# 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

import numpy as np

import paddle
from paddle import Model, base
from paddle.nn.layer.loss import CrossEntropyLoss
from paddle.static import InputSpec
from paddle.vision.models import LeNet


@unittest.skipIf(
    not base.is_compiled_with_cuda(), 'CPU testing is not supported'
)
class TestDistTrainingWithPureFP16(unittest.TestCase):
    def test_amp_training_purefp16(self):
        if not base.is_compiled_with_cuda():
            self.skipTest('module not tested when ONLY_CPU compiling')
        data = np.random.random(size=(4, 1, 28, 28)).astype(np.float32)
        label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)

        paddle.enable_static()
        paddle.set_device('gpu')
        net = LeNet()
        amp_level = "O2"
        inputs = InputSpec([None, 1, 28, 28], "float32", 'x')
        labels = InputSpec([None, 1], "int64", "y")
        model = Model(net, inputs, labels)
        optim = paddle.optimizer.Adam(
            learning_rate=0.001,
            parameters=model.parameters(),
            multi_precision=True,
        )
        amp_configs = {"level": amp_level, "use_fp16_guard": False}
        model.prepare(
            optimizer=optim,
            loss=CrossEntropyLoss(reduction="sum"),
            amp_configs=amp_configs,
        )
        model.train_batch([data], [label])


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