascale from internal repo

- tbd

testing: tbd

* Update argument document of __init__

* update documentation around set_num_gradients_to_accumulate

* added checking code for proper API calling places

* rename internal APIs to make them internal

* updated changelog

* added support for add_param_group and its unit test

* added unit test for set_num_gradients_to_accumulate

* added debias_ewma unit test

* fixed test_set_num_gradients_to_accumulate (need zero_grad() call)

* added missing zero_grad() to test_lr_scheduler

* fixed test_add_param_group with respect to optim.zero_grad()

* added test_gradient_value

* added test_scale_not_equal_default for scale != world_size * grad_accum

* added test_unhook()

* removed print statements

* fixed a typo

* addressed Ben's comment