test_adamw_op.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest
import paddle
import random
import numpy as np
import paddle.fluid as fluid
from op_test import OpTest
from functools import partial
from paddle.framework import core
from paddle.fluid.framework import _test_eager_guard


def adamw_step(inputs, attributes):
    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 'lr_ratio' in attributes:
        lr = lr * attributes['lr_ratio']

    if attributes["with_decay"]:
        coeff = attributes["coeff"]
        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


class TestAdamW(OpTest):
    def setUp(self):
        '''Test AdamW Op with supplied attributes
        '''
        self.op_type = "adamw"
        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,
            "coeff": 0.5,
            "with_decay": True
        }

        param_out, moment1_out, \
            moment2_out = adamw_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()


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestAdamW2(OpTest):
    def setUp(self):
        '''Test AdamW Op with supplied attributes
        '''
        self.op_type = "adamw"
        param = np.random.uniform(-1, 1, (2, 2)).astype("float32")
        grad = np.random.uniform(-1, 1, (2, 2)).astype("float32")
        moment1 = np.random.uniform(-1, 1, (2, 2)).astype("float32")
        # The second moment is positive
        moment2 = np.random.random((2, 2)).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,
            "lr_ratio": 0.1,
            "coeff": 0.5,
            "with_decay": True
        }

        param_out, moment1_out, moment2_out = adamw_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_with_place(core.CUDAPlace(0))


class TestAdamWOp(unittest.TestCase):
    def test_adamw_op_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear = paddle.nn.Linear(13, 5)
        adam = paddle.optimizer.AdamW(
            learning_rate=0.01,
            parameters=linear.parameters(),
            apply_decay_param_fun=lambda name: True,
            weight_decay=0.01)

        for _ in range(2):
            out = linear(a)
            out.backward()
            adam.step()
            adam.clear_gradients()

    def test_adamw_op_coverage(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear = paddle.nn.Linear(13, 5)
        adam = paddle.optimizer.AdamW(
            learning_rate=0.0,
            parameters=linear.parameters(),
            apply_decay_param_fun=lambda name: True,
            weight_decay=0.01)
        assert (adam.__str__() is not None)

    def test_adamw_op(self):
        paddle.enable_static()
        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 = paddle.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.AdamW(
                    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
        paddle.disable_static()

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

    def test_api_eager_dygraph(self):
        with _test_eager_guard():
            self.test_adamw_op_dygraph()
            self.test_adamw_op_invalid_input()


class TestAdamWOpGroup(TestAdamWOp):
    def test_adamw_op_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear_1 = paddle.nn.Linear(13, 5)
        linear_2 = paddle.nn.Linear(5, 3)
        adam = paddle.optimizer.AdamW(
            learning_rate=0.01,
            parameters=[{
                'params': linear_1.parameters()
            }, {
                'params': linear_2.parameters(),
                'weight_decay': 0.001
            }],
            apply_decay_param_fun=lambda name: True,
            weight_decay=0.01)

        for _ in range(2):
            out = linear_1(a)
            out = linear_2(out)
            out.backward()
            adam.step()
            adam.clear_gradients()


class TestAdamWOpGroupWithLR(TestAdamWOp):
    def test_adamw_op_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear_1 = paddle.nn.Linear(13, 5)
        linear_2 = paddle.nn.Linear(5, 3)
        adam = paddle.optimizer.AdamW(
            learning_rate=paddle.optimizer.lr.PiecewiseDecay(
                boundaries=[3, 6], values=[0.1, 0.2, 0.3]),
            parameters=[{
                'params': linear_1.parameters(),
                'learning_rate': 0.1,
            }, {
                'params': linear_2.parameters(),
                'weight_decay': 0.001,
            }],
            apply_decay_param_fun=lambda name: True,
            weight_decay=0.01)

        for _ in range(2):
            out = linear_1(a)
            out = linear_2(out)
            out.backward()
            adam.step()
            adam.clear_gradients()


def simple_lr_setting(param, decay_rate, n_layers):
    if "fc_0" in param.name or "linear_1" in param.name:
        depth = int(param.name.split("_")[2]) + 1
    elif "fc_1" in param.name or "linear_2" in param.name:
        depth = int(param.name.split("_")[2]) + 2
    else:
        depth = 0

    return decay_rate**(n_layers + 2 - depth)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestAdamWOpLayerwiseLR(TestAdamWOp):
    def setUp(self):
        random.seed(2021)
        np.random.seed(2021)
        paddle.seed(2021)

    def test_adamw_op_dygraph(self):
        paddle.disable_static()
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = paddle.to_tensor(value)
        linear1 = paddle.nn.Linear(13, 8)
        linear2 = paddle.nn.Linear(8, 5)

        # fix the linear name, simple_lr_setting function will use the name
        linear1.weight.name = "linear_1.w_0"
        linear1.bias.name = "linear_1.b_0"
        linear2.weight.name = "linear_2.w_0"
        linear2.bias.name = "linear_2.b_0"

        simple_lr_fun = partial(simple_lr_setting, decay_rate=0.8, n_layers=2)

        adam = paddle.optimizer.AdamW(
            learning_rate=0.01,
            parameters=[{
                'params': linear1.parameters()
            }, {
                'params': linear2.parameters(),
            }],
            apply_decay_param_fun=lambda name: True,
            weight_decay=0.01,
            lr_ratio=simple_lr_fun)

        loss_ref = np.array(
            [-1.7267396, -2.81524, -3.9250019, -5.05954, -6.2272625])
        for i in range(5):
            a1 = linear1(a)
            out = linear2(a1)
            out = paddle.mean(out)
            out.backward()
            adam.step()
            adam.clear_gradients()
            np.testing.assert_allclose(out[0].numpy(), loss_ref[i], rtol=1e-6)

    def test_adamw_op(self):
        paddle.enable_static()
        place = fluid.CUDAPlace(0)
        train_prog = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(train_prog, startup):
            with fluid.unique_name.guard():
                x = fluid.data(name='x', shape=[None, 10], dtype='float32')
                y = fluid.data(name='y', shape=[None, 1], dtype='float32')

                fc1 = fluid.layers.fc(input=x, size=32, act=None)
                prediction = fluid.layers.fc(input=fc1, size=1, act=None)
                cost = fluid.layers.square_error_cost(input=prediction, label=y)
                avg_cost = fluid.layers.mean(cost)

                simple_lr_fun = partial(
                    simple_lr_setting, decay_rate=0.8, n_layers=2)

                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.AdamW(
                    learning_rate=1e-5,
                    beta1=beta1,
                    beta2=beta2,
                    weight_decay=0.01,
                    epsilon=1e-8,
                    lr_ratio=simple_lr_fun)
                opt.minimize(avg_cost)

        exe = fluid.Executor(place)
        exe.run(startup)

        loss_ref = np.array(
            [0.33895183, 0.3159437, 0.19472016, 0.17764759, 0.1520702])
        for i in range(5):
            inputs = np.random.random(size=[8, 10]).astype('float32')
            outputs = np.random.random(size=[8, 1]).astype('float32')
            rets = exe.run(train_prog,
                           feed={"x": inputs,
                                 "y": outputs},
                           fetch_list=[avg_cost])
            assert rets[0] is not None
            np.testing.assert_allclose(rets[0], loss_ref[i], rtol=1e-6)

        paddle.disable_static()


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