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models.py
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models.py
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#!/usr/bin/python3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from dataloader import TestDataset, TrainDataset, SingledirectionalOneShotIterator
import random
import pickle
import math
import collections
import itertools
import time
from tqdm import tqdm
import os
def Identity(x):
return x
class BoxOffsetIntersection(nn.Module):
def __init__(self, dim):
super(BoxOffsetIntersection, self).__init__()
self.dim = dim
self.layer1 = nn.Linear(self.dim, self.dim)
self.layer2 = nn.Linear(self.dim, self.dim)
nn.init.xavier_uniform_(self.layer1.weight)
nn.init.xavier_uniform_(self.layer2.weight)
def forward(self, embeddings):
layer1_act = F.relu(self.layer1(embeddings))
layer1_mean = torch.mean(layer1_act, dim=0)
gate = torch.sigmoid(self.layer2(layer1_mean))
offset, _ = torch.min(embeddings, dim=0)
return offset * gate
class CenterIntersection(nn.Module):
def __init__(self, dim):
super(CenterIntersection, self).__init__()
self.dim = dim
self.layer1 = nn.Linear(self.dim, self.dim)
self.layer2 = nn.Linear(self.dim, self.dim)
nn.init.xavier_uniform_(self.layer1.weight)
nn.init.xavier_uniform_(self.layer2.weight)
def forward(self, embeddings):
layer1_act = F.relu(self.layer1(embeddings)) # (num_conj, dim)
attention = F.softmax(self.layer2(layer1_act), dim=0) # (num_conj, dim)
embedding = torch.sum(attention * embeddings, dim=0)
return embedding
class BetaIntersection(nn.Module):
def __init__(self, dim):
super(BetaIntersection, self).__init__()
self.dim = dim
self.layer1 = nn.Linear(2 * self.dim, 2 * self.dim)
self.layer2 = nn.Linear(2 * self.dim, self.dim)
nn.init.xavier_uniform_(self.layer1.weight)
nn.init.xavier_uniform_(self.layer2.weight)
def forward(self, alpha_embeddings, beta_embeddings):
all_embeddings = torch.cat([alpha_embeddings, beta_embeddings], dim=-1)
layer1_act = F.relu(self.layer1(all_embeddings)) # (num_conj, batch_size, 2 * dim)
attention = F.softmax(self.layer2(layer1_act), dim=0) # (num_conj, batch_size, dim)
alpha_embedding = torch.sum(attention * alpha_embeddings, dim=0)
beta_embedding = torch.sum(attention * beta_embeddings, dim=0)
return alpha_embedding, beta_embedding
class BetaProjection(nn.Module):
def __init__(self, entity_dim, relation_dim, hidden_dim, projection_regularizer, num_layers):
super(BetaProjection, self).__init__()
self.entity_dim = entity_dim
self.relation_dim = relation_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.layer1 = nn.Linear(self.entity_dim + self.relation_dim, self.hidden_dim) # 1st layer
self.layer0 = nn.Linear(self.hidden_dim, self.entity_dim) # final layer
for nl in range(2, num_layers + 1):
setattr(self, "layer{}".format(nl), nn.Linear(self.hidden_dim, self.hidden_dim))
for nl in range(num_layers + 1):
nn.init.xavier_uniform_(getattr(self, "layer{}".format(nl)).weight)
self.projection_regularizer = projection_regularizer
def forward(self, e_embedding, r_embedding):
x = torch.cat([e_embedding, r_embedding], dim=-1)
for nl in range(1, self.num_layers + 1):
x = F.relu(getattr(self, "layer{}".format(nl))(x))
x = self.layer0(x)
x = self.projection_regularizer(x)
return x
class Regularizer():
def __init__(self, base_add, min_val, max_val):
self.base_add = base_add
self.min_val = min_val
self.max_val = max_val
def __call__(self, entity_embedding):
return torch.clamp(entity_embedding + self.base_add, self.min_val, self.max_val)
class KGReasoning(nn.Module):
def __init__(self, nentity, nrelation, hidden_dim, gamma,
geo, test_batch_size=1,
box_mode=None, use_cuda=False,
