test_adam_op.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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid import core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
import paddle


class TestAdamOp1(OpTest):
    def setUp(self):
        '''Test Adam Op with supplied attributes
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.004
        beta1 = 0.78
        beta2 = 0.836
        epsilon = 1e-4
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32")
        }

        self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, self.attrs)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOp2(OpTest):
    def setUp(self):
        '''Test Adam Op with supplied attributes
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.001
        beta1 = 0.9
        beta2 = 0.999
        epsilon = 1e-8
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32")
        }

        attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, attributes)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOpMultipleSteps(OpTest):
    def setUp(self):
        '''Test Adam Operator with supplied attributes
        '''
        self.op_type = "adam"
        self.num_steps = 10

        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")

        learning_rate = 0.001
        self.beta1 = 0.9
        self.beta2 = 0.999
        epsilon = 1e-8
        self.beta1_pow = self.beta1**10
        self.beta2_pow = self.beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([self.beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([self.beta2_pow]).astype("float32")
        }

        self.attrs = {
            'epsilon': epsilon,
            'beta1': self.beta1,
            'beta2': self.beta2
        }

    def test_check_output(self):
        for _ in range(self.num_steps):
            param_out, moment1_out, \
                moment2_out = adam_step(self.inputs, self.attrs)

            beta1_pow_out = self.inputs['Beta1Pow'] * self.beta1
            beta2_pow_out = self.inputs['Beta2Pow'] * self.beta2
            self.outputs = {
                'Moment1Out': moment1_out,
                'Moment2Out': moment2_out,
                'ParamOut': param_out,
                'Beta1PowOut': beta1_pow_out,
                'Beta2PowOut': beta2_pow_out
            }

            # Verify output for this step
            self.check_output()

            # Output of this step becomes input for next step
            self.inputs['Param'] = param_out
            self.inputs['Moment1'] = moment1_out
            self.inputs['Moment2'] = moment2_out

            # Update powers of Beta1 and Beta2 for next time step
            self.inputs['Beta1Pow'] = beta1_pow_out
            self.inputs['Beta2Pow'] = beta2_pow_out

            # Randomize gradient for next step
            self.inputs['Grad'] = np.random.uniform(
                -1, 1, (102, 105)).astype("float32")


def adam_step(inputs, attributes):
    '''
    Simulate one step of the adam optimizer
    :param inputs: dict of inputs
    :param attributes: dict of attributes
    :return tuple: tuple of output param, moment1, moment2,
    beta1 power accumulator and beta2 power accumulator
    '''
    param = inputs['Param']
    grad = inputs['Grad']
    moment1 = inputs['Moment1']
    moment2 = inputs['Moment2']
    lr = inputs['LearningRate']
    beta1_pow = inputs['Beta1Pow']
    beta2_pow = inputs['Beta2Pow']

    epsilon = attributes['epsilon']

    if 'beta1' in attributes:
        beta1 = attributes['beta1']
    else:
        beta1 = inputs['Beta1Tensor'][0]
    if 'beta2' in attributes:
        beta2 = attributes['beta2']
    else:
        beta2 = inputs['Beta2Tensor'][0]

    moment1_out = beta1 * moment1 + (1 - beta1) * grad
    moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
    lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
    param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
    return param_out, moment1_out, moment2_out


def adamw_step(inputs, attributes):
    '''
    Simulate one step of the adam optimizer
    :param inputs: dict of inputs
    :param attributes: dict of attributes
    :return tuple: tuple of output param, moment1, moment2,
    beta1 power accumulator and beta2 power accumulator
    '''
    param = inputs['Param']
    grad = inputs['Grad']
    moment1 = inputs['Moment1']
    moment2 = inputs['Moment2']
    lr = inputs['LearningRate']
    beta1_pow = inputs['Beta1Pow']
    beta2_pow = inputs['Beta2Pow']

    epsilon = attributes['epsilon']
    coeff = attributes["coeff"]
    if attributes.get("with_decay", False):
        decay = 1.0 - lr * coeff
        param2 = param * decay
        param = param2.copy()
    if 'beta1' in attributes:
        beta1 = attributes['beta1']
    else:
        beta1 = inputs['Beta1Tensor'][0]
    if 'beta2' in attributes:
        beta2 = attributes['beta2']
    else:
        beta2 = inputs['Beta2Tensor'][0]

