#   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
import numpy as np
from eager_op_test import OpTest
from op import Operator

import paddle
from paddle import fluid
from paddle.fluid import core


def calculate_momentum_by_numpy(
    param,
    grad,
    mu,
    velocity,
    use_nesterov,
    learning_rate,
    regularization_method=None,
    regularization_coeff=1.0,
):
    if regularization_method == "l2_decay":
        grad = grad + regularization_coeff * param

        velocity_out = mu * velocity + grad
        if use_nesterov:
            param_out = param - (grad + velocity_out * mu) * learning_rate
        else:
            param_out = param - learning_rate * velocity_out
    else:
        velocity_out = mu * velocity + grad
        if use_nesterov:
            param_out = (
                param - grad * learning_rate - velocity_out * mu * learning_rate
            )
        else:
            param_out = param - learning_rate * velocity_out

    return param_out, velocity_out


def momentum_wrapper(
    param,
    grad,
    velocity,
    learning_rate=1.0,
    master_param=None,
    mu=0.0,
    use_nesterov=False,
    regularization_method="",
    regularization_coeff=0.0,
    multi_precision=False,
    rescale_grad=1.0,
):
    return paddle._C_ops.momentum_(
        param,
        grad,
        velocity,
        learning_rate,
        master_param,
        mu,
        use_nesterov,
        regularization_method,
        regularization_coeff,
        multi_precision,
        rescale_grad,
    )


class TestMomentumOp1(OpTest):
    def setUp(self):
        self.op_type = "momentum"
        self.python_api = momentum_wrapper
        self.dtype = np.float32
        self.init_dtype()

        param = np.random.random((123, 321)).astype(self.dtype)
        grad = np.random.random((123, 321)).astype(self.dtype)
        velocity = np.zeros((123, 321)).astype(self.dtype)
        learning_rate = np.array([0.001]).astype(np.float32)
        mu = 0.0001
        use_nesterov = False

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Velocity': velocity,
            'LearningRate': learning_rate,
        }

        self.attrs = {'mu': mu}

        param_out, velocity_out = calculate_momentum_by_numpy(
            param=param,
            grad=grad,
            mu=mu,
            velocity=velocity,
            use_nesterov=use_nesterov,
            learning_rate=learning_rate,
        )

        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}

    def init_dtype(self):
        pass

    def test_check_output(self):
        self.check_output()


class TestMomentumOpFp16(TestMomentumOp1):
    def init_dtype(self):
        self.dtype = np.float16

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestMomentumOp2(OpTest):
    '''Test Momentum with default values for attributes'''

    def setUp(self):
        self.op_type = "momentum"
        self.python_api = momentum_wrapper

        param = np.random.random((123, 321)).astype("float32")
        grad = np.random.random((123, 321)).astype("float32")
        velocity = np.zeros((123, 321)).astype("float32")
        learning_rate = np.array([0.001]).astype("float32")
        mu = 0.0001
        use_nesterov = True

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Velocity': velocity,
            'LearningRate': learning_rate,
        }

        self.attrs = {'mu': mu, 'use_nesterov': use_nesterov}

        param_out, velocity_out = calculate_momentum_by_numpy(
            param=param,
            grad=grad,
            mu=mu,
            velocity=velocity,
            use_nesterov=use_nesterov,
            learning_rate=learning_rate,
        )

        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}

    def test_check_output(self):
        self.check_output()


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestLarsMomentumOpWithMP(OpTest):
    def setUp(self):
        self.config()
        self.op_type = "lars_momentum"
        mu = 0.0001
        lars_coeff = 0.001
        lars_weight_decay = 0.0005
        rescale_grad = 1.0

        params = []
        grads = []
        velocitys = []
        learning_rates = []
        master_params = []
        param_outs = []
        velocity_outs = []
        master_param_outs = []
        for i in range(self.params_num):
            master_param = np.random.random((123, 321)).astype("float32")
            param = master_param.astype("float16")
            grad = np.random.random((123, 321)).astype("float16")
            velocity = np.zeros((123, 321)).astype("float32")
            learning_rate = np.array([0.001]).astype("float32")

