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
  import paddle.fluid as fluid
  from paddle.fluid.dygraph import to_variable
  from paddle.fluid.framework import ParamBase, EagerParamBase
  from paddle.jit import ProgramTranslator
  from paddle.fluid.framework import _test_eager_guard, in_dygraph_mode
  
  
  class L1(fluid.Layer):
      def __init__(self):
          super(L1, self).__init__()
          self._param_attr = fluid.ParamAttr(
              initializer=fluid.initializer.Constant(value=0.1))
          self.w1 = self.create_parameter(
              attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False)
          self.w2 = self.create_parameter(
              attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False)
  
      def forward(self):
          return self.w1 + self.w2
  
  
  class L2(fluid.Layer):
      def __init__(self):
          super(L2, self).__init__()
          self.layer1 = L1()
          self.layer2 = L1()
  
      def forward(self):
          return self.layer1() + self.layer2()
  
  
  class L3(fluid.Layer):
      def __init__(self):
          super(L3, self).__init__()
          self.layer1 = L2()
          self.layer2 = L2()
  
      def forward(self):
          return self.layer1() + self.layer2()
  
  
  class TestBaseLayer(unittest.TestCase):
      def func_test_one_level(self):
          with fluid.dygraph.guard():
              l = L1()
              ret = l()
              expected_names = ['l1.w1', 'l1.w2']
              idx = 0
              for name, _ in l.named_parameters(prefix='l1'):
                  self.assertEqual(name, expected_names[idx])
                  idx += 1
              self.assertTrue(np.allclose(ret.numpy(), 0.2 * np.ones([2, 2])))
  
      def test_one_level(self):
          with _test_eager_guard():
              self.func_test_one_level()
          self.func_test_one_level()
  
      def func_test_three_level(self):
          with fluid.dygraph.guard():
              l = L3()
              expected_names = [
                  'l3.layer1.layer1.w1',
                  'l3.layer1.layer1.w2',
                  'l3.layer1.layer2.w1',
                  'l3.layer1.layer2.w2',
                  'l3.layer2.layer1.w1',
                  'l3.layer2.layer1.w2',
                  'l3.layer2.layer2.w1',
                  'l3.layer2.layer2.w2',
              ]
              idx = 0
              for name, _ in l.named_parameters(prefix='l3'):
                  self.assertEqual(name, expected_names[idx])
                  idx += 1
              ret = l()
              self.assertTrue(np.allclose(ret.numpy(), 0.8 * np.ones([2, 2])))
  
      def test_three_level(self):
          with _test_eager_guard():
              self.func_test_three_level()
          self.func_test_three_level()
  
      def func_test_add_parameter_with_error(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              param = net.create_parameter(shape=[1])
  
              with self.assertRaises(TypeError):
                  net.add_parameter(10, param)
  
              with self.assertRaises(KeyError):
                  net.add_parameter("param.name", param)
  
              with self.assertRaises(KeyError):
                  net.add_parameter("", param)
  
              with self.assertRaises(KeyError):
                  net.test_param = 10
                  net.add_parameter("test_param", param)
  
              with self.assertRaises(TypeError):
                  net.add_parameter("no_param", 10)
  
              load_param = net.create_parameter(shape=[1])
              net._loaddict_holder[load_param.name] = load_param
              net.add_parameter("load_param", load_param)
  
      def test_add_parameter_with_error(self):
          with _test_eager_guard():
              self.func_test_add_parameter_with_error()
          self.func_test_add_parameter_with_error()
  
  
  class BufferLayer(fluid.Layer):
      def __init__(self):
          super(BufferLayer, self).__init__()
          buffer_var = to_variable(np.zeros([2, 4]).astype('int32'))
          self.register_buffer("layer_buffer", buffer_var)
  
      def forward(self):
          pass
  
  
  class BufferNet(fluid.Layer):
      def __init__(self):
          super(BufferNet, self).__init__()
          self.buffer_layer = BufferLayer()
          self.w1 = self.create_parameter(
              shape=[2, 2], dtype='float32', is_bias=False)
          buffer_var = to_variable(np.ones([2, 4]).astype('int32'))
          self.register_buffer("net_buffer", buffer_var)
  
          self.new_buffer = to_variable(np.ones([4, 2]).astype('int32'))
  
