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
  
  
  class TestMulOp(OpTest):
      def setUp(self):
          self.op_type = "mul"
          self.inputs = {
              'X': np.random.random((32, 84)).astype("float32"),
              'Y': np.random.random((84, 100)).astype("float32")
          }
          self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad_normal(self):
          self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
  
      def test_check_grad_ingore_x(self):
          self.check_grad(
              ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
  
      def test_check_grad_ingore_y(self):
          self.check_grad(
              ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
  
  
  class TestMulOp2(OpTest):
      def setUp(self):
          self.op_type = "mul"
          self.inputs = {
              'X': np.random.random((15, 4, 12, 10)).astype("float32"),
              'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
          }
          self.attrs = {'x_num_col_dims': 2, 'y_num_col_dims': 2}
          result = np.dot(self.inputs['X'].reshape(15 * 4, 12 * 10),
                          self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
          result = result.reshape(15, 4, 8, 2, 9)
          self.outputs = {'Out': result}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad_normal(self):
          self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
  
      def test_check_grad_ingore_x(self):
          self.check_grad(
              ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X'))
  
      def test_check_grad_ignore_y(self):
          self.check_grad(
              ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
  
  
  class TestFP16MulOp1(OpTest):
      def setUp(self):
          self.op_type = "mul"
          x = np.random.random((32, 84)).astype("float16")
          y = np.random.random((84, 100)).astype("float16")
          self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
          self.outputs = {'Out': np.dot(x, y)}
  
      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-1)
  
  
  class TestFP16MulOp2(OpTest):
      def setUp(self):
          self.op_type = "mul"
          x = np.random.random((15, 4, 12, 10)).astype("float16")
          y = np.random.random((4, 30, 8, 2, 9)).astype("float16")
          self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
          self.attrs = {
              'x_num_col_dims': 2,
              'y_num_col_dims': 2,
          }
          result = np.dot(
              x.reshape(15 * 4, 12 * 10), y.reshape(4 * 30, 8 * 2 * 9))
          result = result.reshape(15, 4, 8, 2, 9)
          self.outputs = {'Out': result}
  
      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=2e-1)
  
  
  if __name__ == "__main__":
      unittest.main()
  