# 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.

import unittest

from hybrid_parallel_mp_model import TestDistMPTraining

import paddle
from paddle.distributed import fleet


class TestMPClipGrad(TestDistMPTraining):
    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=[
                {
                    'params': model.parameters(),
                    'weight_decay': 0.001,
                    'learning_rate': 0.1,
                }
            ],
        )
        return optimizer

    def train_batch(self, batch, model, optimizer, is_mp):
        scaler = paddle.amp.GradScaler(init_loss_scaling=5160)
        if is_mp:
            scaler = fleet.distributed_scaler(scaler)
        with paddle.amp.auto_cast():
            output = model(batch)
            loss = output.mean()

        scaled = scaler.scale(loss)  # scale the loss
        scaled.backward()  # do backward

        scaler.step(optimizer)  # update parameters
        scaler.update()
        optimizer.clear_grad()
        return scaled


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