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layers.py
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layers.py
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import torch
import dgl
import math
import numpy as np
class TimeEncode(torch.nn.Module):
def __init__(self, dim):
super(TimeEncode, self).__init__()
self.dim = dim
self.w = torch.nn.Linear(1, dim)
self.w.weight = torch.nn.Parameter((torch.from_numpy(1 / 10 ** np.linspace(0, 9, dim, dtype=np.float32))).reshape(dim, -1))
self.w.bias = torch.nn.Parameter(torch.zeros(dim))
def forward(self, t):
output = torch.cos(self.w(t.reshape((-1, 1))))
return output
class EdgePredictor(torch.nn.Module):
def __init__(self, dim_in):
super(EdgePredictor, self).__init__()
self.dim_in = dim_in
self.src_fc = torch.nn.Linear(dim_in, dim_in)
self.dst_fc = torch.nn.Linear(dim_in, dim_in)
self.out_fc = torch.nn.Linear(dim_in, 1)
def forward(self, h, neg_samples=1):
num_edge = h.shape[0] // (neg_samples + 2)
h_src = self.src_fc(h[:num_edge])
h_pos_dst = self.dst_fc(h[num_edge:2 * num_edge])
h_neg_dst = self.dst_fc(h[2 * num_edge:])
h_pos_edge = torch.nn.functional.relu(h_src + h_pos_dst)
h_neg_edge = torch.nn.functional.relu(h_src.tile(neg_samples, 1) + h_neg_dst)
return self.out_fc(h_pos_edge), self.out_fc(h_neg_edge)
class TransfomerAttentionLayer(torch.nn.Module):
def __init__(self, dim_node_feat, dim_edge_feat, dim_time, num_head, dropout, att_dropout, dim_out, combined=False):
super(TransfomerAttentionLayer, self).__init__()
self.num_head = num_head
self.dim_node_feat = dim_node_feat
self.dim_edge_feat = dim_edge_feat
self.dim_time = dim_time
self.dim_out = dim_out
self.dropout = torch.nn.Dropout(dropout)
self.att_dropout = torch.nn.Dropout(att_dropout)
self.att_act = torch.nn.LeakyReLU(0.2)
self.combined = combined
if dim_time > 0:
self.time_enc = TimeEncode(dim_time)
if combined:
if dim_node_feat > 0:
self.w_q_n = torch.nn.Linear(dim_node_feat, dim_out)
self.w_k_n = torch.nn.Linear(dim_node_feat, dim_out)
self.w_v_n = torch.nn.Linear(dim_node_feat, dim_out)
if dim_edge_feat > 0:
self.w_k_e = torch.nn.Linear(dim_edge_feat, dim_out)
self.w_v_e = torch.nn.Linear(dim_edge_feat, dim_out)
if dim_time > 0:
self.w_q_t = torch.nn.Linear(dim_time, dim_out)
self.w_k_t = torch.nn.Linear(dim_time, dim_out)
self.w_v_t = torch.nn.Linear(dim_time, dim_out)
else:
if dim_node_feat + dim_time > 0:
self.w_q = torch.nn.Linear(dim_node_feat + dim_time, dim_out)
self.w_k = torch.nn.Linear(dim_node_feat + dim_edge_feat + dim_time, dim_out)
self.w_v = torch.nn.Linear(dim_node_feat + dim_edge_feat + dim_time, dim_out)
self.w_out = torch.nn.Linear(dim_node_feat + dim_out, dim_out)
self.layer_norm = torch.nn.LayerNorm(dim_out)
def forward(self, b):
assert(self.dim_time + self.dim_node_feat + self.dim_edge_feat > 0)
if b.num_edges() == 0:
return torch.zeros((b.num_dst_nodes(), self.dim_out), device=torch.device('cuda:0'))
if self.dim_time > 0:
time_feat = self.time_enc(b.edata['dt'])
zero_time_feat = self.time_enc(torch.zeros(b.num_dst_nodes(), dtype=torch.float32, device=torch.device('cuda:0')))
if self.combined:
Q = torch.zeros((b.num_edges(), self.dim_out), device=torch.device('cuda:0'))
K = torch.zeros((b.num_edges(), self.dim_out), device=torch.device('cuda:0'))
V = torch.zeros((b.num_edges(), self.dim_out), device=torch.device('cuda:0'))
if self.dim_node_feat > 0:
Q += self.w_q_n(b.srcdata['h'][:b.num_dst_nodes()])[b.edges()[1]]
K += self.w_k_n(b.srcdata['h'][b.num_dst_nodes():])[b.edges()[0] - b.num_dst_nodes()]
