message: Shrink the tensor sizes to fix the timeout issue for transpose benchmark.
diff: @@ -115,18 +115,13 @@ class TransposeBenchmark(test.Benchmark):
  [0, 3, 1, 2], [0, 2, 3, 1]
  ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
  - large_shapes = [[2, 100, 100, 100, 32], [2, 100, 100, 100, 64]] * 2 + [[
  - 2, 1000, 1000, 32
  - ], [2, 1000, 1000, 64]] * 2 + [[2, 1000000, 32], [2, 1000000, 64]] * 2
  + large_shapes = [[2, 40, 40, 40, 32], [2, 40, 40, 40, 64]] * 2 + [[
  + 2, 300, 300, 32
  + ], [2, 300, 300, 64]] * 2 + [[2, 100000, 32], [2, 100000, 64]] * 2
  large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
  [0, 3, 1, 2], [0, 2, 3, 1]
  ] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
  - huge_shapes = [[2, 100, 100, 100, 128], [2, 1000, 1000, 128],
  - [2, 1000000, 128]] * 2
  - huge_perms = [[0, 4, 1, 2, 3], [0, 3, 1, 2], [0, 2, 1], [4, 1, 2, 3, 0],
  - [3, 1, 2, 0], [2, 1, 0]]
  -
  num_iters = 40
  for datatype in datatypes:
  for ishape, perm in zip(small_shapes, small_perms):
  @@ -137,11 +132,5 @@ class TransposeBenchmark(test.Benchmark):
  for ishape, perm in zip(large_shapes, large_perms):
  self._run_graph("gpu", ishape, perm, num_iters, datatype)
  - if datatype is not np.complex128:
  - if datatype is not np.float64:
  - if datatype is not np.float16:
  - for ishape, perm in zip(huge_shapes, huge_perms):
  - self._run_graph("gpu", ishape, perm, num_iters, datatype)
  -
  if __name__ == "__main__":
  test.main()
  