rusty1s
commented
5 months ago I have never run METIS on such a large graph, so I sadly cannot give you advice on how much RAM you need. One thing to note is that ClusterData will basically copy the data (so you now have two replicas of the dataset in memory), which might be too much to handle. One thing you can try is to run METIS while only holding edge_index in memory:
graph = dataset[0]
data = Data(edge_index=graph.edge_index, num_nodes=graph.num_nodes)
del graph
del dataset
🐛 Describe the bug
I'm trying to do metis clustering on ogbn-papers100M dataset that has around 110 million nodes and 1.6 billion edges. First since torch_geometric.data.ClusterData expects the graph to be undirected so I'm trying to do so by from torch_geometric.utils import to_undirected function but this throws core dumped error so to tackle this I did the following:
dataset = PygNodePropPredDataset(name = "ogbn-papers100M",root="/DATA/Mohit/SGFomer_related/data/ogbn_papers100m/dataset") graph = dataset[0] edge_index=graph.edge_index row, col = edge_index[0], edge_index[1] row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0) edge_index = torch.stack([row, col], dim=0) graph.edge_index=edge_index
The above approach works but when I pass the graph to ClusterData, it throws core dumped error again and program stops execution without any explicit error. My code is as follows:
from ogb.nodeproppred import PygNodePropPredDataset from torch_geometric.data import ClusterData import torch from torch_geometric.utils import to_undirected num_partitions=10000 dataset = PygNodePropPredDataset(name = "ogbn-papers100M",root="path") graph = dataset[0] edge_index=graph.edge_index row, col = edge_index[0], edge_index[1] row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0) edge_index = torch.stack([row, col], dim=0) graph.edge_index=edge_index
graph.edge_index = to_undirected(graph.edge_index, num_nodes=graph.num_nodes)
split_idx = dataset.get_idx_split() save_path = $path$
adding split ids inside the graph itself (Cluster-GCN idea)
for key, idx in split_idx.items(): mask = torch.zeros(graph.num_nodes, dtype=torch.bool) mask[idx] = True graph[f'{key}_mask'] = mask
cluster_data = ClusterData(graph, num_parts=num_partitions, recursive=False, save_dir=dataset.processed_dir)
my RAM is of 512 GB, Any workaround for this issue? I'm working on such a large dataset for the first time so help is highly appreciated!
Versions
torch==2.0.1+cu117 [pip3] torch-cluster==1.6.3+pt20cu117 [pip3] torch_geometric==2.4.0 [pip3] torch-scatter==2.1.2+pt20cu117 [pip3] torch-sparse==0.6.18+pt20cu117 [pip3] torch-spline-conv==1.2.2+pt20cu117 [pip3] torch-tb-profiler==0.4.3 [pip3] torchaudio==2.0.2+cu117 [pip3] torchvision==0.15.2+cu117