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.
  
  import unittest
  import paddle.fluid as fluid
  import numpy as np
  from paddle.fluid.framework import _test_eager_guard
  
  
  class AutoPruneLayer0(fluid.Layer):
      def __init__(self, input_size):
          super(AutoPruneLayer0, self).__init__()
          self.linear1 = fluid.dygraph.Linear(
              input_size,
              5,
              param_attr=fluid.initializer.ConstantInitializer(value=2),
              bias_attr=False)
          self.linear2 = fluid.dygraph.Linear(
              5,
              5,
              param_attr=fluid.initializer.ConstantInitializer(value=2),
              bias_attr=False)
  
      def forward(self, x, y):
          a = self.linear1(x)
          b = self.linear2(y)
          c = fluid.layers.mul(a, b)
          d = fluid.layers.reduce_mean(c)
          return d
  
  
  class AutoPruneLayer1(fluid.Layer):
      def __init__(self, input_size):
          super(AutoPruneLayer1, self).__init__()
          self.linear1 = fluid.dygraph.Linear(
              input_size,
              5,
              param_attr=fluid.initializer.ConstantInitializer(value=2),
              bias_attr=False)
          self.linear2 = fluid.dygraph.Linear(
              5,
              5,
              param_attr=fluid.initializer.ConstantInitializer(value=2),
              bias_attr=False)
  
      def forward(self, x, y):
          a = self.linear1(x)
          b = self.linear2(y)
          b.stop_gradient = True
          c = fluid.layers.mul(a, b)
          d = fluid.layers.reduce_mean(c)
          return d
  
  
  class AutoPruneLayer2(fluid.Layer):
      def __init__(self, input_size):
          super(AutoPruneLayer2, self).__init__()
          self.linear = fluid.dygraph.Linear(input_size, 10, act=None)
          self.linear2 = fluid.dygraph.Linear(1, 1, act=None)
  
      def forward(self, x, label):
          feature = self.linear(x)
          label = self.linear2(label)
          label = fluid.layers.cast(label, dtype="float32")
          label = fluid.layers.cast(label, dtype='int64')
          # Note that the label is not persistable in fluid.layers.cross_entropy.
          loss = fluid.layers.cross_entropy(input=feature, label=label)
          loss = fluid.layers.mean(loss)
          return loss
  
  
  class AutoPruneLayer3(fluid.Layer):
      def __init__(self, input_size):
          super(AutoPruneLayer3, self).__init__()
          self.linear = fluid.dygraph.Linear(input_size, 20, act=None)
  
      def forward(self, x, label, test_num):
          feature = self.linear(x)
          part1, part2 = fluid.layers.split(
              feature, num_or_sections=[10, 10], dim=1)
          # Note that: part2 is not used.
          loss = fluid.layers.cross_entropy(input=part1, label=label)
          loss = fluid.layers.mean(loss)
          if test_num == 1:
              return loss, part2
          else:
              return loss, part1, part2
  
  
  class MyLayer(fluid.Layer):
      def __init__(self, input_size, vocab_size, size, dtype="float32"):
          super(MyLayer, self).__init__(dtype=dtype)
          self.embed0 = fluid.Embedding(size=(vocab_size, size))
          self.embed1 = fluid.Embedding(size=(vocab_size, size))
          self.linear_0 = fluid.Linear(input_size, size, dtype=dtype)
          self.linear_1 = fluid.Linear(input_size, size, dtype=dtype)
  
      def forward(self, x):
          # this method involves only the linear layers
          loss = fluid.layers.reduce_mean(self.linear_0(x) + self.linear_1(x))
          return loss
  
      def linear0(self, x):
          loss = fluid.layers.reduce_mean(self.linear_0(x))
          return loss
  
      def embed_linear0(self, x):
          loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x)))
          return loss
  
  
  class MyLayer2(fluid.Layer):
      def __init__(self, input_size, vocab_size, size, dtype="float32"):
          super(MyLayer2, self).__init__(dtype=dtype)
          self.embed0 = fluid.Embedding(size=(vocab_size, size))
          self.embed1 = fluid.Embedding(size=(vocab_size, size))
          self.linear_0 = fluid.Linear(input_size, size, dtype=dtype)
          self.linear_1 = fluid.Linear(input_size, size, dtype=dtype)
  
