test_imperative_optimizer_v2.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.

import unittest
import numpy as np
import six
import itertools

import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.optimizer import MomentumOptimizer, LarsMomentumOptimizer, AdagradOptimizer, AdamaxOptimizer, DpsgdOptimizer, DecayedAdagradOptimizer, AdadeltaOptimizer, RMSPropOptimizer, FtrlOptimizer
from paddle.fluid.optimizer import ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer
from paddle.fluid.dygraph import Linear
from test_imperative_base import new_program_scope
from paddle.fluid.framework import _test_eager_guard

# Note(wangzhongpu)
# In dygraph, don't support ModelAverage, DGCMomentumOptimizer, ExponentialMovingAverage, PipelineOptimizer, LookaheadOptimizer, RecomputeOptimizer.


class MLP(fluid.Layer):

    def __init__(self, param_attr=None, bias_attr=None):
        super(MLP, self).__init__()

        self._fc1 = Linear(784, 10)
        self._fc2 = Linear(10, 10)

    def forward(self, inputs):
        y = self._fc1(inputs)
        y = self._fc2(y)
        return y


class TestImperativeOptimizerBase(unittest.TestCase):

    def setUp(self):
        self.batch_num = 20

    def get_optimizer_dygraph(self, parameter_list):
        raise NotImplementedError()

    def get_optimizer(self):
        raise NotImplementedError()

    def reader_decorator(self, reader):

        def _reader_imple():
            for item in reader():
                image = np.array(item[0]).reshape(1, 784)
                label = np.array(item[1]).astype('int64').reshape(1)
                yield image, label

        return _reader_imple

    def _check_exception(self, exception_message, place=None):
        seed = 90
        batch_size = 128
        if place == None:
            place = fluid.CUDAPlace(
                0) if core.is_compiled_with_cuda() else fluid.CPUPlace()

        try:
            paddle.disable_static()
            paddle.seed(seed)
            paddle.framework.random._manual_program_seed(seed)
            mlp = MLP()
            optimizer = self.get_optimizer_dygraph(
                parameter_list=mlp.parameters())
        except Exception as e:
            assert str(e) == exception_message
        finally:
            paddle.enable_static()

    def _check_mlp(self, place=None):
        seed = 90
        batch_size = 128

        if place == None:
            place = fluid.CPUPlace(
            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)

        paddle.disable_static(place)
        paddle.seed(seed)
        paddle.framework.random._manual_program_seed(seed)

        mlp = MLP()
        optimizer = self.get_optimizer_dygraph(parameter_list=mlp.parameters())

        batch_py_reader = fluid.io.PyReader(capacity=1)
        batch_py_reader.decorate_sample_list_generator(paddle.batch(
            self.reader_decorator(paddle.dataset.mnist.train()),
            batch_size=batch_size,
            drop_last=True),
                                                       places=fluid.CPUPlace())

        dy_param_init_value = {}
        for batch_id, data in enumerate(batch_py_reader()):
            if batch_id >= self.batch_num:
                break

            img = data[0]
            label = data[1]

            label.stop_gradient = True

            img = fluid.layers.reshape(img, shape=[batch_size, -1])
            cost = mlp(img)
            avg_loss = fluid.layers.reduce_mean(cost)
            dy_out = avg_loss.numpy()

            if batch_id == 0:
                for param in mlp.parameters():
                    dy_param_init_value[param.name] = param.numpy()

            avg_loss.backward()
            optimizer.minimize(avg_loss)
            if isinstance(optimizer._learning_rate,
                          paddle.optimizer.lr.LRScheduler):
                if isinstance(optimizer._learning_rate,
                              paddle.optimizer.lr.ReduceOnPlateau):
                    optimizer._learning_rate.step(avg_loss)
                else:
                    optimizer._learning_rate.step()
            mlp.clear_gradients()
            dy_param_value = {}
            for param in mlp.parameters():
                dy_param_value[param.name] = param.numpy()

        paddle.enable_static()
        with new_program_scope():
            paddle.seed(seed)
            paddle.framework.random._manual_program_seed(seed)

            if place == None:
                place = fluid.CPUPlace(
                ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)

            exe = fluid.Executor(place)

            mlp = MLP()
            optimizer = self.get_optimizer()
            train_reader = paddle.batch(paddle.dataset.mnist.train(),
                                        batch_size=128,
                                        drop_last=True)

            img = fluid.layers.data(name='pixel',
                                    shape=[1, 28, 28],
                                    dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            img = fluid.layers.reshape(img, shape=[batch_size, 784])
            cost = mlp(img)
            avg_loss = fluid.layers.reduce_mean(cost)
            optimizer.minimize(avg_loss)

