README_ORIGIN.md

<h1 align="center">PyGAS: Auto-Scaling GNNs in PyG</h1>

<img width="100%" src="https://raw.githubusercontent.com/rusty1s/pyg_autoscale/master/figures/overview.png?token=ABU7ZAXZ7WT3RIOSYHIDIVDAEI3SY" />

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*PyGAS* is the practical realization of our *<ins>G</ins>NN<ins>A</ins>uto<ins>S</ins>cale* (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](http://arxiv.org/abs/2106.05609)** *(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](https://pytorch.org/) and utilizes the [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric) (PyG) library.
It provides an easy-to-use interface to convert a common or custom GNN from PyG into its scalable variant:

```python
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](https://github.com/rusty1s/pyg_autoscale/blob/master/torch_geometric_autoscale/models/base.py#L13).

## Requirements

* Install [**PyTorch >= 1.7.0**](https://pytorch.org/get-started/locally/)
* Install [**PyTorch Geometric >= 1.7.0**](https://github.com/rusty1s/pytorch_geometric#installation):

```
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**](https://hydra.cc/) to manage hyperparameter configurations.

## Cite

Please cite [our paper](http://arxiv.org/abs/2106.05609) 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},
}
```