    moment1_out = beta1 * moment1 + (1 - beta1) * grad
    moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
    lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
    param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))

    return param_out, moment1_out, moment2_out


def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad,
                     lazy_mode):
    '''
    Simulate one step of the adam optimizer
    :param inputs: dict of inputs
    :param attributes: dict of attributes
    :return tuple: tuple of output param, moment1, moment2,
    beta1 power accumulator and beta2 power accumulator
    '''
    param = inputs['Param']
    # grad = inputs['Grad']
    moment1 = inputs['Moment1']
    moment2 = inputs['Moment2']
    lr = inputs['LearningRate']
    beta1_pow = inputs['Beta1Pow']
    beta2_pow = inputs['Beta2Pow']

    beta1 = attributes['beta1']
    beta2 = attributes['beta2']
    epsilon = attributes['epsilon']

    moment1_out = np.zeros(shape=[height, row_numel])
    moment2_out = np.zeros(shape=[height, row_numel])
    param_out = np.zeros(shape=[height, row_numel])

    def update_row(row_id, update_value):
        moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
                                                         ) * update_value
        moment2_out[row_id] = beta2 * moment2[row_id] + (
            1 - beta2) * np.square(update_value)
        lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
        param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
            np.sqrt(moment2_out[row_id]) + epsilon))

    if lazy_mode:
        for idx, row_id in enumerate(rows):
            update_row(row_id, np_grad[idx])
    else:
        for row_id in range(param_out.shape[0]):
            update_value = np.zeros(np_grad[0].shape).astype("float32")
            if row_id in rows:
                update_value = np_grad[rows.index(row_id)]
            update_row(row_id, update_value)

    return param_out, moment1_out, moment2_out


class TestSparseAdamOp(unittest.TestCase):
    def setup(self, scope, place, lazy_mode):
        beta1 = 0.78
        beta2 = 0.836
        epsilon = 1e-4
        beta1_pow = np.array([beta1**10]).astype("float32")
        beta2_pow = np.array([beta2**10]).astype("float32")

        height = 10
        rows = [0, 4, 7]
        self.rows = rows
        row_numel = 12
        self.row_numel = row_numel
        self.dense_inputs = {
            "Param": np.full((height, row_numel), 5.0).astype("float32"),
            "Moment1": np.full((height, row_numel), 5.0).astype("float32"),
            "Moment2": np.full((height, row_numel), 5.0).astype("float32"),
            'Beta1Pow': beta1_pow,
            'Beta2Pow': beta2_pow,
            "LearningRate": np.full((1), 2.0).astype("float32")
        }
        self.init_output = np.full((height, row_numel), 0.0).astype("float32")
        self.attrs = {
            'epsilon': epsilon,
            'beta1': beta1,
            'beta2': beta2,
            'min_row_size_to_use_multithread': 2
        }

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

        grad_tensor = grad_selected_rows.get_tensor()
        grad_tensor.set(np_array, place)

        self.sparse_inputs = ["Grad"]

        param_out, mom1, mom2 = adam_step_sparse(self.dense_inputs, self.attrs,
                                                 height, rows, row_numel,
                                                 np_array, lazy_mode)
        self.outputs = {
            "ParamOut": param_out,
            "Moment1Out": mom1,
            "Moment2Out": mom2,
            'Beta1PowOut': beta1_pow * beta1,
            'Beta2PowOut': beta2_pow * beta2
        }

    def check_with_place(self, place, lazy_mode):
        scope = core.Scope()
        self.setup(scope, place, lazy_mode)

        op_args = dict()
        op_args['lazy_mode'] = lazy_mode
        for key, np_array in self.dense_inputs.items():
            var = scope.var(key).get_tensor()
            var.set(np_array, place)
            op_args[key] = key
        for s in self.sparse_inputs:
            op_args[s] = s
        for s in self.outputs:
            var = scope.var(s).get_tensor()
            var.set(self.init_output, place)
            op_args[s] = s
        for k in self.attrs:
            op_args[k] = self.attrs[k]

