test_rnn_nets.py

# 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

bidirectional_list = ["bidirectional", "bidirect"]


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 in bidirectional_list 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)
        mask = paddle.unsqueeze(mask, -1)
        y2 = paddle.multiply(y2, mask)

        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_predict(self):
        predict_test_util(self.place, "SimpleRNN")

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()
        self.test_predict()


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 in bidirectional_list 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)
        mask = paddle.unsqueeze(mask, -1)
        y2 = paddle.multiply(y2, mask)

        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_predict(self):
        predict_test_util(self.place, "GRU")

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()
        self.test_predict()


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 in bidirectional_list 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)
        mask = paddle.unsqueeze(mask, -1)
        y2 = paddle.multiply(y2, mask)

        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):
        predict_test_util(self.place, "LSTM")
        predict_test_util(self.place, "LSTM", False)

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()
        self.test_predict()


def predict_test_util(place, mode, stop_gradient=True):
    place = paddle.set_device(place)
    paddle.seed(123)
    np.random.seed(123)

    class Net(paddle.nn.Layer):

        def __init__(self):
            super(Net, self).__init__()
            self.rnn = getattr(paddle.nn, mode)(16,
                                                32,
                                                2,
                                                direction="bidirectional",
                                                dropout=0.1)

        def forward(self, input):
            return self.rnn(input)

    x = paddle.randn((4, 10, 16))
    x.stop_gradient = stop_gradient
    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, _ = 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, _ = 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/%s_infer" % mode)

    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("./inference/%s_infer" % mode,
                                                exe)
        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 load_tests(loader, tests, pattern):
    suite = unittest.TestSuite()
    devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
        else ["cpu"]
    for direction in ["forward", "bidirectional", "bidirect"]:
        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


if __name__ == '__main__':
    unittest.main()