# Copyright (c) 2022 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 unittest

import numpy as np
from dist_pass_test_base import DistPassTestBase

import paddle
from paddle import nn
from paddle.distributed import fleet
from paddle.distributed.passes import PassManager, new_pass

paddle.enable_static()


class BatchNormAddActNet(nn.Layer):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2D(3, 8, (3, 3), data_format="NHWC")
        self.conv2 = nn.Conv2D(3, 8, (3, 3), data_format="NHWC")
        self.bn1 = nn.BatchNorm2D(8, data_format="NHWC")
        self.bn2 = nn.BatchNorm2D(8, data_format="NHWC")
        self.relu = nn.ReLU()

    def forward(self, x):
        y = self.conv1(x)
        y = self.bn1(y)
        out = self.conv2(x)
        out = self.bn2(out) + y
        out = self.relu(out)
        out = paddle.flatten(out, 1)
        return out


class TestFuseBatchNormAddActPass(DistPassTestBase):
    def init(self):
        self.atol = 1e-4
        self.rtol = 1e-4

    def get_model(self, place, batch_size=32, image_shape=[224, 224, 3]):
        image = paddle.static.data(
            shape=[batch_size] + image_shape, dtype='float32', name='image'
        )

        model = BatchNormAddActNet()
        pred_out = model(image)
        loss = paddle.mean(pred_out)
        optimizer = paddle.optimizer.Adam(learning_rate=1e-3)

        dist_strategy = fleet.DistributedStrategy()
        dist_strategy.fuse_all_reduce_ops = False
        dist_strategy.without_graph_optimization = True
        dist_strategy.amp = True
        dist_strategy.amp_configs = {
            "init_loss_scaling": 32768,
            "use_dynamic_loss_scaling": True,
        }
        fleet.init(is_collective=True, strategy=dist_strategy)
        optimizer = fleet.distributed_optimizer(optimizer)
        optimizer.minimize(loss)

        rank = paddle.distributed.get_rank()

        def reader():
            seed = int(os.environ.get("SEED", 0))
            np.random.seed(seed + rank)
            for _ in range(10):
                image_np = np.random.random(size=image.shape).astype('float32')
                yield image_np,

        main_program = paddle.static.default_main_program()
        startup_program = paddle.static.default_startup_program()
        return main_program, startup_program, [image], [loss], reader

    def apply_passes(self, main_prog, startup_prog):
        pass_manager = PassManager([new_pass("fuse_bn_add_act")])
        pass_manager.apply([main_prog], [startup_prog])
        print(pass_manager.names)

        op_type = []
        for op in main_prog.global_block().ops:
            op_type.append(op.type)
        self.assertTrue("fused_bn_add_activation" in op_type)
        self.assertTrue("fused_bn_add_activation_grad" in op_type)

    def test_fuse_bn_add_act(self):
        self.check_main()


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