# 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 time
import shutil
import tempfile
import numpy as np

import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Linear
from paddle.distributed import fleet
from paddle.fluid.dygraph import nn
from paddle.fluid.framework import _test_eager_guard
from paddle.distributed.sharding import group_sharded_parallel, save_group_sharded_model

epoch = 10
paddle.seed(2022)
np.random.seed(2022)
base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4
batch_size = 100


class MLP(fluid.Layer):

    def __init__(self, linear_size=1000, param_attr=None, bias_attr=None):
        super(MLP, self).__init__()

        self._linear1 = Linear(linear_size, linear_size)
        self._linear2 = Linear(linear_size, linear_size)
        self._linear3 = Linear(linear_size, 10)

    def forward(self, inputs):
        y = self._linear1(inputs)
        y = self._linear2(y)
        y = self._linear3(y)
        return y


def reader_decorator(linear_size=1000):

    def __reader__():
        for _ in range(100):
            img = np.random.rand(linear_size).astype('float32')
            label = np.ones(1).astype('int64')
            yield img, label

    return __reader__


def optimizer_setting(model, use_pure_fp16, opt_group=False):
    clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
    optimizer = paddle.optimizer.Momentum(
        parameters=[{
            "params": list(model.parameters())
        }] if opt_group else list(model.parameters()),
        learning_rate=0.001,
        weight_decay=0.00001,
        grad_clip=clip,
        multi_precision=use_pure_fp16)

    return optimizer


def train_mlp(model, shard_level, use_pure_fp16, output_dir):
    group = paddle.distributed.new_group([0, 1])

    optimizer = optimizer_setting(model=model, use_pure_fp16=use_pure_fp16)
    model = paddle.amp.decorate(models=model, level='O2', save_dtype='float32')
    scaler = paddle.amp.GradScaler(init_loss_scaling=32768)

    model, optimizer, scaler = group_sharded_parallel(model=model,
                                                      optimizer=optimizer,
                                                      level=shard_level,
                                                      scaler=scaler)

    train_reader = paddle.batch(reader_decorator(),
                                batch_size=batch_size,
                                drop_last=True)

    train_loader = paddle.io.DataLoader.from_generator(capacity=32,
                                                       use_double_buffer=True,
                                                       iterable=True,
                                                       return_list=True,
                                                       use_multiprocess=True)
    train_loader.set_sample_list_generator(train_reader)

    for eop in range(epoch):
        model.train()
        for batch_id, data in enumerate(train_loader()):
            img, label = data
            label.stop_gradient = True
            img.stop_gradient = True
            with paddle.amp.auto_cast(True, level='O2'):
                out = model(img)
                loss = paddle.nn.functional.cross_entropy(input=out,
                                                          label=label)
            avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))

            if not use_pure_fp16:
                avg_loss.backward()
                optimizer.step()
            else:
                scaler.scale(avg_loss).backward()
                scaler.step(optimizer)
                scaler.update()

            optimizer.clear_grad()

    save_group_sharded_model(model, output=output_dir, optimizer=optimizer)
    return model.parameters()


def test_sharding_api():
    mlp, mlp1, mlp2 = MLP(), MLP(), MLP()
    state_dict = mlp.state_dict()
    mlp1.set_state_dict(state_dict)
    mlp2.set_state_dict(state_dict)

    output_dir = tempfile.mkdtemp()

    # fp16
    stage2_params = train_mlp(mlp1,
                              shard_level="os_g",
                              use_pure_fp16=True,
                              output_dir=output_dir)
    stage3_params = train_mlp(mlp2,
                              shard_level="p_g_os",
                              use_pure_fp16=True,
                              output_dir=output_dir)

    for i in range(len(stage3_params)):
        np.testing.assert_allclose(stage2_params[i].numpy(),
                                   stage3_params[i].numpy(),
                                   rtol=1e-4,
                                   atol=1e-3)
    shutil.rmtree(output_dir)


if __name__ == '__main__':
    with _test_eager_guard():
        pass
    fleet.init(is_collective=True)
    test_sharding_api()