            fp32_grad = grad.astype("float32")
            pnorm = np.sqrt(np.square(master_param).sum())
            gnorm = np.sqrt(np.square(fp32_grad).sum())
            local_lr = (
                learning_rate
                * lars_coeff
                * pnorm
                / (gnorm + lars_weight_decay * pnorm)
            )
            fp32_grad = fp32_grad * rescale_grad
            velocity_out = mu * velocity + local_lr * (
                fp32_grad + lars_weight_decay * master_param
            )
            p_new = master_param - velocity_out
            param_out = p_new.astype("float16")
            master_param_out = p_new

            params.append(("SubParam_" + str(i), param))
            grads.append(("SubGrad_" + str(i), grad))
            velocitys.append(("SubVelocity_" + str(i), velocity))
            learning_rates.append(("SubLearning_rate_" + str(i), learning_rate))
            velocity_outs.append(("SubVelocity_out_" + str(i), velocity_out))
            param_outs.append(("SubParam_out_" + str(i), param_out))
            master_params.append(("SubMasterParam_" + str(i), master_param))
            master_param_outs.append(
                ("SubMasterParamOut_" + str(i), master_param_out)
            )

        self.inputs = {
            'Param': params,
            'Grad': grads,
            'Velocity': velocitys,
            'LearningRate': learning_rates,
            'MasterParam': master_params,
        }

        self.attrs = {
            'mu': mu,
            'lars_coeff': lars_coeff,
            'lars_weight_decay': [lars_weight_decay],
            'multi_precision': True,
            'rescale_grad': rescale_grad,
        }

        self.outputs = {
            'ParamOut': param_outs,
            'VelocityOut': velocity_outs,
            'MasterParamOut': master_param_outs,
        }

    def test_check_output(self):
        paddle.enable_static()
        if core.is_compiled_with_cuda():
            place = fluid.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, check_dygraph=False)

    def config(self):
        self.params_num = 1


class TestLarsMomentumOp(OpTest):
    def setUp(self):
        self.config()
        self.op_type = "lars_momentum"
        mu = 0.0001
        lars_coeff = 0.001
        lars_weight_decay = 0.0005

        params = []
        grads = []
        velocitys = []
        param_outs = []
        velocity_outs = []
        learning_rates = []
        for i in range(self.params_num):
            param = np.random.random((123, 321)).astype("float32")
            grad = np.random.random((123, 321)).astype("float32")
            velocity = np.zeros((123, 321)).astype("float32")
            learning_rate = np.array([0.001]).astype("float32")
            pnorm = np.sqrt(np.square(param).sum())
            gnorm = np.sqrt(np.square(grad).sum())
            local_lr = (
                learning_rate
                * lars_coeff
                * pnorm
                / (gnorm + lars_weight_decay * param)
            )
            velocity_out = mu * velocity + local_lr * (
                grad + lars_weight_decay * param
            )
            param_out = param - velocity_out

            params.append(("SubParam_" + str(i), param))
            grads.append(("SubGrad_" + str(i), grad))
            velocitys.append(("SubVelocity_" + str(i), velocity))
            learning_rates.append(("SubLearning_rate_" + str(i), learning_rate))
            velocity_outs.append(("SubVelocity_out_" + str(i), velocity_out))
            param_outs.append(("SubParam_out_" + str(i), param_out))

        self.inputs = {
            'Param': params,
            'Grad': grads,
            'Velocity': velocitys,
            'LearningRate': learning_rates,
        }

        self.attrs = {
            'mu': mu,
            'lars_coeff': lars_coeff,
            'lars_weight_decay': [lars_weight_decay],
        }
        self.outputs = {'ParamOut': param_outs, 'VelocityOut': velocity_outs}

    def test_check_output(self):
        paddle.enable_static()
        self.check_output(check_dygraph=False)

    def config(self):
        self.params_num = 1


class TestSparseMomentumOp(unittest.TestCase):
    def setUp(self):
        self.use_nesterov = False
        self.regularization_method = ""
        self.regularization_coeff = 1.0

    def check_with_place(self, place):
        self.init_kernel()
        scope = core.Scope()
        # create and initialize Grad Variable
        height = 10
        rows = [0, 4, 7]
        row_numel = 12
        mu = 1.0
        use_nesterov = self.use_nesterov
        regularization_method = self.regularization_method
        regularization_coeff = self.regularization_coeff

