Graph Transformer
Transformer is an effictive architecture in natural language processing and computer vision. Recently, there have been some applications(Grover, GraphGPS, etc) that combine transformers on graphs. In this tutorial, we will present how to build a graph transformer model via PyG. See <our webinar https://youtu.be/wAYryx3GjLw?si=2vB7imfenP5tUvqd> for in-depth learning on this topic.
Note
Click here to download the full example code
Transformers on Graphs
Compared to Graph Transformers, MPNNs have several drawbacks: (1) WL test: 1-order MPNNs have limited expressivity; (2) Over-smoothing: the features tend to converage to the same value while increasing the number of GNN layers; (3) Over-squashing: Losing information when trying to aggregate messages from many neighbors into a single vector; (4) Cannot capture long-range dependencies.
Feeding the whole graph into the Transformer also brings several pros and cons
Pros
Computation graph structure is decoupled from the input graph structure.
Long-range connections can be handled as all nodes are connected to each other.
Cons
Loss of inductive bias that enables GNNs to work so well on graphs with pronounced locality. Particularly in graphs where edges represent relatedness/closeness.
Language input is squential, but graphs are permutation invariant to node ordering.
Square computational complexity \(O(N^2)\) in the number of nodes whereas message passing GNNs are linear in the number of edges \(O(E)\). Graphs are often sparse \(N \approx E\).
Attention
In Transformer, attention can be multi-head, which consists of multiple attention weights.
Positional and Structural Encodings
We organized PE/SE into 3 categories based on their locality: (1) Local, (2) Global, (3) Relative. Positional encodings (PE) provides an idea of the position in space of a given node within the graph. When two nodes are close to each other within a graph or subgraph, their PE should also be close. Structure encodings (SE) provides an embedding of the structure of graphs or subgraphs to help increasing the expressivity and the generalizability of GNNs. When two nodes share similar subgraphs, or when two graphs are similar, their SE should also be close.
Encoding type |
Positional encodings (PE) |
Structure encodings (SE) |
|---|---|---|
Local (node) |
(1)Distance to cluster center; (2)Sum of non-diagonal elements in m-step random walk. |
(1)Node degree; (2)Random walk diagonals; (3) Enumerate substructures(triangles, rings). |
Global (node) |
(1)Eigenvectors of A/L or distance matrix; (2)Distance to graph’s centroid; (3)Unique ID for each node. |
(1)Eigenvalues of A/L; (2) Graph diameter, girth, degree, etc. |
Relative (edge) |
(1)Pair-wise distance from: Heat Kernels, Random Walks, Graph geodesic, etc; (2)Gradient of eigenvectors |
(1)Gradient of any Local SE; (2)Gradient of sub-structure enumeration |
GPS Layer and GraphGPS Model
Firstly, we introduce the GPS layer, which is combined with local MPNN and global Transformer, and then followed by 2-layer MLP and skip-connecttions. Local MPNN can provide locality bias that is difficult or expensive to achieve in Transformer. In addition, features of edges can be updated and encoded into the node features(GatedGCN, GINE). Transformer can utilize positional and structural encodings. As we don’t need to consider edge features, We can use the existing linear Transformer architecture to reduce the time complexity from \(O(N^2)\) to \(O(N + E)\), like Performer and BigBird.
Warning
BigBird currently is not supported, will be added in the future.
The update function of each layer is described by the equations below.
Local MPNN
h = self.conv(x, edge_index, **kwargs)
h = F.dropout(h, p=self.dropout, training=self.training)
h = h + x
if self.norm1 is not None:
if self.norm_with_batch:
h = self.norm1(h, batch=batch)
else:
h = self.norm1(h)
hs.append(h)
Global Attention
h, mask = to_dense_batch(x, batch)
if isinstance(self.attn, torch.nn.MultiheadAttention):
h, _ = self.attn(h, h, h, key_padding_mask=~mask,
need_weights=False)
elif isinstance(self.attn, PerformerAttention):
h = self.attn(h, mask=mask)
h = h[mask]
h = F.dropout(h, p=self.dropout, training=self.training)
h = h + x # Residual connection.
if self.norm2 is not None:
if self.norm_with_batch:
h = self.norm2(h, batch=batch)
else:
h = self.norm2(h)
hs.append(h)
Combine local and global outputs
out = sum(hs)
out = out + self.mlp(out)
if self.norm3 is not None:
if self.norm_with_batch:
out = self.norm3(out, batch=batch)
else:
out = self.norm3(out)
Next, we introduce GraphGPS architecture. The difference between GraphGPS and GraphTrans is the organization of MPNN and transformer. In GraphTrans, a few layers of MPNNs are comprised before the Transformer, which may be limited by problems of over-smoothing, over-squashing and low expressivity agianst the WL test. These layers could irreparably fail to keep some information in the early stage. The design of GraphGPS is a stacking of MPNN + transformer hybrid, which resolves the local expressivity bottlenecks by allowing information to spread across the graph via full-connectivity.
Train GraphGPS on graph-structured data
In this part, we’ll show how to train a GPSConv GNN model on the ZINC dataset.