            # initialize params and fetch them
            static_param_init_value = {}
            static_param_name_list = []
            for param in mlp.parameters():
                static_param_name_list.append(param.name)

            out = exe.run(fluid.default_startup_program(),
                          fetch_list=static_param_name_list)

            for i in range(len(static_param_name_list)):
                static_param_init_value[static_param_name_list[i]] = out[i]

            for batch_id, data in enumerate(train_reader()):
                if batch_id >= self.batch_num:
                    break

                static_x_data = np.array(
                    [x[0].reshape(1, 28, 28) for x in data]).astype('float32')
                y_data = np.array([x[1] for x in data
                                   ]).astype('int64').reshape([128, 1])

                fetch_list = [avg_loss.name]
                fetch_list.extend(static_param_name_list)
                out = exe.run(fluid.default_main_program(),
                              feed={
                                  "pixel": static_x_data,
                                  "label": y_data
                              },
                              fetch_list=fetch_list)
                if isinstance(optimizer._learning_rate,
                              paddle.optimizer.lr.LRScheduler):
                    if isinstance(optimizer._learning_rate,
                                  paddle.optimizer.lr.ReduceOnPlateau):
                        optimizer._learning_rate.step(out[0])
                    else:
                        optimizer._learning_rate.step()

                static_param_value = {}
                static_out = out[0]
                for i in range(1, len(out)):
                    static_param_value[static_param_name_list[i - 1]] = out[i]

        for key, value in six.iteritems(static_param_init_value):
            np.testing.assert_allclose(value,
                                       dy_param_init_value[key],
                                       rtol=1e-05)

        if core.is_compiled_with_rocm():
            np.testing.assert_allclose(static_out,
                                       dy_out,
                                       rtol=1e-05,
                                       atol=0.001)
        else:
            np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)

        for key, value in six.iteritems(static_param_value):
            if core.is_compiled_with_rocm():
                np.testing.assert_allclose(value,
                                           dy_param_value[key],
                                           rtol=1e-05,
                                           atol=0.001)
            else:
                np.testing.assert_allclose(value,
                                           dy_param_value[key],
                                           rtol=1e-05)


class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        bd = [3, 6, 9]
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.PiecewiseDecay(
                boundaries=bd,
                values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        bd = [3, 6, 9]
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.PiecewiseDecay(
                boundaries=bd,
                values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5,
                                                              gamma=0.9),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.5,
                                                              gamma=0.9))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.ExponentialDecay(
                learning_rate=0.5, gamma=0.9),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.ExponentialDecay(
                learning_rate=0.5, gamma=0.9))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.Adam(
            learning_rate=paddle.optimizer.lr.InverseTimeDecay(
                learning_rate=0.5, gamma=0.9),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.Adam(
            learning_rate=paddle.optimizer.lr.InverseTimeDecay(
                learning_rate=0.5, gamma=0.9))
        return optimizer

    def func_test_adam(self):
        self._check_mlp()

    def test_adam(self):
        with _test_eager_guard():
            self.func_test_adam()
        self.func_test_adam()


class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5,
                                                              decay_steps=5,
                                                              cycle=self.cycle),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.PolynomialDecay(
                learning_rate=0.5, decay_steps=5, cycle=self.cycle))
        return optimizer

    def func_test_sgd_cycle(self):
        self.cycle = True
        self._check_mlp()

    def test_sgd_cycle(self):
        with _test_eager_guard():
            self.func_test_sgd_cycle()
        self.func_test_sgd_cycle()

