test_optimizer.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 os
import tempfile
import unittest

import paddle.fluid as fluid
import paddle.fluid.framework as framework
import paddle.fluid.optimizer as optimizer
import paddle.fluid.core as core
import numpy as np
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import (
    Program,
    program_guard,
    convert_np_dtype_to_dtype_,
)
from paddle.fluid.framework import _test_eager_guard
import paddle
from paddle.io import Dataset
import numpy


class TestOptimizer(unittest.TestCase):
    def test_sgd_optimizer(self):
        def check_sgd_optimizer(optimizer_attr):
            init_program = framework.Program()
            program = framework.Program()
            block = program.global_block()
            mul_x = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="mul.x",
                optimize_attr=optimizer_attr,
            )
            mul_y = block.create_var(
                dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
            )
            mul_out = block.create_var(
                dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
            )
            mean_out = block.create_var(
                dtype="float32", shape=[1], lod_level=0, name="mean.out"
            )
            block.append_op(
                type="mul",
                inputs={"X": mul_x, "Y": mul_y},
                outputs={"Out": mul_out},
                attrs={"x_num_col_dims": 1},
            )
            block.append_op(
                type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
            )
            sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
            opts, _ = sgd_optimizer.minimize(mean_out, init_program)
            return opts

        opts = check_sgd_optimizer({'learning_rate': 1.1})
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "sgd"])

        opts = check_sgd_optimizer({'learning_rate': 1.0})
        self.assertEqual(len(opts), 1)
        self.assertEqual([op.type for op in opts], ["sgd"])


class TestOptimizerBackwardApplygrad(unittest.TestCase):
    def test_sgd_optimizer(self):
        def check_sgd_optimizer(optimizer_attr):
            init_program = framework.Program()
            program = framework.Program()
            block = program.global_block()
            mul_x = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="mul.x",
                optimize_attr=optimizer_attr,
            )
            mul_y = block.create_var(
                dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
            )
            mul_out = block.create_var(
                dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
            )
            mean_out = block.create_var(
                dtype="float32", shape=[1], lod_level=0, name="mean.out"
            )
            block.append_op(
                type="mul",
                inputs={"X": mul_x, "Y": mul_y},
                outputs={"Out": mul_out},
                attrs={"x_num_col_dims": 1},
            )
            block.append_op(
                type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
            )
            sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
            with framework.program_guard(program, init_program):
                p_g = sgd_optimizer.backward(mean_out)
                opts = sgd_optimizer.apply_gradients(p_g)
            return opts

        opts = check_sgd_optimizer({'learning_rate': 1.1})
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "sgd"])

        opts = check_sgd_optimizer({'learning_rate': 1.0})
        self.assertEqual(len(opts), 1)
        self.assertEqual([op.type for op in opts], ["sgd"])


class TestMomentumOptimizer(unittest.TestCase):
    class MockMomentum(optimizer.MomentumOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_velocity_str(self):
            return self._velocity_acc_str

    def test_vanilla_momentum_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        learning_rate = 0.01
        momentum_optimizer = self.MockMomentum(
            learning_rate=learning_rate, momentum=0.2
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = momentum_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        sgd_op = opts[-1]
        self.assertEqual([op.type for op in opts], ["scale", "momentum"])
        self.assertFalse(sgd_op.attr('use_nesterov'))

        # Check accumulators
        accumulators = momentum_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 1)
        self.assertTrue(momentum_optimizer.get_velocity_str() in accumulators)
        velocity_acc = accumulators[momentum_optimizer.get_velocity_str()]
        self.assertEqual(len(velocity_acc), 1)
        self.assertTrue(mul_x.name in velocity_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 2)
        self.assertEqual(init_ops[1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[1].attr('value'), learning_rate)
        self.assertEqual(init_ops[0].type, "fill_constant")
        self.assertAlmostEqual(init_ops[0].attr('value'), 0.0)

    def test_nesterov_momentum_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        momentum_optimizer = self.MockMomentum(
            learning_rate=learning_rate, momentum=0.2, use_nesterov=True
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = momentum_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        sgd_op = opts[-1]
        self.assertEqual([op.type for op in opts], ["scale", "momentum"])
        self.assertTrue(sgd_op.attr('use_nesterov'))

