# Copyright (c) 2021 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 division
from __future__ import print_function

import paddle
import numpy as np
from hybrid_parallel_mp_model import TestDistMPTraning
import unittest
import logging

#log = logging.getLogger("HybridParallel")
#log.setLevel(logging.WARNING)


class TestMPClipGrad(TestDistMPTraning):
    def build_optimizer(self, model):
        grad_clip = paddle.nn.ClipGradByGlobalNorm(2.0)
        scheduler = paddle.optimizer.lr.ExponentialDecay(
            learning_rate=0.001, gamma=0.999, verbose=True)
        optimizer = paddle.optimizer.SGD(scheduler,
                                         grad_clip=grad_clip,
                                         parameters=model.parameters())
        return optimizer


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