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Aggregator.py
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Aggregator.py
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import torch.nn as nn
import numpy as np
import torch
import torch.nn.functional as F
from utils import *
from RGCN import RGCNBlockLayer as RGCNLayer
class RGCNAggregator_global(nn.Module):
def __init__(self, h_dim, dropout, num_nodes, num_rels, num_bases, model, seq_len=10, maxpool=1):
super(RGCNAggregator_global, self).__init__()
self.h_dim = h_dim
self.dropout = nn.Dropout(dropout)
self.seq_len = seq_len
self.num_rels = num_rels
self.num_nodes = num_nodes
self.model = model
self.maxpool = maxpool
self.rgcn1 = RGCNLayer(self.h_dim, self.h_dim, 2 * self.num_rels, num_bases,
activation=F.relu, self_loop=True, dropout=dropout)
self.rgcn2 = RGCNLayer(self.h_dim, self.h_dim, 2 * self.num_rels, num_bases,
activation=None, self_loop=True, dropout=dropout)
def forward(self, t_list, ent_embeds, graph_dict, reverse):
times = list(graph_dict.keys())
time_unit = times[1] - times[0]
time_list = []
len_non_zero = []
num_non_zero = len(torch.nonzero(t_list))
t_list = t_list[:num_non_zero]
for tim in t_list:
length = int(tim // time_unit)
if self.seq_len <= length:
time_list.append(torch.LongTensor(times[length - self.seq_len:length]))
len_non_zero.append(self.seq_len)
else:
time_list.append(torch.LongTensor(times[:length]))
len_non_zero.append(length)
unique_t = torch.unique(torch.cat(time_list))
time_to_idx = dict()
g_list = []
idx = 0
for tim in unique_t:
time_to_idx[tim.item()] = idx
idx += 1
g_list.append(graph_dict[tim.item()])
batched_graph = dgl.batch(g_list)
batched_graph.ndata['h'] = ent_embeds[batched_graph.ndata['id']].view(-1, ent_embeds.shape[1])
move_dgl_to_cuda(batched_graph)
self.rgcn1(batched_graph, reverse)
self.rgcn2(batched_graph, reverse)
if self.maxpool == 1:
global_info = dgl.max_nodes(batched_graph, 'h')
else:
global_info = dgl.mean_nodes(batched_graph, 'h')
batched_graph.ndata.pop('h')
embed_seq_tensor = torch.zeros(len(len_non_zero), self.seq_len, self.h_dim).cuda()
for i, times in enumerate(time_list):
for j, t in enumerate(times):
embed_seq_tensor[i, j, :] = global_info[time_to_idx[t.item()]]
embed_seq_tensor = self.dropout(embed_seq_tensor)
packed_input = torch.nn.utils.rnn.pack_padded_sequence(embed_seq_tensor,
len_non_zero,
batch_first=True)
return packed_input
def predict(self, t, ent_embeds, graph_dict, reverse):
times = list(graph_dict.keys())
id = 0
for tt in times:
if tt >= t:
break
id += 1
if self.seq_len <= id:
timess = torch.LongTensor(times[id - self.seq_len:id])
else:
timess = torch.LongTensor(times[:id])
g_list = []
for tim in timess:
move_dgl_to_cuda(graph_dict[tim.item()])
g_list.append(graph_dict[tim.item()])
batched_graph = dgl.batch(g_list)
batched_graph.ndata['h'] = ent_embeds[batched_graph.ndata['id']].view(-1, ent_embeds.shape[1])
move_dgl_to_cuda(batched_graph)
self.rgcn1(batched_graph, reverse)
self.rgcn2(batched_graph, reverse)
if self.maxpool == 1:
global_info = dgl.max_nodes(batched_graph, 'h')
else:
global_info = dgl.mean_nodes(batched_graph, 'h')
batched_graph.ndata.pop('h')
return global_info
class RGCNAggregator(nn.Module):
def __init__(self, h_dim, dropout, num_nodes, num_rels, num_bases, model, seq_len=10):
super(RGCNAggregator, self).__init__()
self.h_dim = h_dim
self.dropout = nn.Dropout(dropout)
self.seq_len = seq_len
self.num_rels = num_rels
self.num_nodes = num_nodes
self.model = model
self.rgcn1 = RGCNLayer(self.h_dim, self.h_dim, 2*self.num_rels, num_bases,
activation=F.relu, self_loop=True, dropout=dropout)
self.rgcn2 = RGCNLayer(self.h_dim, self.h_dim, 2*self.num_rels, num_bases,
activation=None, self_loop=True, dropout=dropout)
def forward(self, s_hist, s, r, ent_embeds, rel_embeds, graph_dict, global_emb, reverse):
length = 0
for his in s_hist[0]:
length += len(his)
if length == 0:
s_packed_input = None
else:
s_len_non_zero, s_tem, r_tem, g, node_ids_graph, global_emb_list = get_sorted_s_r_embed_rgcn(s_hist, s, r, ent_embeds, graph_dict, global_emb)
if g is None:
s_packed_input = None
else:
self.rgcn1(g, reverse)
self.rgcn2(g, reverse)
embeds_mean = g.ndata.pop('h')
embeds_mean = embeds_mean[torch.LongTensor(node_ids_graph)]
embeds_split = torch.split(embeds_mean, s_len_non_zero.tolist())
global_emb_list_split = torch.split(global_emb_list, s_len_non_zero.tolist())
s_embed_seq_tensor = torch.zeros(len(s_len_non_zero), self.seq_len, 4 * self.h_dim).cuda()
s_embed_seq_tensor_r = torch.zeros(len(s_len_non_zero), self.seq_len, 3 * self.h_dim).cuda()