        # Check accumulators
        accumulators = momentum_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 1)
        self.assertTrue(momentum_optimizer.get_velocity_str() in accumulators)
        velocity_acc = accumulators[momentum_optimizer.get_velocity_str()]
        self.assertEqual(len(velocity_acc), 1)
        self.assertTrue(mul_x.name in velocity_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 2)
        self.assertEqual(init_ops[1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[1].attr('value'), learning_rate)
        self.assertEqual(init_ops[0].type, "fill_constant")
        self.assertAlmostEqual(init_ops[0].attr('value'), 0.0)


class TestAdagradOptimizer(unittest.TestCase):
    class MockAdagrad(optimizer.AdagradOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_moment_str(self):
            return self._moment_acc_str

    def test_adagrad_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        adagrad_optimizer = self.MockAdagrad(
            learning_rate=learning_rate, epsilon=1.0e-6
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = adagrad_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "adagrad"])

        # Check accumulators
        accumulators = adagrad_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 1)
        self.assertTrue(adagrad_optimizer.get_moment_str() in accumulators)
        moment_acc = accumulators[adagrad_optimizer.get_moment_str()]
        self.assertEqual(len(moment_acc), 1)
        self.assertTrue(mul_x.name in moment_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 2)
        self.assertEqual(init_ops[1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[1].attr('value'), learning_rate)
        self.assertEqual(init_ops[0].type, "fill_constant")
        self.assertAlmostEqual(init_ops[0].attr('value'), 0.0)


class TestAdamOptimizer(unittest.TestCase):
    class MockAdam(optimizer.AdamOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_moment1_str(self):
            return self._moment1_acc_str

        def get_moment2_str(self):
            return self._moment2_acc_str

    def test_adam_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        adam_optimizer = self.MockAdam(
            learning_rate=learning_rate, beta1=0.9, beta2=0.999
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = adam_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "adam"])

        # Check accumulators
        accumulators = adam_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 4)
        self.assertTrue(adam_optimizer.get_moment1_str() in accumulators)
        self.assertTrue(adam_optimizer.get_moment2_str() in accumulators)
        moment1_acc = accumulators[adam_optimizer.get_moment1_str()]
        moment2_acc = accumulators[adam_optimizer.get_moment2_str()]
        self.assertEqual(len(moment1_acc), 1)
        self.assertEqual(len(moment2_acc), 1)
        self.assertTrue(mul_x.name in moment1_acc)
        self.assertTrue(mul_x.name in moment2_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 5)
        self.assertEqual(init_ops[-1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[-1].attr('value'), learning_rate)


class TestAdamaxOptimizer(unittest.TestCase):
    class MockAdamax(optimizer.AdamaxOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_moment_str(self):
            return self._moment_acc_str

        def get_inf_norm_str(self):
            return self._inf_norm_acc_str

    def test_adamax_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        adamax_optimizer = self.MockAdamax(
            learning_rate=learning_rate, beta1=0.9, beta2=0.999
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(adamax_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = adamax_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 3)
        self.assertEqual([op.type for op in opts], ["scale", "adamax", "scale"])

        # Check accumulators
        accumulators = adamax_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 3)
        self.assertTrue(adamax_optimizer.get_moment_str() in accumulators)
        self.assertTrue(adamax_optimizer.get_inf_norm_str() in accumulators)
        moment_acc = accumulators[adamax_optimizer.get_moment_str()]
        inf_norm_acc = accumulators[adamax_optimizer.get_inf_norm_str()]
        self.assertEqual(len(moment_acc), 1)
        self.assertEqual(len(inf_norm_acc), 1)
        self.assertTrue(mul_x.name in moment_acc)
        self.assertTrue(mul_x.name in inf_norm_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 4)
        self.assertEqual(init_ops[-1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[-1].attr('value'), learning_rate)