      def forward(self):
          pass
  
  
  class TestBuffer(unittest.TestCase):
      def func_test_buffers_and_named_buffers(self):
          def names(named_buffers):
              return [name for name, _ in named_buffers]
  
          with fluid.dygraph.guard():
              layer = BufferLayer()
              net = BufferNet()
  
              self.assertEqual(len(layer.buffers()), 1)
              self.assertEqual(names(layer.named_buffers()), ['layer_buffer'])
  
              self.assertEqual(len(net.buffers()), 3)
              self.assertEqual(
                  names(net.named_buffers()),
                  ['net_buffer', 'new_buffer', 'buffer_layer.layer_buffer'])
  
              self.assertEqual(len(net.buffers(include_sublayers=False)), 2)
              self.assertEqual(
                  names(net.named_buffers(include_sublayers=False)),
                  ['net_buffer', 'new_buffer'])
  
      def test_buffers_and_named_buffers(self):
          with _test_eager_guard():
              self.func_test_buffers_and_named_buffers()
          self.func_test_buffers_and_named_buffers()
  
      def func_test_register_buffer_with_error(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var = to_variable(np.zeros([1]))
  
              with self.assertRaisesRegexp(TypeError,
                                           "name of buffer should be a string"):
                  net.register_buffer(12, var)
  
              with self.assertRaisesRegexp(TypeError,
                                           "buffer should be a Paddle.Tensor"):
                  if in_dygraph_mode():
                      net.register_buffer("buffer_name",
                                          EagerParamBase([2, 2], 'float32'))
                  else:
                      net.register_buffer("buffer_name",
                                          ParamBase([2, 2], 'float32'))
  
              with self.assertRaisesRegexp(KeyError,
                                           "name of buffer can not contain"):
                  net.register_buffer("buffer.name", var)
  
              with self.assertRaisesRegexp(KeyError,
                                           "name of buffer can not be empty"):
                  net.register_buffer("", var)
  
              net.attr_name = 10
              with self.assertRaisesRegexp(KeyError, "already exists"):
                  net.register_buffer("attr_name", var)
  
              del net.attr_name
              if in_dygraph_mode():
                  net.attr_name = EagerParamBase([2, 2], 'float32')
              else:
                  net.attr_name = ParamBase([2, 2], 'float32')
              with self.assertRaisesRegexp(KeyError, "already exists"):
                  net.register_buffer("attr_name", var)
  
      def test_register_buffer_with_error(self):
          with _test_eager_guard():
              self.func_test_register_buffer_with_error()
          self.func_test_register_buffer_with_error()
  
      def func_test_register_buffer_same_name(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
              var2 = to_variable(np.zeros([2]))
              var3 = to_variable(np.zeros([3]))
  
              net.register_buffer("buffer_name", var1)
              self.assert_var_base_equal(net.buffer_name, var1)
              net.register_buffer("buffer_name", var2)
              self.assert_var_base_equal(net.buffer_name, var2)
              net.register_buffer("buffer_name", var3)
              self.assert_var_base_equal(net.buffer_name, var3)
  
      def test_register_buffer_same_name(self):
          with _test_eager_guard():
              self.func_test_register_buffer_same_name()
          self.func_test_register_buffer_same_name()
  
      def func_test_buffer_not_persistable(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
  
              net.register_buffer("buffer_name", var1, persistable=False)
              self.assertEqual(len(net.buffers()), 1)
              self.assertEqual(len(net.state_dict()), 0)
  
      def test_buffer_not_persistable(self):
          with _test_eager_guard():
              self.func_test_buffer_not_persistable()
          self.func_test_buffer_not_persistable()
  
      def func_test_buffer_not_persistable_del(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
              net.register_buffer("buffer_name", var1, persistable=False)
              del net.buffer_name
              self.assertEqual(len(net.buffers()), 0)
  
      def test_buffer_not_persistable_del(self):
          with _test_eager_guard():
              self.func_test_buffer_not_persistable_del()
          self.func_test_buffer_not_persistable_del()
  
      def func_test_buffer_not_persistable_overwrite(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
              var2 = to_variable(np.zeros([2]))
              net.register_buffer("buffer_name", var1, persistable=False)
              net.register_buffer("buffer_name", var2)
  