# Slow!!!
for i, embeds in enumerate(embeds_split):
s_embed_seq_tensor[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1),
rel_embeds[r_tem[i]].repeat(len(embeds), 1), global_emb_list_split[i]), dim=1)
s_embed_seq_tensor_r[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1), global_emb_list_split[i]), dim=1)
s_embed_seq_tensor = self.dropout(s_embed_seq_tensor)
s_embed_seq_tensor_r = self.dropout(s_embed_seq_tensor_r)
s_packed_input = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor,
s_len_non_zero,
batch_first=True)
s_packed_input_r = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor_r,
s_len_non_zero,
batch_first=True)
return s_packed_input, s_packed_input_r
def predict_batch(self, s_hist, s, r, ent_embeds, rel_embeds, graph_dict, global_emb, reverse):
length = 0
for his in s_hist[0]:
length += len(his)
if length == 0:
s_packed_input = None
s_packed_input_r = None
else:
s_len_non_zero, s_tem, r_tem, g, node_ids_graph, global_emb_list = get_s_r_embed_rgcn(s_hist, s, r, ent_embeds, graph_dict, global_emb)
if g is None:
s_packed_input = None
else:
self.rgcn1(g, reverse)
self.rgcn2(g, reverse)
embeds_mean = g.ndata.pop('h')
embeds_mean = embeds_mean[torch.LongTensor(node_ids_graph)]
embeds_split = torch.split(embeds_mean, s_len_non_zero.tolist())
global_emb_list_split = torch.split(global_emb_list, s_len_non_zero.tolist())
s_embed_seq_tensor = torch.zeros(len(s_len_non_zero), self.seq_len, 4 * self.h_dim).cuda()
s_embed_seq_tensor_r = torch.zeros(len(s_len_non_zero), self.seq_len, 3 * self.h_dim).cuda()
# Slow!!!
for i, embeds in enumerate(embeds_split):
s_embed_seq_tensor[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1),
rel_embeds[r_tem[i]].repeat(len(embeds), 1), global_emb_list_split[i]), dim=1)
s_embed_seq_tensor_r[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1), global_emb_list_split[i]), dim=1)
s_embed_seq_tensor = self.dropout(s_embed_seq_tensor)
s_embed_seq_tensor_r = self.dropout(s_embed_seq_tensor_r)
s_packed_input = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor,
s_len_non_zero,
batch_first=True)
s_packed_input_r = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor_r,
s_len_non_zero,
batch_first=True)
return s_packed_input, s_packed_input_r
def predict(self, s_history, s, r, ent_embeds, rel_embeds, graph_dict, global_emb, reverse):
s_hist = s_history[0]
s_hist_t = s_history[1]
s_len_non_zero, s_tem, r_tem, g, node_ids_graph, global_emb_list = get_s_r_embed_rgcn(([s_hist], [s_hist_t]), s.view(-1,1), r.view(-1,1), ent_embeds,
graph_dict, global_emb)
self.rgcn1(g, reverse)
self.rgcn2(g, reverse)
embeds_mean = g.ndata.pop('h')
embeds = embeds_mean[torch.LongTensor(node_ids_graph)]
inp = torch.zeros(len(s_hist), 4 * self.h_dim).cuda()
inp[torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s].repeat(len(embeds), 1), rel_embeds[r].repeat(len(embeds), 1), global_emb_list), dim=1)
inp_r = torch.zeros(len(s_hist), 3 * self.h_dim).cuda()
inp_r[torch.arange(len(embeds)), :] = torch.cat((embeds, ent_embeds[s].repeat(len(embeds), 1), global_emb_list), dim=1)
return inp, inp_r
class MeanAggregator(nn.Module):
def __init__(self, h_dim, dropout, seq_len=10, gcn=False):
super(MeanAggregator, self).__init__()
self.h_dim = h_dim
self.dropout = nn.Dropout(dropout)
self.seq_len = seq_len
self.gcn = gcn
if gcn:
self.gcn_layer = nn.Linear(h_dim, h_dim)
def forward(self, s_hist, s, r, ent_embeds, rel_embeds):
s_len_non_zero, s_tem, r_tem, embeds_stack, len_s, embeds_split = get_sorted_s_r_embed(s_hist, s, r, ent_embeds)
# To get mean vector at each time
curr = 0
rows = []
cols = []
for i, leng in enumerate(len_s):
rows.extend([i] * leng)
cols.extend(list(range(curr,curr+leng)))
curr += leng
rows = torch.LongTensor(rows)
cols = torch.LongTensor(cols)
idxes = torch.stack([rows,cols], dim=0)
mask_tensor = torch.sparse.FloatTensor(idxes, torch.ones(len(rows)))
mask_tensor = mask_tensor.cuda()
embeds_sum = torch.sparse.mm(mask_tensor, embeds_stack)
embeds_mean = embeds_sum /torch.Tensor(len_s).cuda().view(-1,1)
if self.gcn:
embeds_mean = self.gcn_layer(embeds_mean)
embeds_mean = F.relu(embeds_mean)
embeds_split = torch.split(embeds_mean, s_len_non_zero.tolist())
s_embed_seq_tensor = torch.zeros(len(s_len_non_zero), self.seq_len, 2 * self.h_dim).cuda()