        # create and run sgd operator
        adam_op = Operator("adam", **op_args)
        adam_op.run(scope, place)

        for key, np_array in self.outputs.items():
            out_var = scope.var(key).get_tensor()
            actual = np.array(out_var)
            actual = actual.reshape([actual.size])
            np_array = np_array.reshape([np_array.size])

            for i in range(np_array.size):
                self.assertLess((actual[i] - np_array[i]), 0.00001)

    def test_sparse_adam(self):
        places = [core.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))
        for place in places:
            for lazy_mode in (True, False):
                self.check_with_place(place, lazy_mode)


class TestAdamOpBetaVariable(OpTest):
    def setUp(self):
        '''Test Adam Op with beta as Variable
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")
        beta1 = 0.85
        beta2 = 0.95

        learning_rate = 0.001
        epsilon = 1e-8
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            "Beta1Tensor": np.array([beta1]).astype("float32"),
            "Beta2Tensor": np.array([beta2]).astype("float32"),
        }

        attributes = {'epsilon': epsilon}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, attributes)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOpBetaEpsilonVariable(OpTest):
    def setUp(self):
        '''Test Adam Op with beta/epsilon as Variable
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")
        beta1 = 0.85
        beta2 = 0.95

        learning_rate = 0.001
        epsilon = 1e-8
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            "Beta1Tensor": np.array([beta1]).astype("float32"),
            "Beta2Tensor": np.array([beta2]).astype("float32"),
            "EpsilonTensor": np.array([epsilon]).astype("float32"),
        }

        attributes = {'epsilon': epsilon}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, attributes)

        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1,
            'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOpWithGlobalBetaPow(OpTest):
    def setUp(self):
        '''Test Adam Op with global_beta_pow
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")
        beta1 = 0.85
        beta2 = 0.95

        learning_rate = 0.001
        epsilon = 1e-8
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            "Beta1Tensor": np.array([beta1]).astype("float32"),
            "Beta2Tensor": np.array([beta2]).astype("float32"),
            "EpsilonTensor": np.array([epsilon]).astype("float32"),
        }

        attributes = {'epsilon': epsilon}

        param_out, moment1_out, \
            moment2_out = adam_step(self.inputs, attributes)

        self.attrs = {'use_global_beta_pow': True}

        # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty.
        self.outputs = {
            'Moment1Out': moment1_out,
            'Moment2Out': moment2_out,
            'ParamOut': param_out,
            'Beta1PowOut': np.array([]),
            'Beta2PowOut': np.array([])
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOpWithSkipUpdate(OpTest):
    def setUp(self):
        '''Test Adam Op with global_beta_pow
        '''
        self.op_type = "adam"
        param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((102, 105)).astype("float32")
        beta1 = 0.85
        beta2 = 0.95

        learning_rate = 0.001
        epsilon = 1e-8
        beta1_pow = beta1**10
        beta2_pow = beta2**10

        self.inputs = {
            'Param': param,
            'Grad': grad,
            'Moment1': moment1,
            'Moment2': moment2,
            'LearningRate': np.array([learning_rate]).astype("float32"),
            'Beta1Pow': np.array([beta1_pow]).astype("float32"),
            'Beta2Pow': np.array([beta2_pow]).astype("float32"),
            "Beta1Tensor": np.array([beta1]).astype("float32"),
            "Beta2Tensor": np.array([beta2]).astype("float32"),
            "EpsilonTensor": np.array([epsilon]).astype("float32"),
            "SkipUpdate": np.array([True]).astype("bool"),
        }

        attributes = {'epsilon': epsilon}

        self.attrs = {'use_global_beta_pow': True}

        # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty.
        self.outputs = {
            'Moment1Out': moment1,
            'Moment2Out': moment2,
            'ParamOut': param,
            'Beta1PowOut': self.inputs['Beta1Pow'],
            'Beta2PowOut': self.inputs['Beta2Pow'],
        }

    def test_check_output(self):
        self.check_output()


class TestAdamOpV2(unittest.TestCase):
    def test_adam_op(self):
        place = fluid.CPUPlace()
        shape = [2, 3, 8, 8]
        exe = fluid.Executor(place)
        train_prog = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(train_prog, startup):
            with fluid.unique_name.guard():
                data = fluid.data(name="data", shape=shape)
                conv = fluid.layers.conv2d(data, 8, 3)
                loss = fluid.layers.reduce_mean(conv)

