instead.
torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook(state, bucket)
This DDP communication hook implements PowerSGD gradient
   compression algorithm described in the paper. Once gradient tensors
   are aggregated across all workers, this hook applies compression as
   follows:


Views the input flattened 1D gradient tensor as a list of per-
      parameter tensors, and divides all the tensors into two groups:
 1.1 The tensors that should be compressed before allreduce,
 because the compression can give enough saving in bandwidth.

 1.2 Rest of the tensors will be directly allreduced without
 compression, including all the vector tensors (for biases).



Handles uncompressed tensors:
 2.1. Allocate contiguous memory for those uncompressed
 tensors, and allreduces all the uncompressed tensors as a
 batch, without compression;

 2.2. Copies the individual uncompressed tensors from the


