code: def benchmark(
      model_name: str,
      num_batches: int,
      batch_size: int,
      max_seqlen: int,
      is_half: bool,
  ):
      hf_model = AutoModel.from_pretrained(
          model_name, torch_dtype=torch.float16 if is_half else None
      ).eval()
      hf_model = hf_model.to("cuda:0")
      bt_model = BetterTransformer.transform(
          hf_model, keep_original_model=True
      )
  
      vocab_size = hf_model.config.vocab_size
      input_ids = (
          torch.randint(
              vocab_size - 1,
              (batch_size, max_seqlen),
              dtype=torch.int64,
          )
          + 1
      )
      masks = torch.ones(batch_size, max_seqlen, dtype=torch.int64)
  
      input_ids = input_ids.to("cuda:0")
      masks = masks.to("cuda:0")
  
      decoder_input_ids = torch.ones(
          batch_size, 1, dtype=torch.int64
      ).to("cuda:0")
  
      # Warmup
      _ = hf_model(
          input_ids, masks, decoder_input_ids=decoder_input_ids
      )
      torch.cuda.synchronize()
      _ = bt_model(
          input_ids, masks, decoder_input_ids=decoder_input_ids
      )
      torch.cuda.synchronize()
  
      total_hf_time = timing_cuda(
          hf_model, num_batches, input_ids, masks, decoder_input_ids
      )
      total_bt_time = timing_cuda(
          bt_model, num_batches, input_ids, masks, decoder_input_ids
      )
  
      return total_bt_time, total_hf_time
  
file_path: optimum/tests/bettertransformer/test_gpu.py
imports: import os
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoModel
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils import logging
  from optimum.utils.testing_utils import grid_parameters
label_desc: Use image node_id 2 for calling a global function with example usage: benchmark(model_name, num_batches, batch_size, max_seqlen, is_half) and returns: total_bt_time,  total_hf_time