code: def test_benchmark_choice_in_subproc(self):
      gm = make_fx(
          lambda: torch.zeros(2, 3)
      )()  # a dummy graph to construct the GraphLowering
      graph = GraphLowering(gm)
  
      # the graph handler is neede to create benchmark example value below
      with V.set_graph_handler(graph):
          buf1 = self._create_buffer("mat1", (2, 3))
          buf2 = self._create_buffer("mat2", (3, 2))
          buf3 = self._create_buffer("mat3", (2, 3))
          buf4 = self._create_buffer("mat4", (3, 2))
  
          layout = FixedLayout(
              torch.device("cuda:0"), torch.float32, (2, 2)
          )
  
          mat1 = AlgorithmSelectorCache.benchmark_example_value(buf1)
          mat2 = AlgorithmSelectorCache.benchmark_example_value(buf2)
          mat3 = AlgorithmSelectorCache.benchmark_example_value(buf3)
          mat4 = AlgorithmSelectorCache.benchmark_example_value(buf4)
  
          out = AlgorithmSelectorCache.benchmark_example_value(layout)
          # expected_out = (mat1 @ mat2) + (mat3 @ mat4)
          expected_out = None
  
          choice = aten_mm_plus_mm.bind(
              (buf1, buf2, buf3, buf4), layout
          )
          # use a tensor since the mutation to a python list in a sub process
          # is not synced back to the parent process
          timings = torch.zeros(3, dtype=torch.float32)
          ctx = mp.get_context("spawn")
          child = ctx.Process(
              target=benchmark_choice,
              args=(
                  choice,
                  (mat1, mat2, mat3, mat4),
                  out,
                  expected_out,
                  timings,
              ),
          )
          child.start()
          child.join()
          self.assertEqual(0, child.exitcode)
          print(
              f"timings is {timings}, out {out}, expected_out {expected_out}"
          )
  
imports: import os
  import unittest
  from typing import Callable, List, Optional
  import torch
  from torch import multiprocessing
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.testing import reset_rng_state
  from torch._dynamo.utils import counters
  from torch._inductor import config
  from torch._inductor.autotune_process import BenchmarkRequest, CUDA_VISIBLE_DEVICES, TuningProcessPool
  from torch._inductor.graph import GraphLowering
  from torch._inductor.ir import Buffer, FixedLayout
  from torch._inductor.kernel.mm_plus_mm import aten_mm_plus_mm
  from torch._inductor.select_algorithm import AlgorithmSelectorCache, ChoiceCaller, TritonTemplateCaller
  from torch._inductor.utils import run_and_get_code
  from torch._inductor.virtualized import V
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM75OrLater, SM90OrLater
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, skipIfRocm
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 2 for calling the TestMaxAutotune obj's underlying member method code with example usage: obj.test_benchmark_choice_in_subproc() without return types