# -*- coding: UTF-8 -*-

# 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 os
import numpy as np
import argparse
import ast
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Linear
from paddle.distributed import fleet
from paddle.fluid.framework import _test_eager_guard

from paddle.distributed.fleet.utils.internal_storage import GradStorage
from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.sharding_optimizer_stage2 import ShardingOptimizerStage2

base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4

epoch = 100
batch_size = 32
class_dim = 102


class MLP(fluid.Layer):
    def __init__(self, param_attr=None, bias_attr=None):
        super(MLP, self).__init__()

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

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


def reader_decorator():
    def __reader__():
        for _ in range(100):
            img = np.random.rand(10).astype('float32')
            label = np.ones(1).astype('int64')
            yield img, label

    return __reader__


def optimizer_setting(parameter_list=None):
    optimizer = paddle.optimizer.Momentum(
        learning_rate=base_lr,
        momentum=momentum_rate,
        weight_decay=paddle.regularizer.L2Decay(l2_decay),
        parameters=parameter_list)
    return optimizer


def train_mlp():
    fleet.init(is_collective=True)
    group = paddle.distributed.new_group([0, 1])

    mlp = MLP()

    optimizer = optimizer_setting(parameter_list=mlp.parameters())
    oss_optimizer = ShardingOptimizerStage2(
        params=mlp.parameters(), optim=optimizer, group=group)
    # cover grad_storage code
    trainable_param2align = dict()
    for p in mlp.parameters():
        trainable_param2align[p.name] = 0
    grad_storage = GradStorage(
        10000,
        dtype=paddle.float32,
        device="gpu",
        destination=0,
        parm2align=trainable_param2align)
    for p in mlp.parameters():
        grad_storage.can_add_grad_view(p, trainable_param2align[p.name])
        grad_storage.add_grad(p, trainable_param2align[p.name])
    grad_storage.manumal_relase()
    grad_storage.rebuild()
    grad_storage.reset_checked_in()

    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):
        mlp.train()

        for batch_id, data in enumerate(train_loader()):
            img, label = data
            label.stop_gradient = True
            img.stop_gradient = True

            out = mlp(img)
            loss = paddle.nn.functional.cross_entropy(input=out, label=label)
            avg_loss = paddle.mean(x=loss)
            acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
            acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)

            dy_out = avg_loss.numpy()

            avg_loss.backward()
            oss_optimizer.step()

            # oss_optimizer clear cache
            oss_optimizer._clear_cache()

            # check optimizer.minimize() error
            try:
                oss_optimizer.minimize()
            except:
                print(
                    "====== Find sharding_stage2_optimizer.minimize() error ======"
                )
            return


if __name__ == '__main__':
    with _test_eager_guard():
        pass
    train_mlp()