        # create and initialize Param Variable
        param = scope.var('Param').get_tensor()
        param_array = np.full((height, row_numel), 5.0).astype("float32")
        param.set(param_array, place)
        param_out = scope.var("ParamOut").get_tensor()
        param_out_array = np.full((height, row_numel), 0.0).astype("float32")
        param_out.set(param_out_array, place)

        grad_selected_rows = scope.var('Grad').get_selected_rows()
        grad_selected_rows.set_height(height)
        grad_selected_rows.set_rows(rows)
        grad_np_array = np.ones((len(rows), row_numel)).astype("float32")
        grad_np_array[0, 0] = 2.0
        grad_np_array[2, 8] = 4.0
        grad_tensor = grad_selected_rows.get_tensor()
        grad_tensor.set(grad_np_array, place)

        velocity = scope.var('Velocity').get_tensor()
        velocity_np_array = np.ones((height, row_numel)).astype("float32")
        velocity.set(velocity_np_array, place)
        velocity_out = scope.var('VelocityOut').get_tensor()
        velocity_out_np_array = np.full((height, row_numel), 0.0).astype(
            "float32"
        )
        velocity_out.set(velocity_out_np_array, place)

        # create and initialize LearningRate Variable
        lr = scope.var('LearningRate').get_tensor()
        lr_array = np.full((1), 2.0).astype("float32")
        lr.set(lr_array, place)

        # create and run operator
        op = Operator(
            "momentum",
            Param='Param',
            Grad='Grad',
            Velocity='Velocity',
            ParamOut='ParamOut',
            VelocityOut='VelocityOut',
            LearningRate='LearningRate',
            mu=mu,
            use_nesterov=use_nesterov,
            regularization_method=regularization_method,
            regularization_coeff=regularization_coeff,
        )
        op.run(scope, place)

        # get and compare result
        param_out_np_array = np.array(param_out)
        velocity_out_np_array = np.array(velocity_out)

        # TODO(dzh): add a more suitable general numpy interface
        # for sparse update.
        _grad_np_array = np.full((height, row_numel), 0.0).astype("float32")
        for i in range(len(rows)):
            _grad_np_array[rows[i]] = grad_np_array[i]

        _param = param_array

        _param_out, _velocity_out = calculate_momentum_by_numpy(
            param=_param,
            grad=_grad_np_array,
            mu=mu,
            velocity=velocity_np_array,
            use_nesterov=use_nesterov,
            learning_rate=lr_array,
            regularization_method=regularization_method,
            regularization_coeff=regularization_coeff,
        )

        self.assertTrue((_velocity_out == velocity_out_np_array).all())
        self.assertTrue((_param_out == param_out_np_array).all())

    def init_kernel(self):
        pass

    def test_sparse_momentum(self):
        places = [core.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))
        for place in places:
            self.check_with_place(place)


class TestSparseMomentumOp2(TestSparseMomentumOp):
    def init_kernel(self):
        self.use_nesterov = True


class TestSparseMomentumOpWithMultiPrecision(unittest.TestCase):
    def setUp(self):
        self.init_args()
        self.regularization_method = ""
        self.regularization_coeff = 1.0

    def check_with_place(self, place):
        scope = core.Scope()
        # create and initialize Grad Variable
        height = 10
        rows = [0, 4, 7]
        row_numel = 12
        mu = 1.0
        use_nesterov = self.use_nesterov
        regularization_method = self.regularization_method
        regularization_coeff = self.regularization_coeff

