GitLab

* 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

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

* precise skip, only if agent has only cpu

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

* 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

author: Joshua Meier

* fix, one liner

* adjust so that frozen trunks get spread still, even if this should have little consequences

* removing dead code, hopeful unit test fix

* now with some linting..

* adding a proper unit test case

add a clip gradients util, equivalent to torch's but aware of the sharded states. Add a corresponding unit test

* fixing the issue wrt Apex, validated with Latte, Classy would need another pass

* new unit test to catch rank issues in OSS

Return either the local or global state when queried, depending on a prior consolidation

Make OSS compatible with optimizers which do not support the closure argument

Changes the structure of the returned state dict with respect to the param_groups to make it closer to what a vanilla optimizer would return (un-shard them). Shard again when loading

Make sure that all attributes (not just LR) are in sync in between the OSS.param_groups and the actual wrapped optimizer. Some frameworks make it possible to alter any attribute on a scheduled basis, which proves useful depending on the optimizer, so the keys need to be generically supported (not just "lr"). Not syncing these attributes is a worst case scenario, since these adjustments are silently not propagated, fixing that. 

* Aligning the optimizer state dict with what PyTorch expects

* Adding a check on the dict keys, ensure that `state` and `param_groups` are there

* after installing the specific isort, black and all, one liner to please the linter..

* [fix] optim/oss: work correctly with LRScheduler

Sync lr before every step and before consolidate.

* [fix] optim/oss: support optimizers with additional step kwargs

Some of the optimizers in apex support additional kwargs to step
such as scale.

* move the restored param groups to the original device

* adding a corresponding test

* hotfix a half-cooked optimizer state restoration, the global shared state also needs to be restored

* [cleanup] get 100% coverage on oss.py (#38)
authored-by: Mandeep Singh Baines <msb@fb.com>

* better unit testing, check that the .param_groups attribute is properly in sync with the loaded state
Co-authored-by: msbaines <35972327+msbaines@users.noreply.github.com>

Aligning OSS state dict with `https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer` (#31)

Co-authored-by: Min Xu <m1n@fb.com>