old_contents: #   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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 __future__ import print_function
  
  import unittest
  import numpy as np
  from paddle.fluid.tests.unittests.op_test import OpTest
  import paddle.fluid.core as core
  from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, stable_softmax
  from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
  
  
  class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
      def init_kernel_type(self):
          self.use_mkldnn = True
  
  
  class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
      def get_x_shape(self):
          return [2, 3, 4, 5]
  
  
  class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
      def get_x_shape(self):
          return [2, 3, 4, 5]
  
      def get_axis(self):
          return 0
  
  
  class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
      def get_x_shape(self):
          return [2, 3, 4, 5]
  
      def get_axis(self):
          return 1
  
  
  class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
      def get_x_shape(self):
          return [2, 3, 4, 5]
  
      def get_axis(self):
          return 2
  
  
  # Check if primitives already exist in backward
  class TestSoftmaxMKLDNNPrimitivesAlreadyExist(unittest.TestCase):
      def setUp(self):
          super(TestSoftmaxMKLDNNPrimitivesAlreadyExist, self).setUp()
  
          np.random.seed(123)
          self.op_type = 'softmax'
          self.x = np.random.uniform(-1, 1, 2).astype(np.float32)
          self.out = stable_softmax(self.x)
          self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
          self.x_grad = self.__softmax_bwd(self.out, self.out_grad)
  
      # Softmax grad calculation
      def __softmax_bwd(self, out, out_grad):
          return out * (out_grad - np.dot(out, out_grad))
  
      def test_check(self):
          check_if_mkldnn_primitives_exist_in_bwd(
              self, self.op_type, self.x, self.out, self.out_grad, self.x_grad)
  
  
  if __name__ == '__main__':
      unittest.main()
  
new_contents: #   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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 __future__ import print_function
  
  import unittest
  import numpy as np
  from paddle.fluid.tests.unittests.op_test import OpTest
  import paddle.fluid.core as core
  from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, stable_softmax
  from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
  
  
  class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
      def init_kernel_type(self):
          self.use_mkldnn = True
  
  
  class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
      def get_x_shape(self):
          return [2, 3, 4, 5]
  
  
  # Check if primitives already exist in backward
  class TestSoftmaxMKLDNNPrimitivesAlreadyExist(unittest.TestCase):
      def setUp(self):
          super(TestSoftmaxMKLDNNPrimitivesAlreadyExist, self).setUp()
  
          np.random.seed(123)
          self.op_type = 'softmax'
          self.x = np.random.uniform(-1, 1, 2).astype(np.float32)
          self.out = stable_softmax(self.x)
          self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
          self.x_grad = self.__softmax_bwd(self.out, self.out_grad)
  
      # Softmax grad calculation
      def __softmax_bwd(self, out, out_grad):
          return out * (out_grad - np.dot(out, out_grad))
  
      def test_check(self):
          check_if_mkldnn_primitives_exist_in_bwd(
              self, self.op_type, self.x, self.out, self.out_grad, self.x_grad)
  
  
  if __name__ == '__main__':
      unittest.main()
  
subject: revert test_softmax_cudnn. test=develop