code: def replace_transformer_layer(
      orig_layer_impl, model, checkpoint_dict, config, model_config
  ):
      """Replace bert-style transformer layers with DeepSpeed's transformer layer
      Arguments:
          orig_layer_impl (torch.nn.Module): the original transformer layer implementation to look for,
              e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer
          model (torch.nn.Module): user's nn.module representing their model
          checkpoint_dict: Dictionary for checkpoint passed from the Inference Engine
          config: top-level DS Inference config defined in inference/config.py
          model_config: HuggingFace model config passed from the inference/engine.py
      Returns:
          Updated nn.module with replaced transformer layers
      """
      # defining globals as internally defined functions inherit these everywhere
      quantize = config.dtype == torch.int8
      # todo: Refactor later. In future, let's minimize the style used above and use config.** instead
  
      linear_layer_setting = None
      """
          linear_layer_setting (tuple of modules) [Optional]: shows which two classes are used for linear layers and embedding layers
      """
      micro_batch_size = -1
      seed = -1
      local_rank = -1
  
      mp_replace = ReplaceWithTensorSlicing(
          mp_group=config.tensor_parallel.tp_group,
          mp_size=config.tensor_parallel.tp_size,
      )  # , out_dim=0, in_dim=1)
  
      def replace_with_policy(
          child,
          policy_cls,
          triangular_masking,
          inference=False,
          layer_id=0,
      ):
          policy = policy_cls(child, inference=inference)
          if not policy.cuda_graph_supported:
              # policy says cuda graph is not supported raise an error if set
              assert (
                  not config.enable_cuda_graph
              ), "cuda graph is not supported with this model, please disable"
  
          from deepspeed.moe.layer import MoE
  
          moe = False
          if hasattr(child, "mlp") and isinstance(child.mlp, MoE):
              num_experts = child.mlp.num_experts
              moe = True
  
          # 1. Create a model-specific container object using the policy object.
          _container = policy_to_ds_container(
              policy=policy,
              config=config,
              model_config=model_config,
              layer_id=layer_id,
              child=child,
          )
          _container.set_moe(moe)
  
          # 2. Set the tensor parallelism config
          _container.set_tensor_parallel_config(
              config.tensor_parallel.tp_size,
              config.tensor_parallel.tp_group,
          )
  
          # 3. Initialize tensors
          _container.initialize_tensors()
  
          # 4. deal with data types -- needs refactor to use dtype instead of fp16
          if config.dtype in [
              torch.float16,
              torch.bfloat16,
              torch.int8,
          ]:
              _container.convert_to_required_dtype()
  
          # 5. Set the quantization config
          quantizer = GroupQuantizer(q_int8=quantize)
          _container.set_quantization_config(quantizer)
  
          # 6. create a DS Inference config object
          _container.create_ds_model_config()
  
          # 7. use the config and create the module
          _container.create_module()
  
          # 8. transpose the weights and bias if needed
          _container.transpose()
  
          # 9. deal with tensor parallelism.
          _container.apply_tensor_parallelism(mp_replace)
  
          # 10. copy the tensors from the model-specific container to the new module
          _container.copy_data_to_new_module()
  
          # 11. set global for generic checkpoint loading
          global container_g
  
          if container_g is None:
              container_g = _container
  
          return _container.module
  
      def replace_wo_policy(
          module, all_reduce_linears, prefix="", state_dict=None
      ):
          # mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group)
  
          # 1. Create AutoTP object
          _autotp = AutoTP(
              module,
              all_reduce_linears,
              prefix,
              state_dict,
              linear_layer_setting,
              orig_layer_impl,
          )
  
          # 2. Set the tensor parallelism config
          _autotp.set_tensor_parallel_config(
              config.tensor_parallel.tp_size,
              config.tensor_parallel.tp_group,
          )
  
          # 3. Try to get num_key_heads from model_config.num_key_value_heads
          num_kv_heads = _autotp.get_model_num_kv_heads(model_config)
  
          # 4. When we have num_kv_heads defined, uneven division is possible, otherwise enforce even division
          set_num_kv_heads(num_kv_heads)
  
          # 5. Set linear policies
          _autotp.update_linear_policies()
  
          # 6. Replace modules
          if (
              "lm_head" in all_reduce_linears
              or "embed_out" in all_reduce_linears
          ):
              return _autotp._replace_last_linear_module(module)
          return _autotp._replace_module(module)
  
      def replace_fn(
          child, _policy, layer_id=0, prefix="", state_dict=None
      ):
          training = (
              False  # todo: refactor this part to go in the config
          )
          if training:
              # copy relevant state from child -> new module
              new_module = replace_with_policy(
                  child, _policy, config.triangular_masking
              )
  
          else:
              # copy relevant state from child -> new module
              if config.replace_with_kernel_inject:
                  new_module = replace_with_policy(
                      child,
                      _policy,
                      config.triangular_masking,
                      inference=True,
                      layer_id=layer_id,
                  )
              else:
                  new_module = replace_wo_policy(
                      child,
                      _policy,
                      prefix=prefix,
                      state_dict=state_dict,
                  )
  