        # create and initialize Param Variable
        param_array = np.full((height, row_numel), 5.0).astype("float32")
        param_out_array = np.full((height, row_numel), 0.0).astype("float32")

        param = scope.var('Param').get_tensor()
        param.set(param_array.astype("float16"), place)
        param_out = scope.var("ParamOut").get_tensor()
        param_out.set(param_out_array.astype("float16"), place)

        master_param = scope.var('MasterParam').get_tensor()
        master_param.set(param_array, place)
        master_param_out = scope.var("MasterParamOut").get_tensor()
        master_param_out.set(param_out_array, place)

        grad_selected_rows = scope.var('Grad').get_selected_rows()
        grad_selected_rows.set_height(height)
        grad_selected_rows.set_rows(rows)
        grad_np_array = np.ones((len(rows), row_numel)).astype("float32")
        grad_np_array[0, 0] = 2.0
        grad_np_array[2, 8] = 4.0
        grad_tensor = grad_selected_rows.get_tensor()
        grad_tensor.set(grad_np_array.astype("float16"), place)

        velocity = scope.var('Velocity').get_tensor()
        velocity_np_array = np.ones((height, row_numel)).astype("float32")
        velocity.set(velocity_np_array, place)
        velocity_out = scope.var('VelocityOut').get_tensor()
        velocity_out_np_array = np.full((height, row_numel), 0.0).astype(
            "float32"
        )
        velocity_out.set(velocity_out_np_array, place)

        # create and initialize LearningRate Variable
        lr = scope.var('LearningRate').get_tensor()
        lr_array = np.full((1), 2.0).astype("float32")
        lr.set(lr_array, place)

        # create and run operator
        op = Operator(
            "momentum",
            Param='Param',
            Grad='Grad',
            Velocity='Velocity',
            MasterParam='MasterParam',
            ParamOut='ParamOut',
            VelocityOut='VelocityOut',
            MasterParamOut='MasterParamOut',
            LearningRate='LearningRate',
            mu=mu,
            use_nesterov=use_nesterov,
            regularization_method=regularization_method,
            regularization_coeff=regularization_coeff,
            multi_precision=True,
            rescale_grad=1.0,
        )
        op.run(scope, place)

        # get and compare result
        param_out_np_array = np.array(param_out)
        velocity_out_np_array = np.array(velocity_out)

        _grad_np_array = np.full((height, row_numel), 0.0).astype("float32")
        for i in range(len(rows)):
            _grad_np_array[rows[i]] = grad_np_array[i]

        _param = param_array

        _param_out, _velocity_out = calculate_momentum_by_numpy(
            param=_param,
            grad=_grad_np_array,
            mu=mu,
            velocity=velocity_np_array,
            use_nesterov=use_nesterov,
            learning_rate=lr_array,
            regularization_method=regularization_method,
            regularization_coeff=regularization_coeff,
        )

        self.assertTrue((_velocity_out == velocity_out_np_array).all())
        self.assertTrue((_param_out == param_out_np_array).all())

    def init_args(self):
        self.use_nesterov = False

    def test_sparse_momentum(self):
        if core.is_compiled_with_cuda():
            self.check_with_place(fluid.CUDAPlace(0))


class TestSparseMomentumOpWithMultiPrecision2(
    TestSparseMomentumOpWithMultiPrecision
):
    def init_args(self):
        self.use_nesterov = True


class TestMomentumV2(unittest.TestCase):
    def test_momentum_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear = paddle.nn.Linear(13, 5)
        # This can be any optimizer supported by dygraph.
        adam = paddle.optimizer.Momentum(
            learning_rate=0.01, momentum=0.9, parameters=linear.parameters()
        )
        out = linear(a)
        out.backward()
        adam.step()
        adam.clear_gradients()

    def test_momentum(self):
        paddle.enable_static()
        place = fluid.CPUPlace()
        main = fluid.Program()
        with fluid.program_guard(main):
            x = paddle.static.data(name='x', shape=[-1, 13], dtype='float32')
            y = paddle.static.data(name='y', shape=[-1, 1], dtype='float32')
            y_predict = paddle.static.nn.fc(x, size=1, activation=None)
            cost = paddle.nn.functional.square_error_cost(
                input=y_predict, label=y
            )
            avg_cost = paddle.mean(cost)

            rms_optimizer = paddle.optimizer.Momentum(
                learning_rate=0.1, momentum=0.9
            )
            rms_optimizer.minimize(avg_cost)

