SpatialEncoder¶

class dgl.nn.pytorch.graph_transformer.SpatialEncoder(max_dist, num_heads=1)[source]¶

Bases: torch.nn.modules.module.Module

Spatial Encoder, as introduced in Do Transformers Really Perform Bad for Graph Representation? This module is a learnable spatial embedding module which encodes the shortest distance between each node pair for attention bias.

Parameters

max_dist (int) – Upper bound of the shortest path distance between each node pair to be encoded. All distance will be clamped into the range [0, max_dist].
num_heads (int, optional) – Number of attention heads if multi-head attention mechanism is applied. Default : 1.

Examples

>>> import torch as th
>>> import dgl
>>> from dgl.nn import SpatialEncoder

>>> u = th.tensor([0, 0, 0, 1, 1, 2, 3, 3])
>>> v = th.tensor([1, 2, 3, 0, 3, 0, 0, 1])
>>> g = dgl.graph((u, v))
>>> spatial_encoder = SpatialEncoder(max_dist=2, num_heads=8)
>>> out = spatial_encoder(g)
>>> print(out.shape)
torch.Size([1, 4, 4, 8])

forward(g)[source]¶

Parameters: g (DGLGraph) – A DGLGraph to be encoded, which must be a homogeneous one.
Returns: Return attention bias as spatial encoding of shape \((B, N, N, H)\), where \(N\) is the maximum number of nodes, \(B\) is the batch size of the input graph, and \(H\) is num_heads.
Return type: torch.Tensor