code: def run_layer_benchmark(
      num_repeats: int,
      forward_only: bool = False,
      gsm_mode: str = "baseline",
      create_layer: Callable = LayerFactory.create,
      **kwargs,
  ) -> Tuple[float, Dict[str, int]]:
      """Benchmarks a single layer for runtime and CUDA memory (if applicable).
  
      Args:
          num_repeats: how many times to repeat the forward(/backward) pass
          forward_only: whether to skip the backward pass
          create_layer: function for creating the layer, takes **kwargs
  
      Returns: a tuple consisting of
          Runtime as a float, and
          Memory statistics as a dict
      """
  
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      invalid_mem_flag = False
  
      if torch.cuda.is_available():
          mem = reset_peak_memory_stats(device)
          if mem is None:
              invalid_mem_flag = True
          else:
              invalid_mem_flag |= not (mem.cur_mem == 0)
  
      # setup layer
      layer_fun = create_layer(gsm_mode=gsm_mode, **kwargs)
  
      if forward_only:
          layer_fun.module.eval()
          benchmark_fun = layer_fun.forward_only
      else:
          layer_fun.module.train()
          benchmark_fun = layer_fun.forward_backward
  
      # move layer to device and get memory statistics
      memory_stats = layer_fun.to(device=device)
      invalid_mem_flag |= not (
          sum(v for _, v in memory_stats.items())
          == torch.cuda.memory_allocated(device)
      )
  
      # benchmark.Timer performs its own warmups
      timer = benchmark.Timer(
          stmt="benchmark_fun()",
          globals={"benchmark_fun": benchmark_fun},
          num_threads=1,
      )
      runtime = timer.timeit(num_repeats).mean
  
      # get max memory allocated and reset memory statistics
      mem = reset_peak_memory_stats(device)
      if mem is None:
          invalid_mem_flag = True
      else:
          memory_stats["max_memory"] = mem.prev_max_mem
  
      if invalid_mem_flag:
          return runtime, None
      else:
          return runtime, memory_stats
  
file_path: opacus/benchmarks/benchmark_layer.py
imports: import argparse
  import json
  from typing import Callable, Dict, Tuple
  import torch
  import torch.utils.benchmark
  from benchmarks.layers import LayerFactory, LayerType
  from benchmarks.utils import get_layer_set, reset_peak_memory_stats
label_desc: Use image node_id 1 for calling a global function with example usage: run_layer_benchmark(num_repeats, forward_only, gsm_mode, create_layer) and returns: runtime,  None, runtime,  memory_stats