code: def fp16_compress_hook(
      process_group: dist.ProcessGroup, bucket: dist.GradBucket
  ) -> torch.futures.Future[torch.Tensor]:
      """
      This DDP communication hook implements a simple gradient compression
      approach that casts ``GradBucket`` tensor to half-precision floating-point format (``torch.float16``)
      and then divides it by the process group size.
      It allreduces those ``float16`` gradient tensors. Once compressed gradient
      tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).
  
      Example::
          >>> ddp_model.register_comm_hook(process_group, fp16_compress_hook)
      """
      group_to_use = (
          process_group
          if process_group is not None
          else dist.group.WORLD
      )
      world_size = group_to_use.size()
  
      # Divide first before converting to fp16
      # Use out argument to fuse the division and the conversion.
      compressed_tensor = torch.div(
          bucket.buffer(),
          world_size,
          out=torch.empty_like(bucket.buffer(), dtype=torch.float16),
      )
  
      fut = dist.all_reduce(
          compressed_tensor, group=group_to_use, async_op=True
      ).get_future()
  
      def decompress(fut):
          decompressed_tensor = bucket.buffer()
          # Decompress in place to reduce the peak memory.
          # See: https://github.com/pytorch/pytorch/issues/45968
          decompressed_tensor.copy_(fut.value()[0])
          return decompressed_tensor
  
      # TODO: maybe have a backoff strategy: check if the buffer has inf / NaN, in that case
      # resend with fp32?
      return fut.then(decompress)
  
imports: from typing import Any, Callable
  import torch
  import torch.distributed
label_desc: Use image node_id 1 for calling a global function with example usage: fp16_compress_hook(process_group, bucket) and returns: fut, decompressed_tensor