text: # Copyright (c) 2020 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 paddle
  paddle.set_default_dtype("float64")
  from paddle.fluid.layers import sequence_mask
  
  import numpy as np
  import unittest
  
  from convert import convert_params_for_net
  from rnn_numpy import SimpleRNN, LSTM, GRU
  
  
  class TestSimpleRNN(unittest.TestCase):
      def __init__(self, time_major=True, direction="forward", place="cpu"):
          super(TestSimpleRNN, self).__init__("runTest")
          self.time_major = time_major
          self.direction = direction
          self.num_directions = 2 if direction == "bidirectional" else 1
          self.place = place
  
      def setUp(self):
          # Since `set_device` is global, set `set_device` in `setUp` rather than
          # `__init__` to avoid using an error device set by another test case.
          place = paddle.set_device(self.place)
          paddle.disable_static(place)
          rnn1 = SimpleRNN(
              16, 32, 2, time_major=self.time_major, direction=self.direction)
          rnn2 = paddle.nn.SimpleRNN(
              16, 32, 2, time_major=self.time_major, direction=self.direction)
          convert_params_for_net(rnn1, rnn2)
  
          self.rnn1 = rnn1
          self.rnn2 = rnn2
  
      def test_with_initial_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          prev_h = np.random.randn(2 * self.num_directions, 4, 32)
  
          y1, h1 = rnn1(x, prev_h)
          y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_zero_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
  
          y1, h1 = rnn1(x)
          y2, h2 = rnn2(paddle.to_tensor(x))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_input_lengths(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)
  
          y1, h1 = rnn1(x, sequence_length=sequence_length)
  
          seq_len = paddle.to_tensor(sequence_length)
          mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
          if self.time_major:
              mask = paddle.transpose(mask, [1, 0])
          y2, h2 = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
          y2 = paddle.multiply(y2, mask, axis=0)
  
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def runTest(self):
          self.test_with_initial_state()
          self.test_with_zero_state()
          self.test_with_input_lengths()
  
  
  class TestGRU(unittest.TestCase):
      def __init__(self, time_major=True, direction="forward", place="cpu"):
          super(TestGRU, self).__init__("runTest")
          self.time_major = time_major
          self.direction = direction
          self.num_directions = 2 if direction == "bidirectional" else 1
          self.place = place
  
      def setUp(self):
          # Since `set_device` is global, set `set_device` in `setUp` rather than
          # `__init__` to avoid using an error device set by another test case.
          place = paddle.set_device(self.place)
          paddle.disable_static(place)
          rnn1 = GRU(16,
                     32,
                     2,
                     time_major=self.time_major,
                     direction=self.direction)
          rnn2 = paddle.nn.GRU(16,
                               32,
                               2,
                               time_major=self.time_major,
                               direction=self.direction)
          convert_params_for_net(rnn1, rnn2)
  
          self.rnn1 = rnn1
          self.rnn2 = rnn2
  
      def test_with_initial_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          prev_h = np.random.randn(2 * self.num_directions, 4, 32)
  
          y1, h1 = rnn1(x, prev_h)
          y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_zero_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
  
          y1, h1 = rnn1(x)
          y2, h2 = rnn2(paddle.to_tensor(x))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_input_lengths(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)
  
          y1, h1 = rnn1(x, sequence_length=sequence_length)
  
          seq_len = paddle.to_tensor(sequence_length)
          mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
          if self.time_major:
              mask = paddle.transpose(mask, [1, 0])
          y2, h2 = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
          y2 = paddle.multiply(y2, mask, axis=0)
  
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
  
      def runTest(self):
          self.test_with_initial_state()
          self.test_with_zero_state()
          self.test_with_input_lengths()
  
  
  class TestLSTM(unittest.TestCase):
      def __init__(self, time_major=True, direction="forward", place="cpu"):
          super(TestLSTM, self).__init__("runTest")
          self.time_major = time_major
          self.direction = direction
          self.num_directions = 2 if direction == "bidirectional" else 1
          self.place = place
  