                beta1 = fluid.layers.create_global_var(
                    shape=[1], value=0.85, dtype='float32', persistable=True)
                beta2 = fluid.layers.create_global_var(
                    shape=[1], value=0.95, dtype='float32', persistable=True)
                betas = [beta1, beta2]
                opt = paddle.optimizer.Adam(
                    learning_rate=1e-5,
                    beta1=beta1,
                    beta2=beta2,
                    weight_decay=0.01,
                    epsilon=1e-8)
                opt.minimize(loss)

        exe.run(startup)
        data_np = np.random.random(shape).astype('float32')
        rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
        assert rets[0] is not None

    def test_adam_op_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = fluid.dygraph.to_variable(value)
        linear = fluid.Linear(13, 5, dtype="float32")

        adam = paddle.optimizer.Adam(
            learning_rate=0.01, parameters=linear.parameters())
        out = linear(a)
        out.backward()
        adam.step()
        adam.clear_gradients()
        paddle.enable_static()

    def test_adam_op_with_state_dict(self):

        paddle.disable_static()
        emb = paddle.nn.Embedding(10, 10)

        adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters())
        state_dict = adam.state_dict()
        adam.set_state_dict(state_dict)

        #learning_rate is LRScheduler
        learning_rate = paddle.optimizer.lr.CosineAnnealingDecay(
            learning_rate=0.1, T_max=10)
        adam = paddle.optimizer.Adam(
            learning_rate=learning_rate,
            weight_decay=fluid.regularizer.L2Decay(0.001),
            parameters=emb.parameters())
        lr = adam.get_lr()
        state_dict = adam.state_dict()
        adam.set_state_dict(state_dict)

        #leanrning_rate is Tensor
        with self.assertRaises(TypeError):
            learning_rate = np.array([0.01]).astype("float32")
            learning_rate = paddle.to_tensor(learning_rate)
            adam = paddle.optimizer.Adam(
                learning_rate=learning_rate, parameters=emb.parameters())

        params = adam.get_opti_var_name_list()
        assert (params is not None)
        paddle.enable_static()

    def test_adam_with_grad_clip(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = fluid.dygraph.to_variable(value)
        linear = fluid.Linear(13, 5, dtype="float32")
        clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0)
        adam = paddle.optimizer.Adam(
            0.1, parameters=linear.parameters(), grad_clip=clip)
        out = linear(a)
        out.backward()
        adam.step()
        adam.clear_gradients()
        paddle.enable_static()

    def test_adam_op_with_set_lr(self):
        paddle.disable_static()
        linear = paddle.nn.Linear(10, 10)
        adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())

        lr = 0.01
        adam.set_lr(lr)
        cur_lr = adam.get_lr()
        assert (lr == cur_lr)
        with self.assertRaises(TypeError):
            lr_var = paddle.fluid.layers.create_global_var(
                shape=[1], value=lr, dtype='float32')
            adam.set_lr(lr_var)
        paddle.enable_static()

    def test_adam_op_invalid_input(self):
        paddle.disable_static()
        linear = paddle.nn.Linear(10, 10)
        with self.assertRaises(ValueError):
            adam = paddle.optimizer.Adam(
                0.1, beta1=-1, parameters=linear.parameters())
        with self.assertRaises(ValueError):
            adam = paddle.optimizer.Adam(
                0.1, beta2=-1, parameters=linear.parameters())
        with self.assertRaises(ValueError):
            adam = paddle.optimizer.Adam(
                0.1, epsilon=-1, parameters=linear.parameters())
        paddle.enable_static()

    def test_adam_op_with_sparse_input_and_weight_decay(self):

        paddle.disable_static()
        x_data = np.arange(0, 10).reshape((10, 1)).astype(np.int64)
        x = paddle.to_tensor(x_data, stop_gradient=False)
        emb = paddle.nn.Embedding(10, 10, sparse=True)
        adam = paddle.optimizer.Adam(
            0.001, parameters=emb.parameters(), weight_decay=0.01)

        with self.assertRaises(RuntimeError):
            out = emb(x)
            out.backward()
            adam.step()
        paddle.enable_static()


class TestAdamOptimizer(unittest.TestCase):
    def _test(self,
              place,
              use_tensor=True,
              use_fluid_api=True,
              use_global_beta_pow=False,
              flatten_param_grads=False):
        paddle.enable_static()
        main_prog = paddle.static.Program()
        startup_prog = paddle.static.Program()
        SEED = 2021
        paddle.seed(SEED)
        np.random.seed(SEED)