query_name_dict=None, beta_mode=None):
super(KGReasoning, self).__init__()
self.nentity = nentity
self.nrelation = nrelation
self.hidden_dim = hidden_dim
self.epsilon = 2.0
self.geo = geo
self.use_cuda = use_cuda
self.batch_entity_range = torch.arange(nentity).to(torch.float).repeat(test_batch_size, 1).cuda() if self.use_cuda else torch.arange(nentity).to(torch.float).repeat(test_batch_size, 1) # used in test_step
self.query_name_dict = query_name_dict
self.gamma = nn.Parameter(
torch.Tensor([gamma]),
requires_grad=False
)
self.embedding_range = nn.Parameter(
torch.Tensor([(self.gamma.item() + self.epsilon) / hidden_dim]),
requires_grad=False
)
self.entity_dim = hidden_dim
self.relation_dim = hidden_dim
if self.geo == 'box':
self.entity_embedding = nn.Parameter(torch.zeros(nentity, self.entity_dim)) # centor for entities
activation, cen = box_mode
self.cen = cen # hyperparameter that balances the in-box distance and the out-box distance
if activation == 'none':
self.func = Identity
elif activation == 'relu':
self.func = F.relu
elif activation == 'softplus':
self.func = F.softplus
elif self.geo == 'vec':
self.entity_embedding = nn.Parameter(torch.zeros(nentity, self.entity_dim)) # center for entities
elif self.geo == 'beta':
self.entity_embedding = nn.Parameter(torch.zeros(nentity, self.entity_dim * 2)) # alpha and beta
self.entity_regularizer = Regularizer(1, 0.05, 1e9) # make sure the parameters of beta embeddings are positive
self.projection_regularizer = Regularizer(1, 0.05, 1e9) # make sure the parameters of beta embeddings after relation projection are positive
nn.init.uniform_(
tensor=self.entity_embedding,
a=-self.embedding_range.item(),
b=self.embedding_range.item()
)
self.relation_embedding = nn.Parameter(torch.zeros(nrelation, self.relation_dim))
nn.init.uniform_(
tensor=self.relation_embedding,
a=-self.embedding_range.item(),
b=self.embedding_range.item()
)
if self.geo == 'box':
self.offset_embedding = nn.Parameter(torch.zeros(nrelation, self.entity_dim))
nn.init.uniform_(
tensor=self.offset_embedding,
a=0.,
b=self.embedding_range.item()
)
self.center_net = CenterIntersection(self.entity_dim)
self.offset_net = BoxOffsetIntersection(self.entity_dim)
elif self.geo == 'vec':
self.center_net = CenterIntersection(self.entity_dim)
elif self.geo == 'beta':
hidden_dim, num_layers = beta_mode
self.center_net = BetaIntersection(self.entity_dim)
self.projection_net = BetaProjection(self.entity_dim * 2,
self.relation_dim,
hidden_dim,
self.projection_regularizer,
num_layers)
def forward(self, positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict):
if self.geo == 'box':
return self.forward_box(positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict)
elif self.geo == 'vec':
return self.forward_vec(positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict)
elif self.geo == 'beta':
return self.forward_beta(positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict)
def embed_query_box(self, queries, query_structure, idx):
'''
Iterative embed a batch of queries with same structure using Query2box
queries: a flattened batch of queries
'''
all_relation_flag = True
for ele in query_structure[-1]: # whether the current query tree has merged to one branch and only need to do relation traversal, e.g., path queries or conjunctive queries after the intersection
if ele not in ['r', 'n']:
all_relation_flag = False
break
if all_relation_flag:
if query_structure[0] == 'e':
embedding = torch.index_select(self.entity_embedding, dim=0, index=queries[:, idx])
if self.use_cuda:
offset_embedding = torch.zeros_like(embedding).cuda()
else:
offset_embedding = torch.zeros_like(embedding)
