Graph Transformer in a NutshellΒΆ

The Transformer (Vaswani et al. 2017) has been proven an effective learning architecture in natural language processing and computer vision. Recently, researchers turns to explore the application of transformer in graph learning. They have achieved inital success on many practical tasks, e.g., graph property prediction. Dwivedi et al. (2020) firstly generalize the transformer neural architecture to graph-structured data. Here, we present how to build such a graph transformer with DGLβs sparse matrix APIs.

[ ]:

# Install required packages.
import os
import torch
os.environ['TORCH'] = torch.__version__
os.environ['DGLBACKEND'] = "pytorch"

# Uncomment below to install required packages. If the CUDA version is not 11.8,
# check the https://www.dgl.ai/pages/start.html to find the supported CUDA
# version and corresponding command to install DGL.
#!pip install dgl -f https://data.dgl.ai/wheels/cu118/repo.html > /dev/null
#!pip install ogb >/dev/null

try:
import dgl
installed = True
except ImportError:
installed = False
print("DGL installed!" if installed else "Failed to install DGL!")


Recall the all-pairs scaled-dot-product attention mechanism in vanillar Transformer:

$\text{Attn}=\text{softmax}(\dfrac{QK^T} {\sqrt{d}})V,$

The graph transformer (GT) model employs a Sparse Multi-head Attention block:

$\text{SparseAttn}(Q, K, V, A) = \text{softmax}(\frac{(QK^T) \circ A}{\sqrt{d}})V,$

where $$Q, K, V β\mathbb{R}^{N\times d}$$ are query feature, key feature, and value feature, respectively. $$A\in[0,1]^{N\times N}$$ is the adjacency matrix of the input graph. $$(QK^T)\circ A$$ means that the multiplication of query matrix and key matrix is followed by a Hadamard product (or element-wise multiplication) with the sparse adjacency matrix as illustrated in the figure below:

Essentially, only the attention scores between connected nodes are computed according to the sparsity of $$A$$. This operation is also called Sampled Dense Dense Matrix Multiplication (SDDMM).

Enjoying the batched SDDMM API in DGL, we can parallel the computation on multiple attention heads (different representation subspaces).

[ ]:

import dgl
import dgl.nn as dglnn
import dgl.sparse as dglsp
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from dgl.data import AsGraphPredDataset
from ogb.graphproppred import collate_dgl, DglGraphPropPredDataset, Evaluator
from ogb.graphproppred.mol_encoder import AtomEncoder
from tqdm import tqdm

class SparseMHA(nn.Module):

super().__init__()
self.hidden_size = hidden_size

self.q_proj = nn.Linear(hidden_size, hidden_size)
self.k_proj = nn.Linear(hidden_size, hidden_size)
self.v_proj = nn.Linear(hidden_size, hidden_size)
self.out_proj = nn.Linear(hidden_size, hidden_size)

def forward(self, A, h):
N = len(h)
# [N, dh, nh]
q *= self.scaling
# [N, dh, nh]
# [N, dh, nh]

######################################################################
# (HIGHLIGHT) Compute the multi-head attention with Sparse Matrix API
######################################################################
attn = dglsp.bsddmm(A, q, k.transpose(1, 0))  # (sparse) [N, N, nh]
# Sparse softmax by default applies on the last sparse dimension.
attn = attn.softmax()  # (sparse) [N, N, nh]
out = dglsp.bspmm(attn, v)  # [N, dh, nh]

return self.out_proj(out.reshape(N, -1))


Graph Transformer LayerΒΆ

The GT layer is composed of Multi-head Attention, Batch Norm, and Feed-forward Network, connected by residual links as in vanilla transformer.

[ ]:

class GTLayer(nn.Module):
"""Graph Transformer Layer"""

super().__init__()
self.batchnorm1 = nn.BatchNorm1d(hidden_size)
self.batchnorm2 = nn.BatchNorm1d(hidden_size)
self.FFN1 = nn.Linear(hidden_size, hidden_size * 2)
self.FFN2 = nn.Linear(hidden_size * 2, hidden_size)

def forward(self, A, h):
h1 = h
h = self.MHA(A, h)
h = self.batchnorm1(h + h1)

h2 = h
h = self.FFN2(F.relu(self.FFN1(h)))
h = h2 + h

return self.batchnorm2(h)


Graph Transformer ModelΒΆ

The GT model is constructed by stacking GT layers. The input positional encoding of vanilla transformer is replaced with Laplacian positional encoding (Dwivedi et al. 2020). For the graph-level prediction task, an extra pooler is stacked on top of GT layers to aggregate node feature of the same graph.

[ ]:

class GTModel(nn.Module):
def __init__(
self,
out_size,
hidden_size=80,
pos_enc_size=2,
num_layers=8,
):
super().__init__()
self.atom_encoder = AtomEncoder(hidden_size)
self.pos_linear = nn.Linear(pos_enc_size, hidden_size)
self.layers = nn.ModuleList(
[GTLayer(hidden_size, num_heads) for _ in range(num_layers)]
)
self.pooler = dglnn.SumPooling()
self.predictor = nn.Sequential(
nn.Linear(hidden_size, hidden_size // 2),
nn.ReLU(),
nn.Linear(hidden_size // 2, hidden_size // 4),
nn.ReLU(),
nn.Linear(hidden_size // 4, out_size),
)

def forward(self, g, X, pos_enc):
indices = torch.stack(g.edges())
N = g.num_nodes()
A = dglsp.spmatrix(indices, shape=(N, N))
h = self.atom_encoder(X) + self.pos_linear(pos_enc)
for layer in self.layers:
h = layer(A, h)
h = self.pooler(g, h)

return self.predictor(h)


TrainingΒΆ

We train the GT model on ogbg-molhiv benchmark. The Laplacian positional encoding of each graph is pre-computed (with the API here) as part of the input to the model.

