## This code is from https://github.com/pytorch/examples/blob/master/mnist/main.py
## with modification to do training using onnxruntime as backend on cuda device.
## A private PyTorch build from https://aiinfra.visualstudio.com/Lotus/_git/pytorch (ORTTraining branch) is needed to run the demo.

## Model testing is not complete.

import argparse
import os

import numpy as np  # noqa: F401
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim  # noqa: F401
from mpi4py import MPI
from torchvision import datasets, transforms

from onnxruntime.capi.ort_trainer import IODescription, ModelDescription, ORTTrainer

try:  # noqa: SIM105
    from onnxruntime.capi._pybind_state import set_cuda_device_id
except ImportError:
    pass


class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super().__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out


def my_loss(x, target):
    return F.nll_loss(F.log_softmax(x, dim=1), target)


def train_with_trainer(args, trainer, device, train_loader, epoch):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)  # noqa: PLW2901
        data = data.reshape(data.shape[0], -1)  # noqa: PLW2901

        learning_rate = torch.tensor([args.lr])
        loss = trainer.train_step(data, target, learning_rate)

        # Since the output corresponds to [loss_desc, probability_desc], the first value is taken as loss.
        if batch_idx % args.log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch,
                    batch_idx * len(data),
                    len(train_loader.dataset),
                    100.0 * batch_idx / len(train_loader),
                    loss[0],
                )
            )


# TODO: comple this once ORT training can do evaluation.
def test_with_trainer(args, trainer, device, test_loader):
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)  # noqa: PLW2901
            data = data.reshape(data.shape[0], -1)  # noqa: PLW2901
            output = F.log_softmax(trainer.eval_step(data, fetches=["probability"]), dim=1)
            test_loss += F.nll_loss(output, target, reduction="sum").item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print(
        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
            test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
        )
    )


def mnist_model_description():
    input_desc = IODescription("input1", ["batch", 784], torch.float32)
    label_desc = IODescription(
        "label",
        [
            "batch",
        ],
        torch.int64,
        num_classes=10,
    )
    loss_desc = IODescription("loss", [], torch.float32)
    probability_desc = IODescription("probability", ["batch", 10], torch.float32)
    return ModelDescription([input_desc, label_desc], [loss_desc, probability_desc])


def main():
    # Training settings
    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
    parser.add_argument(
        "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
    )
    parser.add_argument(
        "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
    )
    parser.add_argument("--epochs", type=int, default=10, 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("--no-cuda", action="store_true", default=False, help="disables CUDA training")
    parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
    parser.add_argument(
        "--log-interval",
        type=int,
        default=10,
        metavar="N",
        help="how many batches to wait before logging training status",
    )

    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    kwargs = {"num_workers": 0, "pin_memory": True}
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST(
            "../data",
            train=True,
            download=True,
            transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
        ),
        batch_size=args.batch_size,
        shuffle=True,
        **kwargs,
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST(
            "../data",
            train=False,
            transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]),
        ),
        batch_size=args.test_batch_size,
        shuffle=True,
        **kwargs,
    )

    comm = MPI.COMM_WORLD
    args.local_rank = (
        int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) if ("OMPI_COMM_WORLD_LOCAL_RANK" in os.environ) else 0
    )
    args.world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) if ("OMPI_COMM_WORLD_RANK" in os.environ) else 0
    args.world_size = comm.Get_size()
    if use_cuda:
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        args.n_gpu = 1
        set_cuda_device_id(args.local_rank)
    else:
        device = torch.device("cpu")

    input_size = 784
    hidden_size = 500
    num_classes = 10
    model = NeuralNet(input_size, hidden_size, num_classes)

    model_desc = mnist_model_description()
    # use log_interval as gradient accumulate steps
    trainer = ORTTrainer(
        model,
        my_loss,
        model_desc,
        "SGDOptimizer",
        None,
        IODescription(
            "Learning_Rate",
            [
                1,
            ],
            torch.float32,
        ),
        device,
        1,
        args.world_rank,
        args.world_size,
        use_mixed_precision=False,
        allreduce_post_accumulation=True,
    )
    print("\nBuild ort model done.")

    for epoch in range(1, args.epochs + 1):
        train_with_trainer(args, trainer, device, train_loader, epoch)
        import pdb  # noqa: F401

        test_with_trainer(args, trainer, device, test_loader)


if __name__ == "__main__":
    main()