idx += 1
else:
embedding, offset_embedding, idx = self.embed_query_box(queries, query_structure[0], idx)
for i in range(len(query_structure[-1])):
if query_structure[-1][i] == 'n':
assert False, "box cannot handle queries with negation"
else:
r_embedding = torch.index_select(self.relation_embedding, dim=0, index=queries[:, idx])
r_offset_embedding = torch.index_select(self.offset_embedding, dim=0, index=queries[:, idx])
embedding += r_embedding
offset_embedding += self.func(r_offset_embedding)
idx += 1
else:
embedding_list = []
offset_embedding_list = []
for i in range(len(query_structure)):
embedding, offset_embedding, idx = self.embed_query_box(queries, query_structure[i], idx)
embedding_list.append(embedding)
offset_embedding_list.append(offset_embedding)
embedding = self.center_net(torch.stack(embedding_list))
offset_embedding = self.offset_net(torch.stack(offset_embedding_list))
return embedding, offset_embedding, idx
def embed_query_vec(self, queries, query_structure, idx):
'''
Iterative embed a batch of queries with same structure using GQE
queries: a flattened batch of queries
'''
all_relation_flag = True
for ele in query_structure[-1]: # whether the current query tree has merged to one branch and only need to do relation traversal, e.g., path queries or conjunctive queries after the intersection
if ele not in ['r', 'n']:
all_relation_flag = False
break
if all_relation_flag:
if query_structure[0] == 'e':
embedding = torch.index_select(self.entity_embedding, dim=0, index=queries[:, idx])
idx += 1
else:
embedding, idx = self.embed_query_vec(queries, query_structure[0], idx)
for i in range(len(query_structure[-1])):
if query_structure[-1][i] == 'n':
assert False, "vec cannot handle queries with negation"
else:
r_embedding = torch.index_select(self.relation_embedding, dim=0, index=queries[:, idx])
embedding += r_embedding
idx += 1
else:
embedding_list = []
for i in range(len(query_structure)):
embedding, idx = self.embed_query_vec(queries, query_structure[i], idx)
embedding_list.append(embedding)
embedding = self.center_net(torch.stack(embedding_list))
return embedding, idx
def embed_query_beta(self, queries, query_structure, idx):
'''
Iterative embed a batch of queries with same structure using BetaE
queries: a flattened batch of queries
'''
all_relation_flag = True
for ele in query_structure[-1]: # whether the current query tree has merged to one branch and only need to do relation traversal, e.g., path queries or conjunctive queries after the intersection
if ele not in ['r', 'n']:
all_relation_flag = False
break
if all_relation_flag:
if query_structure[0] == 'e':
embedding = self.entity_regularizer(torch.index_select(self.entity_embedding, dim=0, index=queries[:, idx]))
idx += 1
else:
alpha_embedding, beta_embedding, idx = self.embed_query_beta(queries, query_structure[0], idx)
embedding = torch.cat([alpha_embedding, beta_embedding], dim=-1)
for i in range(len(query_structure[-1])):
if query_structure[-1][i] == 'n':
assert (queries[:, idx] == -2).all()
embedding = 1./embedding
else:
r_embedding = torch.index_select(self.relation_embedding, dim=0, index=queries[:, idx])
embedding = self.projection_net(embedding, r_embedding)
idx += 1
alpha_embedding, beta_embedding = torch.chunk(embedding, 2, dim=-1)
else:
alpha_embedding_list = []
beta_embedding_list = []
for i in range(len(query_structure)):
alpha_embedding, beta_embedding, idx = self.embed_query_beta(queries, query_structure[i], idx)
alpha_embedding_list.append(alpha_embedding)
beta_embedding_list.append(beta_embedding)
alpha_embedding, beta_embedding = self.center_net(torch.stack(alpha_embedding_list), torch.stack(beta_embedding_list))
