code: #   Copyright (c) 2018 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 scipy.special import expit, erf
  import paddle.fluid.core as core
  from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, convert_float_to_uint16
  from paddle.fluid.tests.unittests.test_activation_op import TestActivation, TestRelu, TestTanh, TestSqrt, TestAbs, TestLeakyRelu, TestSwish, TestHardSwish, TestRelu6, TestSigmoid
  from paddle.fluid.tests.unittests.test_gelu_op import gelu
  from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
  
  
  class TestMKLDNNReluDim2(TestRelu):
      def setUp(self):
          super(TestMKLDNNReluDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNRelu6Dim2(TestRelu6):
      def setUp(self):
          super(TestMKLDNNRelu6Dim2, self).setUp()
          self.attrs.update({"use_mkldnn": True})
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNLeakyReluDim2(TestLeakyRelu):
      def setUp(self):
          super(TestMKLDNNLeakyReluDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNGeluDim2(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.float32
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = gelu(x, False)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNGeluDim2Approx(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.float32
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = gelu(x, True)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True, "approximate": True}
  
  
  class TestMKLDNNTanhDim2(TestTanh):
      def setUp(self):
          super(TestMKLDNNTanhDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNSqrtDim2(TestSqrt):
      def setUp(self):
          super(TestMKLDNNSqrtDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNAbsDim2(TestAbs):
      def setUp(self):
          super(TestMKLDNNAbsDim2, self).setUp()
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNSwishDim2(TestSwish):
      def setUp(self):
          super(TestMKLDNNSwishDim2, self).setUp()
  
          self.attrs["use_mkldnn"] = True
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNHardSwishDim2(TestHardSwish):
      def setUp(self):
          super(TestMKLDNNHardSwishDim2, self).setUp()
  
          self.attrs["use_mkldnn"] = True
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNSigmoidDim2(TestSigmoid):
      def setUp(self):
          super(TestMKLDNNSigmoidDim2, self).setUp()
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNReluDim4(TestRelu):
      def setUp(self):
          super(TestMKLDNNReluDim4, self).setUp()
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
          # The same reason with TestAbs
          x[np.abs(x) < 0.005] = 0.02
          out = np.maximum(x, 0)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNLeakyReluDim4(TestLeakyRelu):
      def setUp(self):
          super(TestMKLDNNLeakyReluDim4, self).setUp()
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
          # The same reason with TestAbs
          x[np.abs(x) < 0.005] = 0.02
          out = np.maximum(x, 0.02 * x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNGeluDim4(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.float32
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(self.dtype)
          out = gelu(x, False)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNGeluDim4Approx(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.float32
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(self.dtype)
          out = gelu(x, True)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True, "approximate": True}
  
  
  @unittest.skipIf(not core.supports_bfloat16(),
                   "place does not support BF16 evaluation")
  class TestMKLDNNGeluBf16Dim4(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.uint16
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(np.float32)
          out = convert_float_to_uint16(gelu(x, False))
  
          self.inputs = {'X': convert_float_to_uint16(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
      def test_check_output(self):
          self.check_output_with_place(core.CPUPlace())
  
      def test_check_grad(self):
          pass
  
  
  @unittest.skipIf(not core.supports_bfloat16(),
                   "place does not support BF16 evaluation")
  class TestMKLDNNGeluBf16Dim4Approx(TestActivation):
      def setUp(self):
          self.op_type = "gelu"
          self.dtype = np.uint16
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(np.float32)
          out = convert_float_to_uint16(gelu(x, True))
  
          self.inputs = {'X': convert_float_to_uint16(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True, "approximate": True}
  
      def test_check_output(self):
          self.check_output_with_place(core.CPUPlace())
  
      def test_check_grad(self):
          pass
  
  
  class TestMKLDNNTanhDim4(TestTanh):
      def setUp(self):
          super(TestMKLDNNTanhDim4, self).setUp()
  
          self.inputs = {
              'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
          }
          self.outputs = {'Out': np.tanh(self.inputs['X'])}
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNSqrtDim4(TestSqrt):
      def setUp(self):
          super(TestMKLDNNSqrtDim4, self).setUp()
  
          self.inputs = {
              'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
          }
          self.outputs = {'Out': np.sqrt(self.inputs['X'])}
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNAbsDim4(TestAbs):
      def setUp(self):
          super(TestMKLDNNAbsDim4, self).setUp()
  
          x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
          # The same reason with TestAbs
          x[np.abs(x) < 0.005] = 0.02
          self.inputs = {'X': x}
          self.outputs = {'Out': np.abs(self.inputs['X'])}
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNSwishDim4(TestSwish):
      def setUp(self):
          super(TestMKLDNNSwishDim4, self).setUp()
  
          x = np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype(self.dtype)
          beta = 2.3
          out = x * expit(beta * x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True, "beta": beta}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  def ref_hardswish(x, threshold=6.0, scale=6.0, offset=3.0):
      return (x * np.minimum(np.maximum(x + offset, 0.), threshold) /
              scale).astype(x.dtype)
  
  
  class TestMKLDNNHardSwishDim4(TestHardSwish):
      def setUp(self):
          super(TestMKLDNNHardSwishDim4, self).setUp()
  
          x = np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype(self.dtype)
          threshold = 6.0
          scale = 6.0
          offset = 3.0
          x[np.abs(x + offset) < 0.005] = 0.02
          x[np.abs(x - threshold + offset) < 0.005] = threshold - offset + 0.02
  
          out = ref_hardswish(x, threshold, scale, offset)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
      def init_dtype(self):
          self.dtype = np.float32
  
  
  class TestMKLDNNSigmoidDim4(TestSigmoid):
      def setUp(self):
          super(TestMKLDNNSigmoidDim4, self).setUp()
  
          x = np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype(self.dtype)
          out = 1 / (1 + np.exp(-x))
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
          self.attrs = {"use_mkldnn": True}
  
  
  # Check if primitives already exist in backward
  class TestMKLDNNAbsPrimitivesAlreadyExist(unittest.TestCase):
      def setUp(self):
          super(TestMKLDNNAbsPrimitivesAlreadyExist, self).setUp()
  
          np.random.seed(123)
          self.op_type = 'abs'
          self.x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32)
          self.out = np.abs(self.x)
          self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
          self.x_grad = self.__abs_bwd(self.x, self.out_grad)
  
      # Abs grad calculation
      def __abs_bwd(self, x, out_grad):
          return out_grad * np.sign(x)
  
      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()
  
apis: numpy.sqrt numpy.random.uniform numpy.random.random_sample numpy.sign numpy.random.seed numpy.abs numpy.exp scipy.special.expit numpy.maximum numpy.tanh