    def func_test_sgd(self):
        self.cycle = False
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerCosineAnnealingDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
                learning_rate=0.5, T_max=5),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.CosineAnnealingDecay(
                learning_rate=0.5, T_max=5))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerNoamDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.NoamDecay(d_model=0.01,
                                                        warmup_steps=100,
                                                        verbose=True),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.NoamDecay(d_model=0.01,
                                                        warmup_steps=100))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerLambdaDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.LambdaDecay(
                learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.LambdaDecay(
                learning_rate=0.5, lr_lambda=lambda epoch: 0.9**epoch))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerLinearWarmup(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.LinearWarmup(learning_rate=0.5,
                                                           warmup_steps=20,
                                                           start_lr=0,
                                                           end_lr=0.5),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.LinearWarmup(learning_rate=0.5,
                                                           warmup_steps=20,
                                                           start_lr=0,
                                                           end_lr=0.5,
                                                           verbose=True))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerMultiStepDecay(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.MultiStepDecay(
                learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.MultiStepDecay(
                learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerStepLR(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.StepDecay(learning_rate=0.5,
                                                        step_size=5,
                                                        gamma=0.8),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.StepDecay(
                learning_rate=0.5, step_size=5, gamma=0.8))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestImperativeOptimizerReduceOnPlateau(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
                learning_rate=0.5),
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.SGD(
            learning_rate=paddle.optimizer.lr.ReduceOnPlateau(
                learning_rate=0.5))
        return optimizer

    def func_test_sgd(self):
        self._check_mlp()

    def test_sgd(self):
        with _test_eager_guard():
            self.func_test_sgd()
        self.func_test_sgd()


class TestOptimizerLearningRate(unittest.TestCase):

    def func_test_constant_lr(self):
        with fluid.dygraph.guard():
            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

            linear = fluid.dygraph.nn.Linear(10, 10)

            a = fluid.dygraph.to_variable(a)

            b = linear(a)

            loss = fluid.layers.reduce_mean(b)

            adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())

            np.testing.assert_allclose(adam.get_lr(),
                                       0.001,
                                       rtol=1e-06,
                                       atol=0.0)

            for i in range(10):
                adam.minimize(loss)
                lr = adam.get_lr()

                np.testing.assert_allclose(lr, 0.001, rtol=1e-06, atol=0.0)

    def test_constant_lr(self):
        with _test_eager_guard():
            self.func_test_constant_lr()
        self.func_test_constant_lr()

    def func_test_lr_decay(self):
        with fluid.dygraph.guard():
            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

            linear = fluid.dygraph.nn.Linear(10, 10)

            a = fluid.dygraph.to_variable(a)

            b = linear(a)

            loss = fluid.layers.reduce_mean(b)

            bd = [2, 4, 6, 8]
            value = [0.2, 0.4, 0.6, 0.8, 1.0]

            scheduler = paddle.optimizer.lr.PiecewiseDecay(bd, value)
            adam = paddle.optimizer.Adam(scheduler,
                                         parameters=linear.parameters())

            np.testing.assert_allclose(adam.get_lr(), 0.2, rtol=1e-06, atol=0.0)

            ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
            for i in range(12):
                adam.minimize(loss)
                lr = adam.get_lr()
                np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
                scheduler.step()

    def test_lr_decay(self):
        with _test_eager_guard():
            self.func_test_lr_decay()
        self.func_test_lr_decay()

    def func_test_lr_scheduler_natural_exp(self):
        with fluid.dygraph.guard():
            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

            linear = fluid.dygraph.nn.Linear(10, 10)
            a = fluid.dygraph.to_variable(a)
            b = linear(a)

            loss = fluid.layers.reduce_mean(b)
            base_lr = 1.0

            scheduler = paddle.optimizer.lr.NaturalExpDecay(1.0, gamma=0.5)
            adam = paddle.optimizer.Adam(scheduler,
                                         parameters=linear.parameters())

            np.testing.assert_allclose(adam.get_lr(), 1.0, rtol=1e-06, atol=0.0)

            ret = [1.0, np.exp(-0.5), np.exp(-1)]
            for i in range(3):
                adam.minimize(loss)
                lr = adam.get_lr()
                np.testing.assert_allclose(lr, ret[i], rtol=1e-06, atol=0.0)
                scheduler.step()

    def test_lr_scheduler_natural_exp(self):
        with _test_eager_guard():
            self.func_test_lr_scheduler_natural_exp()
        self.func_test_lr_scheduler_natural_exp()

    def func_test_set_lr(self):
        with fluid.dygraph.guard():
            a = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")

            linear = fluid.dygraph.nn.Linear(10, 10)

            a = fluid.dygraph.to_variable(a)

            b = linear(a)

            loss = fluid.layers.reduce_mean(b)

            adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())

            lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
            for i in range(5):
                adam.set_lr(lr_list[i])
                adam.minimize(loss)
                lr = adam.get_lr()
                np.testing.assert_allclose(lr, lr_list[i], rtol=1e-06, atol=0.0)

