[Gradient Compression] Implement the original layerwise PowerSGD (#49417)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49417
The existing implementation applies PowerSGD to a batch of flattened tensors, which is a coarse-grained compression. This hook now is renamed as "batched_powerSGD_hook".
Now implement the original implementation in the paper, which applies PowerSGD to each per-parameter tensor. This is a layerwise fine-grained compression. Although this original implementation is slower, it is expected to achieve a higher accuracy, especially when the shapes of per-param tensors cannot be aligned.
Also add a test in distributed_test.py.
Original PR issue: Investigate Applying PowerSGD to Communication Hook for Gradient Compression #47202
ghstack-source-id: 118921275
Test Plan:
buck test mode/dev-nosan caffe2/test/distributed:c10d -- test_powerSGD_ddp_comm_hook_nccl
buck test mode/dev-nosan caffe2/test/distributed:distributed_nccl_fork -- test_DistributedDataParallel_powerSGD_ddp_comm_hook
Reviewed By: rohan-varma
Differential Revision: D25511543
fbshipit-source-id: 19ef188bc2d4c7406443c8fa233c1f2c2f27d93c
