test_random_seed.py

#   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.
"""Test cloud role maker."""

import unittest

import numpy as np

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.generator as generator


class TestGeneratorSeed(unittest.TestCase):
    #     """
    #     Test cases for cpu generator seed.
    #     """

    def test_generator_uniform_random_dygraph(self):
        """Test Generator seed."""

        fluid.enable_dygraph()

        gen = paddle.seed(12312321111)
        x = fluid.layers.uniform_random([10], dtype="float32", min=0.0, max=1.0)

        st1 = gen.get_state()
        x1 = fluid.layers.uniform_random(
            [10], dtype="float32", min=0.0, max=1.0
        )

        gen.set_state(st1)
        print(gen.get_state())
        x2 = fluid.layers.uniform_random(
            [10], dtype="float32", min=0.0, max=1.0
        )

        paddle.seed(12312321111)
        x3 = fluid.layers.uniform_random(
            [10], dtype="float32", min=0.0, max=1.0
        )

        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if not core.is_compiled_with_cuda():
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_generator_uniform_random_static(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            result_1 = fluid.layers.uniform_random(shape=[3, 4])
            result_2 = fluid.layers.uniform_random(shape=[3, 4])

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )
            # gen.set_state(cur_state)
            gen.manual_seed(123123143)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            out1_res1 = np.array(out1[0])
            out1_res2 = np.array(out1[1])
            out2_res1 = np.array(out2[0])
            out2_res2 = np.array(out2[1])

            if not core.is_compiled_with_cuda():
                np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
                np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
                self.assertTrue(not np.allclose(out1_res2, out1_res1))

    def test_gen_dropout_dygraph(self):
        fluid.enable_dygraph()

        gen = paddle.seed(111111111)
        st = gen.get_state()
        # x = np.arange(1,101).reshape(2,50).astype("float32")
        x = fluid.layers.uniform_random(
            [2, 10], dtype="float32", min=0.0, max=1.0
        )
        y = fluid.layers.dropout(x, 0.5)
        gen.manual_seed(111111111)
        # gen.set_state(st)
        x1 = fluid.layers.uniform_random(
            [2, 10], dtype="float32", min=0.0, max=1.0
        )
        y1 = fluid.layers.dropout(x1, 0.5)
        y_np = y.numpy()
        y1_np = y1.numpy()

        if not core.is_compiled_with_cuda():
            print(">>>>>>> dropout dygraph >>>>>>>")
            np.testing.assert_allclose(y_np, y1_np, rtol=1e-05)

    def test_gen_dropout_static(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            x_1 = fluid.layers.uniform_random(shape=[2, 10])
            y_1 = fluid.layers.dropout(x_1, 0.5)
            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(train_program, feed={}, fetch_list=[y_1])
            # gen.set_state(cur_state)
            gen.manual_seed(123123143)
            out2 = exe.run(train_program, feed={}, fetch_list=[y_1])
        out1_np = np.array(out1[0])
        out2_np = np.array(out2[0])

        if not core.is_compiled_with_cuda():
            print(">>>>>>> dropout static >>>>>>>")
            np.testing.assert_allclose(out1_np, out2_np, rtol=1e-05)

    def test_generator_gaussian_random_dygraph(self):
        """Test Generator seed."""
        fluid.enable_dygraph()

        gen = paddle.seed(12312321111)
        x = fluid.layers.gaussian_random([10], dtype="float32")
        st1 = gen.get_state()
        x1 = fluid.layers.gaussian_random([10], dtype="float32")
        gen.set_state(st1)
        x2 = fluid.layers.gaussian_random([10], dtype="float32")
        gen.manual_seed(12312321111)
        x3 = fluid.layers.gaussian_random([10], dtype="float32")
        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if not core.is_compiled_with_cuda():
            print(">>>>>>> gaussian random dygraph >>>>>>>")
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_generator_gaussian_random_static(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            result_1 = fluid.layers.gaussian_random(shape=[3, 4])
            result_2 = fluid.layers.gaussian_random(shape=[3, 4])

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )
            # gen.set_state(cur_state)
            gen.manual_seed(123123143)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            out1_res1 = np.array(out1[0])
            out1_res2 = np.array(out1[1])
            out2_res1 = np.array(out2[0])
            out2_res2 = np.array(out2[1])

            if not core.is_compiled_with_cuda():
                print(">>>>>>> gaussian random static >>>>>>>")
                np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
                np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
                self.assertTrue(not np.allclose(out1_res2, out1_res1))

    def test_generator_randint_dygraph(self):
        """Test Generator seed."""
        gen = generator.Generator()

        fluid.enable_dygraph()

        gen = paddle.seed(12312321111)
        x = paddle.randint(low=10, shape=[10], dtype="int32")
        st1 = gen.get_state()
        x1 = paddle.randint(low=10, shape=[10], dtype="int32")
        gen.set_state(st1)
        x2 = paddle.randint(low=10, shape=[10], dtype="int32")
        gen.manual_seed(12312321111)
        x3 = paddle.randint(low=10, shape=[10], dtype="int32")
        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if not core.is_compiled_with_cuda():
            print(">>>>>>> randint dygraph >>>>>>>")
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_generator_uniform_random_static_1(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            result_1 = fluid.layers.uniform_random(shape=[3, 4])
            result_2 = fluid.layers.uniform_random(shape=[3, 4])

