class_docstr: Store state for all-reducing gradients globally until given step, then locally after.
  
  Stores the state for all-reducing gradients globally using ``process_group`` until step ``start_localSGD_iter``,
  and all-reducing gradients locally using ``subgroup`` afterwards.
  
  If ``process_group`` is ``None``, the global process group will be used.
  If ``subgroup`` is ``None``, the intra-node process group on each machine will be used.
  
  Additionally, ``post_local_gradient_allreduce`` may be worth tuning,
  because both true and false may give a faster convergence.
docstr: Initialize state object with given parameters and log when localSGD start.
docstr_tok: ["Initialize","state","object","with","given","parameters","and","log","when","localSGD","start","."]
code: def __init__(
      self,
      process_group,
      subgroup,
      start_localSGD_iter,
      post_local_gradient_allreduce=True,
  ):
      """Initialize state object with given parameters and log when localSGD start."""
      logger.info(
          "Local SGD will be started after %s iterations",
          start_localSGD_iter,
      )
  
      # The group used for all-reducing gradients globally.
      self.process_group = process_group
      # The group used for all-reducing gradients locally.
      self.subgroup = subgroup
      self.start_localSGD_iter = start_localSGD_iter
      # Allreduce gradients locally since iteration `start_localSGD_iter`.
      # This may help with the convergence efficiency at the cost of relatively cheap intra-subgroup communication.
      self.post_local_gradient_allreduce = post_local_gradient_allreduce
      # Iteration/step in the training loop.
      self.iter = 0
  
file_path: pytorch/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py
imports: import logging
  import torch
  import torch.distributed
  from .None import default_hooks
label_desc: Use image node_id 1 to create a new PostLocalSGDState object  with example: obj = PostLocalSGDState(process_group, subgroup, start_localSGD_iter, post_local_gradient_allreduce)