text: #   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.
  
  import unittest
  import numpy as np
  import paddle.fluid.core as core
  from op_test import OpTest
  from scipy.special import expit
  
  
  class TestExp(OpTest):
      def setUp(self):
          self.op_type = "exp"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          out = np.exp(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Exp(TestExp):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSigmoid(OpTest):
      def setUp(self):
          self.op_type = "sigmoid"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = 1 / (1 + np.exp(-x))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.01)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Sigmoid(TestSigmoid):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestLogSigmoid(OpTest):
      def setUp(self):
          self.op_type = "logsigmoid"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = np.log(1 / (1 + np.exp(-x)))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.008)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16LogSigmoid(TestLogSigmoid):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestTanh(OpTest):
      def setUp(self):
          self.op_type = "tanh"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          out = np.tanh(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Tanh(TestTanh):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestTanhShrink(OpTest):
      def setUp(self):
          self.op_type = "tanh_shrink"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [10, 17]).astype(self.dtype)
          out = x - np.tanh(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.008)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16TanhShrink(TestTanhShrink):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestHardShrink(OpTest):
      def setUp(self):
          self.op_type = "hard_shrink"
          self.dtype = np.float32
          self.init_dtype()
  
          threshold = 0.5
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.copy(x)
          out[(out >= -threshold) & (out <= threshold)] = 0
  
          self.attrs = {'lambda': threshold}
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.005)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16HardShrink(TestHardShrink):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSoftShrink(OpTest):
      def setUp(self):
          self.op_type = "softshrink"
          self.dtype = np.float32
          self.init_dtype()
  
          lambda_val = 0.1
          x = np.random.uniform(0.25, 10, [4, 4]).astype(self.dtype)
          out = np.copy(x)
          out = (out < -lambda_val) * (out + lambda_val) + (out > lambda_val) * (
              out - lambda_val)
  
          self.attrs = {'lambda': lambda_val}
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16SoftShrink(TestSoftShrink):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSqrt(OpTest):
      def setUp(self):
          self.op_type = "sqrt"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          out = np.sqrt(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Sqrt(TestSqrt):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestAbs(OpTest):
      def setUp(self):
          self.op_type = "abs"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          # Because we set delta = 0.005 in caculating numeric gradient,
          # if x is too small, such as 0.002, x_neg will be -0.003
          # x_pos will be 0.007, so the numeric gradient is unaccurate.
          # we should avoid this
          x[np.abs(x) < 0.005] = 0.02
          out = np.abs(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Abs(TestAbs):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestCeil(OpTest):
      def setUp(self):
          self.op_type = "ceil"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.ceil(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Ceil(TestCeil):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestFloor(OpTest):
      def setUp(self):
          self.op_type = "floor"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.floor(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Floor(TestFloor):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestCos(OpTest):
      def setUp(self):
          self.op_type = "cos"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.cos(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Cos(TestCos):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSin(OpTest):
      def setUp(self):
          self.op_type = "sin"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.sin(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Sin(TestSin):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestRound(OpTest):
      def setUp(self):
          self.op_type = "round"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          out = np.round(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Round(TestRound):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestRelu(OpTest):
      def setUp(self):
          self.op_type = "relu"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          # 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}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Relu(TestRelu):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestBRelu(OpTest):
      def setUp(self):
          self.op_type = "brelu"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
          t_min = 1.0
          t_max = 4.0
          # The same with TestAbs
          x[np.abs(x - t_min) < 0.005] = t_min + 0.02
          x[np.abs(x - t_max) < 0.005] = t_max + 0.02
          t = np.copy(x)
          t[t < t_min] = t_min
          t[t > t_max] = t_max
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.attrs = {'t_min': t_min, 't_max': t_max}
          self.outputs = {'Out': t}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.02)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16BRelu(TestBRelu):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestRelu6(OpTest):
      def setUp(self):
          self.op_type = "relu6"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [4, 10]).astype(self.dtype)
          threshold = 6.0
          # The same with TestAbs
          x[np.abs(x) < 0.005] = 0.02
          x[np.abs(x - threshold) < 0.005] = threshold + 0.02
          out = np.minimum(np.maximum(x, 0), threshold)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.attrs = {'threshold': threshold}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.02)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Relu6(TestRelu6):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSoftRelu(OpTest):
      def setUp(self):
          self.op_type = "soft_relu"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-3, 3, [4, 4]).astype(self.dtype)
          threshold = 2.0
          # The same reason with TestAbs
          x[np.abs(x - threshold) < 0.005] = threshold + 0.02
          x[np.abs(x + threshold) < 0.005] = -threshold + 0.02
          t = np.copy(x)
          t[t < -threshold] = -threshold
          t[t > threshold] = threshold
          out = np.log((np.exp(t) + 1))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.attrs = {'threshold': threshold}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.02)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16SoftRelu(TestSoftRelu):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestELU(OpTest):
      def setUp(self):
          self.op_type = "elu"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-3, 3, [4, 4]).astype(self.dtype)
          alpha = 1.
          out = np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
          # Note: unlike other Relu extensions, point 0 on standard ELU function (i.e. alpha = 1)
          # is differentiable, so we can skip modifications like x[np.abs(x) < 0.005] = 0.02 here
          self.inputs = {'X': x}
          self.attrs = {'alpha': alpha}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.02)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16ELU(TestELU):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestReciprocal(OpTest):
      def setUp(self):
          self.op_type = "reciprocal"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
          out = np.reciprocal(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.01)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Reciprocal(TestReciprocal):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestLog(OpTest):
      def setUp(self):
          self.op_type = "log"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          out = np.log(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Log(TestLog):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSquare(OpTest):
      def setUp(self):
          self.op_type = "square"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          out = np.square(x)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Square(TestSquare):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestPow(OpTest):
      def setUp(self):
          self.op_type = "pow"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
          out = np.power(x, 3)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.attrs = {'factor': 3.0}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.02)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Pow(TestPow):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=5e-2)
  