return alpha_embedding, beta_embedding, idx
def cal_logit_beta(self, entity_embedding, query_dist):
alpha_embedding, beta_embedding = torch.chunk(entity_embedding, 2, dim=-1)
entity_dist = torch.distributions.beta.Beta(alpha_embedding, beta_embedding)
logit = self.gamma - torch.norm(torch.distributions.kl.kl_divergence(entity_dist, query_dist), p=1, dim=-1)
return logit
def forward_beta(self, positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict):
all_idxs, all_alpha_embeddings, all_beta_embeddings = [], [], []
all_union_idxs, all_union_alpha_embeddings, all_union_beta_embeddings = [], [], []
for query_structure in batch_queries_dict:
if 'u' in self.query_name_dict[query_structure] and 'DNF' in self.query_name_dict[query_structure]:
alpha_embedding, beta_embedding, _ = \
self.embed_query_beta(self.transform_union_query(batch_queries_dict[query_structure],
query_structure),
self.transform_union_structure(query_structure),
0)
all_union_idxs.extend(batch_idxs_dict[query_structure])
all_union_alpha_embeddings.append(alpha_embedding)
all_union_beta_embeddings.append(beta_embedding)
else:
alpha_embedding, beta_embedding, _ = self.embed_query_beta(batch_queries_dict[query_structure],
query_structure,
0)
all_idxs.extend(batch_idxs_dict[query_structure])
all_alpha_embeddings.append(alpha_embedding)
all_beta_embeddings.append(beta_embedding)
if len(all_alpha_embeddings) > 0:
all_alpha_embeddings = torch.cat(all_alpha_embeddings, dim=0).unsqueeze(1)
all_beta_embeddings = torch.cat(all_beta_embeddings, dim=0).unsqueeze(1)
all_dists = torch.distributions.beta.Beta(all_alpha_embeddings, all_beta_embeddings)
if len(all_union_alpha_embeddings) > 0:
all_union_alpha_embeddings = torch.cat(all_union_alpha_embeddings, dim=0).unsqueeze(1)
all_union_beta_embeddings = torch.cat(all_union_beta_embeddings, dim=0).unsqueeze(1)
all_union_alpha_embeddings = all_union_alpha_embeddings.view(all_union_alpha_embeddings.shape[0]//2, 2, 1, -1)
all_union_beta_embeddings = all_union_beta_embeddings.view(all_union_beta_embeddings.shape[0]//2, 2, 1, -1)
all_union_dists = torch.distributions.beta.Beta(all_union_alpha_embeddings, all_union_beta_embeddings)
if type(subsampling_weight) != type(None):
subsampling_weight = subsampling_weight[all_idxs+all_union_idxs]
if type(positive_sample) != type(None):
if len(all_alpha_embeddings) > 0:
positive_sample_regular = positive_sample[all_idxs] # positive samples for non-union queries in this batch
positive_embedding = self.entity_regularizer(torch.index_select(self.entity_embedding, dim=0, index=positive_sample_regular).unsqueeze(1))
positive_logit = self.cal_logit_beta(positive_embedding, all_dists)
else:
positive_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_alpha_embeddings) > 0:
positive_sample_union = positive_sample[all_union_idxs] # positive samples for union queries in this batch
positive_embedding = self.entity_regularizer(torch.index_select(self.entity_embedding, dim=0, index=positive_sample_union).unsqueeze(1).unsqueeze(1))
positive_union_logit = self.cal_logit_beta(positive_embedding, all_union_dists)
positive_union_logit = torch.max(positive_union_logit, dim=1)[0]
else:
positive_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
positive_logit = torch.cat([positive_logit, positive_union_logit], dim=0)
else:
positive_logit = None
if type(negative_sample) != type(None):
if len(all_alpha_embeddings) > 0:
negative_sample_regular = negative_sample[all_idxs]
batch_size, negative_size = negative_sample_regular.shape
negative_embedding = self.entity_regularizer(torch.index_select(self.entity_embedding, dim=0, index=negative_sample_regular.view(-1)).view(batch_size, negative_size, -1))