      def setUp(self):
          # Since `set_device` is global, set `set_device` in `setUp` rather than
          # `__init__` to avoid using an error device set by another test case.
          place = paddle.set_device(self.place)
          paddle.disable_static(place)
          rnn1 = LSTM(
              16, 32, 2, time_major=self.time_major, direction=self.direction)
          rnn2 = paddle.nn.LSTM(
              16, 32, 2, time_major=self.time_major, direction=self.direction)
          convert_params_for_net(rnn1, rnn2)
  
          self.rnn1 = rnn1
          self.rnn2 = rnn2
  
      def test_with_initial_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          prev_h = np.random.randn(2 * self.num_directions, 4, 32)
          prev_c = np.random.randn(2 * self.num_directions, 4, 32)
  
          y1, (h1, c1) = rnn1(x, (prev_h, prev_c))
          y2, (h2, c2) = rnn2(
              paddle.to_tensor(x),
              (paddle.to_tensor(prev_h), paddle.to_tensor(prev_c)))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_zero_state(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
  
          y1, (h1, c1) = rnn1(x)
          y2, (h2, c2) = rnn2(paddle.to_tensor(x))
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_with_input_lengths(self):
          rnn1 = self.rnn1
          rnn2 = self.rnn2
  
          x = np.random.randn(12, 4, 16)
          if not self.time_major:
              x = np.transpose(x, [1, 0, 2])
          sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)
  
          y1, (h1, c1) = rnn1(x, sequence_length=sequence_length)
  
          seq_len = paddle.to_tensor(sequence_length)
          mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
          if self.time_major:
              mask = paddle.transpose(mask, [1, 0])
          y2, (h2, c2) = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
          y2 = paddle.multiply(y2, mask, axis=0)
  
          np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
          np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
  
      def test_predict(self):
          place = paddle.set_device(self.place)
          paddle.seed(123)
          np.random.seed(123)
  
          class Net(paddle.nn.Layer):
              def __init__(self):
                  super(Net, self).__init__()
                  self.rnn1 = paddle.nn.LSTM(
                      16, 32, 2, direction="bidirectional", dropout=0.1)
  
              def forward(self, input):
                  return self.rnn1(input)
  
          x = paddle.randn((4, 10, 16))
          x.stop_gradient = False
          seq_len = paddle.to_tensor(np.array([10, 6, 8, 5]))
          mask = sequence_mask(seq_len, maxlen=10, dtype=x.dtype)
          mask = paddle.unsqueeze(mask, [2])
          rnn = Net()
          y, (h, c) = rnn(x)
          y = y * mask
          loss = paddle.mean(y)
          loss.backward()
          optimizer = paddle.optimizer.Adam(
              learning_rate=0.1, parameters=rnn.parameters())
          optimizer.step()
          rnn.eval()
          y, (h, c) = rnn(x)
          # `jit.to_static` would include a train_program, eval mode might cause
          # some errors currently, such as dropout grad op gets `is_test == True`.
          rnn.train()
  
          rnn = paddle.jit.to_static(
              rnn,
              [paddle.static.InputSpec(
                  shape=[None, None, 16], dtype=x.dtype)])
          paddle.jit.save(rnn, "./inference/lstm_infer")
  
          paddle.enable_static()
  
          new_scope = paddle.static.Scope()
          with paddle.static.scope_guard(new_scope):
              exe = paddle.static.Executor(place)
              [inference_program, feed_target_names,
               fetch_targets] = paddle.static.load_inference_model(
                   dirname="./inference",
                   executor=exe,
                   model_filename="lstm_infer.pdmodel",
                   params_filename="lstm_infer.pdiparams")
              results = exe.run(inference_program,
                                feed={feed_target_names[0]: x.numpy()},
                                fetch_list=fetch_targets)
              np.testing.assert_equal(
                  y.numpy(), results[0])  # eval results equal predict results
          paddle.disable_static()
  
      def runTest(self):
          self.test_with_initial_state()
          self.test_with_zero_state()
          self.test_with_input_lengths()
          self.test_predict()
  
  
  def load_tests(loader, tests, pattern):
      suite = unittest.TestSuite()
      devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
          else ["cpu"]
      for direction in ["forward", "backward", "bidirectional"]:
          for time_major in [True, False]:
              for device in devices:
                  for test_class in [TestSimpleRNN, TestLSTM, TestGRU]:
                      suite.addTest(test_class(time_major, direction, device))
      return suite
  