  
  class TestSTanh(OpTest):
      def setUp(self):
          self.op_type = "stanh"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          scale_a = 2.0 / 3.0
          scale_b = 1.7159
          out = scale_b * np.tanh(x * scale_a)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.attrs = {'scale_a': scale_a, 'scale_b': scale_b}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16STanh(TestSTanh):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSoftplus(OpTest):
      def setUp(self):
          self.op_type = "softplus"
          self.dtype = np.float64
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = np.log(1 + np.exp(x))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Softplus(TestSoftplus):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSoftsign(OpTest):
      def setUp(self):
          self.op_type = "softsign"
          self.dtype = np.float32
          self.init_dtype()
  
          x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
          out = np.divide(x, 1 + np.abs(x))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.007)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Softsign(TestSoftsign):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestThresholdedRelu(OpTest):
      def setUp(self):
          self.op_type = "thresholded_relu"
          self.dtype = np.float32
          self.init_dtype()
  
          threshold = 0.25
          self.relative_error = 0.005
          X = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
  
          # Same reason as TestAbs
          X[np.abs(X - threshold) < self.relative_error] = threshold + 0.2
          out = (X > threshold) * X
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
          self.attrs = {'threshold': threshold}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=self.relative_error)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16ThresholdedRelu(TestThresholdedRelu):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestHardSigmoid(OpTest):
      def setUp(self):
          self.op_type = "hard_sigmoid"
          self.dtype = np.float32
          self.init_dtype()
  
          self.relative_error = 0.002
  
          X = np.random.uniform(-5, 5, [2, 2]).astype("float32")
          slope = 0.2
          offset = 0.5
          lower_threshold = -offset / slope
          upper_threshold = (1 - offset) / slope
  
          # Same reason as TestAbs
          X[np.abs(X - lower_threshold) < self.relative_error] = \
              lower_threshold + 0.2
          X[np.abs(X - upper_threshold) < self.relative_error] = \
              upper_threshold - 0.2
  
          temp = X * slope + offset
          out = np.maximum(0.0, np.minimum(1.0, temp))
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.002)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16HardSigmoid(TestHardSigmoid):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  class TestSwish(OpTest):
      def setUp(self):
          self.op_type = "swish"
          self.dtype = np.float32
          self.init_dtype()
  
          X = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
          beta = 2.3
          out = X * expit(beta * X)
  
          self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
          self.attrs = {'beta': beta}
          self.outputs = {'Out': out}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          if self.dtype == np.float16:
              return
          self.check_grad(['X'], 'Out', max_relative_error=0.008)
  
      def init_dtype(self):
          pass
  
  
  class TestFP16Swish(TestSwish):
      def init_dtype(self):
          self.dtype = np.float16
  
      def test_check_output(self):
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place):
                  self.check_output_with_place(place, atol=1e-3)
  
  
  #--------------------test MKLDNN--------------------
  class TestMKLDNNReluDim2(TestRelu):
      def setUp(self):
          super(TestMKLDNNReluDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNTanhDim2(TestTanh):
      def setUp(self):
          super(TestMKLDNNTanhDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNSqrtDim2(TestSqrt):
      def setUp(self):
          super(TestMKLDNNSqrtDim2, self).setUp()
  
          self.attrs = {"use_mkldnn": True}
  
  
  class TestMKLDNNAbsDim2(TestAbs):
      def setUp(self):
          super(TestMKLDNNAbsDim2, 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}
  
  
  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}
  
  
  if __name__ == "__main__":
      unittest.main()
  