        a_np = np.random.random(size=(2, 2)).astype('float32')
        b_np = np.random.random(size=(2, 2)).astype('float32')
        label_np = np.random.randint(2, size=(2, 1)).astype('int64')
        weight_attr1 = paddle.ParamAttr(
            name="weight1",
            initializer=fluid.initializer.Constant(value=1.0),
            trainable=True)
        weight_attr2 = paddle.ParamAttr(
            name="weight2",
            initializer=fluid.initializer.Constant(value=2.0),
            trainable=True)
        clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)

        with paddle.static.program_guard(main_prog, startup_prog):
            with paddle.utils.unique_name.guard():
                a = paddle.static.data(name="a", shape=[2, 2], dtype='float32')
                b = paddle.static.data(name="b", shape=[2, 2], dtype='float32')
                label = paddle.static.data(
                    name="label", shape=[2, 1], dtype='int64')

                sum = paddle.add(a, b)
                z = paddle.pow(sum, 2.0)

                fc_1 = fluid.layers.fc(input=z, size=2, param_attr=weight_attr1)
                prediction = fluid.layers.fc(input=fc_1,
                                             size=2,
                                             param_attr=weight_attr2,
                                             act='softmax')

                cost = fluid.layers.cross_entropy(input=prediction, label=label)
                loss = fluid.layers.reduce_mean(cost)
                beta1_init = 0.9
                beta2_init = 0.999
                epsilon_init = 1e-8
                if use_tensor:
                    beta1 = fluid.layers.create_global_var(
                        shape=[1],
                        value=float(beta1_init),
                        dtype='float32',
                        persistable=True,
                        name="beta1")
                    beta2 = fluid.layers.create_global_var(
                        shape=[1],
                        value=float(beta2_init),
                        dtype='float32',
                        persistable=True,
                        name="beta2")
                    epsilon = fluid.layers.create_global_var(
                        shape=[1],
                        value=float(epsilon_init),
                        dtype='float32',
                        persistable=True,
                        name="epsilon")
                    if use_fluid_api:
                        adam = fluid.optimizer.Adam(
                            learning_rate=0.01,
                            beta1=beta1,
                            beta2=beta2,
                            epsilon=epsilon,
                            use_global_beta_pow=use_global_beta_pow,
                            flatten_param_grads=flatten_param_grads,
                            align_size=256,
                            grad_clip=clip)
                    else:
                        adam = paddle.optimizer.Adam(
                            learning_rate=0.01,
                            beta1=beta1,
                            beta2=beta2,
                            epsilon=epsilon,
                            grad_clip=clip)
                else:
                    if use_fluid_api:
                        adam = fluid.optimizer.Adam(
                            learning_rate=0.01,
                            beta1=beta1_init,
                            beta2=beta2_init,
                            epsilon=epsilon_init,
                            use_global_beta_pow=use_global_beta_pow,
                            flatten_param_grads=flatten_param_grads,
                            align_size=256,
                            grad_clip=clip)
                    else:
                        adam = fluid.optimizer.Adam(
                            learning_rate=0.01,
                            beta1=beta1_init,
                            beta2=beta2_init,
                            epsilon=epsilon_init,
                            grad_clip=clip)

                adam.minimize(loss)

        scope = fluid.Scope()
        with fluid.scope_guard(scope):
            exe = paddle.static.Executor(place)
            exe.run(startup_prog)

            print("Start run on {}".format(place))
            for epoch in range(10):
                pred_res, loss_res = exe.run(
                    main_prog,
                    feed={"a": a_np,
                          "b": b_np,
                          "label": label_np},
                    fetch_list=[prediction, loss])
                print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
                    epoch, pred_res[0], loss_res))
            paddle.disable_static()
            return pred_res, loss_res

    def _test_with_place(self, place):
        preds = []
        losses = []

        for use_tensor in [True, False]:
            for use_fluid_api in [True, False]:
                for use_global_beta_pow in [True, False]:
                    for flatten_param_grads in [True, False]:
                        pred, loss = self._test(
                            place, use_tensor, use_fluid_api,
                            use_global_beta_pow, flatten_param_grads)
                        preds.append(pred)
                        losses.append(loss)
        for pred in preds:
            self.assertTrue(np.allclose(pred, preds[0]))
        for loss in losses:
            self.assertTrue(np.allclose(loss, losses[0]))

