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

# 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 shutil
import tempfile

import numpy as np

import paddle
from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_optimizer_stage2 import (
    GroupShardedOptimizerStage2,
)
from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_stage2 import (
    GroupShardedStage2,
)
from paddle.nn import Linear

seed = 2022
epoch = 2
linear_size = 1000

np.random.seed(seed)
paddle.seed(seed)


class MLP(paddle.nn.Layer):
    def __init__(self, linear_size=1000, param_attr=None, bias_attr=None):
        super().__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


class RandomDataset(paddle.io.Dataset):
    def __init__(self, num_samples=2000, linear_size=1000):
        self.num_samples = num_samples
        self.linear_size = linear_size

    def __getitem__(self, idx):
        img = np.random.rand(self.linear_size).astype('float32')
        label = np.ones(1).astype('int64')
        return img, label

    def __len__(self):
        return self.num_samples


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

    return optimizer


def train_mlp(
    model,
    sharding_stage,
    batch_size=100,
    use_pure_fp16=False,
    accumulate_grad=False,
    opt_group=False,
    save_model=False,
    test_minimize=False,
):
    if sharding_stage != "dp":
        group = paddle.distributed.new_group([0, 1], backend="nccl")
    if opt_group:
        optimizer = optimizer_setting(
            model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group
        )
    else:
        optimizer = optimizer_setting(model=model, use_pure_fp16=use_pure_fp16)

    if sharding_stage == 2:
        optimizer = GroupShardedOptimizerStage2(
            params=optimizer._parameter_list, optim=optimizer, group=group
        )

        model = GroupShardedStage2(
            model, optimizer, group=group, buffer_max_size=2**21
        )
    else:
        model = paddle.DataParallel(model)

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

    paddle.seed(2023)
    np.random.seed(2023)
    train_loader = paddle.io.DataLoader(
        RandomDataset(),
        batch_size=batch_size,
        shuffle=False,
        drop_last=True,
        num_workers=0,
    )

    if sharding_stage == 2:
        model.to(device="gpu")

    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

            out = model(img)
            loss = paddle.nn.functional.cross_entropy(input=out, label=label)

            avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))
            if batch_size == 20:
                avg_loss = avg_loss / 5
            avg_loss.backward()

            if not accumulate_grad:
                optimizer.step()
                optimizer.clear_grad()

        if accumulate_grad:
            optimizer.step()
            optimizer.clear_grad()

    if save_model:
        return model, optimizer
    return model.parameters()


def test_dp_stage2():
    paddle.distributed.init_parallel_env()
    mlp = MLP()
    state_dict = mlp.state_dict()
    mlp1 = MLP()
    mlp2 = MLP()
    mlp3 = MLP()
    mlp4 = MLP()
    mlp5 = MLP()
    mlp6 = MLP()
    mlp7 = MLP()
    mlp1.set_state_dict(state_dict)
    mlp2.set_state_dict(state_dict)
    mlp3.set_state_dict(state_dict)
    mlp4.set_state_dict(state_dict)
    mlp5.set_state_dict(state_dict)
    mlp6.set_state_dict(state_dict)
    mlp7.set_state_dict(state_dict)

    # DP VS stage2
    dp_params = train_mlp(
        mlp1, sharding_stage="dp", use_pure_fp16=False, opt_group=False
    )
    stage2_params = train_mlp(
        mlp2, sharding_stage=2, use_pure_fp16=False, opt_group=False
    )
    for i in range(len(dp_params)):
        np.testing.assert_allclose(
            dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6
        )

    # stage2 accumulate grad
    stage2_params = train_mlp(mlp3, sharding_stage=2, accumulate_grad=True)
    stage2_accumulate_grad = train_mlp(
        mlp4, sharding_stage=2, batch_size=20, accumulate_grad=True
    )
    for i in range(len(stage2_params)):
        np.testing.assert_allclose(
            stage2_params[i].numpy(),
            stage2_accumulate_grad[i].numpy(),
            rtol=1e-5,
            atol=1e-5,
        )

    # stage2 param list VS param group
    stage2_params = train_mlp(
        mlp5, sharding_stage=2, use_pure_fp16=False, opt_group=True
    )
    for i in range(len(dp_params)):
        np.testing.assert_allclose(
            dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6
        )

    # save/load model
    output_dir = tempfile.mkdtemp()
    model_file = os.path.join(output_dir, "model.pdmodel")
    optimizer_file = os.path.join(output_dir, "model.pdopt")
    model_stage2, optimizer_stage2 = train_mlp(
        mlp6,
        sharding_stage=2,
        use_pure_fp16=False,
        opt_group=False,
        save_model=True,
    )
    paddle.save(model_stage2.state_dict(), model_file)
    paddle.save(optimizer_stage2.state_dict(), optimizer_file)
    m_state_dict = paddle.load(model_file)
    opt_state_dict = paddle.load(optimizer_file)
    model_stage2.set_state_dict(m_state_dict)
    optimizer_stage2.set_state_dict(opt_state_dict)
    shutil.rmtree(output_dir)

    # check optimizer.minimize() error
    train_mlp(mlp7, sharding_stage=2, test_minimize=True)
    return


if __name__ == '__main__':
    test_dp_stage2()