          return new_module
  
      def set_lm_head(module):
          embedding_weight = None
          for n, p in module.named_parameters():
              if (
                  "word_embeddings." in n
                  or "embed_tokens." in n
                  or "wte." in n
              ):
                  embedding_weight = p
          if (
              embedding_weight is not None
              and hasattr(module, "lm_head")
              and hasattr(module.lm_head, "weight")
              and module.lm_head.weight.is_meta
          ):
              module.lm_head.weight = embedding_weight
          # enable tensor parallel for the last linear
          if (
              hasattr(module, "lm_head")
              and hasattr(module.lm_head, "weight")
              and not module.lm_head.weight.is_meta
              and isinstance(module.lm_head, torch.nn.Linear)
          ):
              module = replace_wo_policy(
                  module, ("lm_head",), 0, "lm_head"
              )
          elif (
              hasattr(module, "embed_out")
              and hasattr(module.embed_out, "weight")
              and not module.embed_out.weight.is_meta
              and isinstance(module.embed_out, torch.nn.Linear)
          ):
              module = replace_wo_policy(
                  module, ("embed_out",), 0, "embed_out"
              )
          return module
  
      if (
          checkpoint_dict is not None
          and not config.replace_with_kernel_inject
      ):
          # AutoTP shard loading
          checkpoint = checkpoint_dict["checkpoints"]
          pbar = tqdm.tqdm(
              total=len(checkpoint),
              desc=f"Loading {len(checkpoint)} checkpoint shards",
          )
          for i in range(len(checkpoint)):
              checkpoint_file = os.path.join(
                  config.base_dir, checkpoint[i]
              )
              replaced_module = replace_module(
                  model=model,
                  orig_class=orig_layer_impl,
                  replace_fn=replace_fn,
                  _replace_policy=config.injection_policy_tuple,
                  checkpoint=checkpoint_file,
              )
              pbar.update(1)
              gc.collect()
          replaced_module = set_lm_head(replaced_module)
      else:
          replaced_module = replace_module(
              model=model,
              orig_class=orig_layer_impl,
              replace_fn=replace_fn,
              _replace_policy=config.injection_policy_tuple,
          )
  
      quantizer = GroupQuantizer(q_int8=quantize)
      world_size = dist.get_world_size() if dist.is_initialized() else 1
      rank = dist.get_rank() if dist.is_initialized() else 0
      if (
          checkpoint_dict is not None
          and config.replace_with_kernel_inject
      ):
          assert (
              container_g.ckpt_load_enabled
          ), f"Meta Tensor checkpoint loading not supported in {container_g.__class__.__name__} container"
          start_time = time.time()
          checkpoint = checkpoint_dict["checkpoints"]
          ckpt_list = (
              checkpoint["tp"]
              if type(checkpoint) is dict
              else checkpoint
          )
          ckpt_type = checkpoint_dict.get("parallelization", "pp")
          ckpt_mp_size = checkpoint_dict.get("tp_size", len(ckpt_list))
          ckpt_mp_size = checkpoint_dict.get("mp_size", ckpt_mp_size)
          base_dir1 = checkpoint_dict.get("base_dir", config.base_dir)
  
          if ckpt_type == "pp" and type(checkpoint) is list:
              pbar = tqdm.tqdm(
                  total=len(checkpoint),
                  desc=f"Loading {len(checkpoint)} checkpoint shards",
              )
  
              for i in range(len(checkpoint)):
                  sd = [
                      torch.load(
                          os.path.join(base_dir1, checkpoint[i]),
                          map_location="cpu",
                      )
                  ]
                  load_model_with_checkpoint(
                      replaced_module,
                      sd,
                      mp_replace,
                      ckpt_type,
                      ckpt_mp_size,
                      quantizer,
                      container=container_g,
                  )
                  pbar.update(1)
          else:
              num_checkpoints = len(ckpt_list) // ckpt_mp_size
              tp_split_size = world_size / ckpt_mp_size
              sd_offset = int(rank / tp_split_size)
              sd_count = (
                  int((rank + max(1, tp_split_size)) / tp_split_size)
                  - sd_offset
              )
              pbar = tqdm.tqdm(
                  total=num_checkpoints,
                  desc=f"Loading {num_checkpoints} checkpoint shards",
              )
              for i in range(num_checkpoints):
                  pbar.update(1)
                  ckpt_index = i * ckpt_mp_size + sd_offset
                  ckpt_files = [
                      os.path.join(base_dir1, ckpt_list[ckpt_index + j])
                      if base_dir1
                      else ckpt_list[ckpt_index + j]
                      for j in range(sd_count)
                  ]
                  sds = [
                      torch.load(ckpt_file, map_location="cpu")
                      for ckpt_file in ckpt_files
                  ]
                  load_model_with_checkpoint(
                      replaced_module,
                      sds,
                      mp_replace,
                      ckpt_type,
                      ckpt_mp_size,
                      quantizer,
                      int(rank % tp_split_size),
                      container=container_g,
                  )
                  sds = [None for _ in sds]
                  gc.collect()
  