    def test_adam_api(self):
        # NOTE(zhiqiu): cpu and gpu has different seed, so should compare separatly.
        self._test_with_place(paddle.CPUPlace())
        if core.is_compiled_with_cuda():
            self._test_with_place(paddle.CUDAPlace(0))

    def test_adam_flatten_param_grads_with_regularizer(self):
        # flatten_param_grads + regularizer is not supported yet.
        paddle.enable_static()
        main = fluid.Program()
        weight_attr = paddle.ParamAttr(
            name="weight1",
            initializer=fluid.initializer.Constant(value=1.0),
            regularizer=fluid.regularizer.L1DecayRegularizer(
                regularization_coeff=0.1),
            trainable=True)
        with fluid.program_guard(main):
            x = fluid.data(name='x', shape=[None, 13], dtype='float32')
            y = fluid.data(name='y', shape=[None, 1], dtype='float32')
            y_predict = fluid.layers.fc(input=x,
                                        size=1,
                                        act=None,
                                        param_attr=weight_attr)
            cost = fluid.layers.square_error_cost(input=y_predict, label=y)
            avg_cost = fluid.layers.mean(cost)

            adam = fluid.optimizer.AdamOptimizer(
                0.01, flatten_param_grads=True, align_size=256)
            adam.minimize(avg_cost)
            paddle.disable_static()

            self.assertEqual(adam._flatten_param_grads, False)

    def test_adam_exception(self):
        paddle.enable_static()
        a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
        b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
        label = paddle.static.data(name="label", shape=[32, 1], dtype='int64')

        sum = paddle.add(a, b)
        z = paddle.pow(sum, 2.0)

        fc_1 = fluid.layers.fc(input=z, size=128)
        prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')

        cost = fluid.layers.cross_entropy(input=prediction, label=label)
        loss = fluid.layers.reduce_mean(cost)
        adam = fluid.optimizer.Adam(use_global_beta_pow=True)
        adam.minimize(loss)
        self.assertRaises(Exception, adam._get_global_accumulator, 'tmp')
        adam._add_global_accumulator(
            'tmp', type=core.VarDesc.VarType.LOD_TENSOR)
        adam._get_global_accumulator('tmp')
        self.assertRaises(
            Exception,
            adam._add_global_accumulator,
            adam._beta1_pow_acc_str,
            type=core.VarDesc.VarType.LOD_TENSOR)
        paddle.disable_static()

    def test_adam_save_load(self):
        paddle.disable_static()
        a = paddle.rand([4, 10])
        linear = paddle.nn.Linear(10, 10)
        b = linear(a)
        state_dict = linear.state_dict()
        fluid.save_dygraph(state_dict, "paddle_dy")

        scheduler = paddle.optimizer.lr.NoamDecay(
            d_model=0.01, warmup_steps=100, verbose=True)
        adam = paddle.fluid.optimizer.Adam(
            learning_rate=scheduler,
            parameter_list=linear.parameters(),
            use_global_beta_pow=True)
        adam.minimize(b)
        state_dict = adam.state_dict()
        fluid.save_dygraph(state_dict, "paddle_dy")
        para_state_dict, opt_state_dict = fluid.load_dygraph("paddle_dy")
        adam.set_state_dict(opt_state_dict)

        paddle.enable_static()

    def test_adam_save_load_error(self):
        paddle.disable_static()

        def get_opt(dtype, shape):
            with paddle.utils.unique_name.guard():
                paddle.set_default_dtype(dtype)
                a = paddle.rand([4, 10])
                linear = paddle.nn.Linear(10, 10)
                b = linear(a)
                state_dict = linear.state_dict()
                fluid.save_dygraph(state_dict, "paddle_dy")

                scheduler = paddle.optimizer.lr.NoamDecay(
                    d_model=0.01, warmup_steps=100, verbose=True)
                adam = paddle.fluid.optimizer.Adam(
                    learning_rate=scheduler,
                    parameter_list=linear.parameters(),
                    use_global_beta_pow=True)
                adam.minimize(b)
                return adam

        adam = get_opt('float32', [10, 10])

        state_dict = adam.state_dict()
        fluid.save_dygraph(state_dict, "paddle_dy")
        para_state_dict, opt_state_dict = fluid.load_dygraph("paddle_dy")
        adam.set_state_dict(opt_state_dict)