class TestDpsgdOptimizer(unittest.TestCase):
    def test_dpsgd_optimizer(self):
        def check_dpsgd_optimizer(optimizer_attr):
            init_program = framework.Program()
            program = framework.Program()
            block = program.global_block()
            mul_x = block.create_parameter(
                dtype="float32",
                shape=[5, 10],
                lod_level=0,
                name="mul.x",
                optimize_attr=optimizer_attr,
            )
            mul_y = block.create_var(
                dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
            )
            mul_out = block.create_var(
                dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
            )
            block.append_op(
                type="mul",
                inputs={"X": mul_x, "Y": mul_y},
                outputs={"Out": mul_out},
                attrs={"x_num_col_dims": 1},
            )
            mean_out = block.create_var(
                dtype="float32", shape=[1], lod_level=0, name="mean.out"
            )
            block.append_op(
                type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
            )
            dpsgd_optimizer = optimizer.DpsgdOptimizer(
                learning_rate=0.01, clip=100.0, batch_size=16.0, sigma=0.0
            )
            opts, _ = dpsgd_optimizer.minimize(mean_out, init_program)
            return opts

        opts = check_dpsgd_optimizer(
            {
                'learning_rate': 1.1,
                'clip': 100.0,
                'batch_size': 16.0,
                'sigma': 4.0,
            }
        )
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "dpsgd"])


class TestDecayedAdagradOptimizer(unittest.TestCase):
    class MockDecayedAdagrad(optimizer.DecayedAdagradOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_moment_str(self):
            return self._moment_acc_str

    def test_decayed_adagrad_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        decayed_adagrad_optimizer = self.MockDecayedAdagrad(
            learning_rate=learning_rate, decay=0.95, epsilon=1.0e-6
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = decayed_adagrad_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "decayed_adagrad"])

        # Check accumulators
        accumulators = decayed_adagrad_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 1)
        self.assertTrue(
            decayed_adagrad_optimizer.get_moment_str() in accumulators
        )
        moment_acc = accumulators[decayed_adagrad_optimizer.get_moment_str()]
        self.assertEqual(len(moment_acc), 1)
        self.assertTrue(mul_x.name in moment_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 2)
        self.assertEqual(init_ops[1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[1].attr('value'), learning_rate)
        self.assertEqual(init_ops[0].type, "fill_constant")
        self.assertAlmostEqual(init_ops[0].attr('value'), 0.0)


class TestFtrlOptimizer(unittest.TestCase):
    class MockFtrl(optimizer.FtrlOptimizer):
        def get_accumulators(self):
            return self._accumulators

        def get_squared_str(self):
            return self._squared_acc_str

        def get_linear_str(self):
            return self._linear_acc_str

    def test_ftrl_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )
        learning_rate = 0.01
        ftrl_optimizer = self.MockFtrl(
            learning_rate=learning_rate, l1=0.0, l2=0.0, lr_power=-0.5
        )
        params_grads = append_backward(mean_out)
        self.assertEqual(len(params_grads), 1)
        self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
        with framework.program_guard(program, init_program):
            opts = ftrl_optimizer.apply_gradients(params_grads)
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "ftrl"])

        # Check accumulators
        accumulators = ftrl_optimizer.get_accumulators()
        self.assertEqual(len(accumulators), 2)
        self.assertTrue(ftrl_optimizer.get_squared_str() in accumulators)
        self.assertTrue(ftrl_optimizer.get_linear_str() in accumulators)
        squared_acc = accumulators[ftrl_optimizer.get_squared_str()]
        linear_acc = accumulators[ftrl_optimizer.get_linear_str()]
        self.assertEqual(len(squared_acc), 1)
        self.assertEqual(len(linear_acc), 1)
        self.assertTrue(mul_x.name in squared_acc)
        self.assertTrue(mul_x.name in linear_acc)

        # Check init_program
        init_ops = init_program.global_block().ops
        self.assertEqual(len(init_ops), 3)
        self.assertEqual(init_ops[-1].type, "fill_constant")
        self.assertAlmostEqual(init_ops[-1].attr('value'), learning_rate)


class TestLookaheadOptimizer(unittest.TestCase):
    def test_lookahead_optimizer(self):
        init_program = framework.Program()
        program = framework.Program()
        block = program.global_block()
        init_block = init_program.global_block()
        mul_x = block.create_parameter(
            dtype="float32",
            shape=[5, 10],
            lod_level=0,
            name="mul.x",
            optimize_attr={'learning_rate': 1.1},
        )
        init_mul_x = init_block.create_parameter(
            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x"
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )

        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        block.append_op(
            type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
        )

        sgd = optimizer.SGD(learning_rate=0.01)
        lookahead = optimizer.LookaheadOptimizer(sgd, alpha=0.5, k=5)
        with framework.program_guard(program, init_program):
            opts, _ = lookahead.minimize(mean_out)
        self.assertEqual(len(opts), 2)
        self.assertEqual([op.type for op in opts], ["scale", "sgd"])


class TestRecomputeOptimizer(unittest.TestCase):
    def net(self, return_input=False, with_dropout=False, with_seed=False):
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x"
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )

        if with_dropout is True:
            mul_out_drop = block.create_var(
                dtype="float32",
                shape=[5, 8],
                lod_level=0,
                name="mul.out.dropout",
            )
            mul_out_mask = block.create_var(
                dtype="uint8", shape=[5, 8], lod_level=0, name="mul.out.mask"
            )
            if with_seed is True:
                seed_out = block.create_var(
                    dtype="int32", shape=[1], name="seed.out"
                )

        b1 = block.create_parameter(
            dtype="float32", shape=[5, 8], lod_level=0, name="b1"
        )
        b1_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="b1_out"
        )
        b2 = block.create_parameter(
            dtype="float32", shape=[5, 8], lod_level=0, name="b2"
        )
        b2_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="b2_out"
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )

        if with_dropout is True:
            dropout_inputs = {'X': [mul_out]}
            if with_seed is True:
                block.append_op(
                    type='seed',
                    outputs={'Out': seed_out},
                    attrs={
                        'deterministic': True,
                        'rng_name': 'rng0',
                        'force_cpu': True,
                    },
                )
                dropout_inputs = {'X': [mul_out], 'Seed': [seed_out]}

            block.append_op(
                type='dropout',
                inputs=dropout_inputs,
                outputs={'Out': [mul_out_drop], 'Mask': [mul_out_mask]},
                attrs={
                    'dropout_prob': 0.5,
                },
            )
            block.append_op(
                type="elementwise_add",
                inputs={"X": mul_out_drop, "Y": b1},
                outputs={"Out": b1_out},
            )
        else:
            block.append_op(
                type="elementwise_add",
                inputs={"X": mul_out, "Y": b1},
                outputs={"Out": b1_out},
            )

        block.append_op(
            type="elementwise_add",
            inputs={"X": b1_out, "Y": b2},
            outputs={"Out": b2_out},
        )
        block.append_op(
            type="mean", inputs={"X": b2_out}, outputs={"Out": mean_out}
        )

        if return_input:
            return mul_x, mul_out, b1_out, b2_out, mean_out
        return mul_out, b1_out, b2_out, mean_out

    def test_no_checkpoint(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 12)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_one_checkpoint(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 13)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "mul",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_str_checkpoints(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out.name])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 13)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "mul",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_multi_checkpoint(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([mul_out, b2_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 13)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add",
                "elementwise_add_grad",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_adjacent_checkpoint(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([mul_out, b1_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 12)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_out_of_order_checkpoint(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b2_out, mul_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 13)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add",
                "elementwise_add_grad",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_input_as_checkpoints(self):
        mul_x, mul_out, b1_out, b2_out, mean_out = self.net(return_input=True)
        self.assertEqual(len(mean_out.block.ops), 4)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([mul_x, b2_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 14)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "mul",
                "elementwise_add",
                "elementwise_add_grad",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_apply_gradients(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out])
        # apply backward
        params_grads = recompute_optimizer.backward(
            mean_out,
            startup_program=None,
            parameter_list=None,
            no_grad_set=None,
        )