            fetch_list = [avg_cost]
            train_reader = paddle.batch(
                paddle.dataset.uci_housing.train(), batch_size=1
            )
            feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            for data in train_reader():
                exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)

    def test_raise_error(self):
        self.assertRaises(
            ValueError, paddle.optimizer.Momentum, learning_rate=None
        )
        self.assertRaises(ValueError, paddle.optimizer.Momentum, momentum=None)


class TestMomentumOpWithDecay(OpTest):
    def setUp(self):
        self.op_type = "momentum"
        self.python_api = momentum_wrapper
        self.dtype = np.float32
        self.use_nesterov = True
        self.regularization_method = 'l2_decay'
        self.regularization_coeff = 0.9
        self.init_config()

        param = np.random.random((123, 321)).astype(self.dtype)
        grad = np.random.random((123, 321)).astype(self.dtype)
        velocity = np.zeros((123, 321)).astype(self.dtype)
        learning_rate = np.array([0.001]).astype(np.float32)
        mu = 0.0001
        use_nesterov = self.use_nesterov
        regularization_method = self.regularization_method
        regularization_coeff = self.regularization_coeff

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Velocity': velocity,
            'LearningRate': learning_rate,
        }

        self.attrs = {
            'mu': mu,
            'use_nesterov': use_nesterov,
            'regularization_method': regularization_method,
            'regularization_coeff': regularization_coeff,
        }

        grad = grad + regularization_coeff * param

        param_out, velocity_out = calculate_momentum_by_numpy(
            param=param,
            grad=grad,
            mu=mu,
            velocity=velocity,
            use_nesterov=use_nesterov,
            learning_rate=learning_rate,
        )

        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}

    def init_config(self):
        pass

    def test_check_output(self):
        paddle.enable_static()
        self.check_output()


class TestMomentumOpWithDecayFP16(TestMomentumOpWithDecay):
    def init_config(self):
        self.dtype = np.float16

    def test_check_output(self):
        paddle.enable_static()
        self.check_output(atol=1e-3)


class TestMomentumOpWithDecay2(TestMomentumOpWithDecay):
    def init_config(self):
        self.use_nesterov = False


class TestSparseMomentumOpWithDecay(TestSparseMomentumOp):
    def setUp(self):
        self.use_nesterov = False
        self.regularization_method = 'l2_decay'
        self.regularization_coeff = 0.9


class TestSparseMomentumOpWithDecay2(TestSparseMomentumOpWithDecay):
    def init_kernel(self):
        self.use_nesterov = True


class TestMomentumOpWithDecayAPI(unittest.TestCase):
    def _test_momentum_dygraph_common(self, regularization):
        paddle.disable_static()
        inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
        linear = paddle.nn.Linear(10, 10)
        inp = paddle.to_tensor(inp)
        out = linear(inp)
        loss = paddle.mean(out)
        # This can be any optimizer supported by dygraph.
        momentum = paddle.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameters=linear.parameters(),
            weight_decay=regularization,
        )
        momentum.minimize(loss)

    def test_momentum_dygraph_1(self):
        self._test_momentum_dygraph_common(
            regularization=paddle.regularizer.L2Decay(coeff=0.1)
        )

    def test_momentum_static(self):
        paddle.enable_static()
        place = fluid.CPUPlace()
        main = fluid.Program()
        with fluid.program_guard(main):
            x = paddle.static.data(name='x', shape=[-1, 13], dtype='float32')
            y = paddle.static.data(name='y', shape=[-1, 1], dtype='float32')
            y_predict = paddle.static.nn.fc(x, size=1, activation=None)
            cost = paddle.nn.functional.square_error_cost(
                input=y_predict, label=y
            )
            avg_cost = paddle.mean(cost)

            momentum_optimizer = paddle.optimizer.Momentum(
                learning_rate=0.1, momentum=0.9
            )
            momentum_optimizer.minimize(avg_cost)

            fetch_list = [avg_cost]
            train_reader = paddle.batch(
                paddle.dataset.uci_housing.train(), batch_size=1
            )
            feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            for data in train_reader():
                exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)


