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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import time
from layers import SpGraphAttentionLayer, ConvKB
CUDA = torch.cuda.is_available() # checking cuda availability
class SpGAT(nn.Module):
def __init__(self, num_nodes, nfeat, nhid, relation_dim, dropout, alpha, nheads):
"""
Sparse version of GAT
nfeat -> Entity Input Embedding dimensions
nhid -> Entity Output Embedding dimensions
relation_dim -> Relation Embedding dimensions
num_nodes -> number of nodes in the Graph
nheads -> Used for Multihead attention
"""
super(SpGAT, self).__init__()
self.dropout = dropout
self.dropout_layer = nn.Dropout(self.dropout)
self.attentions = [SpGraphAttentionLayer(num_nodes, nfeat,
nhid,
relation_dim,
dropout=dropout,
alpha=alpha,
concat=True)
for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
# W matrix to convert h_input to h_output dimension
self.W = nn.Parameter(torch.zeros(size=(relation_dim, nheads * nhid)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.out_att = SpGraphAttentionLayer(num_nodes, nhid * nheads,
nheads * nhid, nheads * nhid,
dropout=dropout,
alpha=alpha,
concat=False
)
def forward(self, Corpus_, batch_inputs, entity_embeddings, relation_embed,
edge_list, edge_type, edge_embed, edge_list_nhop, edge_type_nhop):
x = entity_embeddings
edge_embed_nhop = relation_embed[
edge_type_nhop[:, 0]] + relation_embed[edge_type_nhop[:, 1]]
x = torch.cat([att(x, edge_list, edge_embed, edge_list_nhop, edge_embed_nhop)
for att in self.attentions], dim=1)
x = self.dropout_layer(x)
out_relation_1 = relation_embed.mm(self.W)
edge_embed = out_relation_1[edge_type]
edge_embed_nhop = out_relation_1[
edge_type_nhop[:, 0]] + out_relation_1[edge_type_nhop[:, 1]]
x = F.elu(self.out_att(x, edge_list, edge_embed,
edge_list_nhop, edge_embed_nhop))
return x, out_relation_1
class SpKBGATModified(nn.Module):
def __init__(self, initial_entity_emb, initial_relation_emb, entity_out_dim, relation_out_dim,
drop_GAT, alpha, nheads_GAT):
'''Sparse version of KBGAT
entity_in_dim -> Entity Input Embedding dimensions
entity_out_dim -> Entity Output Embedding dimensions, passed as a list
num_relation -> number of unique relations
relation_dim -> Relation Embedding dimensions
num_nodes -> number of nodes in the Graph
nheads_GAT -> Used for Multihead attention, passed as a list '''
super().__init__()
self.num_nodes = initial_entity_emb.shape[0]
self.entity_in_dim = initial_entity_emb.shape[1]
self.entity_out_dim_1 = entity_out_dim[0]
self.nheads_GAT_1 = nheads_GAT[0]
self.entity_out_dim_2 = entity_out_dim[1]
self.nheads_GAT_2 = nheads_GAT[1]
# Properties of Relations
self.num_relation = initial_relation_emb.shape[0]
self.relation_dim = initial_relation_emb.shape[1]
self.relation_out_dim_1 = relation_out_dim[0]
self.drop_GAT = drop_GAT
self.alpha = alpha # For leaky relu
self.final_entity_embeddings = nn.Parameter(
torch.randn(self.num_nodes, self.entity_out_dim_1 * self.nheads_GAT_1))
self.final_relation_embeddings = nn.Parameter(
torch.randn(self.num_relation, self.entity_out_dim_1 * self.nheads_GAT_1))
self.entity_embeddings = nn.Parameter(initial_entity_emb)
self.relation_embeddings = nn.Parameter(initial_relation_emb)
self.sparse_gat_1 = SpGAT(self.num_nodes, self.entity_in_dim, self.entity_out_dim_1, self.relation_dim,
self.drop_GAT, self.alpha, self.nheads_GAT_1)
self.W_entities = nn.Parameter(torch.zeros(
size=(self.entity_in_dim, self.entity_out_dim_1 * self.nheads_GAT_1)))
nn.init.xavier_uniform_(self.W_entities.data, gain=1.414)
def forward(self, Corpus_, adj, batch_inputs, train_indices_nhop):