negative_logit = self.cal_logit_beta(negative_embedding, all_dists)
else:
negative_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_alpha_embeddings) > 0:
negative_sample_union = negative_sample[all_union_idxs]
batch_size, negative_size = negative_sample_union.shape
negative_embedding = self.entity_regularizer(torch.index_select(self.entity_embedding, dim=0, index=negative_sample_union.view(-1)).view(batch_size, 1, negative_size, -1))
negative_union_logit = self.cal_logit_beta(negative_embedding, all_union_dists)
negative_union_logit = torch.max(negative_union_logit, dim=1)[0]
else:
negative_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
negative_logit = torch.cat([negative_logit, negative_union_logit], dim=0)
else:
negative_logit = None
return positive_logit, negative_logit, subsampling_weight, all_idxs+all_union_idxs
def transform_union_query(self, queries, query_structure):
'''
transform 2u queries to two 1p queries
transform up queries to two 2p queries
'''
if self.query_name_dict[query_structure] == '2u-DNF':
queries = queries[:, :-1] # remove union -1
elif self.query_name_dict[query_structure] == 'up-DNF':
queries = torch.cat([torch.cat([queries[:, :2], queries[:, 5:6]], dim=1), torch.cat([queries[:, 2:4], queries[:, 5:6]], dim=1)], dim=1)
queries = torch.reshape(queries, [queries.shape[0]*2, -1])
return queries
def transform_union_structure(self, query_structure):
if self.query_name_dict[query_structure] == '2u-DNF':
return ('e', ('r',))
elif self.query_name_dict[query_structure] == 'up-DNF':
return ('e', ('r', 'r'))
def cal_logit_box(self, entity_embedding, query_center_embedding, query_offset_embedding):
delta = (entity_embedding - query_center_embedding).abs()
distance_out = F.relu(delta - query_offset_embedding)
distance_in = torch.min(delta, query_offset_embedding)
logit = self.gamma - torch.norm(distance_out, p=1, dim=-1) - self.cen * torch.norm(distance_in, p=1, dim=-1)
return logit
def forward_box(self, positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict):
all_center_embeddings, all_offset_embeddings, all_idxs = [], [], []
all_union_center_embeddings, all_union_offset_embeddings, all_union_idxs = [], [], []
for query_structure in batch_queries_dict:
if 'u' in self.query_name_dict[query_structure]:
center_embedding, offset_embedding, _ = \
self.embed_query_box(self.transform_union_query(batch_queries_dict[query_structure],
query_structure),
self.transform_union_structure(query_structure),
0)
all_union_center_embeddings.append(center_embedding)
all_union_offset_embeddings.append(offset_embedding)
all_union_idxs.extend(batch_idxs_dict[query_structure])
else:
center_embedding, offset_embedding, _ = self.embed_query_box(batch_queries_dict[query_structure],
query_structure,
0)
all_center_embeddings.append(center_embedding)
all_offset_embeddings.append(offset_embedding)
all_idxs.extend(batch_idxs_dict[query_structure])
if len(all_center_embeddings) > 0 and len(all_offset_embeddings) > 0:
all_center_embeddings = torch.cat(all_center_embeddings, dim=0).unsqueeze(1)
all_offset_embeddings = torch.cat(all_offset_embeddings, dim=0).unsqueeze(1)
if len(all_union_center_embeddings) > 0 and len(all_union_offset_embeddings) > 0:
all_union_center_embeddings = torch.cat(all_union_center_embeddings, dim=0).unsqueeze(1)
all_union_offset_embeddings = torch.cat(all_union_offset_embeddings, dim=0).unsqueeze(1)
all_union_center_embeddings = all_union_center_embeddings.view(all_union_center_embeddings.shape[0]//2, 2, 1, -1)
all_union_offset_embeddings = all_union_offset_embeddings.view(all_union_offset_embeddings.shape[0]//2, 2, 1, -1)