class TestFusedMomentumWithDecayAPI(unittest.TestCase):
    def get_program(self, weight_attr, bias_attr=False):
        main_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        with paddle.static.program_guard(
            main_program=main_program, startup_program=startup_program
        ):
            x = paddle.static.data(name='x', shape=[10, 10])
            linear = paddle.nn.Linear(
                10, 10, weight_attr=weight_attr, bias_attr=bias_attr
            )
            out = linear(x)
            loss = paddle.mean(out)
            optimizer = paddle.optimizer.Momentum(
                learning_rate=0.01,
                momentum=0.9,
                weight_decay=paddle.regularizer.L2Decay(0.5),
            )
            optimizer.minimize(loss)
        return main_program

    def test_param_has_l2decay(self):
        paddle.enable_static()
        weight_attr = paddle.ParamAttr(
            name="weight",
            initializer=paddle.nn.initializer.Constant(value=0.5),
            regularizer=paddle.regularizer.L2Decay(0.1),
        )
        program = self.get_program(weight_attr, bias_attr=False)
        ops = program.global_block().ops

        self.assertEqual(ops[-1].attr('regularization_method'), 'l2_decay')
        self.assertEqual(ops[-1].attr('regularization_coeff'), np.float32(0.1))
        for i in range(len(ops)):
            self.assertTrue('sum' not in ops[i].type)
            self.assertTrue('scale' not in ops[i].type)

    def test_param_has_l1decay(self):
        paddle.enable_static()
        weight_attr = paddle.ParamAttr(
            name="weight",
            initializer=paddle.nn.initializer.Constant(value=0.5),
            regularizer=paddle.regularizer.L1Decay(0.1),
        )
        bias_attr = paddle.ParamAttr(
            name="bias",
            initializer=paddle.nn.initializer.Constant(value=0.0),
            regularizer=None,
        )
        program = self.get_program(weight_attr, bias_attr)
        ops = program.global_block().ops

        self.assertEqual(ops[-1].type, 'momentum')
        self.assertEqual(ops[-2].type, 'momentum')
        self.assertEqual(ops[-3].type, 'sum')
        self.assertEqual(ops[-4].type, 'scale')
        self.assertEqual(ops[-5].type, 'sign')
        self.assertEqual(ops[-6].type, 'matmul_v2_grad')
        if 'weight' in ops[-1].input('Param'):
            self.assertEqual(ops[-1].attr('regularization_method'), '')
            self.assertEqual(ops[-1].attr('regularization_coeff'), 0)
        if 'bias' in ops[-2].input('Param'):
            self.assertEqual(ops[-2].attr('regularization_method'), 'l2_decay')
            self.assertEqual(
                ops[-2].attr('regularization_coeff'), np.float32(0.5)
            )

    def test_param_has_no_regularizer(self):
        paddle.enable_static()
        program = self.get_program(weight_attr=None)
        ops = program.global_block().ops
        self.assertEqual(ops[-1].attr('regularization_method'), 'l2_decay')
        self.assertEqual(ops[-1].attr('regularization_coeff'), np.float32(0.5))
        for i in range(len(ops)):
            self.assertTrue('sum' not in ops[i].type)
            self.assertTrue('scale' not in ops[i].type)


class TestMomentumOpVsMomentumOpWithDecayAPI(unittest.TestCase):
    def __update_params(self, momentum, linear):
        for i in range(10):
            inp = paddle.full(
                shape=[2, 2], fill_value=i, dtype='float32'
            ).astype("float32")
            inp = paddle.to_tensor(inp)
            out = linear(inp)
            loss = paddle.mean(out)
            loss.backward()
            momentum.minimize(loss)
            linear.clear_gradients()

    def __test_vs(self, place=fluid.CPUPlace()):
        paddle.disable_static(place=place)

        linear_old = paddle.nn.Linear(
            2,
            2,
            weight_attr=paddle.nn.initializer.Constant(value=2.0),
            bias_attr=paddle.nn.initializer.Constant(value=2.0),
        )
        momentum_old = paddle.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameters=linear_old.parameters(),
            weight_decay=paddle.regularizer.L2Decay(coeff=0.1),
        )
        self.__update_params(momentum=momentum_old, linear=linear_old)