Load dataset
transform = T.AddRandomWalkPE(walk_length=20, attr_name='pe')
train_dataset = ZINC(path, subset=True, split='train', pre_transform=transform)
val_dataset = ZINC(path, subset=True, split='val', pre_transform=transform)
test_dataset = ZINC(path, subset=True, split='test', pre_transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64)
test_loader = DataLoader(test_dataset, batch_size=64)
Define model
class RedrawProjection:
def __init__(self, model: torch.nn.Module,
redraw_interval: Optional[int] = None):
self.model = model
self.redraw_interval = redraw_interval
self.num_last_redraw = 0
def redraw_projections(self):
if not self.model.training or self.redraw_interval is None:
return
if self.num_last_redraw >= self.redraw_interval:
fast_attentions = [
module for module in self.model.modules()
if isinstance(module, PerformerAttention)
]
for fast_attention in fast_attentions:
fast_attention.redraw_projection_matrix()
self.num_last_redraw = 0
return
self.num_last_redraw += 1
class GPS(torch.nn.Module):
def __init__(self, channels: int, pe_dim: int, num_layers: int,
attn_type: str, attn_kwargs: Dict[str, Any]):
super().__init__()
self.node_emb = Embedding(28, channels - pe_dim)
self.pe_lin = Linear(20, pe_dim)
self.pe_norm = BatchNorm1d(20)
self.edge_emb = Embedding(4, channels)
self.convs = ModuleList()
for _ in range(num_layers):
nn = Sequential(
Linear(channels, channels),
ReLU(),
Linear(channels, channels),
)
conv = GPSConv(channels, GINEConv(nn), heads=4,
attn_type=attn_type, attn_kwargs=attn_kwargs)
self.convs.append(conv)
self.mlp = Sequential(
Linear(channels, channels // 2),
ReLU(),
Linear(channels // 2, channels // 4),
ReLU(),
Linear(channels // 4, 1),
)
self.redraw_projection = RedrawProjection(
self.convs,
redraw_interval=1000 if attn_type == 'performer' else None)
def forward(self, x, pe, edge_index, edge_attr, batch):
x_pe = self.pe_norm(pe)
x = torch.cat((self.node_emb(x.squeeze(-1)), self.pe_lin(x_pe)), 1)
edge_attr = self.edge_emb(edge_attr)
for conv in self.convs:
x = conv(x, edge_index, batch, edge_attr=edge_attr)
x = global_add_pool(x, batch)
return self.mlp(x)
Train and evaluate
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
attn_kwargs = {'dropout': 0.5}
model = GPS(channels=64, pe_dim=8, num_layers=10, attn_type=args.attn_type,
attn_kwargs=attn_kwargs).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20,
min_lr=0.00001)
def train():
model.train()
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
model.redraw_projection.redraw_projections()
out = model(data.x, data.pe, data.edge_index, data.edge_attr,
data.batch)
loss = (out.squeeze() - data.y).abs().mean()
loss.backward()
total_loss += loss.item() * data.num_graphs
optimizer.step()
return total_loss / len(train_loader.dataset)
@torch.no_grad()
def test(loader):
model.eval()
total_error = 0
for data in loader:
data = data.to(device)
out = model(data.x, data.pe, data.edge_index, data.edge_attr,
data.batch)
total_error += (out.squeeze() - data.y).abs().sum().item()
return total_error / len(loader.dataset)
for epoch in range(1, 101):
loss = train()
val_mae = test(val_loader)
test_mae = test(test_loader)
scheduler.step(val_mae)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_mae:.4f}, '
f'Test: {test_mae:.4f}')
Epoch: 01, Loss: 0.7216, Val: 0.5316, Test: 0.5454
Epoch: 02, Loss: 0.5519, Val: 0.5895, Test: 0.6288
Epoch: 03, Loss: 0.5009, Val: 0.5029, Test: 0.4924
Epoch: 04, Loss: 0.4751, Val: 0.4801, Test: 0.4786
Epoch: 05, Loss: 0.4363, Val: 0.4438, Test: 0.4352
Epoch: 06, Loss: 0.4276, Val: 0.4931, Test: 0.4994
Epoch: 07, Loss: 0.3956, Val: 0.3502, Test: 0.3439
Epoch: 08, Loss: 0.4021, Val: 0.3143, Test: 0.3296
Epoch: 09, Loss: 0.3761, Val: 0.4012, Test: 0.3858
Epoch: 10, Loss: 0.3739, Val: 0.3343, Test: 0.3032
Epoch: 11, Loss: 0.3532, Val: 0.3679, Test: 0.3334
Epoch: 12, Loss: 0.3683, Val: 0.3094, Test: 0.2754
Epoch: 13, Loss: 0.3457, Val: 0.4007, Test: 0.4023
Epoch: 14, Loss: 0.3460, Val: 0.3986, Test: 0.3589
Epoch: 15, Loss: 0.3369, Val: 0.3478, Test: 0.3124
Epoch: 16, Loss: 0.3222, Val: 0.3043, Test: 0.2651
Epoch: 17, Loss: 0.3190, Val: 0.4496, Test: 0.4070
Epoch: 18, Loss: 0.3317, Val: 0.3803, Test: 0.3450
Epoch: 19, Loss: 0.3179, Val: 0.2671, Test: 0.2408
Epoch: 20, Loss: 0.3143, Val: 0.4168, Test: 0.3901
Epoch: 21, Loss: 0.3238, Val: 0.3183, Test: 0.2926
Epoch: 22, Loss: 0.3132, Val: 0.9534, Test: 1.0879
Epoch: 23, Loss: 0.3088, Val: 0.3705, Test: 0.3360
Epoch: 24, Loss: 0.3032, Val: 0.3051, Test: 0.2692
Epoch: 25, Loss: 0.2968, Val: 0.2829, Test: 0.2571
Epoch: 26, Loss: 0.2915, Val: 0.3145, Test: 0.2820
Epoch: 27, Loss: 0.2871, Val: 0.3127, Test: 0.2965
Epoch: 28, Loss: 0.2953, Val: 0.4415, Test: 0.4144
Epoch: 29, Loss: 0.2916, Val: 0.3118, Test: 0.2733
Epoch: 30, Loss: 0.3074, Val: 0.4497, Test: 0.4418