code: def run_optimize_gpu_model(
      self,
      Model,
      Tokenizer,
      model_path,
      self_attn,
      layer_norm,
      lower_bound,
  ):
      with torch.inference_mode():
  
          def forward_hook(module, input, output, layer_name):
              self.layer_outputs.append(output)
  
          def pre_forward_hook(module, input, output, layer_name):
              self.pre_layer_outputs.append(output)
  
          tokenizer = Tokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          input_ids = tokenizer.encode(prompt, return_tensors="pt").to(
              device
          )
  
          model = Model.from_pretrained(
              model_path,
              load_in_4bit=True,
              optimize_model=False,
              trust_remote_code=True,
          )
          model = model.to(device)
  
          for layer_name, layer_module in model.named_modules():
              if layer_name == layer_norm:
                  layer_module.register_forward_hook(
                      lambda module, input, output, layer_name=layer_name: pre_forward_hook(
                          module, input, output, layer_name
                      )
                  )
              if layer_name == self_attn:
                  layer_module.register_forward_hook(
                      lambda module, input, output, layer_name=layer_name: forward_hook(
                          module, input, output, layer_name
                      )
                  )
          logits_base_model = (model(input_ids)).logits
          # the list `layer_output` has only one element.
          layer_tensor = self.layer_outputs.pop()
          model.to("cpu")
  
          opt_model = Model.from_pretrained(
              model_path,
              load_in_4bit=True,
              optimize_model=True,
              trust_remote_code=True,
          )
          opt_model = opt_model.to(device)
  
          def replace_forward_hook(module, input, output, layer_name):
              output = self.pre_layer_outputs[0]
              return output
  
          for layer_name, layer_module in opt_model.named_modules():
              if layer_name == layer_norm:
                  layer_module.register_forward_hook(
                      lambda module, input, output, layer_name=layer_name: replace_forward_hook(
                          module, input, output, layer_name
                      )
                  )
              if layer_name == self_attn:
                  layer_module.register_forward_hook(
                      lambda module, input, output, layer_name=layer_name: forward_hook(
                          module, input, output, layer_name
                      )
                  )
          logits_optimized_model = (opt_model(input_ids)).logits
          # the list `layer_output` has only one element.
          opt_layer_tensor = self.layer_outputs[0]
          opt_model.to("cpu")
  
          attn_output_diff = []
          for i, (t1, t2) in enumerate(
              zip(layer_tensor, opt_layer_tensor)
          ):
              if t1 is not None and t2 is not None:
                  if isinstance(t1, torch.Tensor) and isinstance(
                      t2, torch.Tensor
                  ):
                      # 'attn_output' is of type torch.Tensor.
                      attn_output_diff.append(t1 - t2)
                  else:
                      # 'past_key_value'is of type tuple as default.
                      for i, (t3, t4) in enumerate(zip(t1, t2)):
                          if (
                              model.config.architectures[0]
                              == "ChatGLMModel"
                              and hasattr(
                                  model.config, "padded_vocab_size"
                              )
                              and model.config.padded_vocab_size
                              == 65024
                          ):
                              # chatglm2's past_key_value is expanded 16x for some speedup.
                              # We need to narrow it here.
                              t4 = t4[:, :, 15:17, :]
                          attn_output_diff.append(t3 - t4)
  
          max_diff_tensor = [
              torch.max(item).item() for item in attn_output_diff
          ]
          print(max_diff_tensor)
          assert all(
              max_diff <= lower_bound for max_diff in max_diff_tensor
          )
  
file_path: BigDL/python/llm/test/inference_gpu/test_transformers_api.py
imports: import os, time
  import pytest
  import torch
  from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel, AutoModelForSpeechSeq2Seq
  from transformers import LlamaTokenizer, AutoTokenizer
label_desc: Use image node_id 2 for calling the Test_Optimize_Gpu_Model obj's underlying member method code with example usage: obj.run_optimize_gpu_model(Model, Tokenizer, model_path, _attn, layer_norm, lower_bound) and returns: output