      def forward(self, indices):
          # mind the difference with MyLayer
          # In this example, the forward method involes all params
          loss = fluid.layers.reduce_mean(
              self.linear_0(self.embed0(indices)) + self.linear_1(
                  self.embed1(indices)))
          return loss
  
      def linear0(self, x):
          loss = fluid.layers.reduce_mean(self.linear_0(x))
          return loss
  
      def embed_linear0(self, x):
          loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x)))
          return loss
  
  
  class TestImperativeAutoPrune(unittest.TestCase):
      def func_auto_prune(self):
          with fluid.dygraph.guard():
              case1 = AutoPruneLayer0(input_size=5)
              value1 = np.arange(25).reshape(5, 5).astype("float32")
              value2 = np.arange(25).reshape(5, 5).astype("float32")
              v1 = fluid.dygraph.to_variable(value1)
              v2 = fluid.dygraph.to_variable(value2)
              loss = case1(v1, v2)
              loss.backward()
              self.assertTrue(case1.linear2.weight._grad_ivar() is not None)
              self.assertTrue(case1.linear1.weight._grad_ivar() is not None)
  
      def test_auto_prune(self):
          with _test_eager_guard():
              self.func_auto_prune()
          self.func_auto_prune()
  
      def func_auto_prune2(self):
          with fluid.dygraph.guard():
              case2 = AutoPruneLayer1(input_size=5)
              value1 = np.arange(25).reshape(5, 5).astype("float32")
              value2 = np.arange(25).reshape(5, 5).astype("float32")
              v1 = fluid.dygraph.to_variable(value1)
              v2 = fluid.dygraph.to_variable(value2)
              loss = case2(v1, v2)
  
              loss.backward()
              self.assertTrue(case2.linear2.weight._grad_ivar() is None)
              self.assertTrue(case2.linear1.weight._grad_ivar() is not None)
  
      def test_auto_prune2(self):
          with _test_eager_guard():
              self.func_auto_prune2()
          self.func_auto_prune2()
  
      def test_auto_prune3(self):
          with fluid.dygraph.guard():
              case3 = AutoPruneLayer3(input_size=784)
              value1 = np.arange(784).reshape(1, 784).astype("float32")
              value2 = np.arange(1).reshape(1, 1).astype("int64")
              v1 = fluid.dygraph.to_variable(value1)
              v2 = fluid.dygraph.to_variable(value2)
              loss, part2 = case3(v1, v2, 1)
              loss.backward()
              self.assertTrue(case3.linear.weight._grad_ivar() is not None)
              self.assertTrue((part2.gradient() == 0).all())
  
      def test_auto_prune4(self):
          with fluid.dygraph.guard():
              case4 = AutoPruneLayer3(input_size=784)
              value1 = np.arange(784).reshape(1, 784).astype("float32")
              value2 = np.arange(1).reshape(1, 1).astype("int64")
              v1 = fluid.dygraph.to_variable(value1)
              v2 = fluid.dygraph.to_variable(value2)
              loss, part2 = case4(v1, v2, 1)
              part2.backward()
              self.assertTrue(case4.linear.weight._grad_ivar() is not None)
              self.assertTrue((part2.gradient() == 1).all())
  
      def test_auto_prune5(self):
          with fluid.dygraph.guard():
              case4 = AutoPruneLayer3(input_size=784)
              value1 = np.arange(784).reshape(1, 784).astype("float32")
              value2 = np.arange(1).reshape(1, 1).astype("int64")
              v1 = fluid.dygraph.to_variable(value1)
              v2 = fluid.dygraph.to_variable(value2)
              loss, part1, part2 = case4(v1, v2, 2)
              part1.backward()
              self.assertTrue(case4.linear.weight._grad_ivar() is not None)
              self.assertTrue((part2.gradient() == 0).all())
  
