GitLab

* 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>

[fix] Revert change that removed the option to run OffloadModel with out activation checkpointing. (#608)

* revert change made

* add tests and revert sync shard changes

* add tests

* remove file checked in by error

* inine var

* fix lint errors

* add checkpoint activation

* fix mypy

* use a bigger model

* modify tests for now

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

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

* making APIs more private
* linting

This test is flaky for torch >= 1.8.0.

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

Fixes: #548

* renaming/adding error messages

* address comments

* address comments

* add more comments

* add more comments

[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

* enabling disabled tests

* [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

* 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>