# Copyright 2019 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.
# ============================================================================
import datetime
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.nn.optim import Momentum
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.ops import operations as P
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.common import dtype as mstype

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
init('nccl')

epoch = 5
total = 5000
batch_size = 32
mini_batch = total // batch_size


class LeNet(nn.Cell):
    def __init__(self):
        super(LeNet, self).__init__()

        self.relu = P.ReLU()
        self.batch_size = 32
        weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
        weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
        self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
        self.reshape = P.Reshape()

        weight1 = Tensor(np.ones([120, 400]).astype(np.float32) * 0.01)
        self.fc1 = nn.Dense(400, 120, weight_init=weight1)

        weight2 = Tensor(np.ones([84, 120]).astype(np.float32) * 0.01)
        self.fc2 = nn.Dense(120, 84, weight_init=weight2)

        weight3 = Tensor(np.ones([10, 84]).astype(np.float32) * 0.01)
        self.fc3 = nn.Dense(84, 10, weight_init=weight3)

    def construct(self, input_x):
        output = self.conv1(input_x)
        output = self.relu(output)
        output = self.pool(output)
        output = self.conv2(output)
        output = self.relu(output)
        output = self.pool(output)
        output = self.reshape(output, (self.batch_size, -1))
        output = self.fc1(output)
        output = self.fc2(output)
        output = self.fc3(output)
        return output


def multisteplr(total_steps, gap, base_lr=0.9, gamma=0.1, dtype=mstype.float32):
    lr = []
    for step in range(total_steps):
        lr_ = base_lr * gamma ** (step//gap)
        lr.append(lr_)
    return Tensor(np.array(lr), dtype)


def test_lenet_nccl():
    net = LeNet()
    net.set_train()

    learning_rate = multisteplr(epoch, 2)
    momentum = Tensor(np.array([0.9]).astype(np.float32))
    mom_optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
    net_with_criterion = WithLossCell(net, criterion)
    context.set_auto_parallel_context(parallel_mode="data_parallel", mirror_mean=True, device_num=get_group_size())
    train_network = TrainOneStepCell(net_with_criterion, mom_optimizer)
    train_network.set_train()
    losses = []

    data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([net.batch_size]).astype(np.int32))
    start = datetime.datetime.now()
    for i in range(epoch):
        for step in range(mini_batch):
            loss = train_network(data, label)
            losses.append(loss.asnumpy())
    end = datetime.datetime.now()
    with open("ms_time.txt", "w") as fo1:
        fo1.write("time:")
        fo1.write(str(end - start))
    with open("ms_loss.txt", "w") as fo2:
        fo2.write("loss:")
        fo2.write(str(losses[-5:]))
    assert(losses[-1] < 0.01)