Scaling Up GNNs via Remote Backends
PyG (2.2 and beyond) includes numerous primitives to easily integrate with simple paradigms for scalable graph machine learning, enabling users to train GNNs on graphs far larger than the size of their machine’s available memory.
It does so by introducing simple, easy-to-use, and extensible abstractions of a
torch_geometric.data.FeatureStore and a
torch_geometric.data.GraphStore that plug directly into existing familiar PyG interfaces.
FeatureStore allows users to leverage node (and soon, edge) features stored remotely, and defining a
GraphStore allows users to leverage graph structure information stored remotely.
Together, they allow for powerful GNN scalability with low developer friction.
The remote backend APIs discussed here may change in the future as we continuously work to improve their ease-of-use and generalizability.
An instantiated Graph Neural Network consists of two types of data:
Node and/or edge feature information: Dense vectors corresponding to attributes of the nodes and edges in a graph
Graph structure information: The nodes in the graph and the edges that connect them
An immediate observation of GNNs is that scaling to data larger than the available memory of a chosen accelerator requires training on sampled subgraphs (which form mini-batches), instead of the full graph at once (full-batch training). While this method adds stochasticity to the learning process, it reduces the memory requirements of the accelerator to those of the sampled subgraphs.
However, while mini-batch training reduces the memory requirements of the chosen accelerator, it is not a silver bullet for all graph learning scalability problems. In particular, since one must sample subgraphs to pass to the accelerator at each iteration of the learning process, the graph and features are traditionally required to be stored in the CPU DRAM of a user’s machine. At large scale, this requirement can become quite burdensome:
Acquiring instances with enough CPU DRAM to store a graph and features is challenging
Training with data parallelism requires replicating the graph and features in each compute node
Graphs and features can easily be much larger than the memory of a single machine
Scalability to very large graphs and features beyond the memory requirements of a single machine thus requires moving these data structures out-of-core and only processing sampled subgraphs on a node that performs computation.
In order to achieve this goal, PyG relies on two primary abstractions to store feature information and graph structure:
Features are stored in a key-value
FeatureStore, which must support efficient random access.
Graph information is stored in a
GraphStore, which must support efficient sampling for the samplers defined to operate on the
In PyG (2.2 and beyond), the separation of graph data into its features and structure information, the storage of this information in locations potentially remote to the actual training node, and the interactions between these components, are all completely abstracted from the end user.
As long as the
GraphStore are defined appropriately (keeping in mind the aforementioned performance requirements), PyG handles the rest!
torch_geometric.data.FeatureStore holds features for the nodes and edges of a graph.
Feature storage is often the primary storage bottleneck in graph learning applications, as storing a graph’s layout information (i.e. the
edge_index) is relatively cheap (~32 bytes per edge).
PyG provides a common interface for various
FeatureStore implementations to interface with its core learning API.
The implementation details of a
FeatureStore are abstracted from PyG through a CRUD-like interface.
In particular, implementors of the
FeatureStore abstraction are expected to primarily override
Doing so both enables PyG to leverage the features stored in the inmplementation and allows a user to employ a pythonic interface to inspect and modify the
feature_store = CustomFeatureStore() paper_features = ... # [num_papers, num_paper_features] author_features = ... # [num_authors, num_author_features] # Add features: feature_store['paper', 'x', None] = paper_features feature_store['author', 'x', None] = author_features # Access features: assert torch.equal(feature_store['paper', 'x'], paper_features) assert torch.equal(feature_store['paper'].x, paper_features) assert torch.equal(feature_store['author', 'x', 0:20], author_features[0:20])
Common implementations of the
FeatureStore abstractions are key-value stores, e.g., backends such as
RocksDB are all viable performant options.
Graph Store and Sampler
torch_geometric.data.GraphStore holds the edge indices that define relationships between nodes in a graph.
The goal of the
GraphStore is to store graph information in a manner that allows for efficient sampling from root nodes, according to a sampling algorithm of the developer’s choice.
Similar to the
FeatureStore, PyG provides a common interface for various
GraphStore implementations to interface with its core learning API.
However, unlike the
GraphStore does not need to provide efficient random access for all its elements; rather, it needs to define a representation that provides efficient subgraph sampling.
An example usage of the interface is shown below:
graph_store = CustomGraphStore() edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]]) # Put edges: graph_store['edge', 'coo'] = coo # Access edges: row, col = graph_store['edge', 'coo'] assert torch.equal(row, edge_index) assert torch.equal(col, edge_index)
Common implementations of the
GraphStore are graph databases, e.g.,
ArangoDB are all viable performant options.
A graph sampler is tightly coupled to the given
GraphStore, and operates on the
GraphStore to produce sampled subgraphs from input nodes.
Different sampling algorithms are implemented behind the
By default, PyG’s default in-memory sampler pulls all edge indices from the
GraphStore into the training node memory, converts them to compressed sparse column (CSC) format, and leverages pre-built in-memory sampling routines.
However, custom sampler implementations may choose to call specialized
GraphStore methods by implementing the
sample_from_edges() of the
BaseSampler class for efficiency reasons (e.g., for performing sampling directly on the remote
# `CustomGraphSampler` knows how to sample on `CustomGraphStore`: node_sampler = CustomGraphSampler( graph_store=graph_store, num_neighbors=[10, 20], ... )
PyG provides two data loaders out-of-the-box: a
torch_geometric.loader.NodeLoader that samples subgraphs from input nodes for use in node classification tasks, and a
torch_geometric.loader.LinkLoader that samples subgraphs from either side of an edge for use in link prediction tasks.
These data loaders require a
GraphStore, and a graph sampler as input, and internally call the sampler’s
sample_from_edges() method to perform subgraph sampling:
# Instead of passing PyG data objects, we now pass a tuple # of the `FeatureStore` and `GraphStore as input data: loader = NodeLoader( data=(feature_store, graph_store), node_sampler=node_sampler, batch_size=20, input_nodes='paper', ) for batch in loader: pass
Putting it All Together
At a high level, the components listed above all work together to provide support for scaling up GNNs within PyG.
Upon receipt of a response, the data loader subsequently queries the
FeatureStorefor features associated with the nodes and edges of the sampled subgraphs.
The data loader subsequently constructs a final mini-batch from graph structure and feature information to send to the accelerator for forward/backward passes.
Repeat until convergence.
All of the outlined classes speak through common interfaces, making them extensible, generalizable, and easy to integrate with the PyG you use today:
To get started with scalability, we recommend inspecting the interfaces listed above and defining your own
BaseSampler implementations behind them.
GraphStore, and a
BaseSampler are correctly implemented, simply pass them as parameters to a
NodeLoader or a
LinkLoader, and the rest of PyG will work seamlessly and similar to any pure in-memory application.