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import os
from datetime import datetime
import argparse
import torch.multiprocessing as mp
import torchvision
import torchvision.transforms as transforms
import torch
import torch.nn as nn
import torch.distributed as dist
from apex.parallel import DistributedDataParallel as DDP
from apex import amp


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N',
                        help='number of data loading workers (default: 4)')
    parser.add_argument('-g', '--gpus', default=1, type=int,
                        help='number of gpus per node')
    parser.add_argument('-nr', '--nr', default=0, type=int,
                        help='ranking within the nodes')
    parser.add_argument('--epochs', default=2, type=int, metavar='N',
                        help='number of total epochs to run')
    args = parser.parse_args()
    args.world_size = args.gpus * args.nodes
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '8888'
    mp.spawn(train, nprocs=args.gpus, args=(args,))


class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out


def train(gpu, args):
    rank = args.nr * args.gpus + gpu
    dist.init_process_group(
        backend='nccl',
        init_method='env://',
        world_size=args.world_size,
        rank=rank)
    torch.manual_seed(0)
    model = ConvNet()
    torch.cuda.set_device(gpu)
    model.cuda(gpu)
    batch_size = 100
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(gpu)
    optimizer = torch.optim.SGD(model.parameters(), 1e-4)
    # Wrap the model
    model, optimizer = amp.initialize(model, optimizer, opt_level='O2')
    model = DDP(model)
    # Data loading code
    train_dataset = torchvision.datasets.MNIST(
        root='./data',
        train=True,
        transform=transforms.ToTensor(),
        download=True
    )
    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset,
        num_replicas=args.world_size,
        rank=rank)
    train_loader = torch.utils.data.DataLoader(
        dataset=train_dataset,
        batch_size=batch_size,
        shuffle=False,
        num_workers=0,
        pin_memory=True,
        sampler=train_sampler
    )

    start = datetime.now()
    total_step = len(train_loader)
    for epoch in range(args.epochs):
        for i, (images, labels) in enumerate(train_loader):
            images = images.cuda(non_blocking=True)
            labels = labels.cuda(non_blocking=True)
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)

            # Backward and optimize
            optimizer.zero_grad()
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
            optimizer.step()
            if (i + 1) % 100 == 0 and gpu == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(
                    epoch + 1,
                    args.epochs,
                    i + 1,
                    total_step,
                    loss.item())
                )
    if gpu == 0:
        print("Training complete in: " + str(datetime.now() - start))


if __name__ == '__main__':
    main()