code: def module_replacement(
      model, module_name, compression_technique=None, mpu=None
  ):
      """
      Replace a module with a new module.
      Args:
          model (`torch.nn.Module`)
              The model to replace the module in.
          module_name (`str`)
              The name of the module to replace.
          compression_technique (`str`)
              The compression technique to use for the new module.
      """
  
      # Get the old module
      old_module = recursive_getattr(model, module_name)
  
      need_bias = False
      if hasattr(old_module, "bias") and old_module.bias is not None:
          need_bias = True
  
      # Initialize the new module
      if isinstance(old_module, LinearLayer_Compress) or isinstance(
          old_module, torch.nn.Linear
      ):
          if isinstance(old_module, LinearLayer_Compress):
              new_module = old_module
          else:
              new_module = LinearLayer_Compress(
                  old_module.in_features,
                  old_module.out_features,
                  bias=need_bias,
              ).to(
                  device=old_module.weight.device,
                  dtype=old_module.weight.dtype,
              )
              new_module.weight.data = old_module.weight.data
              if need_bias:
                  new_module.bias.data = old_module.bias.data
      elif isinstance(old_module, Conv2dLayer_Compress) or isinstance(
          old_module, torch.nn.Conv2d
      ):
          if isinstance(old_module, Conv2dLayer_Compress):
              new_module = old_module
          else:
              new_module = Conv2dLayer_Compress(
                  old_module.in_channels,
                  old_module.out_channels,
                  old_module.kernel_size,
                  old_module.stride,
                  old_module.padding,
                  old_module.dilation,
                  old_module.groups,
                  need_bias,
                  old_module.padding_mode,
              ).to(
                  device=old_module.weight.device,
                  dtype=old_module.weight.dtype,
              )
              new_module.weight.data = old_module.weight.data
              if need_bias:
                  new_module.bias.data = old_module.bias.data
      elif isinstance(old_module, torch.nn.BatchNorm2d):
          new_module = BNLayer_Compress(
              old_module.num_features,
              old_module.eps,
              old_module.momentum,
              old_module.affine,
              old_module.track_running_stats,
          ).to(old_module.weight.device, old_module.weight.dtype)
          new_module.weight.data = old_module.weight.data
          if need_bias:
              new_module.bias.data = old_module.bias.data
          new_module.running_mean.data = old_module.running_mean.data
          new_module.running_var.data = old_module.running_var.data
      elif isinstance(old_module, Embedding_Compress) or isinstance(
          old_module, torch.nn.Embedding
      ):
          if isinstance(old_module, Embedding_Compress):
              new_module = old_module
          else:
              new_module = Embedding_Compress(
                  old_module.num_embeddings,
                  old_module.embedding_dim,
                  old_module.padding_idx,
                  old_module.max_norm,
                  old_module.norm_type,
                  old_module.scale_grad_by_freq,
                  old_module.sparse,
              ).to(
                  device=old_module.weight.device,
                  dtype=old_module.weight.dtype,
              )
              new_module.weight.data = old_module.weight.data
      elif mpu is not None and (
          isinstance(old_module, ColumnParallelLinear_Compress)
          or isinstance(old_module, mpu.ColumnParallelLinear)
      ):
          if isinstance(old_module, ColumnParallelLinear_Compress):
              new_module = old_module
          else:
              new_module = ColumnParallelLinear_Compress(
                  mpu,
                  old_module.input_size,
                  old_module.output_size,
                  gather_output=old_module.gather_output,
                  skip_bias_add=old_module.skip_bias_add,
                  bias=need_bias,
              ).to(
                  device=old_module.weight.device,
                  dtype=old_module.weight.dtype,
              )
              new_module.weight.data = old_module.weight.data
              if need_bias:
                  new_module.bias.data = old_module.bias.data
      elif mpu is not None and (
          isinstance(old_module, RowParallelLinear_Compress)
          or isinstance(old_module, mpu.RowParallelLinear)
      ):
          if isinstance(old_module, RowParallelLinear_Compress):
              new_module = old_module
          else:
              new_module = RowParallelLinear_Compress(
                  mpu,
                  old_module.input_size,
                  old_module.output_size,
                  input_is_parallel=old_module.input_is_parallel,
                  skip_bias_add=old_module.skip_bias_add,
                  bias=need_bias,
              ).to(
                  device=old_module.weight.device,
                  dtype=old_module.weight.dtype,
              )
              new_module.weight.data = old_module.weight.data
              if need_bias:
                  new_module.bias.data = old_module.bias.data
      else:
          new_module = None
  
      if compression_technique is not None:
          for k, v in compression_technique.items():
              if k == SPARSE_PRUNING:
                  if v[SPARSE_PRUNING_ENABLED]:
                      new_module.enable_sparse_pruning(
                          v[SPARSE_PRUNING_DENSE_RATIO],
                          v[SPARSE_PRUNING_METHOD],
                      )
              elif k == ROW_PRUNING:
                  if v[ROW_PRUNING_ENABLED]:
                      new_module.enable_row_pruning(
                          v[ROW_PRUNING_DENSE_RATIO],
                          v[ROW_PRUNING_METHOD],
                      )
              elif k == HEAD_PRUNING:
                  if v[HEAD_PRUNING_ENABLED]:
                      new_module.enable_head_pruning(
                          v[HEAD_PRUNING_DENSE_RATIO],
                          v[HEAD_PRUNING_METHOD],
                          v[HEAD_PRUNING_NUM_HEADS],
                      )
              elif k == ACTIVATION_QUANTIZATION:
                  if v[ACTIVATION_QUANTIZATION_ENABLED]:
                      new_module.enable_activation_quantization(
                          v[ACTIVATION_QUANTIZE_BITS],
                          v[ACTIVATION_QUANTIZE_TYPE],
                          v[ACTIVATION_QUANTIZE_RANGE],
                      )
              elif k == WEIGHT_QUANTIZATION:
                  if v[WEIGHT_QUANTIZE_ENABLED]:
                      new_module.enable_weight_quantization(
                          v[WEIGHT_QUANTIZE_START_BITS],
                          v[WEIGHT_QUANTIZE_TARGET_BITS],
                          v[WEIGHT_QUANTIZATION_PERIOD],
                          v[WEIGHT_QUANTIZE_IN_FORWARD_ENABLED],
                          v[WEIGHT_QUANTIZE_TYPE],
                          v[WEIGHT_QUANTIZE_GROUPS],
                      )
              elif k == CHANNEL_PRUNING:
                  if v[CHANNEL_PRUNING_ENABLED]:
                      new_module.enable_channel_pruning(
                          v[CHANNEL_PRUNING_DENSE_RATIO],
                          v[CHANNEL_PRUNING_METHOD],
                      )
              else:
                  raise NotImplementedError(
                      "Compression technique {} is not implemented".format(
                          k
                      )
                  )
  
      # Replace the old module with the new one
      recursive_setattr(model, module_name, new_module)
  
file_path: deepspeed/deepspeed/compression/helper.py
imports: import torch
  from .basic_layer import Embedding_Compress, LinearLayer_Compress, Conv2dLayer_Compress, BNLayer_Compress, ColumnParallelLinear_Compress, RowParallelLinear_Compress
  from .constants import *
  from deepspeed.utils import logger
label_desc: Use image node_id 3 for calling a global function with example usage: module_replacement(model, module_name, compression_technique, mpu) without return types