        linear_new = paddle.nn.Linear(
            2,
            2,
            weight_attr=paddle.nn.initializer.Constant(value=2.0),
            bias_attr=paddle.nn.initializer.Constant(value=2.0),
        )
        momentum_new = paddle.optimizer.Momentum(
            learning_rate=0.01,
            momentum=0.9,
            parameters=linear_new.parameters(),
            weight_decay=paddle.regularizer.L2Decay(coeff=0.1),
        )
        self.__update_params(momentum=momentum_new, linear=linear_new)

        self.assertEqual(
            (linear_old.weight.numpy() == linear_new.weight.numpy()).all(),
            True,
            'the param weight updated by two Momentum optimizers should equal',
        )

    def test_vs(self, place=fluid.CPUPlace()):
        places = [fluid.CPUPlace()]
        if paddle.fluid.core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))

        for place in places:
            self.__test_vs(place=place)


class TestMomentumV2Group(TestMomentumV2):
    def test_momentum_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear_1 = paddle.nn.Linear(13, 5)
        linear_2 = paddle.nn.Linear(5, 3)
        # This can be any optimizer supported by dygraph.
        adam = paddle.optimizer.Momentum(
            learning_rate=0.01,
            parameters=[
                {'params': linear_1.parameters()},
                {
                    'params': linear_2.parameters(),
                    'weight_decay': 0.001,
                    'learning_rate': 0.1,
                    'momentum': 0.99,
                },
            ],
            weight_decay=0.1,
            momentum=0.9,
        )
        out = linear_1(a)
        out = linear_2(out)
        out.backward()
        adam.step()
        adam.clear_gradients()


class TestMultiTensorMomentumDygraph(unittest.TestCase):
    def _momentum_optimize_dygraph(
        self,
        place,
        use_param_attr=False,
        use_param_group=False,
        use_amp=False,
        use_multi_tensor=False,
    ):
        paddle.disable_static()
        paddle.seed(10)
        paddle.set_device(place)
        input = paddle.randn((5, 5))
        weight_attr = paddle.ParamAttr(
            learning_rate=0.5,
            regularizer=paddle.regularizer.L2Decay(1.0),
            trainable=True,
        )
        if use_param_attr:
            model = paddle.nn.Linear(5, 5, weight_attr)
        else:
            model = paddle.nn.Linear(5, 5)
        if not use_param_group:
            optimizer = paddle.optimizer.Momentum(
                parameters=model.parameters(),
                use_multi_tensor=use_multi_tensor,
                multi_precision=use_amp,
            )
        else:
            parameters = list(model.parameters())
            n = len(parameters)
            optimizer = paddle.optimizer.Momentum(
                parameters=[
                    {
                        'params': parameters[: int(n / 2)],
                        'weight_decay': 0.001,
                        'learning_rate': 0.1,
                        'momentum': 0.99,
                    },
                    {
                        'params': parameters[int(n / 2) :],
                        'weight_decay': 0.001,
                        'learning_rate': 0.1,
                        'momentum': 0.99,
                    },
                ],
                use_multi_tensor=use_multi_tensor,
                multi_precision=use_amp,
            )
        for idx in range(5):
            if place == 'gpu' and use_amp:
                model = paddle.amp.decorate(models=model, level='O2')
                scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
            if place == 'gpu' and use_amp:
                with paddle.amp.auto_cast(level='O2'):
                    output = model(input)
                    loss = paddle.mean(output)
                scaled = scaler.scale(loss)
                scaled.backward()
                scaler.step(optimizer)
                optimizer.clear_grad(set_to_zero=False)
            else:
                output = model(input)
                loss = paddle.mean(output)
                # This can be any optimizer supported by dygraph.
                loss.backward()
                optimizer.step()
                optimizer.clear_grad(set_to_zero=False)
        return output, model.parameters()

    def _get_places(self):
        places = ['cpu']
        if paddle.is_compiled_with_cuda():
            places.append('gpu')
        return places

