Repository: snap-stanford/ogb
Issue: products /paper100M doesn't have node id for graphsage
Root cause: Documentation Deficiency

I want to train paper100M using graphsage.
It doesn't have node ids, I tried to use the method described at  https://github.com/pyg-team/pytorch_geometric/discussions/3528
But still failed.


```python
import torch
from torch_geometric.loader import NeighborSampler
from ogb.nodeproppred import PygNodePropPredDataset
from torch import Tensor
from torch_geometric.nn import SAGEConv
import torch.nn.functional as F

class SAGE(torch.nn.Module):

    def __init__(self,
                 in_channels,
                 hidden_channels,
                 out_channels,
                 num_layers=2):
        super(SAGE, self).__init__()
        self.num_layers = num_layers

        self.convs = torch.nn.ModuleList()
        self.convs.append(SAGEConv(in_channels, hidden_channels))
        for _ in range(self.num_layers - 2):
            self.convs.append(SAGEConv(hidden_channels, hidden_channels))
        self.convs.append(SAGEConv(hidden_channels, out_channels))

    def forward(self, x: Tensor, adjs: list) -> Tensor:
        for i, (edge_index, _, size) in enumerate(adjs):
            x_target = x[:size[1]]  # Target nodes are always placed first.
            x = self.convs[i]((x, x_target), edge_index)
            if i != self.num_layers - 1:
                x = F.relu(x)
                # x = F.dropout(x, p=0.5, training=self.training)
        return x.log_softmax(dim=-1)

    @torch.no_grad()
    def inference(self, x_all, device, subgraph_loader):
        for i in range(self.num_layers):
            xs = []
            for batch_size, n_id, adj in subgraph_loader:
                edge_index, _, size = adj.to(device)
                x = x_all[n_id].to(device)
                x_target = x[:size[1]]
                x = self.convs[i]((x, x_target), edge_index)
                if i != self.num_layers - 1:
                    x = F.relu(x)
                xs.append(x)

            x_all = torch.cat(xs, dim=0)

        return x_all

def main():
    dataset = PygNodePropPredDataset(name="ogbn-products", root="/data/ogb/")
    data = dataset[0]
    rank = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
    torch.cuda.set_device(rank)
    torch.manual_seed(12345)
    model = SAGE(data.num_features, 256, dataset.num_classes).to(rank)
    y = data.y
    x = data.x
    split_idx = dataset.get_idx_split()
    train_idx = split_idx['train']
    data.n_id = torch.arange(data.num_nodes)
    print("data.n_id", data.n_id.shape) # data.n_id torch.Size([2449029])
    train_loader = NeighborSampler(data.edge_index,
        node_idx=train_idx,
        sizes=[5,5],
        batch_size=1024,
        shuffle=False,
        num_workers=14,
    ) 
    print("loader finished")
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    for epoch in range(1, 2):
        model.train()
        n_id, batch_size, adjs  = next(iter(train_loader)) 
        optimizer.zero_grad()
        print("n_id", n_id.shape)
        print("batch_size", batch_size)
        target_node = data.n_id[:batch_size]
        adjs = [adj.to(rank) for adj in adjs]
        out = model(x[n_id].to(rank), adjs)
        loss =  F.nll_loss(out, y[target_node].squeeze(1).to(rank))
        loss.backward()
        optimizer.step()
        print(loss)
    print("train finished")

if __name__ == "__main__":
    main()
```


```
Traceback (most recent call last):
  File "paper100mneisampler.py", line 104, in <module>
    main()
  File "paper100mneisampler.py", line 88, in main
    print("n_id", n_id.shape)
AttributeError: 'int' object has no attribute 'shape'
```

I saw `pytorch_geometric/benchmark/training/training_benchmark.py` seems to skip node id. 

It needs to be `batch_size, n_id, adjs  = next(iter(train_loader))`.

> It needs to be `batch_size, n_id, adjs = next(iter(train_loader))`.

Thanks , it works.