        # apply gradient
        program = mean_out.block.program
        with framework.program_guard(program, None):
            optimize_ops = recompute_optimizer.apply_gradients(params_grads)

        self.assertEqual(len(mean_out.block.ops), 13)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "mul",
                "elementwise_add_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_load(self):
        mul_out, b1_out, b2_out, mean_out = self.net()
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out])
        try:
            state_dict = {}
            recompute_optimizer.load(state_dict)
        except NotImplementedError as e:
            self.assertEqual(
                "load function is not supported by Recompute Optimizer for now",
                str(e),
            )

    def test_dropout(self):
        """
        If there are dropout layers in the forward nets, we should add a
        seed op
        """
        mul_out, b1_out, b2_out, mean_out = self.net(with_dropout=True)
        self.assertEqual(len(mean_out.block.ops), 5)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            ["mul", "dropout", "elementwise_add", "elementwise_add", "mean"],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 17)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "seed",
                "dropout",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "mul",
                "dropout",
                "elementwise_add_grad",
                "dropout_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_dropout_with_determinate_seed(self):
        mul_out, b1_out, b2_out, mean_out = self.net(
            with_dropout=True, with_seed=True
        )
        self.assertEqual(len(mean_out.block.ops), 6)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "seed",
                "dropout",
                "elementwise_add",
                "elementwise_add",
                "mean",
            ],
        )
        sgd_optimizer = optimizer.SGD(learning_rate=1.0)
        recompute_optimizer = optimizer.RecomputeOptimizer(sgd_optimizer)
        recompute_optimizer._set_checkpoints([b1_out])
        opts, params_grads = recompute_optimizer.minimize(mean_out)

        self.assertEqual(len(mean_out.block.ops), 17)
        self.assertEqual(
            [op.type for op in mean_out.block.ops],
            [
                "mul",
                "seed",
                "dropout",
                "elementwise_add",
                "elementwise_add",
                "mean",
                "fill_constant",
                "mean_grad",
                "elementwise_add_grad",
                "mul",
                "dropout",
                "elementwise_add_grad",
                "dropout_grad",
                "mul_grad",
                "sgd",
                "sgd",
                "sgd",
            ],
        )

    def test_dropout_with_seed(self):
        """
        when we recompute a dropout op, make sure that the recomputed one
        is the same as the original var.
        """

        def gen_data():
            return {
                "x": np.random.random(size=(100, 3)).astype('float32'),
                "y": np.random.randint(2, size=(100, 1)).astype('int64'),
            }

        def mlp(input_x, input_y):
            drop_res = fluid.layers.dropout(
                input_x, dropout_prob=0.5, name="dropout_with_seed_cpu"
            )
            prediction = fluid.layers.fc(
                input=[drop_res], size=2, act='softmax'
            )
            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
            sum_cost = fluid.layers.reduce_mean(cost)
            return drop_res, prediction, sum_cost

        main_program = Program()
        startup_program = Program()
        scope = fluid.Scope()
        with fluid.scope_guard(scope):
            with program_guard(main_program, startup_program):
                input_x = fluid.layers.data(
                    name="x", shape=[3], dtype='float32'
                )
                input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
                drop_res, prediction, cost = mlp(input_x, input_y)
                sgd = fluid.optimizer.Adam(learning_rate=0.01)
                sgd = fluid.optimizer.RecomputeOptimizer(sgd)
                sgd._set_checkpoints([prediction])
                sgd.minimize(cost)

                place = fluid.CPUPlace()
                exe = fluid.Executor(place)
                exe.run(fluid.default_startup_program())
                feed_data = gen_data()
                drop_vec = exe.run(
                    feed=feed_data,
                    program=fluid.default_main_program(),
                    fetch_list=[
                        "dropout_with_seed_cpu.tmp_1",
                        "dropout_with_seed_cpu.tmp_1.subprog_0",
                    ],
                )
                self.assertEqual(drop_vec[0].tolist(), drop_vec[1].tolist())


