old_contents: # RUN: %PYTHON %s 2>&1 | FileCheck %s
  
  # This file contains small benchmarks with reasonably-sized problem/tiling sizes
  # and codegen options.
  
  from ..core.experts import *
  from .util import *
  
  all_experts = [
      SingleTilingExpert(
          sizes=[8, 16, 32],
          interchange=[0, 1, 2],
          pad=[0, 1, 2],
          peel=False,
          hoist_padding=[3])
  ]
  
  
  # CHECK-NOT: FAILURE
  def main():
    n_iters = 100
    problem_size_list = [[128, 192], [104, 96], [8000, 6000]]
    for np_type in [np.float32]:
      for problem_sizes in problem_size_list:
        M, N = problem_sizes
        # Init printing.
        print(
            f'\n###############################################################\n'
            f'Problem size {M}x{N} -> {M}')
        print('MLIR Codegen')
        for expert in all_experts:
          compile_and_test_linalg_reduction(M, N, n_iters, np_type, expert)
        # For single-threaded apples-to-apples comparisons, run with:
        # MKL_NUM_THREADS=1 ATEN_NUM_THREADS=1 OMP_NUM_THREADS=1 TBB_NUM_THREADS=1
        import os
        if os.environ.get('BENCHMARK_NUMPY'):
          print('Numpy')
          test_numpy_reduction(M, N, n_iters, np_type)
        if os.environ.get('BENCHMARK_TORCH'):
          print('Torch')
          test_torch_reduction(M, N, n_iters, np_type, 1)
  
  
  if __name__ == '__main__':
    main()
  
new_contents: # RUN: %PYTHON %s 2>&1 | FileCheck %s
  
  # This file contains small benchmarks with reasonably-sized problem/tiling sizes
  # and codegen options.
  
  from ..core.experts import *
  from .util import *
  
  all_experts = [
      SingleTilingExpert(
          sizes=[8, 16, 32],
          interchange=[0, 1, 2],
          peel=False,
          pad=True,
          pack_padding=[],
          hoist_padding=[])
  ]
  
  
  # CHECK-NOT: FAILURE
  def main():
    n_iters = 100
    problem_size_list = [[128, 192], [104, 96], [8000, 6000]]
    for np_type in [np.float32]:
      for problem_sizes in problem_size_list:
        M, N = problem_sizes
        # Init printing.
        print(
            f'\n###############################################################\n'
            f'Problem size {M}x{N} -> {M}')
        print('MLIR Codegen')
        for expert in all_experts:
          compile_and_test_linalg_reduction(M, N, n_iters, np_type, expert)
        # For single-threaded apples-to-apples comparisons, run with:
        # MKL_NUM_THREADS=1 ATEN_NUM_THREADS=1 OMP_NUM_THREADS=1 TBB_NUM_THREADS=1
        import os
        if os.environ.get('BENCHMARK_NUMPY'):
          print('Numpy')
          test_numpy_reduction(M, N, n_iters, np_type)
        if os.environ.get('BENCHMARK_TORCH'):
          print('Torch')
          test_torch_reduction(M, N, n_iters, np_type, 1)
  
  
  if __name__ == '__main__':
    main()
  
subject: Update SingleTilingExpert parameters in reduction benchmark.