if type(subsampling_weight) != type(None):
subsampling_weight = subsampling_weight[all_idxs+all_union_idxs]
if type(positive_sample) != type(None):
if len(all_center_embeddings) > 0:
positive_sample_regular = positive_sample[all_idxs]
positive_embedding = torch.index_select(self.entity_embedding, dim=0, index=positive_sample_regular).unsqueeze(1)
positive_logit = self.cal_logit_box(positive_embedding, all_center_embeddings, all_offset_embeddings)
else:
positive_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_center_embeddings) > 0:
positive_sample_union = positive_sample[all_union_idxs]
positive_embedding = torch.index_select(self.entity_embedding, dim=0, index=positive_sample_union).unsqueeze(1).unsqueeze(1)
positive_union_logit = self.cal_logit_box(positive_embedding, all_union_center_embeddings, all_union_offset_embeddings)
positive_union_logit = torch.max(positive_union_logit, dim=1)[0]
else:
positive_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
positive_logit = torch.cat([positive_logit, positive_union_logit], dim=0)
else:
positive_logit = None
if type(negative_sample) != type(None):
if len(all_center_embeddings) > 0:
negative_sample_regular = negative_sample[all_idxs]
batch_size, negative_size = negative_sample_regular.shape
negative_embedding = torch.index_select(self.entity_embedding, dim=0, index=negative_sample_regular.view(-1)).view(batch_size, negative_size, -1)
negative_logit = self.cal_logit_box(negative_embedding, all_center_embeddings, all_offset_embeddings)
else:
negative_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_center_embeddings) > 0:
negative_sample_union = negative_sample[all_union_idxs]
batch_size, negative_size = negative_sample_union.shape
negative_embedding = torch.index_select(self.entity_embedding, dim=0, index=negative_sample_union.view(-1)).view(batch_size, 1, negative_size, -1)
negative_union_logit = self.cal_logit_box(negative_embedding, all_union_center_embeddings, all_union_offset_embeddings)
negative_union_logit = torch.max(negative_union_logit, dim=1)[0]
else:
negative_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
negative_logit = torch.cat([negative_logit, negative_union_logit], dim=0)
else:
negative_logit = None
return positive_logit, negative_logit, subsampling_weight, all_idxs+all_union_idxs
def cal_logit_vec(self, entity_embedding, query_embedding):
distance = entity_embedding - query_embedding
logit = self.gamma - torch.norm(distance, p=1, dim=-1)
return logit
def forward_vec(self, positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict):
all_center_embeddings, all_idxs = [], []
all_union_center_embeddings, all_union_idxs = [], []
for query_structure in batch_queries_dict:
if 'u' in self.query_name_dict[query_structure]:
center_embedding, _ = self.embed_query_vec(self.transform_union_query(batch_queries_dict[query_structure],
query_structure),
self.transform_union_structure(query_structure), 0)
all_union_center_embeddings.append(center_embedding)
all_union_idxs.extend(batch_idxs_dict[query_structure])
else:
center_embedding, _ = self.embed_query_vec(batch_queries_dict[query_structure], query_structure, 0)
all_center_embeddings.append(center_embedding)
all_idxs.extend(batch_idxs_dict[query_structure])
if len(all_center_embeddings) > 0:
all_center_embeddings = torch.cat(all_center_embeddings, dim=0).unsqueeze(1)
if len(all_union_center_embeddings) > 0:
all_union_center_embeddings = torch.cat(all_union_center_embeddings, dim=0).unsqueeze(1)
all_union_center_embeddings = all_union_center_embeddings.view(all_union_center_embeddings.shape[0]//2, 2, 1, -1)
if type(subsampling_weight) != type(None):
