# This file is duplicated in release/ml_user_tests/horovod
import argparse
import os
from filelock import FileLock

import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed

import horovod.torch as hvd
from horovod.ray import RayExecutor


def metric_average(val, name):
    tensor = torch.tensor(val)
    avg_tensor = hvd.allreduce(tensor, name=name)
    return avg_tensor.item()


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)


def train_fn(
    data_dir=None,
    seed=42,
    use_cuda=False,
    batch_size=64,
    use_adasum=False,
    lr=0.01,
    momentum=0.5,
    num_epochs=10,
    log_interval=10,
):
    # Horovod: initialize library.
    hvd.init()
    torch.manual_seed(seed)

    if use_cuda:
        # Horovod: pin GPU to local rank.
        torch.cuda.set_device(hvd.local_rank())
        torch.cuda.manual_seed(seed)

    # Horovod: limit # of CPU threads to be used per worker.
    torch.set_num_threads(1)

    kwargs = {"num_workers": 1, "pin_memory": True} if use_cuda else {}
    data_dir = data_dir or "./data"
    with FileLock(os.path.expanduser("~/.horovod_lock")):
        train_dataset = datasets.MNIST(
            data_dir,
            train=True,
            download=True,
            transform=transforms.Compose(
                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
            ),
        )
    # Horovod: use DistributedSampler to partition the training data.
    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset, num_replicas=hvd.size(), rank=hvd.rank()
    )
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=batch_size, sampler=train_sampler, **kwargs
    )

    model = Net()

    # By default, Adasum doesn't need scaling up learning rate.
    lr_scaler = hvd.size() if not use_adasum else 1

    if use_cuda:
        # Move model to GPU.
        model.cuda()
        # If using GPU Adasum allreduce, scale learning rate by local_size.
        if use_adasum and hvd.nccl_built():
            lr_scaler = hvd.local_size()

    # Horovod: scale learning rate by lr_scaler.
    optimizer = optim.SGD(model.parameters(), lr=lr * lr_scaler, momentum=momentum)

    # Horovod: wrap optimizer with DistributedOptimizer.
    optimizer = hvd.DistributedOptimizer(
        optimizer,
        named_parameters=model.named_parameters(),
        op=hvd.Adasum if use_adasum else hvd.Average,
    )

    for epoch in range(1, num_epochs + 1):
        model.train()
        # Horovod: set epoch to sampler for shuffling.
        train_sampler.set_epoch(epoch)
        for batch_idx, (data, target) in enumerate(train_loader):
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            optimizer.zero_grad()
            output = model(data)
            loss = F.nll_loss(output, target)
            loss.backward()
            optimizer.step()
            if batch_idx % log_interval == 0:
                # Horovod: use train_sampler to determine the number of
                # examples in this worker's partition.
                print(
                    "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                        epoch,
                        batch_idx * len(data),
                        len(train_sampler),
                        100.0 * batch_idx / len(train_loader),
                        loss.item(),
                    )
                )


def main(
    num_workers, use_gpu, timeout_s=30, placement_group_timeout_s=100, kwargs=None
):
    kwargs = kwargs or {}
    if use_gpu:
        kwargs["use_cuda"] = True
    settings = RayExecutor.create_settings(
        timeout_s=timeout_s, placement_group_timeout_s=placement_group_timeout_s
    )
    executor = RayExecutor(settings, use_gpu=use_gpu, num_workers=num_workers)
    executor.start()
    executor.run(train_fn, kwargs=kwargs)


if __name__ == "__main__":
    # Training settings
    parser = argparse.ArgumentParser(
        description="PyTorch MNIST Example",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        default=64,
        metavar="N",
        help="input batch size for training (default: 64)",
    )
    parser.add_argument(
        "--num-epochs",
        type=int,
        default=5,
        metavar="N",
        help="number of epochs to train (default: 10)",
    )
    parser.add_argument(
        "--lr",
        type=float,
        default=0.01,
        metavar="LR",
        help="learning rate (default: 0.01)",
    )
    parser.add_argument(
        "--momentum",
        type=float,
        default=0.5,
        metavar="M",
        help="SGD momentum (default: 0.5)",
    )
    parser.add_argument(
        "--use-cuda", action="store_true", default=False, help="enables CUDA training"
    )
    parser.add_argument(
        "--seed", type=int, default=42, metavar="S", help="random seed (default: 42)"
    )
    parser.add_argument(
        "--log-interval",
        type=int,
        default=10,
        metavar="N",
        help="how many batches to wait before logging training status",
    )
    parser.add_argument(
        "--use-adasum",
        action="store_true",
        default=False,
        help="use adasum algorithm to do reduction",
    )
    parser.add_argument(
        "--num-workers",
        type=int,
        default=4,
        help="Number of Ray workers to use for training.",
    )
    parser.add_argument(
        "--data-dir",
        help="location of the training dataset in the local filesystem ("
        "will be downloaded if needed)",
    )
    parser.add_argument(
        "--address",
        required=False,
        type=str,
        default=None,
        help="Address of Ray cluster.",
    )

    args = parser.parse_args()

    import ray

    if args.address:
        ray.init(args.address)
    else:
        ray.init()

    kwargs = {
        "data_dir": args.data_dir,
        "seed": args.seed,
        "use_cuda": args.use_cuda if args.use_cuda else False,
        "batch_size": args.batch_size,
        "use_adasum": args.use_adasum if args.use_adasum else False,
        "lr": args.lr,
        "momentum": args.momentum,
        "num_epochs": args.num_epochs,
        "log_interval": args.log_interval,
    }

    main(
        num_workers=args.num_workers,
        use_gpu=args.use_cuda if args.use_cuda else False,
        kwargs=kwargs,
    )