# Slow!!!
for i, embeds in enumerate(embeds_split):
s_embed_seq_tensor[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1)), dim=1)
s_embed_seq_tensor = self.dropout(s_embed_seq_tensor)
s_packed_input = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor,
s_len_non_zero,
batch_first=True)
return s_packed_input
def predict(self, s_history, s, r, ent_embeds, rel_embeds):
inp = torch.zeros(len(s_history), 2 * self.h_dim).cuda()
for i, s_o in enumerate(s_history):
tem = torch.mean(ent_embeds[s_o], dim=0)
if self.gcn:
tem = F.relu(self.gcn_layer(tem))
inp[i] = torch.cat(
(tem, ent_embeds[s]), dim=0)
return inp
class AttnAggregator(nn.Module):
def __init__(self, h_dim, dropout, seq_len=10):
super(AttnAggregator, self).__init__()
self.h_dim = h_dim
self.dropout = nn.Dropout(dropout)
self.seq_len = seq_len
self.attn_s = nn.Linear(3 * h_dim, h_dim)
self.v_s = nn.Parameter(torch.Tensor(h_dim, 1))
nn.init.xavier_uniform_(self.v_s, gain=nn.init.calculate_gain('relu'))
def forward(self, s_hist, s, r, ent_embeds, rel_embeds):
s_len_non_zero, s_tem, r_tem, embeds_stack, len_s, embeds_split = get_sorted_s_r_embed(s_hist, s, r, ent_embeds)
s_embed_seq_tensor = torch.zeros(len(s_len_non_zero), self.seq_len, 3 * self.h_dim).cuda()
curr = 0
for i, s_l in enumerate(s_len_non_zero):
# Make a batch, get first elements from all sequences, and get second elements from all sequences
em = embeds_split[curr:curr + s_l]
len_s = list(map(len, em))
curr += s_l
em_cat = torch.cat(em, dim=0)
ss = ent_embeds[s_tem[i]]
rr = rel_embeds[r_tem[i]]
ss = ss.repeat(len(em_cat), 1)
rr = rr.repeat(len(em_cat), 1)
em_s_r = torch.cat((em_cat, ss, rr), dim=1)
weights = F.tanh(self.attn_s(em_s_r)) @ self.v_s
weights_split = torch.split(weights, len_s)
weights_cat = list(map(lambda x: F.softmax(x, dim=0), weights_split))
embeds = torch.stack(list(map(lambda x, y: torch.sum(x * y, dim=0), weights_cat, em)))
s_embed_seq_tensor[i, torch.arange(len(embeds)), :] = torch.cat(
(embeds, ent_embeds[s_tem[i]].repeat(len(embeds), 1),
rel_embeds[r_tem[i]].repeat(len(embeds), 1)), dim=1)
s_embed_seq_tensor = self.dropout(s_embed_seq_tensor)
s_packed_input = torch.nn.utils.rnn.pack_padded_sequence(s_embed_seq_tensor,
s_len_non_zero,
batch_first=True)
return s_packed_input
def predict(self, s_history, s, r, ent_embeds, rel_embeds):
inp = torch.zeros(len(s_history), 3 * self.h_dim).cuda()
for i, s_s in enumerate(s_history):
emb_s = ent_embeds[s_s]
ss = ent_embeds[s].repeat(len(emb_s), 1)
rr = rel_embeds[r].repeat(len(emb_s), 1)
emb_s_r = torch.cat((emb_s, ss, rr), dim=1)
weights = F.softmax(F.tanh(self.attn_s(emb_s_r)) @ self.v_s, dim=0)
inp[i] = torch.cat((torch.sum(weights * emb_s, dim=0), ent_embeds[s], rel_embeds[r]), dim=0)
return inp