code: def _reducer_allreduce_and_upcast_hook(
      hook_state: _AllreduceUpcastHookState, bucket: dist.GradBucket
  ) -> torch.futures.Future[torch.Tensor]:
      """
      Perform allreduce in precision ``reduce_dtype``, upcast to prepare for optimizer.
  
      Performs allreduce in the reduced precision given by DDP's mixed precision
      reduce_dtype, and upcasts parameters and gradients to fp32 in preparation
      to run the optimizer.
      """
      ddp_weakref = hook_state.ddp_weakref
      reducer, process_group = (
          ddp_weakref().reducer,
          ddp_weakref().process_group,
      )
      gradient_is_bucket_view = ddp_weakref().gradient_as_bucket_view
      # Cast bucket if different than param_dtype.
      if (
          ddp_weakref().mixed_precision.param_dtype
          != ddp_weakref().mixed_precision.reduce_dtype
      ):
          # Cast bucket tensor to reduce_dtype
          bucket.set_buffer(
              bucket.buffer().to(
                  ddp_weakref().mixed_precision.reduce_dtype
              )
          )
      fut = reducer._run_allreduce_hook(bucket)
      ret_fut = torch.futures.Future()
      stream = hook_state.upcast_stream
      with torch.cuda.stream(stream):
          fut.wait()
          bucket.buffer().div_(process_group.size())
          ret_fut.set_result(bucket.buffer())
  
          # Upcast parameters and gradients so optimizer step can run in fp32.
          params, grads = bucket.parameters(), bucket.gradients()
          for p, g in zip(params, grads):
              p.data = p._fp_param
              # free storage for mp param as it will be allocated again in next
              # forward pass.
              _free_storage(p._mp_param)
              p.grad.data = p.grad.to(p.data.dtype)
  
      # enqueue a callback to wait for this stream at end of backward
      def wait_for_stream_cb():
          torch.cuda.current_stream().wait_stream(stream)
          # Remove post-backward hooks since they are re-installed in next
          # iteration, similar to FSDP.
          # Parameters that don't require grad still needed to be casted since
          # they may participate in computation. However, they would not be recast
          # by hook above as they don't have a grad hook installed, so cast them
          # back here.
          for n, p in ddp_weakref().module.named_parameters():
              if hasattr(p, "_ddp_mp_hook_state"):
                  p._ddp_mp_hook_state[1].remove()
                  delattr(p, "_ddp_mp_hook_state")
              if not p.requires_grad and not hasattr(p, "_ddp_ignored"):
                  p.data = p._fp_param
  
          # reset for next backward pass
          hook_state.wait_for_stream_enqueued = False
  
      if not hook_state.wait_for_stream_enqueued:
          Variable._execution_engine.queue_callback(wait_for_stream_cb)
          # mark that the callback is enqueued
          hook_state.wait_for_stream_enqueued = True
  
      return ret_fut
  
file_path: pytorch/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py
imports: import torch
  import torch.distributed
  from torch.autograd import Variable
  from dataclasses import dataclass
  from typing import Any, no_type_check
  from torch.distributed.utils import _free_storage
label_desc: Use image node_id 1 for calling a global function with example usage: _reducer_allreduce_and_upcast_hook(hook_state, bucket) and returns: ret_fut