GitLab

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)

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

Add FusedAdam, update benchmark and add tests.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>