code: def test_performance_half(self):
      mlp = MLP(mlp_sizes).cuda().half()
  
      mlp_layers = []
      for i in range(mlp.num_layers):
          linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
          mlp.weights[i].data.copy_(linear.weight)
          mlp.biases[i].data.copy_(linear.bias)
          mlp_layers.append(linear)
          mlp_layers.append(nn.ReLU(inplace=True))
  
      ref_mlp = nn.Sequential(*mlp_layers).cuda().half()
  
      test_input = (
          torch.empty(
              batch_size, mlp_sizes[0], device="cuda", dtype=torch.half
          )
          .fill_(10.0)
          .requires_grad_()
      )
      ref_input = (
          torch.empty(
              batch_size, mlp_sizes[0], device="cuda", dtype=torch.half
          )
          .fill_(10.0)
          .requires_grad_()
      )
  
      # Warm up GPU
      for _ in range(100):
          ref_out = ref_mlp(ref_input)
          ref_loss = ref_out.mean()
          ref_mlp.zero_grad()
          ref_loss.backward()
          mlp_out = mlp(test_input)
          test_loss = mlp_out.mean()
          mlp.zero_grad()
          test_loss.backward()
  
      torch.cuda.profiler.start()
      torch.cuda.synchronize()
      start_time = time()
      for _ in range(num_iters):
          ref_out = ref_mlp(ref_input)
          ref_loss = ref_out.mean()
          ref_mlp.zero_grad()
          ref_loss.backward()
      torch.cuda.synchronize()
      stop_time = time()
      ref_time = (stop_time - start_time) * 1000.0 / num_iters
      print(f"\nPytorch MLP time {ref_time:.4f} ms")
  
      torch.cuda.synchronize()
      start_time = time()
      for _ in range(num_iters):
          mlp_out = mlp(test_input)
          test_loss = mlp_out.mean()
          mlp.zero_grad()
          test_loss.backward()
      torch.cuda.synchronize()
      stop_time = time()
      actual_time = (stop_time - start_time) * 1000.0 / num_iters
      print(f"C++ MLP time {actual_time:.4f} ms")
      torch.cuda.profiler.stop()
      self.assertLessEqual(
          actual_time,
          ref_time,
          msg=f"Custom extension took {actual_time:.4f} while PyTorch took {ref_time:.4f}",
      )
  
file_path: apex/tests/L0/run_mlp/test_mlp.py
imports: from itertools import product
  from time import time
  import torch
  from torch import nn
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
  from torch.testing._internal.common_device_type import onlyCUDA
  from apex.mlp import MLP
label_desc: Use image node_id 7 for calling the TestMLP obj's underlying member method code with example usage: obj.test_performance_half() without return types