      def test_auto_prune6(self):
          with fluid.dygraph.guard():
              value0 = np.arange(26).reshape(2, 13).astype("float32")
              value1 = np.arange(6).reshape(2, 3).astype("float32")
              value2 = np.arange(10).reshape(2, 5).astype("float32")
              linear = fluid.Linear(13, 5, dtype="float32")
              linear2 = fluid.Linear(3, 3, dtype="float32")
              a = fluid.dygraph.to_variable(value0)
              b = fluid.dygraph.to_variable(value1)
              c = fluid.dygraph.to_variable(value2)
              out1 = linear(a)
              out2 = linear2(b)
              out1.stop_gradient = True
              out = fluid.layers.concat(input=[out1, out2, c], axis=1)
              out.backward()
              self.assertTrue(linear.weight.gradient() is None)
              self.assertTrue(out1.gradient() is None)
  
      def test_auto_prune7(self):
          with fluid.dygraph.guard():
              value0 = np.arange(26).reshape(2, 13).astype("float32")
              value1 = np.arange(6).reshape(2, 3).astype("float32")
              value2 = np.arange(10).reshape(2, 5).astype("float32")
              linear = fluid.Linear(13, 5, dtype="float32")
              linear2 = fluid.Linear(3, 3, dtype="float32")
              a = fluid.dygraph.to_variable(value0)
              b = fluid.dygraph.to_variable(value1)
              c = fluid.dygraph.to_variable(value2)
              out1 = linear(a)
              out2 = linear2(b)
              out1.stop_gradient = True
              out = fluid.layers.concat(input=[out1, out2, c], axis=1)
              out.backward()
              self.assertTrue(linear.weight.gradient() is None)
              self.assertTrue(out1.gradient() is None)
  
      def test_auto_prune8(self):
          with fluid.dygraph.guard():
              value0 = np.arange(26).reshape(2, 13).astype("float32")
              value1 = np.arange(6).reshape(2, 3).astype("float32")
              value2 = np.arange(10).reshape(2, 5).astype("float32")
              linear = fluid.Linear(13, 5, dtype="float32")
              linear2 = fluid.Linear(5, 3, dtype="float32")
              a = fluid.dygraph.to_variable(value0)
              b = fluid.dygraph.to_variable(value1)
              c = fluid.dygraph.to_variable(value2)
              out1 = linear(a)
              linear_origin = linear.weight.numpy()
              out2 = linear2(out1)
              linear2_origin = linear2.weight.numpy()
              linear2.weight.stop_gradient = True
              out2.backward()
              optimizer = fluid.optimizer.SGD(
                  learning_rate=0.003,
                  parameter_list=(linear.parameters() + linear2.parameters()))
              optimizer.minimize(out2)
              self.assertTrue(
                  np.array_equal(linear2_origin, linear2.weight.numpy()))
              self.assertFalse(
                  np.array_equal(linear_origin, linear.weight.numpy()))
  
      def test_auto_prune9(self):
          with fluid.dygraph.guard():
              value0 = np.arange(26).reshape(2, 13).astype("float32")
              value1 = np.arange(6).reshape(2, 3).astype("float32")
              value2 = np.arange(10).reshape(2, 5).astype("float32")
              linear = fluid.Linear(13, 5, dtype="float32")
              linear2 = fluid.Linear(5, 3, dtype="float32")
              a = fluid.dygraph.to_variable(value0)
              b = fluid.dygraph.to_variable(value1)
              c = fluid.dygraph.to_variable(value2)
              out1 = linear(a)
              linear_origin = linear.weight.numpy()
              out2 = linear2(out1)
              linear2_origin = linear2.weight.numpy()
              out2.stop_gradient = True
              out2.backward()
              optimizer = fluid.optimizer.SGD(
                  learning_rate=0.003,
                  parameter_list=(linear.parameters() + linear2.parameters()))
              optimizer.minimize(out2)
              self.assertTrue(
                  np.array_equal(linear2_origin, linear2.weight.numpy()))
              self.assertTrue(
                  np.array_equal(linear_origin, linear.weight.numpy()))
              try:
                  linear2.weight.gradient()
              except ValueError as e:
                  assert type(e) == ValueError
  