              if "non_tp" in checkpoint:
                  pbar = tqdm.tqdm(
                      total=len(checkpoint["non_tp"]),
                      desc=f"Loading {len(checkpoint['non_tp'])} checkpoint shards",
                  )
  
                  for i in range(len(checkpoint["non_tp"])):
                      pbar.update(1)
                      ckpt_file = (
                          os.path.join(
                              base_dir1, checkpoint["non_tp"][i]
                          )
                          if base_dir1
                          else checkpoint["non_tp"][i]
                      )
                      sds = [torch.load(ckpt_file, map_location="cpu")]
                      load_model_with_checkpoint(
                          replaced_module,
                          sds,
                          mp_replace,
                          ckpt_type,
                          ckpt_mp_size,
                          quantizer,
                          int(rank % tp_split_size),
                          container=container_g,
                      )
                      sds = [None for _ in sds]
                      gc.collect()
          set_lm_head(replaced_module)
          print(
              f"checkpoint loading time at rank {rank}: {time.time()-start_time} sec"
          )
  
      if config.save_mp_checkpoint_path is not None:
          from collections import OrderedDict
          import json
  
          num_partitions = 8
  
          if checkpoint_dict is None:
              ckpt_name = "ds_model"
              try:
                  from transformers.models.bloom.modeling_bloom import (
                      BloomForCausalLM,
                  )
  
                  if isinstance(model, BloomForCausalLM):
                      ckpt_name = "bloom"
              except ImportError:
                  ckpt_name = "ds_model"
          else:
              ckpt_name = checkpoint_dict["type"]
          if dist.is_initialized():
              dist.barrier()
          transformer_name = get_transformer_name(replaced_module)
          non_tp_ckpt_name = f"non-tp.pt"
          ckpt_files = [non_tp_ckpt_name]
          os.makedirs(config.save_mp_checkpoint_path, exist_ok=True)
  
          if not dist.is_initialized() or dist.get_rank() == 0:
              print("Saving tp-sharded checkpoints")
              torch.save(
                  OrderedDict(
                      {
                          k: v
                          for k, v in dict(
                              replaced_module.state_dict()
                          ).items()
                          if transformer_name not in k
                      }
                  ),
                  f"{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}",
              )
  
              dtype_reprs = {
                  torch.float32: "float32",
                  torch.float16: "float16",
                  torch.int8: "int8",
                  torch.bfloat16: "bfloat16",
              }
  
              ckpt_config = json.dumps(
                  {
                      "type": ckpt_name,
                      "base_dir": f"{config.save_mp_checkpoint_path}",
                      "checkpoints": {
                          "non_tp": ckpt_files,
                          "tp": [
                              f"tp_{r:0>2d}_{m:0>2d}.pt"
                              for m in range(num_partitions)
                              for r in range(world_size)
                          ],
                      },
                      "version": 1.0,
                      "parallelization": "tp",
                      "tp_size": world_size,
                      "dtype": dtype_reprs[config.dtype],
                  }
              )
              with open(
                  f"{config.save_mp_checkpoint_path}/ds_inference_config.json",
                  "w",
              ) as cfg:
                  cfg.write(ckpt_config)
  
          rep_sd = replaced_module.state_dict()
          for n, p in replaced_module.named_parameters():
              if hasattr(p, "scale"):
                  rep_sd[n] = [p, p.scale]
          keys = list(rep_sd.keys())
          partition_size = len(keys) // num_partitions + 1
          for m in range(num_partitions):
              torch.save(
                  OrderedDict(
                      {
                          k: [rep_sd[k], rep_sd[k].scale]
                          if hasattr(rep_sd[k], "scale")
                          else rep_sd[k]
                          for k in keys[
                              m
                              * partition_size : (m + 1)
                              * partition_size
                          ]
                          if transformer_name in k
                      }
                  ),
                  f"{config.save_mp_checkpoint_path}/tp_{rank:0>2d}_{m:0>2d}.pt",
              )
  
      return replaced_module
  
imports: import os
  import torch
  import tqdm
  import deepspeed
  import deepspeed.ops.transformer
  from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention
  from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock
  from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from .replace_policy import replace_policies, generic_policies
  from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading
  from deepspeed import comm
  from deepspeed.module_inject.tp_shard import set_num_kv_heads
  from .load_checkpoint import load_model_with_checkpoint
  import time
  from .utils import policy_to_ds_container
  import gc
  from ..pipe import PipelineModule
  import re
label_desc: Use image node_id 4 for calling a global function with example usage: replace_transformer_layer(orig_layer_impl, model, checkpoint_dict, config, model_config) and returns: replaced_module, _container, _autotp, new_module, module, _autotp