code: def test(self, tmpdir):
      from deepspeed.runtime.comm.nccl import NcclBackend
  
      size = dist.get_world_size()
      rank = dist.get_rank()
      backend = NcclBackend()
      local_rank = dist.get_rank()
      device = torch.device("cuda", dist.get_rank())
  
      # A simulated compression function using deepspeed.comm
      def torch_sim(a):
          a_sign = a.sign().add_(1).bool().float().add_(-0.5).mul_(2.0)
          scale = a.norm() / np.sqrt(a.numel())
          a_compressed = scale * a_sign
          a_sign = None
          worker_error = a - a_compressed
          dist.all_reduce(a_compressed)
          a_compressed.mul_(1 / dist.get_world_size())
          a_server_sign = (
              a_compressed.sign()
              .add_(1)
              .bool()
              .float()
              .add_(-0.5)
              .mul_(2.0)
          )
          a_list = torch.chunk(
              a_compressed, chunks=dist.get_world_size()
          )
          server_scale = [
              chunk_a.norm() / np.sqrt(chunk_a.numel())
              for chunk_a in a_list
          ]
          a_sign_list = torch.chunk(
              a_server_sign, dist.get_world_size()
          )
          a_server_compressed = torch.cat(
              [
                  server_scale[i] * a_sign_list[i]
                  for i in range(dist.get_world_size())
              ]
          )
          rank = dist.get_rank()
          server_error = (
              a_list[rank] - server_scale[rank] * a_sign_list[rank]
          )
          torch.cuda.synchronize()
          dist.barrier()
          return a_server_compressed, worker_error, server_error
  
      tensor_size = 300 * 2**20
      server_size = int(tensor_size / size)
      if tensor_size % (8 * size) != 0:
          right_tensor_size = tensor_size + (
              8 * size - (tensor_size % (8 * size))
          )
      else:
          right_tensor_size = tensor_size
      right_server_size = right_tensor_size // size
  
      # Adding bias to the initialization of the gradient we are communicating
      # In order to get rid of the case where some elements in the gradient are too small
      a = (torch.rand(tensor_size, device=device) - 0.5) + 0.01 * rank
  
      worker_error = torch.zeros(right_tensor_size, device=device)
      server_error = torch.zeros(right_server_size, device=device)
  
      a_torch, worker_error_torch, server_error_torch = torch_sim(a)
      torch.cuda.empty_cache()
  
      a_after = backend.compressed_allreduce(
          a, worker_error, server_error, local_rank
      )
  
      threshold = 1e-6
      magnitude_threshold = 1e-6
      diff_mask = (a_after - a_torch) > threshold
      diff_server_mask = torch.chunk(diff_mask, size)[rank]
      mpi_server = torch.chunk(a_after, size)[rank] + server_error
      torch_server = (
          torch.chunk(a_torch, size)[rank] + server_error_torch
      )
  
      # If the number in the compensated_server_m is too small (e.g 1e-8), then calling sign() might be problematic
      # The test would skip those numbers that are too small in compensated_server_m
      check_mag_mask = (
          mpi_server[diff_server_mask] > magnitude_threshold
      )
      if torch.sum(check_mag_mask) != 0:
          print(
              "Fails at {} of positions".format(
                  torch.sum(check_mag_mask)
              )
          )
      assert (
          torch.sum(diff_server_mask) == 0
          or torch.sum(check_mag_mask) == 0
      )
  
imports: import torch
  import torch.nn
  import deepspeed.comm
  import deepspeed
  import pytest
  import copy
  import os
  import numpy
  from deepspeed.runtime.pipe.topology import PipeDataParallelTopology
  from deepspeed.ops.op_builder import OpBuilder
  from deepspeed.runtime.pipe.module import PipelineModule
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel, random_dataloader
  from unit.alexnet_model import AlexNetPipe, train_cifar
label_desc: Use image node_id 1 for calling the TestCompressedAllReduceBasic obj's underlying member method code with example usage: obj.test(tmpdir) and returns: a_server_compressed,  worker_error,  server_error