              # Allow to overwrite a non-persistable buffer with a persistable var.
              self.assertEqual(len(net.buffers()), 1)
              self.assertEqual(len(net.state_dict()), 1)
  
              net.register_buffer("buffer_name", var1, persistable=False)
              self.assertEqual(len(net.buffers()), 1)
              self.assertEqual(len(net.state_dict()), 0)
  
      def test_buffer_not_persistable_overwrite(self):
          with _test_eager_guard():
              self.func_test_buffer_not_persistable_overwrite()
          self.func_test_buffer_not_persistable_overwrite()
  
      def func_test_buffer_not_persistable_assign(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
              net.register_buffer("buffer_name", var1, persistable=False)
  
              # Assigning Nones will remove the buffer, but allow to re-assign
              # to remark it as buffer.
              net.buffer_name = None
              self.assertEqual(len(net.buffers()), 0)
              self.assertEqual(len(net.state_dict()), 0)
  
              net.buffer_name = var1
              self.assertEqual(len(net.buffers()), 1)
              self.assertEqual(len(net.state_dict()), 0)
  
              # Re-assign a ParamBase will remove the buffer.
              if in_dygraph_mode():
                  net.buffer_name = EagerParamBase([2, 2], 'float32')
              else:
                  net.buffer_name = ParamBase([2, 2], 'float32')
              self.assertEqual(len(net.buffers()), 0)
              self.assertEqual(len(net.state_dict()), 1)
  
      def test_buffer_not_persistable_assign(self):
          with _test_eager_guard():
              self.func_test_buffer_not_persistable_assign()
          self.func_test_buffer_not_persistable_assign()
  
      def func_test_buffer_not_persistable_load(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([1]))
              net.register_buffer("buffer_name", var1, persistable=False)
              net.load_dict({})
  
      def test_buffer_not_persistable_load(self):
          with _test_eager_guard():
              self.func_test_buffer_not_persistable_load()
          self.func_test_buffer_not_persistable_load()
  
      def func_test_buffer_state_dict(self):
          with fluid.dygraph.guard():
              net = fluid.Layer()
              var1 = to_variable(np.zeros([2, 3]))
              var2 = to_variable(np.zeros([3, 2]))
              net.register_buffer("buffer_var1", var1)
              net.register_buffer("buffer_var2", var2, persistable=False)
  
              self.assertEqual(len(net.state_dict()), 1)
              self.assertEqual([name for name, _ in net.state_dict().items()],
                               ["buffer_var1"])
  
              # load state_dict
              net_load = fluid.Layer()
              var = to_variable(np.ones([2, 3]))
              net_load.register_buffer("buffer_var1", var)
              net_load.load_dict(net.state_dict())
  
              self.assert_var_base_equal(net_load.buffer_var1, var1)
  
      def test_buffer_state_dict(self):
          with _test_eager_guard():
              self.func_test_buffer_state_dict()
          self.func_test_buffer_state_dict()
  
      def assert_var_base_equal(self, var1, var2):
          self.assertTrue(np.array_equal(var1.numpy(), var2.numpy()))
  
  
  class BufferNetWithModification(paddle.nn.Layer):
      def __init__(self, shape):
          super(BufferNetWithModification, self).__init__()
  
          self.buffer1 = paddle.zeros(shape, 'int32')
          self.buffer2 = paddle.zeros(shape, 'int32')
  
      @paddle.jit.to_static
      def forward(self, x):
          self.buffer1 += x
          self.buffer2 = self.buffer1 + x
  
          out = self.buffer1 + self.buffer2
  
          return out
  
  
  class TestModifiedBuffer(unittest.TestCase):
      def funcsetUp(self):
          paddle.disable_static()
          self.prog_trans = ProgramTranslator()
          self.shape = [10, 16]
  
      def _run(self, to_static=False):
          self.prog_trans.enable(to_static)
  
          x = paddle.ones([1], 'int32')
          net = BufferNetWithModification(self.shape)
          out = net(x)
  
          return out, net.buffer1, net.buffer2
  
      def func_test_modified(self):
          self.funcsetUp()
          dy_outs = self._run(False)
          st_outs = self._run(True)
  
          for i in range(len(dy_outs)):
              self.assertTrue(
                  np.array_equal(dy_outs[i].numpy(), st_outs[i].numpy()))
  
      def test_modified(self):
          with _test_eager_guard():
              self.func_test_modified()
          self.func_test_modified()
  