Note that we down-sample the dataset to make this demo runs faster. See the *example script* for the performance on the full dataset.

[ ]:

@torch.no_grad()
model.eval()
y_true = []
y_pred = []
batched_g, labels = batched_g.to(device), labels.to(device)
y_hat = model(batched_g, batched_g.ndata["feat"], batched_g.ndata["PE"])
y_true.append(labels.view(y_hat.shape).detach().cpu())
y_pred.append(y_hat.detach().cpu())
y_true = torch.cat(y_true, dim=0).numpy()
y_pred = torch.cat(y_pred, dim=0).numpy()
input_dict = {"y_true": y_true, "y_pred": y_pred}
return evaluator.eval(input_dict)["rocauc"]

def train(model, dataset, evaluator, device):
dataset[dataset.train_idx],
batch_size=256,
shuffle=True,
collate_fn=collate_dgl,
)
dataset[dataset.val_idx], batch_size=256, collate_fn=collate_dgl
)
dataset[dataset.test_idx], batch_size=256, collate_fn=collate_dgl
)
num_epochs = 20
scheduler = optim.lr_scheduler.StepLR(
optimizer, step_size=num_epochs, gamma=0.5
)
loss_fcn = nn.BCEWithLogitsLoss()

for epoch in range(num_epochs):
model.train()
total_loss = 0.0
batched_g, labels = batched_g.to(device), labels.to(device)
logits = model(
batched_g, batched_g.ndata["feat"], batched_g.ndata["PE"]
)
loss = loss_fcn(logits, labels.float())
total_loss += loss.item()
loss.backward()
optimizer.step()
scheduler.step()
val_metric = evaluate(model, valid_dataloader, evaluator, device)
test_metric = evaluate(model, test_dataloader, evaluator, device)
print(
f"Epoch: {epoch:03d}, Loss: {avg_loss:.4f}, "
f"Val: {val_metric:.4f}, Test: {test_metric:.4f}"
)

# Training device.
dev = torch.device("cpu")
# Uncomment the code below to train on GPU. Be sure to install DGL with CUDA support.
#dev = torch.device("cuda:0")

pos_enc_size = 8
dataset = AsGraphPredDataset(
DglGraphPropPredDataset("ogbg-molhiv", "./data/OGB")
)
evaluator = Evaluator("ogbg-molhiv")

# Down sample the dataset to make the tutorial run faster.
import random
random.seed(42)
train_size = len(dataset.train_idx)
val_size = len(dataset.val_idx)
test_size = len(dataset.test_idx)
dataset.train_idx = dataset.train_idx[
torch.LongTensor(random.sample(range(train_size), 2000))
]
dataset.val_idx = dataset.val_idx[
torch.LongTensor(random.sample(range(val_size), 1000))
]
dataset.test_idx = dataset.test_idx[
torch.LongTensor(random.sample(range(test_size), 1000))
]

# Laplacian positional encoding.
indices = torch.cat([dataset.train_idx, dataset.val_idx, dataset.test_idx])
for idx in tqdm(indices, desc="Computing Laplacian PE"):
g, _ = dataset[idx]

# Create model.
model = GTModel(out_size=out_size, pos_enc_size=pos_enc_size).to(dev)

# Kick off training.
train(model, dataset, evaluator, dev)

Computing Laplacian PE:   1%|          | 25/4000 [00:00<00:16, 244.77it/s]/usr/local/lib/python3.8/dist-packages/dgl/backend/pytorch/tensor.py:52: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at ../aten/src/ATen/native/Copy.cpp:250.)
return th.as_tensor(data, dtype=dtype)
Computing Laplacian PE: 100%|ββββββββββ| 4000/4000 [00:13<00:00, 296.04it/s]

Epoch: 000, Loss: 0.2486, Val: 0.3082, Test: 0.3068
Epoch: 001, Loss: 0.1695, Val: 0.4684, Test: 0.4572
Epoch: 002, Loss: 0.1428, Val: 0.5887, Test: 0.4721
Epoch: 003, Loss: 0.1237, Val: 0.6375, Test: 0.5010
Epoch: 004, Loss: 0.1127, Val: 0.6628, Test: 0.4854
Epoch: 005, Loss: 0.1047, Val: 0.6811, Test: 0.4983
Epoch: 006, Loss: 0.0949, Val: 0.6751, Test: 0.5409
Epoch: 007, Loss: 0.0901, Val: 0.6340, Test: 0.5357
Epoch: 008, Loss: 0.0811, Val: 0.6717, Test: 0.5543
Epoch: 009, Loss: 0.0643, Val: 0.7861, Test: 0.5628
Epoch: 010, Loss: 0.0489, Val: 0.7319, Test: 0.5341
Epoch: 011, Loss: 0.0340, Val: 0.7884, Test: 0.5299
Epoch: 012, Loss: 0.0285, Val: 0.5887, Test: 0.4293
Epoch: 013, Loss: 0.0361, Val: 0.5514, Test: 0.3419
Epoch: 014, Loss: 0.0451, Val: 0.6795, Test: 0.4964
Epoch: 015, Loss: 0.0429, Val: 0.7405, Test: 0.5527
Epoch: 016, Loss: 0.0331, Val: 0.7859, Test: 0.4994
Epoch: 017, Loss: 0.0177, Val: 0.6544, Test: 0.4457
Epoch: 018, Loss: 0.0201, Val: 0.8250, Test: 0.6073
Epoch: 019, Loss: 0.0093, Val: 0.7356, Test: 0.5561