cf1cc19bb79ae4128ef5437279de475835374a46,benchmark/runtime/dgl/train.py,,train_runtime,#Any#Any#Any#Any#,9

Before Change

    mask = torch.tensor(data.train_mask, dtype=torch.uint8, device=device)
    y = torch.tensor(data.labels, dtype=torch.long, device=device)[mask]
    g.add_edges(g.nodes(), g.nodes())
    norm = torch.pow(g.in_degrees().float(), -0.5)
    norm[torch.isinf(norm)] = 0
    g.ndata["norm"] = norm.unsqueeze(1).to(device)

    model = Net(g, x.size(1), data.num_labels).to(device)
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

After Change

    for epoch in range(epochs):
        optimizer.zero_grad()
        out = model(x)
        loss = F.nll_loss(out[mask], y.view(-1))
        loss.backward()
        optimizer.step()

Instances