        adam2 = get_opt('float64', [10, 10])  # dtype not match
        self.assertRaises(AssertionError, adam2.set_state_dict, opt_state_dict)

        adam3 = get_opt('float32', [10, 10])  # shape not match
        opt_state_dict['beta1_pow_acc_0'] = np.array(
            [0.9, 0.9], dtype='float32')
        self.assertRaises(AssertionError, adam3.set_state_dict, opt_state_dict)
        paddle.enable_static()


class TestAdamOpV2Group(TestAdamOpV2):
    def test_adam_op(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.Adam(
            learning_rate=0.01,
            parameters=[{
                'params': linear_1.parameters()
            }, {
                'params': linear_2.parameters(),
                'weight_decay': 0.001,
                'beta1': 0.1,
                'beta2': 0.99
            }],
            weight_decay=0.1)
        out = linear_1(a)
        out = linear_2(out)
        out.backward()
        adam.step()
        adam.clear_gradients()


class TestMultiTensorAdam(unittest.TestCase):
    def _adam_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.Adam(
                parameters=model.parameters(),
                use_multi_tensor=use_multi_tensor,
                multi_precision=use_amp)
        else:
            optimizer = paddle.optimizer.Adam(
                parameters=[{
                    'params': model.parameters(),
                    'weight_decay': 0.001,
                    'beta1': 0.1,
                    'beta2': 0.99
                }],
                use_multi_tensor=use_multi_tensor,
                multi_precision=use_amp)

        for idx in range(2):
            if place == 'gpu' and use_amp == True:
                model = paddle.amp.decorate(models=model, level='O2')
                scaler = paddle.amp.GradScaler(init_loss_scaling=1024)

            if place == 'gpu' and use_amp == True:
                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()
            else:
                output = model(input)
                loss = paddle.mean(output)
                loss.backward()
                optimizer.step()
                optimizer.clear_grad()

        return output, model.parameters()

    def _adam_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.Adam(
            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.fluid.layers.mean(hidden)
            optimizer.minimize(loss)
        exe.run(startup_program)
        if use_amp:
            optimizer.amp_init(place=place, scope=paddle.static.global_scope())
            x = np.random.random(size=(2, 2)).astype('float16')
        else:
            x = np.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):
        # test dygraph mode
        output_dygraph1, params_dygraph1 = self._adam_optimize_dygraph(
            place=place, use_amp=use_amp, use_multi_tensor=True)
        output_dygraph2, params_dygraph2 = self._adam_optimize_dygraph(
            place=place, use_amp=use_amp, use_multi_tensor=False)
        self.assertEqual(
            np.allclose(
                output_dygraph1, output_dygraph2, rtol=1e-05), True)
        for idx in range(len(params_dygraph1)):
            self.assertEqual(
                np.allclose(
                    params_dygraph1[idx], params_dygraph2[idx], rtol=1e-05),
                True)
        # test static mode
        output_static1 = self._adam_optimize_static(
            place=place, use_amp=use_amp, use_multi_tensor=True)
        output_static2 = self._adam_optimize_static(
            place=place, use_amp=use_amp, use_multi_tensor=False)
        for idx in range(len(output_static1)):
            self.assertEqual(
                np.allclose(
                    output_static1[idx], output_static2[idx], rtol=1e-05),
                True)

    def _check_with_param_arrt(self, place, use_amp):
        output1, params1 = self._adam_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_attr=True,
            use_multi_tensor=True)
        output2, params2 = self._adam_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_attr=True,
            use_multi_tensor=False)

        self.assertEqual(np.allclose(output1, output2, rtol=1e-05), True)
        for idx in range(len(params1)):
            self.assertEqual(
                np.allclose(
                    params1[idx], params2[idx], rtol=1e-05), True)

    def _check_with_param_group(self, place, use_amp):
        output1, params1 = self._adam_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_group=True,
            use_multi_tensor=True)
        output2, params2 = self._adam_optimize_dygraph(
            place=place,
            use_amp=use_amp,
            use_param_group=True,
            use_multi_tensor=False)

        self.assertEqual(np.allclose(output1, output2, rtol=1e-05), True)
        for idx in range(len(params1)):
            self.assertEqual(
                np.allclose(
                    params1[idx], params2[idx], rtol=1e-05), True)

    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)


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