import dgl
from . import register_model, BaseModel
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import math
import dgl.function as fn
[docs]
@register_model('GATNE-T')
class GATNE(BaseModel):
@classmethod
def build_model_from_args(cls, args, hg):
return cls(hg.num_nodes(), args.dim, args.edge_dim, hg.etypes, len(hg.etypes), args.att_dim)
def __init__(
self,
num_nodes,
embedding_size,
embedding_u_size,
edge_types,
edge_type_count,
att_dim,
):
super(GATNE, self).__init__()
self.num_nodes = num_nodes
self.embedding_size = embedding_size
self.embedding_u_size = embedding_u_size
self.edge_types = edge_types
self.edge_type_count = edge_type_count
self.att_dim = att_dim
self.node_embeddings = Parameter(torch.FloatTensor(num_nodes, embedding_size))
self.node_type_embeddings = Parameter(
torch.FloatTensor(num_nodes, edge_type_count, embedding_u_size)
)
self.trans_weights = Parameter(
torch.FloatTensor(edge_type_count, embedding_u_size, embedding_size)
)
self.trans_weights_s1 = Parameter(
torch.FloatTensor(edge_type_count, embedding_u_size, att_dim)
)
self.trans_weights_s2 = Parameter(torch.FloatTensor(edge_type_count, att_dim, 1))
self.reset_parameters()
def reset_parameters(self):
self.node_embeddings.data.uniform_(-1.0, 1.0)
self.node_type_embeddings.data.uniform_(-1.0, 1.0)
self.trans_weights.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
self.trans_weights_s1.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
self.trans_weights_s2.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
# embs: [batch_size, embedding_size]
def forward(self, block):
input_nodes = block.srcdata[dgl.NID]
output_nodes = block.dstdata[dgl.NID]
batch_size = block.number_of_dst_nodes()
node_embed = self.node_embeddings
node_type_embed = []
with block.local_scope():
for i in range(self.edge_type_count):
edge_type = self.edge_types[i]
block.srcdata[edge_type] = self.node_type_embeddings[input_nodes, i]
block.dstdata[edge_type] = self.node_type_embeddings[output_nodes, i]
block.update_all(
fn.copy_u(edge_type, "m"), fn.sum("m", edge_type), etype=edge_type
)
node_type_embed.append(block.dstdata[edge_type])
node_type_embed = torch.stack(node_type_embed, 1)
tmp_node_type_embed = node_type_embed.unsqueeze(2).view(
-1, 1, self.embedding_u_size
)
trans_w = (
self.trans_weights.unsqueeze(0)
.repeat(batch_size, 1, 1, 1)
.view(-1, self.embedding_u_size, self.embedding_size)
)
trans_w_s1 = (
self.trans_weights_s1.unsqueeze(0)
.repeat(batch_size, 1, 1, 1)
.view(-1, self.embedding_u_size, self.att_dim)
)
trans_w_s2 = (
self.trans_weights_s2.unsqueeze(0)
.repeat(batch_size, 1, 1, 1)
.view(-1, self.att_dim, 1)
)
attention = (
F.softmax(
torch.matmul(
torch.tanh(torch.matmul(tmp_node_type_embed, trans_w_s1)),
trans_w_s2,
)
.squeeze(2)
.view(-1, self.edge_type_count),
dim=1,
)
.unsqueeze(1)
.repeat(1, self.edge_type_count, 1)
)
node_type_embed = torch.matmul(attention, node_type_embed).view(
-1, 1, self.embedding_u_size
)
node_embed = node_embed[output_nodes].unsqueeze(1).repeat(
1, self.edge_type_count, 1
) + torch.matmul(node_type_embed, trans_w).view(
-1, self.edge_type_count, self.embedding_size
)
last_node_embed = F.normalize(node_embed, dim=2)
return last_node_embed # [batch_size, edge_type_count, embedding_size]
class NSLoss(nn.Module):
def __init__(self, num_nodes, num_sampled, embedding_size):
super(NSLoss, self).__init__()
self.num_nodes = num_nodes
self.num_sampled = num_sampled
self.embedding_size = embedding_size
self.weights = Parameter(torch.FloatTensor(num_nodes, embedding_size))
# [ (log(i+2) - log(i+1)) / log(num_nodes + 1)]
self.sample_weights = F.normalize(
torch.Tensor(
[
(math.log(k + 2) - math.log(k + 1)) / math.log(num_nodes + 1)
for k in range(num_nodes)
]
),
dim=0,
)
self.reset_parameters()
def reset_parameters(self):
self.weights.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
def forward(self, input, embs, label):
n = input.shape[0]
log_target = torch.log(
torch.sigmoid(torch.sum(torch.mul(embs, self.weights[label]), 1))
)
negs = torch.multinomial(
self.sample_weights, self.num_sampled * n, replacement=True
).view(n, self.num_sampled)
noise = torch.neg(self.weights[negs])
sum_log_sampled = torch.sum(
torch.log(torch.sigmoid(torch.bmm(noise, embs.unsqueeze(2)))), 1
).squeeze()
loss = log_target + sum_log_sampled
return -loss.sum() / n