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestRecomputeOptimizerCUDA(unittest.TestCase):
    def test_dropout_with_seed(self):
        """
        when we recompute a dropout op, make sure that the recomputed one
        is the same as the original var.
        """

        def gen_data():
            return {
                "x": np.random.random(size=(100, 3)).astype('float32'),
                "y": np.random.randint(2, size=(100, 1)).astype('int64'),
            }

        def mlp(input_x, input_y):
            drop_res = fluid.layers.dropout(
                input_x, dropout_prob=0.5, name="dropout_with_seed_gpu"
            )
            prediction = fluid.layers.fc(
                input=[drop_res], size=2, act='softmax'
            )
            cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
            sum_cost = fluid.layers.reduce_mean(cost)
            return drop_res, prediction, sum_cost

        main_program = Program()
        startup_program = Program()
        scope = fluid.Scope()
        with fluid.scope_guard(scope):
            with program_guard(main_program, startup_program):
                input_x = fluid.layers.data(
                    name="x", shape=[3], dtype='float32'
                )
                input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
                drop_res, prediction, cost = mlp(input_x, input_y)
                sgd = fluid.optimizer.Adam(learning_rate=0.01)
                sgd = fluid.optimizer.RecomputeOptimizer(sgd)
                sgd._set_checkpoints([prediction])
                sgd.minimize(cost)

                place = fluid.CUDAPlace(0)
                exe = fluid.Executor(place)
                exe.run(fluid.default_startup_program())
                feed_data = gen_data()
                drop_vec = exe.run(
                    feed=feed_data,
                    program=fluid.default_main_program(),
                    fetch_list=[
                        "dropout_with_seed_gpu.tmp_1",
                        "dropout_with_seed_gpu.tmp_1.subprog_0",
                    ],
                )
                self.assertEqual(drop_vec[0].tolist(), drop_vec[1].tolist())


class TestGradientMergeOptimizer(unittest.TestCase):
    def net(self):
        program = framework.Program()
        block = program.global_block()
        mul_x = block.create_parameter(
            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x"
        )
        mul_y = block.create_var(
            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
        )
        mul_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
        )
        b1 = block.create_parameter(
            dtype="float32", shape=[5, 8], lod_level=0, name="b1"
        )
        b1_out = block.create_var(
            dtype="float32", shape=[5, 8], lod_level=0, name="b1_out"
        )
        mean_out = block.create_var(
            dtype="float32", shape=[1], lod_level=0, name="mean.out"
        )
        block.append_op(
            type="mul",
            inputs={"X": mul_x, "Y": mul_y},
            outputs={"Out": mul_out},
            attrs={"x_num_col_dims": 1},
        )
        block.append_op(
            type="elementwise_add",
            inputs={"X": mul_out, "Y": b1},
            outputs={"Out": b1_out},
        )
        block.append_op(
            type="mean", inputs={"X": b1_out}, outputs={"Out": mean_out}
        )
        return mean_out

    def test_program_desc(
        self,
    ):
        cost = self.net()
        main_program = cost.block.program
        init_program = framework.Program()
        self.assertEqual(main_program.num_blocks, 1)
        self.assertEqual(len(cost.block.ops), 3)
        self.assertEqual(
            [op.type for op in cost.block.ops],
            ["mul", "elementwise_add", "mean"],
        )

        opt = optimizer.SGD(learning_rate=1.0)
        opt = optimizer.GradientMergeOptimizer(opt, k_steps=4)
        with framework.program_guard(main_program, init_program):
            ops, params_grads = opt.minimize(cost)

        self.assertEqual(main_program.num_blocks, 2)

        # main block
        self.assertEqual(len(cost.block.ops), 13)
        self.assertEqual(
            [op.type for op in cost.block.ops],
            [
                'mul',
                'elementwise_add',
                'mean',
                'fill_constant',
                'mean_grad',
                'elementwise_add_grad',
                'mul_grad',
                'increment',  # step += 1
                'elementwise_mod',  # step %= k_steps
                'equal',  # cond_var == (step == 0)
                'elementwise_add',
                'elementwise_add',
                'conditional_block',
            ],
        )

