code: def write_benchmark_log(
      filename,
      model_size,
      cache_size,
      hidden_size,
      gpu_peak_mem,
      prefill_latency,
      prefill_throughput,
      decode_latency,
      decode_throughput,
      total_latency,
      total_throughput,
  ):
      log_str = (
          f"model size: {model_size/GB:.3f} GB\t"
          f"cache size: {cache_size/GB:.3f} GB\t"
          f"hidden size (p): {hidden_size/GB:.3f} GB\n"
          f"peak gpu mem: {gpu_peak_mem / GB:.3f} GB\t"
          f"prefill latency: {prefill_latency:.3f} s\t"
          f"prefill throughput: {prefill_throughput:.3f} token/s\n"
          f"decode latency: {decode_latency:.3f} s\t"
          f"decode throughput: {decode_throughput:.3f} token/s\n"
          f"total latency: {total_latency:.3f} s\t"
          f"total throughput: {total_throughput:.3f} token/s"
      )
      with open(filename, "a") as fout:
          fout.write(log_str + "\n")
  
      return log_str
  
file_path: deepspeedexamples/inference/huggingface/zero_inference/utils.py
imports: import torch
  import time
label_desc: Use image node_id 9 for calling a global function with example usage: write_benchmark_log(filename, model_size, cache_size, hidden_size, gpu_peak_mem, prefill_latency, prefill_throughput, decode_latency, decode_throughput, total_latency, total_throughput) and returns: log_str