      def test_auto_prune10(self):
          with fluid.dygraph.guard():
              value0 = np.arange(26).reshape(2, 13).astype("float32")
              value1 = np.arange(6).reshape(2, 3).astype("float32")
              value2 = np.arange(10).reshape(2, 5).astype("float32")
              linear = fluid.Linear(13, 5, dtype="float32")
              linear2 = fluid.Linear(3, 3, dtype="float32")
              a = fluid.dygraph.to_variable(value0)
              b = fluid.dygraph.to_variable(value1)
              c = fluid.dygraph.to_variable(value2)
              out1 = linear(a)
              out2 = linear2(b)
              out1.stop_gradient = True
              out = fluid.layers.concat(input=[out1, out2, c], axis=1)
              fluid.set_flags({'FLAGS_sort_sum_gradient': True})
              out.backward()
              self.assertTrue(linear.weight.gradient() is None)
              self.assertTrue(out1.gradient() is None)
  
      def test_auto_prune_with_optimizer(self):
          vocab_size = 100
          size = 20
          batch_size = 16
  
          indices = np.random.randint(
              low=0, high=100, size=(batch_size, 1)).astype("int64")
          embed = np.random.randn(batch_size, size).astype("float32")
  
          place = fluid.CPUPlace()
          with fluid.dygraph.guard(place):
              model = MyLayer(size, vocab_size, size)
              grad_clip = fluid.clip.GradientClipByGlobalNorm(0.001)
              optimizer = fluid.optimizer.AdamOptimizer(
                  0.001, parameter_list=model.parameters(), grad_clip=grad_clip)
  
              indices = fluid.dygraph.to_variable(indices)
              embed = fluid.dygraph.to_variable(embed)
              dummy_loss = model(embed)
  
              loss = model.embed_linear0(indices)
              loss.backward()
              _, params_grads = optimizer.minimize(loss)
              for items in params_grads:
                  assert items[0].name is not model.embed1.weight.name
                  assert items[0].name is not model.linear_1.weight.name
              assert model.embed1.weight._grad_ivar() is None
              assert model.linear_1.weight._grad_ivar() is None
  
          with fluid.dygraph.guard(place):
              model = MyLayer2(size, vocab_size, size)
              grad_clip = fluid.clip.GradientClipByGlobalNorm(0.001)
              optimizer = fluid.optimizer.AdamOptimizer(
                  0.001, parameter_list=model.parameters(), grad_clip=grad_clip)
  
              indices = fluid.dygraph.to_variable(indices)
              emebd = fluid.dygraph.to_variable(embed)
              dummy_loss = model(indices)
  
              loss = model.embed_linear0(indices)
              loss.backward()
              optimizer.minimize(loss)
              for items in params_grads:
                  assert items[0].name is not model.embed1.weight.name
                  assert items[0].name is not model.linear_1.weight.name
              assert model.embed1.weight._grad_ivar() is None
              assert model.linear_1.weight._grad_ivar() is None
  
      def test_case2_prune_no_grad_branch(self):
          with fluid.dygraph.guard():
              value1 = np.arange(784).reshape(1, 784)
              value2 = np.arange(1).reshape(1, 1)
              v1 = fluid.dygraph.to_variable(value1).astype("float32")
              v2 = fluid.dygraph.to_variable(value2).astype("float32")
              case3 = AutoPruneLayer2(input_size=784)
              loss = case3(v1, v2)
              loss.backward()
              self.assertTrue(case3.linear2.weight._grad_ivar() is None)
              self.assertTrue(case3.linear.weight._grad_ivar() is not None)
  
      def test_case3_prune_no_grad_branch2(self):
          with fluid.dygraph.guard():
              value1 = np.arange(1).reshape(1, 1)
              linear = fluid.dygraph.Linear(1, 1, act=None)
              label = fluid.dygraph.to_variable(value1).astype("float32")
              label = linear(label)
              label = fluid.layers.cast(label, dtype="float32")
              label = fluid.layers.cast(label, dtype='int64')
              out = fluid.layers.one_hot(input=label, depth=100)
              loss = fluid.layers.mean(out)
              loss.backward()
              self.assertTrue(linear.weight._grad_ivar() is None)
  
      def test_case4_with_no_grad_op_maker(self):
          with fluid.dygraph.guard():
              out = fluid.layers.gaussian_random(shape=[20, 30])
              loss = fluid.layers.mean(out)
              loss.backward()
              self.assertTrue(out._grad_ivar() is None)
  
  
  if __name__ == '__main__':
      unittest.main()
  