GitLab

* refactoring FlattenParamWrapper

- use a FlatParameter class to encapsulate the logic of
  flattening and expanding into views.
- this will make it easier to have multiple groups of flatten
  parameters

* fixed testing context issues for both temp files and temp dirs

* fixing test_fsdp_metadata

* fix pickling of FlatParameter

* fixed test_fsdp_optimizer_utils.py

* minor

* fix assert

* lint

* remove nesting from the test

* step 1.5: remove the code related unnecessary nesting support in FPW

* Update fairscale/nn/misc/flatten_params_wrapper.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* address comment
Co-authored-by: Min Xu <min.xu.public@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

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>