Compiled Graph Neural Networks

torch.compile() is the latest method to speed up your code in torch >= 2.0.0! torch.compile() makes PyTorch code run faster by JIT-compiling it into optimized kernels, all while required minimal code changes.

Under the hood, torch.compile() captures programs via TorchDynamo, canonicalizes over 2,000 operators via PrimTorch, and finally generates fast code out of it across multiple accelerators and backends via the deep learning compiler TorchInductor.

Note

See here for a general tutorial on how to leverage torch.compile(), and here for a description of its interface.

In this tutorial, we show how to optimize your custom model via torch.compile().

Note

From 2.5 (and onwards), torch.compile() is now fully compatible with all GNN layers. If you are on an earlier version of , consider using torch_geometric.compile() instead.

Basic Usage

Once you have a model defined, simply wrap it with torch.compile() to obtain its optimized version:

import torch
from torch_geometric.nn import GraphSAGE

model = GraphSAGE(in_channels, hidden_channels, num_layers, out_channels)
model = model.to(device)

model = torch.compile(model)

and execute it as usual:

from torch_geometric.datasets import Planetoid

dataset = Planetoid(root, name="Cora")
data = dataset[0].to(device)

out = model(data.x, data.edge_index)

Maximizing Performance

The torch.compile() method provides two important arguments to be aware of:

  • Most of the mini-batches observed in are dynamic by nature, meaning that their shape varies across different mini-batches. For these scenarios, we can enforce dynamic shape tracing in via the dynamic=True argument:

    torch.compile(model, dynamic=True)

    With this, will up-front attempt to generate a kernel that is as dynamic as possible to avoid recompilations when sizes change across mini-batches changes. Note that when dynamic is set to False, will never generate dynamic kernels, and thus only works when graph sizes are guaranteed to never change (e.g., in full-batch training on small graphs). By default, dynamic is set to None in >= 2.1.0, and will automatically detect if dynamism has occured. Note that support for dynamic shape tracing requires >= 2.1.0 to be installed.

  • In order to maximize speedup, graphs breaks in the compiled model should be limited. We can force compilation to raise an error upon the first graph break encountered by using the fullgraph=True argument:

    torch.compile(model, fullgraph=True)

    It is generally a good practice to confirm that your written model does not contain any graph breaks. Importantly, there exists a few operations in that will currently lead to graph breaks (but workaround exists), e.g.:

    1. global_mean_pool() (and other pooling operators) perform device synchronization in case the batch size size is not passed, leading to a graph break.

    2. remove_self_loops() and add_remaining_self_loops() mask the given edge_index, leading to a device synchronization to compute its final output shape. As such, we recommend to augment your graph before inputting it into your GNN, e.g., via the AddSelfLoops or GCNNorm transformations, and setting add_self_loops=False/normalize=False when initializing layers such as GCNConv.

Exampe Scripts

We have incorporated multiple examples in examples/compile that further show the practical usage of torch.compile():

  1. Node Classification via GCN (dynamic=False)

  2. Graph Classification via GIN (dynamic=True)

If you notice that torch.compile() fails for a certain model, do not hesitate to reach out either on GitHub or Slack. We are very eager to improve torch.compile() support across the whole code base.

Benchmark

torch.compile() works fantastically well for many models. Overall, we observe runtime improvements of nearly up to 300%.

Specifically, we benchmark GCN, GraphSAGE and GIN and compare runtimes obtained from traditional eager mode and torch.compile(). We use a synthetic graph with 10,000 nodes and 200,000 edges, and a hidden feature dimensionality of 64. We report runtimes over 500 optimization steps:

Model

Mode

Forward

Backward

Total

Speedup

GCN

Eager

2.6396s

2.1697s

4.8093s

GCN

Compiled

1.1082s

0.5896s

1.6978s

2.83x

GraphSAGE

Eager

1.6023s

1.6428s

3.2451s

GraphSAGE

Compiled

0.7033s

0.7465s

1.4498s

2.24x

GIN

Eager

1.6701s

1.6990s

3.3690s

GIN

Compiled

0.7320s

0.7407s

1.4727s

2.29x

To reproduce these results, run

python test/nn/models/test_basic_gnn.py

from the root folder of your checked out repository from .