# getting edge list
edge_list = adj[0]
edge_type = adj[1]
edge_list_nhop = torch.cat(
(train_indices_nhop[:, 3].unsqueeze(-1), train_indices_nhop[:, 0].unsqueeze(-1)), dim=1).t()
edge_type_nhop = torch.cat(
[train_indices_nhop[:, 1].unsqueeze(-1), train_indices_nhop[:, 2].unsqueeze(-1)], dim=1)
if(CUDA):
edge_list = edge_list.cuda()
edge_type = edge_type.cuda()
edge_list_nhop = edge_list_nhop.cuda()
edge_type_nhop = edge_type_nhop.cuda()
edge_embed = self.relation_embeddings[edge_type]
start = time.time()
self.entity_embeddings.data = F.normalize(
self.entity_embeddings.data, p=2, dim=1).detach()
# self.relation_embeddings.data = F.normalize(
# self.relation_embeddings.data, p=2, dim=1)
out_entity_1, out_relation_1 = self.sparse_gat_1(
Corpus_, batch_inputs, self.entity_embeddings, self.relation_embeddings,
edge_list, edge_type, edge_embed, edge_list_nhop, edge_type_nhop)
mask_indices = torch.unique(batch_inputs[:, 2]).cuda()
mask = torch.zeros(self.entity_embeddings.shape[0]).cuda()
mask[mask_indices] = 1.0
entities_upgraded = self.entity_embeddings.mm(self.W_entities)
out_entity_1 = entities_upgraded + \
mask.unsqueeze(-1).expand_as(out_entity_1) * out_entity_1
out_entity_1 = F.normalize(out_entity_1, p=2, dim=1)
self.final_entity_embeddings.data = out_entity_1.data
self.final_relation_embeddings.data = out_relation_1.data
return out_entity_1, out_relation_1
class SpKBGATConvOnly(nn.Module):
def __init__(self, initial_entity_emb, initial_relation_emb, entity_out_dim, relation_out_dim,
drop_GAT, drop_conv, alpha, alpha_conv, nheads_GAT, conv_out_channels):
'''Sparse version of KBGAT
entity_in_dim -> Entity Input Embedding dimensions
entity_out_dim -> Entity Output Embedding dimensions, passed as a list
num_relation -> number of unique relations
relation_dim -> Relation Embedding dimensions
num_nodes -> number of nodes in the Graph
nheads_GAT -> Used for Multihead attention, passed as a list '''
super().__init__()
self.num_nodes = initial_entity_emb.shape[0]
self.entity_in_dim = initial_entity_emb.shape[1]
self.entity_out_dim_1 = entity_out_dim[0]
self.nheads_GAT_1 = nheads_GAT[0]
self.entity_out_dim_2 = entity_out_dim[1]
self.nheads_GAT_2 = nheads_GAT[1]
# Properties of Relations
self.num_relation = initial_relation_emb.shape[0]
self.relation_dim = initial_relation_emb.shape[1]
self.relation_out_dim_1 = relation_out_dim[0]
self.drop_GAT = drop_GAT
self.drop_conv = drop_conv
self.alpha = alpha # For leaky relu
self.alpha_conv = alpha_conv
self.conv_out_channels = conv_out_channels
self.final_entity_embeddings = nn.Parameter(
torch.randn(self.num_nodes, self.entity_out_dim_1 * self.nheads_GAT_1))
self.final_relation_embeddings = nn.Parameter(
torch.randn(self.num_relation, self.entity_out_dim_1 * self.nheads_GAT_1))
self.convKB = ConvKB(self.entity_out_dim_1 * self.nheads_GAT_1, 3, 1,
self.conv_out_channels, self.drop_conv, self.alpha_conv)
def forward(self, Corpus_, adj, batch_inputs):
conv_input = torch.cat((self.final_entity_embeddings[batch_inputs[:, 0], :].unsqueeze(1), self.final_relation_embeddings[
batch_inputs[:, 1]].unsqueeze(1), self.final_entity_embeddings[batch_inputs[:, 2], :].unsqueeze(1)), dim=1)
out_conv = self.convKB(conv_input)
return out_conv
def batch_test(self, batch_inputs):
conv_input = torch.cat((self.final_entity_embeddings[batch_inputs[:, 0], :].unsqueeze(1), self.final_relation_embeddings[
batch_inputs[:, 1]].unsqueeze(1), self.final_entity_embeddings[batch_inputs[:, 2], :].unsqueeze(1)), dim=1)
out_conv = self.convKB(conv_input)
return out_conv