            with self.assertRaises(TypeError):
                lr_var = fluid.layers.create_global_var(shape=[1],
                                                        value=0.7,
                                                        dtype='float32')
                adam.set_lr(lr_var)

            with self.assertRaises(RuntimeError):
                adam = paddle.optimizer.Adam(
                    paddle.optimizer.lr.NaturalExpDecay(learning_rate=0.1,
                                                        gamma=0.5),
                    parameters=linear.parameters())
                adam.set_lr(0.01)

    def test_set_lr(self):
        with _test_eager_guard():
            self.func_test_set_lr()
        self.func_test_set_lr()


class TestImperativeMomentumOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = MomentumOptimizer(learning_rate=0.001,
                                      momentum=0.9,
                                      parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
        return optimizer

    def func_test_momentum(self):
        self._check_mlp()

    def test_momentum(self):
        with _test_eager_guard():
            self.func_test_momentum()
        self.func_test_momentum()


class TestImperativeLarsMomentumOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = LarsMomentumOptimizer(learning_rate=0.001,
                                          momentum=0.9,
                                          parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = LarsMomentumOptimizer(learning_rate=0.001, momentum=0.9)
        return optimizer

    def func_test_larsmomentum(self):
        self._check_mlp()

    def test_larsmomentum(self):
        with _test_eager_guard():
            self.func_test_larsmomentum()
        self.func_test_larsmomentum()


class TestImperativeAdagradOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = AdagradOptimizer(learning_rate=0.2,
                                     parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = AdagradOptimizer(learning_rate=0.2)
        return optimizer

    def func_test_adagrad(self):
        self._check_mlp()

    def test_adagrad(self):
        with _test_eager_guard():
            self.func_test_adagrad()
        self.func_test_adagrad()


class TestImperativeAdamaxOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = AdamaxOptimizer(learning_rate=0.2,
                                    parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = AdamaxOptimizer(learning_rate=0.2)
        return optimizer

    def func_test_adamax(self):
        self._check_mlp()

    def test_adamax(self):
        with _test_eager_guard():
            self.func_test_adamax()
        self.func_test_adamax()


class TestImperativeDpsgdOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = DpsgdOptimizer(learning_rate=0.01,
                                   clip=10.0,
                                   batch_size=16.0,
                                   sigma=1.0,
                                   parameter_list=parameter_list)
        optimizer._seed = 100
        return optimizer

    def get_optimizer(self):
        optimizer = DpsgdOptimizer(learning_rate=0.01,
                                   clip=10.0,
                                   batch_size=16.0,
                                   sigma=1.0)
        optimizer._seed = 100
        return optimizer

    def func_test_dpsgd(self):
        self._check_mlp(place=fluid.CPUPlace())

    def test_dpsgd(self):
        with _test_eager_guard():
            self.func_test_dpsgd()
        self.func_test_dpsgd()


class TestImperativeDecayedAdagradOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = DecayedAdagradOptimizer(learning_rate=0.2,
                                            parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = DecayedAdagradOptimizer(learning_rate=0.2)
        return optimizer

    def func_test_decayadagrad(self):
        self._check_mlp()

    def test_decayadagrad(self):
        with _test_eager_guard():
            self.func_test_decayadagrad()
        self.func_test_decayadagrad()


class TestImperativeAdadeltaOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = AdadeltaOptimizer(learning_rate=0.0003,
                                      epsilon=1.0e-6,
                                      rho=0.95,
                                      parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = AdadeltaOptimizer(learning_rate=0.0003,
                                      epsilon=1.0e-6,
                                      rho=0.95)
        return optimizer

    def func_test_adadelta(self):
        self._check_mlp()

    def test_adadelta(self):
        with _test_eager_guard():
            self.func_test_adadelta()
        self.func_test_adadelta()


class TestImperativeRMSPropOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = RMSPropOptimizer(learning_rate=0.1,
                                     parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = RMSPropOptimizer(learning_rate=0.1)
        return optimizer

    def func_test_rmsprop(self):
        self._check_mlp()

    def test_rmsprop(self):
        with _test_eager_guard():
            self.func_test_rmsprop()
        self.func_test_rmsprop()


class TestImperativeFtrlOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = FtrlOptimizer(learning_rate=0.1,
                                  parameter_list=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = FtrlOptimizer(learning_rate=0.1)
        return optimizer

    def func_test_ftrl(self):
        self._check_mlp()

    def test_ftrl(self):
        with _test_eager_guard():
            self.func_test_ftrl()
        self.func_test_ftrl()


def exclude_fn(param):
    return param.name.endswith('.b_0')


class TestImperativeLambOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.Lamb(
            learning_rate=0.002,
            exclude_from_weight_decay_fn=exclude_fn,
            parameters=parameter_list)
        return optimizer

    def get_optimizer(self):
        optimizer = paddle.optimizer.Lamb(
            learning_rate=0.002, exclude_from_weight_decay_fn=exclude_fn)
        return optimizer

