GitLab

* [fix] mypy and flaky test

- CI didn't seem to catch this or maybe I merged incorrectly yesterday
- this should fix the mypy error on master
- also updated a test that seems to be flaky due to tcp port conflict

* another flaky test, hopefully more determinism helps

* CR

* skip 1.6

* fix

* minor
Co-authored-by: Min Xu <min.xu@acm.org>

* FSDP: fixing training with freezing weights

- an assert is changed to catch this case correctly
- unit test added (based on Quentin's test code) for this case and
  compare DDP and FSDP

fixes: #610

* added test file to list 1

* Use better and simpler code as suggested by Myle

* testing both methods of freezing as well
Co-authored-by: Min Xu <min.xu@acm.org>

replacing multip-process pipe implementation with more flexible one

Initial implementation of proposal pytorch/pytorch#55256

* Adding a unit test which checks for multiple FW passes on the same block
* Adding an embedding table, but still no problem to show for it

* Properly handle .train() and .eval() modes
* showing that the unit test works, now fixed
* code review

This test is flaky for torch >= 1.8.0.

NCCL all_to_all is now supported in PyTorch (since v1.8.0)

Fixes: #548

[fix] FSDP: disable single rank process group for auto_wrap_bn and fixed mixed precision regnet test (#556)

* [fix] disable single rank process group for auto_wrap_bn

- beefed up unit test with regnet-like model
- found that single-rank process group is causing problem
- disabled it to enable convergence tests on the vissl side
- use `raise e from None` to get a better assertion output
  in testing.py.

* [test] fix regnet test for ddp+mixed_precision

- need AMP context in FSDP
- workaround different between ddp & fsdp when bias=True
- fixed a bug in input data generation that caused different ranks have
  the same data with wrong iteration count.
- added TODO for need a better loss and grad_scaler and reduced
  iters so there is no nan.
- added a (disabled) debugging code

* lint

* lint

* add scaler

* lint

* scaler

* add a real loss

* seeding in the ranks

* blance tests

* run AMP DDP==FSDP test only on cuda version 11 and up

* add relu inplace and comment

* make wrap_bn covers more cases in full precision mode

* survive the model being moved to device post-construction
* make sure that a unit test would catch a regression

- added DDP equivalency test
- added rmf, state_dict_norm functions to testing utils
- added more debugging output to objects_are_equal

* re-activating unit test
* removing changed that slipped in

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

* Add new test for weight init (fails)
* Set FSDP.compute_device so summon_full_params works before module moves to CUDA
* Override FSDP.apply to enable custom weight init

* param buckets
* unifying the buckets

* extracting the buckets in a dedicated class, fixing the resize_ bug
* adding a unit test
* copyright

* [feat] FSDP: add auto_wrap_bn

- add an utility function to handle wrapping of BN

* changelog

* [feature] FSDP: enable pytorch SyncBN

- not fully validated yet but at least not asserting
- this enables VISSL to move forward with its next PR

* add the test file

* changelog and lint

* addressed comment

* Deactivating buckets for a single rank, not crashing but not useful

* 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

* [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

* [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

* [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