subsampling_weight = subsampling_weight[all_idxs+all_union_idxs]
if type(positive_sample) != type(None):
if len(all_center_embeddings) > 0:
positive_sample_regular = positive_sample[all_idxs]
positive_embedding = torch.index_select(self.entity_embedding, dim=0, index=positive_sample_regular).unsqueeze(1)
positive_logit = self.cal_logit_vec(positive_embedding, all_center_embeddings)
else:
positive_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_center_embeddings) > 0:
positive_sample_union = positive_sample[all_union_idxs]
positive_embedding = torch.index_select(self.entity_embedding, dim=0, index=positive_sample_union).unsqueeze(1).unsqueeze(1)
positive_union_logit = self.cal_logit_vec(positive_embedding, all_union_center_embeddings)
positive_union_logit = torch.max(positive_union_logit, dim=1)[0]
else:
positive_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
positive_logit = torch.cat([positive_logit, positive_union_logit], dim=0)
else:
positive_logit = None
if type(negative_sample) != type(None):
if len(all_center_embeddings) > 0:
negative_sample_regular = negative_sample[all_idxs]
batch_size, negative_size = negative_sample_regular.shape
negative_embedding = torch.index_select(self.entity_embedding, dim=0, index=negative_sample_regular.view(-1)).view(batch_size, negative_size, -1)
negative_logit = self.cal_logit_vec(negative_embedding, all_center_embeddings)
else:
negative_logit = torch.Tensor([]).to(self.entity_embedding.device)
if len(all_union_center_embeddings) > 0:
negative_sample_union = negative_sample[all_union_idxs]
batch_size, negative_size = negative_sample_union.shape
negative_embedding = torch.index_select(self.entity_embedding, dim=0, index=negative_sample_union.view(-1)).view(batch_size, 1, negative_size, -1)
negative_union_logit = self.cal_logit_vec(negative_embedding, all_union_center_embeddings)
negative_union_logit = torch.max(negative_union_logit, dim=1)[0]
else:
negative_union_logit = torch.Tensor([]).to(self.entity_embedding.device)
negative_logit = torch.cat([negative_logit, negative_union_logit], dim=0)
else:
negative_logit = None
return positive_logit, negative_logit, subsampling_weight, all_idxs+all_union_idxs
@staticmethod
def train_step(model, optimizer, train_iterator, args, step):
model.train()
optimizer.zero_grad()
positive_sample, negative_sample, subsampling_weight, batch_queries, query_structures = next(train_iterator)
batch_queries_dict = collections.defaultdict(list)
batch_idxs_dict = collections.defaultdict(list)
for i, query in enumerate(batch_queries): # group queries with same structure
batch_queries_dict[query_structures[i]].append(query)
batch_idxs_dict[query_structures[i]].append(i)
for query_structure in batch_queries_dict:
if args.cuda:
batch_queries_dict[query_structure] = torch.LongTensor(batch_queries_dict[query_structure]).cuda()
else:
batch_queries_dict[query_structure] = torch.LongTensor(batch_queries_dict[query_structure])
if args.cuda:
positive_sample = positive_sample.cuda()
negative_sample = negative_sample.cuda()
subsampling_weight = subsampling_weight.cuda()
positive_logit, negative_logit, subsampling_weight, _ = model(positive_sample, negative_sample, subsampling_weight, batch_queries_dict, batch_idxs_dict)
negative_score = F.logsigmoid(-negative_logit).mean(dim=1)
positive_score = F.logsigmoid(positive_logit).squeeze(dim=1)
positive_sample_loss = - (subsampling_weight * positive_score).sum()
negative_sample_loss = - (subsampling_weight * negative_score).sum()
positive_sample_loss /= subsampling_weight.sum()
negative_sample_loss /= subsampling_weight.sum()
loss = (positive_sample_loss + negative_sample_loss)/2