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )
            # gen.set_state(cur_state)
            gen.manual_seed(123123143)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            out1_res1 = np.array(out1[0])
            out1_res2 = np.array(out1[1])
            out2_res1 = np.array(out2[0])
            out2_res2 = np.array(out2[1])

            if not core.is_compiled_with_cuda():
                np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
                np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
                self.assertTrue(not np.allclose(out1_res2, out1_res1))

    def test_generator_randint_dygraph_1(self):
        """Test Generator seed."""
        fluid.enable_dygraph()

        gen = paddle.seed(12312321111)
        x = paddle.randint(low=1)
        st1 = gen.get_state()
        x1 = paddle.randint(low=1)
        gen.set_state(st1)
        x2 = paddle.randint(low=1)
        gen.manual_seed(12312321111)
        x3 = paddle.randint(low=1)
        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()
        if not core.is_compiled_with_cuda():
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_generator_ranint_static(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            result_1 = paddle.randint(low=10, shape=[3, 4])
            result_2 = paddle.randint(low=10, shape=[3, 4])

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )
            # gen.set_state(cur_state)
            gen.manual_seed(123123143)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            out1_res1 = np.array(out1[0])
            out1_res2 = np.array(out1[1])
            out2_res1 = np.array(out2[0])
            out2_res2 = np.array(out2[1])

            if not core.is_compiled_with_cuda():
                print(">>>>>>> randint static >>>>>>>")
                np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
                np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
                self.assertTrue(not np.allclose(out1_res2, out1_res1))

    def test_generator_randperm_dygraph(self):
        """Test Generator seed."""

        fluid.enable_dygraph()

        gen = paddle.seed(12312321111)
        x = paddle.randperm(10)
        st1 = gen.get_state()
        x1 = paddle.randperm(10)
        gen.set_state(st1)
        x2 = paddle.randperm(10)
        gen.manual_seed(12312321111)
        x3 = paddle.randperm(10)
        x_np = x.numpy()
        x1_np = x1.numpy()
        x2_np = x2.numpy()
        x3_np = x3.numpy()

        if not core.is_compiled_with_cuda():
            print(">>>>>>> randperm dygraph >>>>>>>")
            np.testing.assert_allclose(x1_np, x2_np, rtol=1e-05)
            np.testing.assert_allclose(x_np, x3_np, rtol=1e-05)

    def test_generator_randperm_static(self):

        fluid.disable_dygraph()

        paddle.seed(123123143)

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            result_1 = paddle.randperm(10)
            result_2 = paddle.randperm(10)

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            paddle.seed(123123143)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

            out1_res1 = np.array(out1[0])
            out1_res2 = np.array(out1[1])
            out2_res1 = np.array(out2[0])
            out2_res2 = np.array(out2[1])

            if not core.is_compiled_with_cuda():
                print(">>>>>>> randperm static >>>>>>>")
                np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
                np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
                self.assertTrue(not np.allclose(out1_res2, out1_res1))

    def test_gen_TruncatedNormal_initializer(self):
        fluid.disable_dygraph()

        gen = paddle.seed(123123143)
        cur_state = gen.get_state()

        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            # example 1:
            # attr shape is a list which doesn't contain tensor Variable.
            x = fluid.layers.uniform_random(shape=[2, 10])
            result_1 = fluid.layers.fc(
                input=x,
                size=10,
                param_attr=fluid.initializer.TruncatedNormal(
                    loc=0.0, scale=2.0
                ),
            )
            result_2 = fluid.layers.fc(
                input=x,
                size=10,
                param_attr=fluid.initializer.TruncatedNormal(
                    loc=0.0, scale=2.0
                ),
            )

            exe = fluid.Executor(fluid.CPUPlace())
            exe.run(startup_program)
            out1 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

        gen.manual_seed(123123143)
        with fluid.program_guard(train_program, startup_program):
            exe.run(startup_program)
            out2 = exe.run(
                train_program, feed={}, fetch_list=[result_1, result_2]
            )

        out1_res1 = np.array(out1[0])
        out1_res2 = np.array(out1[1])
        out2_res1 = np.array(out2[0])
        out2_res2 = np.array(out2[1])

        if not core.is_compiled_with_cuda():
            print(">>>>>>> sampling id static >>>>>>>")
            np.testing.assert_allclose(out1_res1, out2_res1, rtol=1e-05)
            np.testing.assert_allclose(out1_res2, out2_res2, rtol=1e-05)
            self.assertTrue(not np.allclose(out1_res2, out1_res1))


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