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* extending the current state_dict interface, make it possible to do everything in a single call, and to checkpoint on all ranks

* FSDP: multi-pass autograd graph and mixed precision

- added BACKWARD_PRE/POST checking
- better assert_state
- fixed issue of backward hook misfiring

* fix

* cleanup

* Update fairscale/nn/data_parallel/fully_sharded_data_parallel.py
Co-authored-by: Myle Ott <myleott@fb.com>
Co-authored-by: Myle Ott <myleott@fb.com>

* Adding a hard sync barrier before the broadcast, mostly useful for Gloo actually, NCCL is synced behind the scene
* adding a proper unit test
* adding a unit test for https://github.com/facebookresearch/fairscale/pull/510

* experimental: Add spectrain support

* Address review comments

* Address review comments

* seemingly fix flakyness for gloo by checking all coms handles

* smal fix, remove unused flags

* remove usused flag

* add back max_batch flag

* adding back lazy_construction

* adding back lazy_construction

* add missing device arg
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* [chore] 0.3.1 release

- mainly because vissl needs the new version
- added a doc on release steps

* Update CHANGELOG.md
Co-authored-by: anj-s <32556631+anj-s@users.noreply.github.com>

* review comments
Co-authored-by: anj-s <32556631+anj-s@users.noreply.github.com>

* Document FSDP tips and tricks in a separate file

* Fix packed sequence apply

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

* [fix]: handle inputs with containers

- this is an issue surfaces by vissl as well
- fix seems to be super simple
- also cleaned up two tests with respect to multiple such tests
  running back to back (they don't do that presently)

* cleanup

* fix

* lint

Update example as style arg was removed in https://github.com/facebookresearch/fairscale/pull/345

:(

* [refactor] enhance wrap and auto_wrap

- Two things were done in this PR
  1. We don't need to import FSDP in wrap.py since the wrapper class
     type is stored in the context now.
  2. We can use a `auto_wrap_policy` function to customize wrapping policy
     for auto_wrap, including size of module, blacklist, exclude list
- The auto_wrap function got simplified a bit as a minor side effect.

* Update fairscale/nn/wrap/auto_wrap.py
Co-authored-by: Sean Naren <sean@grid.ai>

* addressed comments

* addressed more comments
Co-authored-by: Sean Naren <sean@grid.ai>

* [perf][minor] cache the rank lookups, small shardedddp perf fix
* tiny improvement, code quality

* change empty shard handling for OSS, do not rely on asserts
* code review

* [hotfix]: fix a bug in CI command

* debug

* debug

* bump cache ver

* fix

* eq

* check

* bump

* addressed comment

* [feat]: checkpoint and normalization

- added special handling of BN for track_running_stats and checkpointing
- we test BN/LN and checkpointing
- we test them with mixed precision

* Fix ampnet unit test by adding delegate object

* Remove comments

- cover them in terms of code path only
- numerically, AdaScale is different on SDP/FSDP than DDP, mainly
  due to partial view of the gradients.
- this doesn't mean it is definitely not useful but it is yet to
  be validated.
- not going to spend too much time until we have a real use case.

* [chore] move a test script

* add a shortcut for installing

* more skipping

* keep apt-get part

[refactor] Use logging in place of print statements, remove unused functions and other minor refactoring changes. (#461)

* fix pipe logging and other cleanups

* more log/debug changes

This adds a context manager that assists in making child modules with similar defaults.
Usage:
```
from fairscale.nn.misc import enable_wrap, wrap

with enable_wrap(**handleful_of_important_params):
    layer_1 = wrap(torch.nn.Linear(5, 5))
    layer_2 = wrap(torch.nn.Linear(5, 5), flatten_parameters=True) # Override parameters if you'd like

# without the context manager, creates Linear layer
layer_1 = wrap(torch.nn.Linear(5, 5))
```
If not within the FSDP context, this would be a no-op. This makes it easier to annotate layers without having to copy any changes in parameters.