        # optimize block
        self.assertEqual(len(main_program.block(1).ops), 6)
        self.assertEqual(
            [op.type for op in main_program.block(1).ops],
            ['scale', 'scale', 'sgd', 'sgd', 'fill_constant', 'fill_constant'],
        )


class TestOptimizerDtype(unittest.TestCase):
    '''
    The dtype of optimizer should be inferred by parameters, and the learning rate
    is cteated with the same dtype.
    '''

    def check_with_dtype(self, dtype):
        class MyLayer(paddle.nn.Layer):
            def __init__(self, dtype):
                super().__init__()
                self._w = self.create_parameter([2, 3], dtype=dtype)
                self._b = self.create_parameter([2, 3], dtype=dtype)

            def forward(self, x):
                return x * self._w + self._b

        with paddle.fluid.dygraph.guard():
            model = MyLayer(dtype)
            x = paddle.rand([10, 2, 3], dtype=dtype)
            loss = model(x)
            adam = paddle.optimizer.Adam(parameters=model.parameters())
            loss.backward()
            adam.step()
            self.assertEqual(adam._dtype, convert_np_dtype_to_dtype_(dtype))

    def test_float64(self):
        self.check_with_dtype('float64')

    def test_float32(self):
        self.check_with_dtype('float32')

    def test_api_eager_dygraph(self):
        with _test_eager_guard():
            self.test_float64()
            self.test_float32()


class TestMasterWeightSaveForFP16(unittest.TestCase):
    '''
    For Amp-O2, some optimizer(Momentum, Adam ...) will create master weights for parameters to improve the accuracy.
    Master weights will be saved by optimizer::state_dict.
    '''

    def setUp(self):
        self.temp_dir = tempfile.TemporaryDirectory()

    def tearDown(self):
        self.temp_dir.cleanup()

    def check_with_opt_state_dict(self, use_save_load=True):
        paddle.seed(100)
        numpy.random.seed(100)

        class SimpleNet(paddle.nn.Layer):
            def __init__(self, input_size, output_size):
                super().__init__()
                self.linears = paddle.nn.LayerList(
                    [
                        paddle.nn.Linear(input_size, output_size)
                        for i in range(1)
                    ]
                )

            def forward(self, x):
                for i, l in enumerate(self.linears):
                    x = self.linears[i](x)
                return x

        input_size = 2  # 设为较大的值
        output_size = 2  # 设为较大的值
        batch_size = 2  # batch_size 为8的倍数
        nums_batch = 10

        class RandomDataset(Dataset):
            def __init__(self, num_samples):
                self.num_samples = num_samples

            def __getitem__(self, idx):
                data = numpy.random.random([input_size]).astype('float16')
                label = numpy.random.random([output_size]).astype('float16')
                return data, label

            def __len__(self):
                return self.num_samples

        dataset = RandomDataset(nums_batch * batch_size)
        loader = paddle.io.DataLoader(
            dataset,
            batch_size=batch_size,
            shuffle=False,
            drop_last=True,
            num_workers=0,
        )

        mse = paddle.nn.MSELoss()
        model = SimpleNet(input_size, output_size)  # 定义模型
        optimizer = paddle.optimizer.Momentum(
            learning_rate=0.0001,
            parameters=model.parameters(),
            multi_precision=True,
        )  # 定义优化器
        scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
        model = paddle.amp.decorate(models=model, level='O2')

        for i, (data, label) in enumerate(loader):
            with paddle.amp.auto_cast(level='O2'):
                output = model(data)
                loss = mse(output, label)
            scaled = scaler.scale(loss)
            scaled.backward()
            scaler.step(optimizer)
            scaler.update()
            optimizer.clear_grad(set_to_zero=False)

            if use_save_load and i == 5:
                model_path = os.path.join(self.temp_dir.name, "model.pdparams")
                optimizer_path = os.path.join(self.temp_dir.name, "opt.pdopt")
                paddle.save(model.state_dict(), model_path)
                paddle.save(optimizer.state_dict(), optimizer_path)
                model.set_state_dict(paddle.load(model_path))
                optimizer.set_state_dict(paddle.load(optimizer_path))

        return loss.numpy()

    def test_with_state_dict(self):
        if core.is_compiled_with_cuda():
            with fluid.dygraph.guard():
                out_use_state_dict = self.check_with_opt_state_dict(
                    use_save_load=True
                )
                out_no_state_dict = self.check_with_opt_state_dict(
                    use_save_load=False
                )
            np.testing.assert_array_equal(out_use_state_dict, out_no_state_dict)


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