V += self.w_v_n(b.srcdata['h'][b.num_dst_nodes():])[b.edges()[0] - b.num_dst_nodes()]
if self.dim_edge_feat > 0:
K += self.w_k_e(b.edata['f'])
V += self.w_v_e(b.edata['f'])
if self.dim_time > 0:
Q += self.w_q_t(zero_time_feat)[b.edges()[1]]
K += self.w_k_t(time_feat)
V += self.w_v_t(time_feat)
Q = torch.reshape(Q, (Q.shape[0], self.num_head, -1))
K = torch.reshape(K, (K.shape[0], self.num_head, -1))
V = torch.reshape(V, (V.shape[0], self.num_head, -1))
att = dgl.ops.edge_softmax(b, self.att_act(torch.sum(Q*K, dim=2)))
att = self.att_dropout(att)
V = torch.reshape(V*att[:, :, None], (V.shape[0], -1))
b.edata['v'] = V
b.update_all(dgl.function.copy_edge('v', 'm'), dgl.function.sum('m', 'h'))
else:
if self.dim_time == 0 and self.dim_node_feat == 0:
Q = torch.ones((b.num_edges(), self.dim_out), device=torch.device('cuda:0'))
K = self.w_k(b.edata['f'])
V = self.w_v(b.edata['f'])
elif self.dim_time == 0 and self.dim_edge_feat == 0:
Q = self.w_q(b.srcdata['h'][:b.num_dst_nodes()])[b.edges()[1]]
K = self.w_k(b.srcdata['h'][b.num_dst_nodes():])
V = self.w_v(b.srcdata['h'][b.num_dst_nodes():])
elif self.dim_time == 0:
Q = self.w_q(b.srcdata['h'][:b.num_dst_nodes()])[b.edges()[1]]
K = self.w_k(torch.cat([b.srcdata['h'][b.num_dst_nodes():], b.edata['f']], dim=1))
V = self.w_v(torch.cat([b.srcdata['h'][b.num_dst_nodes():], b.edata['f']], dim=1))
elif self.dim_node_feat == 0:
Q = self.w_q(zero_time_feat)[b.edges()[1]]
K = self.w_k(torch.cat([b.edata['f'], time_feat], dim=1))
V = self.w_v(torch.cat([b.edata['f'], time_feat], dim=1))
elif self.dim_edge_feat == 0:
Q = self.w_q(torch.cat([b.srcdata['h'][:b.num_dst_nodes()], zero_time_feat], dim=1))[b.edges()[1]]
K = self.w_k(torch.cat([b.srcdata['h'][b.num_dst_nodes():], time_feat], dim=1))
V = self.w_v(torch.cat([b.srcdata['h'][b.num_dst_nodes():], time_feat], dim=1))
else:
Q = self.w_q(torch.cat([b.srcdata['h'][:b.num_dst_nodes()], zero_time_feat], dim=1))[b.edges()[1]]
K = self.w_k(torch.cat([b.srcdata['h'][b.num_dst_nodes():], b.edata['f'], time_feat], dim=1))
V = self.w_v(torch.cat([b.srcdata['h'][b.num_dst_nodes():], b.edata['f'], time_feat], dim=1))
Q = torch.reshape(Q, (Q.shape[0], self.num_head, -1))
K = torch.reshape(K, (K.shape[0], self.num_head, -1))
V = torch.reshape(V, (V.shape[0], self.num_head, -1))
att = dgl.ops.edge_softmax(b, self.att_act(torch.sum(Q*K, dim=2)))
att = self.att_dropout(att)
V = torch.reshape(V*att[:, :, None], (V.shape[0], -1))
b.srcdata['v'] = torch.cat([torch.zeros((b.num_dst_nodes(), V.shape[1]), device=torch.device('cuda:0')), V], dim=0)
b.update_all(dgl.function.copy_src('v', 'm'), dgl.function.sum('m', 'h'))
if self.dim_node_feat != 0:
rst = torch.cat([b.dstdata['h'], b.srcdata['h'][:b.num_dst_nodes()]], dim=1)
else:
rst = b.dstdata['h']
rst = self.w_out(rst)
rst = torch.nn.functional.relu(self.dropout(rst))
return self.layer_norm(rst)
class IdentityNormLayer(torch.nn.Module):
def __init__(self, dim_out):
super(IdentityNormLayer, self).__init__()
self.norm = torch.nn.LayerNorm(dim_out)
def forward(self, b):
return self.norm(b.srcdata['h'])
class JODIETimeEmbedding(torch.nn.Module):
def __init__(self, dim_out):
super(JODIETimeEmbedding, self).__init__()
self.dim_out = dim_out
class NormalLinear(torch.nn.Linear):
# From Jodie code
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.normal_(0, stdv)
if self.bias is not None:
self.bias.data.normal_(0, stdv)
self.time_emb = NormalLinear(1, dim_out)
def forward(self, h, mem_ts, ts):
time_diff = (ts - mem_ts) / (ts + 1)
rst = h * (1 + self.time_emb(time_diff.unsqueeze(1)))
return rst