import mindspore.nn as nn
from mindspore import ops
from src.model_utils.config import config


class AlexNet(nn.Cell):
    """
    Alexnet
    """

    def __init__(self):
        super(AlexNet, self).__init__()
        self.fc1 = nn.Dense(128, 256, has_bias=False)
        self.fc1.matmul.shard(((config.dp, 1), (config.mp, 1)))
        self.fc2 = nn.Dense(256, 512, has_bias=False)
        self.fc2.matmul.shard(((config.dp, config.mp), (1, config.mp)))

    def construct(self, x):
        """define network"""
        x = self.fc1(x)
        x = self.fc2(x)
        x = ops.reduce_mean(x, 0)
        x = ops.reduce_mean(x, 0)

        return x

2.  分布式训练脚本：

```python
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
######################## train alexnet example ########################
train alexnet and get network model files(.ckpt) :
python train.py --data_path /YourDataPath
"""

import os

from mindspore.nn import Cell
from mindspore.profiler import Profiler
from mindspore.train.callback import LossMonitor, TimeMonitor, CheckpointConfig, ModelCheckpoint

from src.model_utils.config import config
from src.model_utils.moxing_adapter import moxing_wrapper
from src.model_utils.device_adapter import get_device_id, get_rank_id, get_job_id
from src.dataset import create_dataset_cifar10
from src.generator_lr import get_lr_cifar10, get_lr_imagenet
from src.alexnet import AlexNet
from src.get_param_groups import get_param_groups

import mindspore.nn as nn
from mindspore.communication.management import init, get_rank
from mindspore import dataset as de
from mindspore import context
from mindspore import Tensor, ops
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.common import set_seed

import numpy as np
from termcolor import cprint

set_seed(1)
de.config.set_seed(1)


class OpsReduceSum(Cell):
    def __init__(self):
        super(OpsReduceSum, self).__init__()

    def construct(self, x, y):
        return ops.add(x, y)


def modelarts_pre_process():
    pass


@moxing_wrapper(pre_process=modelarts_pre_process)
def train_alexnet():
    print(config)
    cprint(f'device id: {get_device_id()}', on_color='on_red')
    # print('device num:', config.device_num)
    cprint(f'rank id: {get_rank_id()}', on_color='on_red')
    cprint(f'job id: {get_job_id()}', on_color='on_red')
    device_num = config.dp * config.mp

    context.set_context(mode=context.GRAPH_MODE,
                        save_graphs=True,
                        device_target='Ascend',
                        device_id=get_device_id(),
                        save_graphs_path=f"bs{config.batch_size}_dn{config.device_num}_dp{config.dp}_mp{config.mp}")
    print("set save_graphs ....")

    cprint(f"device_num={device_num}, device_id={get_device_id()}", on_color='on_red')

    context.reset_auto_parallel_context()
    context.set_auto_parallel_context(device_num=device_num, full_batch=True, global_rank=0,
                                      parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=True)

    profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
    init()

    network = AlexNet()
    cprint(network.trainable_params(), on_color='on_red')

    metrics = None
    step_per_epoch = 10000
    lr = Tensor(get_lr_imagenet(config.learning_rate, config.epoch_size, step_per_epoch))
    opt = nn.Momentum(params=network.trainable_params(),
                      learning_rate=lr,
                      momentum=config.momentum)

    from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
    if config.is_dynamic_loss_scale == 1:
        loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
    else:
        loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)

    model = Model(network, loss_fn=OpsReduceSum(), optimizer=opt, metrics=metrics, amp_level="O2", keep_batchnorm_fp32=False,
                  loss_scale_manager=loss_scale_manager)

    cprint("============== Starting Training ==============", on_color='on_red')

    for i in range(1):
        model.train_network.compile(Tensor(np.ones([config.batch_size, 128]).astype(np.float32)),
                                    Tensor(np.ones([1]).astype(np.int32)))

    profiler.analyse()


if __name__ == "__main__":
    train_alexnet()

3. 执行训练任务

```shell
export RANK_TABLE_FILE=/home/z00448363/mindspore/model_zoo/utils/hccl_tools/hccl_1p_6_0.0.0.22.json
export DEVICE_ID=6
export RANK_ID=0

python train.py --device_id=6 --dp=2 --mp=1


### Describe the expected behavior / 预期结果 (Mandatory / 必填)
 预期顺利编译成功

### Related log / screenshot / 日志 / 截图 (Mandatory / 必填)
 打印了部分结果：
![输入图片说明](https://images.gitee.com/uploads/images/2022/0224/113927_8ecbf9ea_78977.png "屏幕截图.png")

### Special notes for this issue/备注 (Optional / 选填)