code: def run_transformer_int4_gpu_win(
      repo_id,
      local_model_hub,
      in_out_pairs,
      warm_up,
      num_trials,
      num_beams,
      low_bit,
      cpu_embedding,
  ):
      from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
      from transformers import (
          AutoTokenizer,
          GPTJForCausalLM,
          LlamaTokenizer,
      )
      import intel_extension_for_pytorch as ipex
  
      reserved_mem_list = []
      model_path = get_model_path(repo_id, local_model_hub)
      # Load model in 4 bit,
      # which convert the relevant layers in the model into INT4 format
      st = time.perf_counter()
      if repo_id in CHATGLM_IDS:
          model = AutoModel.from_pretrained(
              model_path,
              load_in_low_bit=low_bit,
              optimize_model=True,
              trust_remote_code=True,
              use_cache=True,
              cpu_embedding=cpu_embedding,
          ).eval()
          tokenizer = AutoTokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          model = model.to("xpu")
      elif repo_id in LLAMA_IDS:
          model = AutoModelForCausalLM.from_pretrained(
              model_path,
              load_in_low_bit=low_bit,
              trust_remote_code=True,
              use_cache=True,
              cpu_embedding=cpu_embedding,
          ).eval()
          tokenizer = LlamaTokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          model = model.to("xpu")
      elif repo_id in LLAVA_IDS:
          llava_repo_dir = os.environ.get("LLAVA_REPO_DIR")
          sys.path.append(rf"{llava_repo_dir}")
          from llava.model.language_model.llava_llama import (
              LlavaLlamaForCausalLM,
          )
  
          model = AutoModelForCausalLM.from_pretrained(
              model_path,
              load_in_low_bit=low_bit,
              optimize_model=True,
              trust_remote_code=True,
              use_cache=True,
              cpu_embedding=cpu_embedding,
          ).eval()
          tokenizer = AutoTokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          model = model.to("xpu")
      else:
          model = AutoModelForCausalLM.from_pretrained(
              model_path,
              optimize_model=True,
              load_in_low_bit=low_bit,
              trust_remote_code=True,
              use_cache=True,
              cpu_embedding=cpu_embedding,
          ).eval()
          tokenizer = AutoTokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          model = model.to("xpu")
          if isinstance(model, GPTJForCausalLM):
              # For gpt-j model family, this optimization can provide a better performance.
              model = ipex.optimize(model.eval(), inplace=True)
      end = time.perf_counter()
      print(">> loading of model costs {}s".format(end - st))
      reserved_mem_list.append(
          torch.xpu.memory.memory_reserved() / (1024**3)
      )
  
      model = BenchmarkWrapper(model)
  
      result = {}
      with torch.inference_mode():
          for in_out in in_out_pairs:
              try:
                  in_out_len = in_out.split("-")
                  in_len = int(in_out_len[0])
                  out_len = int(in_out_len[1])
                  # As different tokenizer has different encodings,
                  # in_len.txt maybe shorter than we need,
                  # use much longer context to make sure input length
                  test_length = min(in_len * 2, 8192)
                  while test_length not in [32, 256, 1024, 2048, 8192]:
                      test_length = test_length * 2
                  input_str = open(
                      f"prompt/{test_length}.txt", "r"
                  ).read()
                  # As different tokenizer has different encodings,
                  # slice the input_ids to ensure the prompt length is required length.
                  input_ids = tokenizer.encode(
                      input_str, return_tensors="pt"
                  )
                  input_ids = input_ids[:, :in_len]
                  true_str = tokenizer.batch_decode(input_ids)[0]
                  input_ids = tokenizer.encode(
                      true_str, return_tensors="pt"
                  ).to("xpu")
                  actual_in_len = input_ids.shape[1]
                  result[in_out] = []
                  for i in range(num_trials + warm_up):
                      st = time.perf_counter()
                      output_ids = model.generate(
                          input_ids,
                          do_sample=False,
                          max_new_tokens=out_len,
                          num_beams=num_beams,
                      )
                      torch.xpu.synchronize()
                      end = time.perf_counter()
                      reserved_mem_list.append(
                          torch.xpu.memory.memory_reserved()
                          / (1024**3)
                      )
                      gpu_peak_mem = max(
                          reserved_mem_list
                      )  # always keep the peak gpu mem at current stage
                      output_ids = output_ids.cpu()
                      print("model generate cost: " + str(end - st))
                      output = tokenizer.batch_decode(output_ids)
                      print(output[0])
                      actual_out_len = (
                          output_ids.shape[1] - actual_in_len
                      )
                      if i >= warm_up:
                          result[in_out].append(
                              [
                                  model.first_cost,
                                  model.rest_cost_mean,
                                  model.encoder_time,
                                  actual_in_len,
                                  actual_out_len,
                                  gpu_peak_mem,
                              ]
                          )
                      # torch.xpu.empty_cache() # this may make first token slower
              except RuntimeError:
                  traceback.print_exc()
                  pass
      model.to("cpu")
      torch.xpu.synchronize()
      torch.xpu.empty_cache()
      del model
      gc.collect()
      return result
  
imports: import torch
  import time
  import gc
  import traceback
  import threading
  import numpy
  from datetime import date
  import os
  import sys
  from benchmark_util import BenchmarkWrapper
  from bigdl.llm.utils.common.log4Error import invalidInputError
label_desc: Use image node_id 13 for calling a global function with example usage: run_transformer_int4_gpu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, cpu_embedding) and returns: result