PyGAS: Auto-Scaling GNNs in PyG

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PyGAS is the practical realization of our GNNAutoScale (GAS) framework, which scales arbitrary message-passing GNNs to large graphs, as described in our paper:

Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec: GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings (ICML 2021)

GAS prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations, leading to constant GPU memory consumption in respect to input mini-batch size, and maximally expressivity.

PyGAS is implemented in PyTorch and utilizes the PyTorch Geometric (PyG) library. It provides an easy-to-use interface to convert a common or custom GNN from PyG into its scalable variant:

from torch_geometric.nn import SAGEConv
from torch_geometric_autoscale import ScalableGNN
from torch_geometric_autoscale import metis, permute, SubgraphLoader

class GNN(ScalableGNN):
    def __init__(self, num_nodes, in_channels, hidden_channels,
                 out_channels, num_layers):
        # * pool_size determines the number of pinned CPU buffers
        # * buffer_size determines the size of pinned CPU buffers,
        #   i.e. the maximum number of out-of-mini-batch nodes

        super().__init__(num_nodes, hidden_channels, num_layers,
                         pool_size=2, buffer_size=5000)

        self.convs = ModuleList()
        self.convs.append(SAGEConv(in_channels, hidden_channels))
        for _ in range(num_layers - 2):
            self.convs.append(SAGEConv(hidden_channels, hidden_channels))
        self.convs.append(SAGEConv(hidden_channels, out_channels))

    def forward(self, x, adj_t, *args):
        for conv, history in zip(self.convs[:-1], self.histories):
            x = conv(x, adj_t).relu_()
            x = self.push_and_pull(history, x, *args)
        return self.convs[-1](x, adj_t)

perm, ptr = metis(data.adj_t, num_parts=40, log=True)
data = permute(data, perm, log=True)
loader = SubgraphLoader(data, ptr, batch_size=10, shuffle=True)

model = GNN(...)
for batch, *args in loader:
    out = model(batch.x, batch.adj_t, *args)

A detailed description of ScalableGNN can be found in its implementation.

Requirements

• Install PyTorch >= 1.7.0
• Install PyTorch Geometric >= 1.7.0:

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

where ${TORCH} should be replaced by either 1.7.0 or 1.8.0, and ${CUDA} should be replaced by either cpu, cu92, cu101, cu102, cu110 or cu111, depending on your PyTorch installation.

Installation

pip install git+https://github.com/rusty1s/pyg_autoscale.git

or

python setup.py install

Project Structure

• torch_geometric_autoscale/ contains the source code of PyGAS
• examples/ contains examples to demonstrate how to apply GAS in practice
• small_benchmark/ includes experiments to evaluate GAS performance on small-scale graphs
• large_benchmark/ includes experiments to evaluate GAS performance on large-scale graphs

We use Hydra to manage hyperparameter configurations.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{Fey/etal/2021,
  title={{GNNAutoScale}: Scalable and Expressive Graph Neural Networks via Historical Embeddings},
  author={Fey, M. and Lenssen, J. E. and Weichert, F. and Leskovec, J.},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2021},
}