loss.backward()
optimizer.step()
log = {
'positive_sample_loss': positive_sample_loss.item(),
'negative_sample_loss': negative_sample_loss.item(),
'loss': loss.item(),
}
return log
@staticmethod
def test_step(model, easy_answers, hard_answers, args, test_dataloader, query_name_dict, save_result=False, save_str="", save_empty=False):
model.eval()
step = 0
total_steps = len(test_dataloader)
logs = collections.defaultdict(list)
with torch.no_grad():
for negative_sample, queries, queries_unflatten, query_structures in tqdm(test_dataloader, disable=not args.print_on_screen):
batch_queries_dict = collections.defaultdict(list)
batch_idxs_dict = collections.defaultdict(list)
for i, query in enumerate(queries):
batch_queries_dict[query_structures[i]].append(query)
batch_idxs_dict[query_structures[i]].append(i)
for query_structure in batch_queries_dict:
if args.cuda:
batch_queries_dict[query_structure] = torch.LongTensor(batch_queries_dict[query_structure]).cuda()
else:
batch_queries_dict[query_structure] = torch.LongTensor(batch_queries_dict[query_structure])
if args.cuda:
negative_sample = negative_sample.cuda()
_, negative_logit, _, idxs = model(None, negative_sample, None, batch_queries_dict, batch_idxs_dict)
queries_unflatten = [queries_unflatten[i] for i in idxs]
query_structures = [query_structures[i] for i in idxs]
argsort = torch.argsort(negative_logit, dim=1, descending=True)
ranking = argsort.clone().to(torch.float)
if len(argsort) == args.test_batch_size: # if it is the same shape with test_batch_size, we can reuse batch_entity_range without creating a new one
ranking = ranking.scatter_(1, argsort, model.batch_entity_range) # achieve the ranking of all entities
else: # otherwise, create a new torch Tensor for batch_entity_range
if args.cuda:
ranking = ranking.scatter_(1,
argsort,
torch.arange(model.nentity).to(torch.float).repeat(argsort.shape[0],
1).cuda()
) # achieve the ranking of all entities
else:
ranking = ranking.scatter_(1,
argsort,
torch.arange(model.nentity).to(torch.float).repeat(argsort.shape[0],
1)
) # achieve the ranking of all entities
for idx, (i, query, query_structure) in enumerate(zip(argsort[:, 0], queries_unflatten, query_structures)):
hard_answer = hard_answers[query]
easy_answer = easy_answers[query]
num_hard = len(hard_answer)
num_easy = len(easy_answer)
assert len(hard_answer.intersection(easy_answer)) == 0
cur_ranking = ranking[idx, list(easy_answer) + list(hard_answer)]
cur_ranking, indices = torch.sort(cur_ranking)
masks = indices >= num_easy
if args.cuda:
answer_list = torch.arange(num_hard + num_easy).to(torch.float).cuda()
else:
answer_list = torch.arange(num_hard + num_easy).to(torch.float)
cur_ranking = cur_ranking - answer_list + 1 # filtered setting
cur_ranking = cur_ranking[masks] # only take indices that belong to the hard answers
mrr = torch.mean(1./cur_ranking).item()
h1 = torch.mean((cur_ranking <= 1).to(torch.float)).item()
h3 = torch.mean((cur_ranking <= 3).to(torch.float)).item()
h10 = torch.mean((cur_ranking <= 10).to(torch.float)).item()
logs[query_structure].append({
'MRR': mrr,
'HITS1': h1,
'HITS3': h3,
'HITS10': h10,
'num_hard_answer': num_hard,
})
if step % args.test_log_steps == 0:
logging.info('Evaluating the model... (%d/%d)' % (step, total_steps))
step += 1
metrics = collections.defaultdict(lambda: collections.defaultdict(int))
for query_structure in logs:
for metric in logs[query_structure][0].keys():
if metric in ['num_hard_answer']:
continue
metrics[query_structure][metric] = sum([log[metric] for log in logs[query_structure]])/len(logs[query_structure])
metrics[query_structure]['num_queries'] = len(logs[query_structure])
return metrics