import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from cogdl.utils import check_mh_spmm, mh_spmm, mul_edge_softmax, spmm, get_activation, get_norm_layer
[docs]class GATLayer(nn.Module):
"""
Sparse version GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(
self, in_features, out_features, nhead=1, alpha=0.2, attn_drop=0.5, activation=None, residual=False, norm=None
):
super(GATLayer, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.nhead = nhead
self.W = nn.Parameter(torch.FloatTensor(in_features, out_features * nhead))
self.a_l = nn.Parameter(torch.zeros(size=(1, nhead, out_features)))
self.a_r = nn.Parameter(torch.zeros(size=(1, nhead, out_features)))
self.dropout = nn.Dropout(attn_drop)
self.leakyrelu = nn.LeakyReLU(self.alpha)
self.act = None if activation is None else get_activation(activation)
self.norm = None if norm is None else get_norm_layer(norm, out_features * nhead)
if residual:
self.residual = nn.Linear(in_features, out_features * nhead)
else:
self.register_buffer("residual", None)
self.reset_parameters()
[docs] def reset_parameters(self):
def reset(tensor):
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
reset(self.a_l)
reset(self.a_r)
reset(self.W)
[docs] def forward(self, graph, x):
h = torch.matmul(x, self.W).view(-1, self.nhead, self.out_features)
h[torch.isnan(h)] = 0.0
row, col = graph.edge_index
# Self-attention on the nodes - Shared attention mechanism
h_l = (self.a_l * h).sum(dim=-1)[row]
h_r = (self.a_r * h).sum(dim=-1)[col]
edge_attention = self.leakyrelu(h_l + h_r)
# edge_attention: E * H
edge_attention = mul_edge_softmax(graph, edge_attention)
edge_attention = self.dropout(edge_attention)
if check_mh_spmm() and next(self.parameters()).device.type != "cpu":
if self.nhead > 1:
h_prime = mh_spmm(graph, edge_attention, h)
out = h_prime.view(h_prime.shape[0], -1)
else:
edge_weight = edge_attention.view(-1)
with graph.local_graph():
graph.edge_weight = edge_weight
out = spmm(graph, h.squeeze(1))
else:
with graph.local_graph():
h_prime = []
h = h.permute(1, 0, 2).contiguous()
for i in range(self.nhead):
edge_weight = edge_attention[:, i]
graph.edge_weight = edge_weight
hidden = h[i]
assert not torch.isnan(hidden).any()
h_prime.append(spmm(graph, hidden))
out = torch.cat(h_prime, dim=1)
if self.residual:
res = self.residual(x)
out += res
if self.norm is not None:
out = self.norm(out)
if self.act is not None:
out = self.act(out)
return out
def __repr__(self):
return self.__class__.__name__ + " (" + str(self.in_features) + " -> " + str(self.out_features) + ")"