GitLab

- fixed typing
- make it run less often to reduce CI time

testing: run it in a loop make sure it is run in the right frequency.

The expert annotation is used by clip_grads and DDP.

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

* new unit test to catch rank issues in OSS

Currently only implemented for a single process and expert.

* Add unittest for checkpoint & DDP

- this change adds test cases to reproduce the error with checkpoint & DDP
- mandeep mentioned that there is also deadlock in this case, but this
  change doesn't cover that.
- we cover cases where weight sharing is OK
- however, same module multiple checkpoint or find_unused_parameters are
  both not OK

* added norm checks

Same bucketing strategy for OSS and SDP:
sort everything ahead of time, per rank and per size, smaller tensors first. Bucket the smallest elements in a fixed buffer, send async, then send all the others async, and get back to the bucket. Once done then scatter the contents if needed

- adding the buffer broadcast option
- minor cleanup in shardedDDP

Adds support for distributing pipeline stages across multiple processes (and therefore multiple machines)
* Adds a style argument to the Pipe constructor, defaulting to PipelineStyle.SingleProcess, but also supporting PipelineStyle.MultiProcess
* Added support for lazy construction of modules (see lazy_construction for an example)
* Added two implementations of inter-process communication: one based on rpc with globally visible queues, one based on send/recv
* Copied all the relevant tests from tests/pipe to tests/pipe_process and modified them to exercise PipelineStyle.MultiProcess

- rename oss_ddp to ShardedDataParallel
- some refactoring
- ShardedDataParallel owns the sharded optimizer, exposed if need be
- some small perf bumps

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. 

Add GradScaler to Fairscale, subclassing PyTorch's GradScaler. Use GradScaler in the pipe benchmark; though it is not needed in this case, it is a good example of how to use gradient scaling for larger models that do require gradient scaling in order to converge.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

* 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.

- added train(mode) method to be aware of eval mode

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

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

Implement scaling of optimizer state when using pure-fp16 training to avoid underflow. Update benchmark to use pure-fp16. Modify state_dict methods to store and load the optimizer state scale.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Set the torch seed for tests. xfail mixed precision and memory-efficient mixed-precision state_dict tests due to their states being cast to FP16 and back to FP32 during load_state_dict.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

* move the restored param groups to the original device

* adding a corresponding test

Refactor tests to remove duplicated code. Fix the state_dict test to instantiate the second optimizer with the correct precision. Fix Adam.load_state_dict to make optimizer state the right type.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Allow training with optimizer state in fp16. Use an enum to select from full-precision, mixed precision, memory efficient mixed precision and pure fp16. Improve clarity of testing code
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Add support for mixed-precision (half precision params, full precision gradients) and memory-efficient (half precision params and half precision gradients) training with Adam
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

* 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)