  
  class TestLayerTo(unittest.TestCase):
      def funcsetUp(self):
          paddle.disable_static()
          self.linear = paddle.nn.Linear(2, 2)
          self.new_grad = np.random.random([2, 2])
          self.linear.weight._set_grad_ivar(paddle.to_tensor(self.new_grad))
          buffer = paddle.to_tensor([0.0], dtype='float32')
          self.linear.register_buffer("buf_name", buffer, persistable=True)
  
          sublayer = paddle.nn.Conv1D(3, 2, 3)
          self.linear.add_sublayer("1", sublayer)
  
      def func_test_to_api(self):
          self.linear.to(dtype='double')
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
  
          self.linear.to()
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          for p in self.linear.parameters():
              if in_dygraph_mode():
                  self.assertTrue(
                      isinstance(p, paddle.fluid.framework.EagerParamBase))
              else:
                  self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
  
          if paddle.fluid.is_compiled_with_cuda():
              self.linear.to(device=paddle.CUDAPlace(0))
              self.assertTrue(self.linear.weight.place.is_gpu_place())
              self.assertEqual(self.linear.weight.place.gpu_device_id(), 0)
              self.assertTrue(self.linear.buf_name.place.is_gpu_place())
              self.assertEqual(self.linear.buf_name.place.gpu_device_id(), 0)
              self.assertTrue(self.linear.weight._grad_ivar().place.is_gpu_place(
              ))
              self.assertEqual(
                  self.linear.weight._grad_ivar().place.gpu_device_id(), 0)
  
              self.linear.to(device='gpu:0')
              self.assertTrue(self.linear.weight.place.is_gpu_place())
              self.assertEqual(self.linear.weight.place.gpu_device_id(), 0)
              self.assertTrue(self.linear.buf_name.place.is_gpu_place())
              self.assertEqual(self.linear.buf_name.place.gpu_device_id(), 0)
              self.assertTrue(self.linear.weight._grad_ivar().place.is_gpu_place(
              ))
              self.assertEqual(
                  self.linear.weight._grad_ivar().place.gpu_device_id(), 0)
              for p in self.linear.parameters():
                  if in_dygraph_mode():
                      self.assertTrue(
                          isinstance(p, paddle.fluid.framework.EagerParamBase))
                  else:
                      self.assertTrue(
                          isinstance(p, paddle.fluid.framework.ParamBase))
  
          self.linear.to(device=paddle.CPUPlace())
          self.assertTrue(self.linear.weight.place.is_cpu_place())
          self.assertTrue(self.linear.buf_name.place.is_cpu_place())
          self.assertTrue(self.linear.weight._grad_ivar().place.is_cpu_place())
  
          self.linear.to(device='cpu')
          self.assertTrue(self.linear.weight.place.is_cpu_place())
          self.assertTrue(self.linear.buf_name.place.is_cpu_place())
          self.assertTrue(self.linear.weight._grad_ivar().place.is_cpu_place())
  
          self.assertRaises(ValueError, self.linear.to, device=1)
  
          self.assertRaises(AssertionError, self.linear.to, blocking=1)
  
      def func_test_to_api_paddle_dtype(self):
          self.linear.to(dtype=paddle.float64)
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
  
          self.linear.to()
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          for p in self.linear.parameters():
              if in_dygraph_mode():
                  self.assertTrue(
                      isinstance(p, paddle.fluid.framework.EagerParamBase))
              else:
                  self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
  
      def func_test_to_api_numpy_dtype(self):
          self.linear.to(dtype=np.float64)
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
  
          self.linear.to()
          self.assertEqual(self.linear.weight.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertEqual(self.linear.buf_name.dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          self.assertTrue(
              np.allclose(self.linear.weight.grad.numpy(), self.new_grad))
          self.assertEqual(self.linear.weight._grad_ivar().dtype,
                           paddle.fluid.core.VarDesc.VarType.FP64)
          for p in self.linear.parameters():
              if in_dygraph_mode():
                  self.assertTrue(
                      isinstance(p, paddle.fluid.framework.EagerParamBase))
              else:
                  self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase))
  
      def test_main(self):
          with _test_eager_guard():
              self.funcsetUp()
              self.func_test_to_api()
              self.func_test_to_api_paddle_dtype()
              self.func_test_to_api_numpy_dtype()
          self.funcsetUp()
          self.func_test_to_api()
          self.func_test_to_api_paddle_dtype()
          self.func_test_to_api_numpy_dtype()
  
  
  if __name__ == '__main__':
      paddle.enable_static()
      unittest.main()
  