text: # Copyright 2019 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from akg.utils import kernel_exec as utils
  import numpy as np
  from akg.topi.util import get_const_tuple
  from akg.ops.nn.relu_ad import relu_ad
  from tests.common.tensorio import compare_tensor
  from tests.common.base import get_rtol_atol
  
  
  def relu_ad_run(shape, dtype, attrs):
      if 'tuning' in attrs.keys():
          t = attrs.get("tuning", False)
          kernel_name = attrs.get("kernel_name", False)
          mod = utils.op_build_test(relu_ad, [shape, shape], [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=t)
          if t:
              expect, head_np, input_np, output = gen_data(dtype, shape)
              return mod, expect, (head_np, input_np, output)
          else:
              return mod
      else:
          mod = utils.op_build_test(relu_ad, [shape, shape], [dtype, dtype], kernel_name='relu_ad', attrs=attrs)
          expect, head_np, input_np, output = gen_data(dtype, shape)
          output = utils.mod_launch(mod, (head_np, input_np, output), expect=expect)
          rtol, atol = get_rtol_atol("relu_ad", dtype)
          return (input_np, head_np), output, expect, compare_tensor(output, expect, rtol=rtol, atol=atol)
  
  
  def gen_data(dtype, shape):
      input_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(shape)).astype(dtype)
      head_np = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
      expect = head_np * (input_np > 0)
      output = np.full(expect.shape, np.nan, dtype)
      return expect, head_np, input_np, output
  