  import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
import torchrec as trec

def _get_random_dataloader(num_embeddings, batch_size, pin_memory):
    return torch.utils.data.DataLoader(
        torch.randint(0, num_embeddings, (batch_size,)),
        batch_size=batch_size,
        pin_memory=pin_memory,
    )

def train(num_embeddings, embedding_dim, dense_arch_layer_sizes, over_arch_layer_sizes, learning_rate):
    # Initialize the process group, device, rank, and backend
    dist.init_process_group(backend="nccl")
    device = torch.device("cuda")
    rank = dist.get_rank()
    world_size = dist.get_world_size()

    # Construct a DLRM model
    model = trec.DLRM(
        num_embeddings,
        embedding_dim,
        dense_arch_layer_sizes,
        over_arch_layer_sizes,
    )

    # Enable optimizer fusion
    model.enable_optimizer_fusion()

    # Distribute the model across devices
    model = model.to(device)
    model = dist.DistributedDataParallel(model, device_ids=[rank])

    # Overlap communication, compute, and device transfer during training
    model.overlap_communication_and_compute()

    # Construct a training iterator
    train_loader = _get_random_dataloader(num_embeddings, 32, True)
    train_iterator = iter(train_loader)

    # Train the model
    optimizer = optim.SGD(model.parameters(), lr=learning_rate)
    for epoch in range(10):
        for batch in train_iterator:
            optimizer.zero_grad()
            output = model(batch)
            loss = output.sum()
            loss.backward()
            optimizer.step()

if __name__ == "__main__":
    train(1000, 128, [128, 128], [128, 128], 0.001)