    def _check_with_place_amp(self, place, use_amp):
        output1, params1 = self._momentum_optimize_dygraph(
            place=place, use_amp=use_amp, use_multi_tensor=True
        )
        output2, params2 = self._momentum_optimize_dygraph(
            place=place, use_amp=use_amp, use_multi_tensor=False
        )

        np.testing.assert_allclose(output1, output2, rtol=1e-05)
        for idx in range(len(params1)):
            np.testing.assert_allclose(params1[idx], params2[idx], rtol=1e-05)

    def _check_with_param_arrt(self, place, use_amp):
        output1, params1 = self._momentum_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_attr=True,
            use_multi_tensor=True,
        )
        output2, params2 = self._momentum_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_attr=True,
            use_multi_tensor=False,
        )
        np.testing.assert_allclose(output1, output2, rtol=1e-05)
        for idx in range(len(params1)):
            np.testing.assert_allclose(params1[idx], params2[idx], rtol=1e-05)

    def _check_with_param_group(self, place, use_amp):
        output1, params1 = self._momentum_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_group=True,
            use_multi_tensor=True,
        )
        output2, params2 = self._momentum_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_group=True,
            use_multi_tensor=False,
        )
        np.testing.assert_allclose(output1, output2, rtol=1e-05)
        for idx in range(len(params1)):
            np.testing.assert_allclose(params1[idx], params2[idx], rtol=1e-05)

    def test_main(self):
        for place in self._get_places():
            use_amp_list = [True, False]
            for use_amp in use_amp_list:
                self._check_with_place_amp(place, use_amp)
                self._check_with_param_arrt(place, use_amp)
                self._check_with_param_group(place, use_amp)


class TestMultiTensorMomentumStatic(unittest.TestCase):
    def _momentum_optimize_static(
        self, place, use_amp=False, use_multi_tensor=False
    ):
        paddle.enable_static()
        paddle.seed(10)
        np.random.seed(10)
        if place == 'cpu':
            use_amp = False
        exe = paddle.static.Executor(place=place)
        train_program = paddle.static.Program()
        startup_program = paddle.static.Program()
        optimizer = paddle.optimizer.Momentum(
            multi_precision=use_amp, use_multi_tensor=use_multi_tensor
        )
        if use_amp:
            optimizer = paddle.static.amp.decorate(
                optimizer,
                init_loss_scaling=128.0,
                use_dynamic_loss_scaling=True,
                use_pure_fp16=True,
                use_fp16_guard=False,
            )
        with paddle.static.program_guard(train_program, startup_program):
            if use_amp:
                data = paddle.static.data(
                    shape=[2, 2], name='X', dtype='float16'
                )
            else:
                data = paddle.static.data(
                    shape=[2, 2], name='X', dtype='float32'
                )
            hidden = paddle.static.nn.fc(x=data, size=10)
            loss = paddle.mean(hidden)
            optimizer.minimize(loss)
        exe.run(startup_program)
        if use_amp:
            optimizer.amp_init(
                place=paddle.CUDAPlace(0), scope=paddle.static.global_scope()
            )
            x = numpy.random.random(size=(2, 2)).astype('float16')
        else:
            x = numpy.random.random(size=(2, 2)).astype('float32')
        out = []
        for idx in range(5):
            (loss_data,) = exe.run(
                train_program, feed={"X": x}, fetch_list=[loss.name]
            )
            out.append(loss_data)
        return out

    def _get_places(self):
        places = ['cpu']
        if paddle.is_compiled_with_cuda():
            places.append('gpu')
        return places

    def _check_with_place_amp(self, place, use_amp):
        output1 = self._momentum_optimize_static(
            place=place, use_amp=use_amp, use_multi_tensor=True
        )
        output2 = self._momentum_optimize_static(
            place=place, use_amp=use_amp, use_multi_tensor=False
        )
        for idx in range(len(output1)):
            np.testing.assert_allclose(output1[idx], output2[idx], rtol=1e-05)

    def test_main(self):
        for place in self._get_places():
            use_amp_list = [True, False]
            for use_amp in use_amp_list:
                self._check_with_place_amp(place, use_amp)


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