paddlepaddle--paddle/test/rnn/test_rnn_cells_static.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.framework.set_default_dtype("float64")
paddle.enable_static()

import sys
import unittest

import numpy as np
from convert import convert_params_for_cell_static

sys.path.append("../../rnn")
from rnn_numpy import GRUCell, LSTMCell, SimpleRNNCell


class TestSimpleRNNCell(unittest.TestCase):
    def __init__(self, bias=True, place="cpu"):
        super().__init__(methodName="runTest")
        self.bias = bias
        self.place = (
            paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
        )

    def test_with_initial_state(self):
        rnn1 = SimpleRNNCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.SimpleRNNCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)
        prev_h = np.random.randn(4, 32)

        y1, h1 = rnn1(x, prev_h)

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            init_h = paddle.static.data(
                "init_h",
                [-1, 32],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, h = rnn2(x_data, init_h)

        feed_dict = {x_data.name: x, init_h.name: prev_h}
        with paddle.static.scope_guard(scope):
            y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h])

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = SimpleRNNCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.SimpleRNNCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)

        y1, h1 = rnn1(x)

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, h = rnn2(x_data)

        feed_dict = {x_data.name: x}

        with paddle.static.scope_guard(scope):
            y2, h2 = exe.run(
                mp, feed=feed_dict, fetch_list=[y, h], use_prune=True
            )

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)

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


class TestGRUCell(unittest.TestCase):
    def __init__(self, bias=True, place="cpu"):
        super().__init__(methodName="runTest")
        self.bias = bias
        self.place = (
            paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
        )

    def test_with_initial_state(self):
        rnn1 = GRUCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.GRUCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)
        prev_h = np.random.randn(4, 32)

        y1, h1 = rnn1(x, prev_h)

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            init_h = paddle.static.data(
                "init_h",
                [-1, 32],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, h = rnn2(x_data, init_h)

        feed_dict = {x_data.name: x, init_h.name: prev_h}
        with paddle.static.scope_guard(scope):
            y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h])

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = GRUCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.GRUCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)

        y1, h1 = rnn1(x)

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, h = rnn2(x_data)

        feed_dict = {x_data.name: x}

        with paddle.static.scope_guard(scope):
            y2, h2 = exe.run(
                mp, feed=feed_dict, fetch_list=[y, h], use_prune=True
            )

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)

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


class TestLSTMCell(unittest.TestCase):
    def __init__(self, bias=True, place="cpu"):
        super().__init__(methodName="runTest")
        self.bias = bias
        self.place = (
            paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
        )

    def test_with_initial_state(self):
        rnn1 = LSTMCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.LSTMCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)
        prev_h = np.random.randn(4, 32)
        prev_c = np.random.randn(4, 32)

        y1, (h1, c1) = rnn1(x, (prev_h, prev_c))

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            init_h = paddle.static.data(
                "init_h",
                [-1, 32],
                dtype=paddle.framework.get_default_dtype(),
            )
            init_c = paddle.static.data(
                "init_c",
                [-1, 32],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, (h, c) = rnn2(x_data, (init_h, init_c))

        feed_dict = {x_data.name: x, init_h.name: prev_h, init_c.name: prev_c}
        with paddle.static.scope_guard(scope):
            y2, h2, c2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h, c])

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(c1, c2, atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = LSTMCell(16, 32, bias=self.bias)

        mp = paddle.static.Program()
        sp = paddle.static.Program()
        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            rnn2 = paddle.nn.LSTMCell(
                16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias
            )

        place = self.place
        exe = paddle.static.Executor(place)
        scope = paddle.base.Scope()
        with paddle.static.scope_guard(scope):
            exe.run(sp)
            convert_params_for_cell_static(rnn1, rnn2, place)

        x = np.random.randn(4, 16)

        y1, (h1, c1) = rnn1(x)

        with (
            paddle.base.unique_name.guard(),
            paddle.static.program_guard(mp, sp),
        ):
            x_data = paddle.static.data(
                "input",
                [-1, 16],
                dtype=paddle.framework.get_default_dtype(),
            )
            y, (h, c) = rnn2(x_data)

        feed_dict = {x_data.name: x}

        with paddle.static.scope_guard(scope):
            y2, h2, c2 = exe.run(
                mp, feed=feed_dict, fetch_list=[y, h, c], use_prune=True
            )

        np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(c1, c2, atol=1e-8, rtol=1e-5)

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


def load_tests(loader, tests, pattern):
    suite = unittest.TestSuite()
    devices = ["cpu", "gpu"] if paddle.base.is_compiled_with_cuda() else ["cpu"]
    for bias in [True, False]:
        for device in devices:
            for test_class in [TestSimpleRNNCell, TestGRUCell, TestLSTMCell]:
                suite.addTest(test_class(bias, device))
    return suite


if __name__ == "__main__":
    paddle.enable_static()
    unittest.main()