    # should fix: may fail in CI-windows
    def _test_lamb(self):
        self._check_mlp()


class TestImperativeModelAverage(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = ModelAverage(0.15,
                                 min_average_window=10000,
                                 max_average_window=12500)
        return optimizer

    def func_test_modelaverage(self):
        exception_message = "In dygraph, don't support ModelAverage."
        self._check_exception(exception_message)

    def test_modelaverage(self):
        with _test_eager_guard():
            self.func_test_modelaverage()
        self.func_test_modelaverage()


class TestImperativeDGCMomentumOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = DGCMomentumOptimizer(learning_rate=0.0001,
                                         momentum=0.9,
                                         rampup_step=1000,
                                         rampup_begin_step=1252,
                                         sparsity=[0.999, 0.999])
        return optimizer

    def func_test_dgcmomentum(self):
        exception_message = "In dygraph, don't support DGCMomentumOptimizer."
        self._check_exception(exception_message)

    def test_dgcmomentum(self):
        with _test_eager_guard():
            self.func_test_dgcmomentum()
        self.func_test_dgcmomentum()


class TestImperativeExponentialMovingAverage(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = ExponentialMovingAverage(0.999)
        return optimizer

    def func_test_exponentialmoving(self):
        exception_message = "In dygraph, don't support ExponentialMovingAverage."
        self._check_exception(exception_message)

    def test_exponentialmoving(self):
        with _test_eager_guard():
            self.func_test_exponentialmoving()
        self.func_test_exponentialmoving()


class TestImperativePipelineOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(learning_rate=0.5,
                                         parameters=parameter_list)
        optimizer = PipelineOptimizer(optimizer)
        return optimizer

    def func_test_pipline(self):
        exception_message = "In dygraph, don't support PipelineOptimizer."
        self._check_exception(exception_message)

    def test_pipline(self):
        with _test_eager_guard():
            self.func_test_pipline()
        self.func_test_pipline()


class TestImperativeLookaheadOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(learning_rate=0.5,
                                         parameters=parameter_list)
        optimizer = LookaheadOptimizer(optimizer, alpha=0.5, k=5)
        return optimizer

    def func_test_lookahead(self):
        exception_message = "In dygraph, don't support LookaheadOptimizer."
        self._check_exception(exception_message)

    def test_lookahead(self):
        with _test_eager_guard():
            self.func_test_lookahead()
        self.func_test_lookahead()


class TestImperativeRecomputeOptimizer(TestImperativeOptimizerBase):

    def get_optimizer_dygraph(self, parameter_list):
        optimizer = paddle.optimizer.SGD(learning_rate=0.5,
                                         parameters=parameter_list)
        optimizer = RecomputeOptimizer(optimizer)
        return optimizer

    def func_test_recompute(self):
        exception_message = "In dygraph, don't support RecomputeOptimizer."
        self._check_exception(exception_message)

    def test_recompute(self):
        with _test_eager_guard():
            self.func_test_recompute()
        self.func_test_recompute()


class TestImperativeOptimizerList(unittest.TestCase):

    def func_test_parameter_list(self):
        with fluid.dygraph.guard():
            linear_1 = Linear(10, 10)
            linear_2 = Linear(10, 10)

            sgd = paddle.optimizer.SGD(1.0,
                                       parameters=itertools.chain(
                                           linear_1.parameters(),
                                           linear_2.parameters()))

            in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
            in_data = fluid.dygraph.to_variable(in_np)

            y = linear_1(in_data)
            y = linear_2(y)
            loss = fluid.layers.reduce_mean(y)
            loss.backward()
            sgd.minimize(loss)

            self.assertTrue(
                len(sgd._parameter_list) == len(linear_1.parameters() +
                                                linear_2.parameters()))

    def test_parameter_list(self):
        with _test_eager_guard():
            self.func_test_parameter_list()
        self.func_test_parameter_list()


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