text: #   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 paddle.fluid as fluid
  from paddle.fluid.dygraph.base import to_variable
  from paddle.fluid.dygraph.nn import Embedding
  from paddle.fluid.optimizer import SGDOptimizer
  import numpy as np
  import paddle.fluid.core as core
  import paddle
  from paddle.fluid.framework import _test_eager_guard
  
  
  class SimpleNet(paddle.nn.Layer):
  
      def __init__(self, vocab_size, hidden_size, dtype):
          super(SimpleNet, self).__init__()
          self.emb = fluid.dygraph.Embedding(size=[vocab_size, hidden_size],
                                             dtype=dtype,
                                             param_attr='emb.w',
                                             is_sparse=True)
  
      def forward(self, input):
          input_emb = self.emb(input)
          return input_emb, self.emb
  
  
  class TestSimpleNet(unittest.TestCase):
  
      def func_selectedrows_gradient1(self):
          places = [fluid.CPUPlace()]
          if core.is_compiled_with_cuda():
              places.append(fluid.CUDAPlace(0))
  
          for place in places:
              for dtype in ["float32", "float64"]:
                  for sort_sum_gradient in [True, False]:
                      paddle.disable_static(place)
                      fluid.set_flags(
                          {'FLAGS_sort_sum_gradient': sort_sum_gradient})
                      # grad_clip = fluid.clip.GradientClipByGlobalNorm(5.0)
  
                      input_word = np.array([[1, 2], [2, 1]]).astype('int64')
                      input = paddle.to_tensor(input_word)
  
                      simplenet = SimpleNet(20, 32, dtype)
                      adam = SGDOptimizer(learning_rate=0.001,
                                          parameter_list=simplenet.parameters()
                                          )  # grad_clip=grad_clip
                      input_emb, emb = simplenet(input)
  
                      self.assertTrue(emb.weight.gradient() is None)
                      self.assertTrue(input_emb.gradient() is None)
  
                      input_emb.backward()
                      adam.minimize(input_emb)
                      self.assertTrue(emb.weight.gradient() is not None)
  
                      emb.clear_gradients()
                      self.assertTrue(emb.weight.gradient() is None)
  
                      input_emb.clear_gradient()
                      self.assertTrue(input_emb.gradient() is not None)
                      paddle.enable_static()
  
      def test_selectedrows_gradient1(self):
          fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
          with _test_eager_guard():
              self.func_selectedrows_gradient1()
          self.func_selectedrows_gradient1()
          fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
  
      def func_selectedrows_gradient2(self):
          places = [fluid.CPUPlace()]
          if core.is_compiled_with_cuda():
              places.append(fluid.CUDAPlace(0))
  
          for place in places:
              for sort_sum_gradient in [True, False]:
                  with fluid.dygraph.guard(place):
                      fluid.set_flags(
                          {'FLAGS_sort_sum_gradient': sort_sum_gradient})
                      grad_clip = fluid.clip.GradientClipByGlobalNorm(5.0)
  
                      input_word = np.array([[1, 2], [2, 1]]).astype('int64')
                      input = to_variable(input_word)
  
                      simplenet = SimpleNet(20, 32, "float32")
                      adam = SGDOptimizer(learning_rate=0.001,
                                          parameter_list=simplenet.parameters(),
                                          grad_clip=grad_clip)
                      input_emb, emb = simplenet(input)
  
                      self.assertTrue(emb.weight.gradient() is None)
                      self.assertTrue(input_emb.gradient() is None)
  
                      input_emb.backward()
                      adam.minimize(input_emb)
                      self.assertTrue(emb.weight.gradient() is not None)
  
                      emb.clear_gradients()
                      self.assertTrue(emb.weight.gradient() is None)
  
                      input_emb.clear_gradient()
                      self.assertTrue(input_emb.gradient() is not None)
  
      def test_selectedrows_gradient2(self):
          fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
          with _test_eager_guard():
              self.func_selectedrows_gradient2()
          self.func_selectedrows_gradient2()
          fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
  
  
  if __name__ == '__main__':
      unittest.main()
  