code: def run_transformer_int4_gpu(
      repo_id,
      local_model_hub,
      in_out_pairs,
      warm_up,
      num_trials,
      num_beams,
      low_bit,
  ):
      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,
          ).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,
          ).eval()
          tokenizer = LlamaTokenizer.from_pretrained(
              model_path, trust_remote_code=True
          )
          model = model.to("xpu")
      else:
          if "starcoder" in repo_id:
              # Load starcoder-15.5b model in bf16 format to avoid CPU OOM.
              model = AutoModelForCausalLM.from_pretrained(
                  model_path,
                  optimize_model=True,
                  load_in_low_bit=low_bit,
                  trust_remote_code=True,
                  use_cache=True,
                  torch_dtype=torch.bfloat16,
              ).eval()
              # Convert the low-bit model back to fp32 for performance considerations.
              model = model.float()
          else:
              model = AutoModelForCausalLM.from_pretrained(
                  model_path,
                  optimize_model=True,
                  load_in_low_bit=low_bit,
                  trust_remote_code=True,
                  use_cache=True,
              ).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:
              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]
                  and test_length < 8192
              ):
                  test_length = test_length * 2
              # For the sequence length not in [32, 256, 1024, 2048, 8192], it will be truncated from 8192.txt.
              test_length = min(test_length, 8192)
              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] = []
              thread = threading.Thread(
                  target=run_model_in_thread,
                  args=(
                      model,
                      in_out,
                      tokenizer,
                      result,
                      warm_up,
                      num_beams,
                      input_ids,
                      out_len,
                      actual_in_len,
                      num_trials,
                      reserved_mem_list,
                  ),
              )
              thread.start()
              thread.join()
      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 8 for calling a global function with example usage: run_transformer_int4_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit) and returns: result