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import copy
import logging
import torch
import torch.nn as nn
import numpy as np
from models.embedding_models.bert_embedding_model import BertEmbedModel
from models.embedding_models.pretrained_embedding_model import PretrainedEmbedModel
from modules.token_embedders.bert_encoder import BertLinear
from collections import defaultdict
logger = logging.getLogger(__name__)
class EntRelJointDecoder(nn.Module):
def __init__(self, cfg, vocab, ent_rel_file):
"""__init__ constructs `EntRelJointDecoder` components and
sets `EntRelJointDecoder` parameters. This class adopts a joint
decoding algorithm for entity relation joint decoding and facilitates
the interaction between entity and relation.
Args:
cfg (dict): config parameters for constructing multiple models
vocab (Vocabulary): vocabulary
ent_rel_file (dict): entity and relation file (joint id, entity id, relation id, symmetric id, asymmetric id)
"""
super().__init__()
self.vocab = vocab
self.max_span_length = cfg.max_span_length
self.activation = nn.GELU()
self.device = cfg.device
self.separate_threshold = cfg.separate_threshold
if cfg.embedding_model == 'bert':
self.embedding_model = BertEmbedModel(cfg, vocab)
elif cfg.embedding_model == 'pretrained':
self.embedding_model = PretrainedEmbedModel(cfg, vocab)
self.encoder_output_size = self.embedding_model.get_hidden_size()
self.head_mlp = BertLinear(input_size=self.encoder_output_size,
output_size=cfg.mlp_hidden_size,
activation=self.activation,
dropout=cfg.dropout)
self.tail_mlp = BertLinear(input_size=self.encoder_output_size,
output_size=cfg.mlp_hidden_size,
activation=self.activation,
dropout=cfg.dropout)
self.U = nn.Parameter(
torch.FloatTensor(self.vocab.get_vocab_size('ent_rel_id'), cfg.mlp_hidden_size + 1,
cfg.mlp_hidden_size + 1))
self.U.data.zero_()
if cfg.logit_dropout > 0:
self.logit_dropout = nn.Dropout(p=cfg.logit_dropout)
else:
self.logit_dropout = lambda x: x
self.none_idx = self.vocab.get_token_index('None', 'ent_rel_id')
self.symmetric_label = torch.LongTensor(ent_rel_file["symmetric"])
self.asymmetric_label = torch.LongTensor(ent_rel_file["asymmetric"])
self.ent_label = torch.LongTensor(ent_rel_file["entity"])
self.rel_label = torch.LongTensor(ent_rel_file["relation"])
if self.device > -1:
self.symmetric_label = self.symmetric_label.cuda(device=self.device, non_blocking=True)
self.asymmetric_label = self.asymmetric_label.cuda(device=self.device, non_blocking=True)
self.ent_label = self.ent_label.cuda(device=self.device, non_blocking=True)
self.rel_label = self.rel_label.cuda(device=self.device, non_blocking=True)
self.element_loss = nn.CrossEntropyLoss()
def forward(self, batch_inputs):
"""forward
Arguments:
batch_inputs {dict} -- batch input data
Returns:
dict -- results: ent_loss, ent_pred
"""
results = {}
batch_seq_tokens_lens = batch_inputs['tokens_lens']
batch_tokens = batch_inputs['tokens']
self.embedding_model(batch_inputs)
batch_seq_tokens_encoder_repr = batch_inputs['seq_encoder_reprs']
batch_seq_tokens_head_repr = self.head_mlp(batch_seq_tokens_encoder_repr)
batch_seq_tokens_head_repr = torch.cat(
[batch_seq_tokens_head_repr,
torch.ones_like(batch_seq_tokens_head_repr[..., :1])], dim=-1)
batch_seq_tokens_tail_repr = self.tail_mlp(batch_seq_tokens_encoder_repr)
batch_seq_tokens_tail_repr = torch.cat(
[batch_seq_tokens_tail_repr,
torch.ones_like(batch_seq_tokens_tail_repr[..., :1])], dim=-1)
batch_joint_score = torch.einsum('bxi, oij, byj -> boxy', batch_seq_tokens_head_repr, self.U,
batch_seq_tokens_tail_repr).permute(0, 2, 3, 1)
batch_normalized_joint_score = torch.softmax(
batch_joint_score, dim=-1) * batch_inputs['joint_label_matrix_mask'].unsqueeze(-1).float()
if not self.training:
# tokens = [self.vocab.get_token_from_index(token, 'tokens') for token in batch_inputs['tokens'][batch_seq_tokens_lens]]
# print("tokens: ", tokens)
results['joint_label_preds'] = torch.argmax(batch_normalized_joint_score, dim=-1)
# three step decoding happens in soft_joint_decoding func!
separate_position_preds, ent_preds, rel_preds = self.soft_joint_decoding(
batch_normalized_joint_score, batch_tokens, batch_seq_tokens_lens)
results['all_separate_position_preds'] = separate_position_preds
results['all_ent_preds'] = ent_preds
results['all_rel_preds'] = rel_preds
return results
results['element_loss'] = self.element_loss(
self.logit_dropout(batch_joint_score[batch_inputs['joint_label_matrix_mask']]),
batch_inputs['joint_label_matrix'][batch_inputs['joint_label_matrix_mask']])
batch_symmetric_normalized_joint_score = batch_normalized_joint_score[..., self.symmetric_label]
results['symmetric_loss'] = torch.abs(batch_symmetric_normalized_joint_score -
batch_symmetric_normalized_joint_score.transpose(1, 2)).sum(
dim=-1)[batch_inputs['joint_label_matrix_mask']].mean()
batch_rel_normalized_joint_score = torch.max(batch_normalized_joint_score[..., self.rel_label], dim=-1).values
batch_diag_ent_normalized_joint_score = torch.max(
batch_normalized_joint_score[..., self.ent_label].diagonal(0, 1, 2),
dim=1).values.unsqueeze(-1).expand_as(batch_rel_normalized_joint_score)
results['implication_loss'] = (
torch.relu(batch_rel_normalized_joint_score - batch_diag_ent_normalized_joint_score).sum(dim=2) +
torch.relu(batch_rel_normalized_joint_score.transpose(1, 2) - batch_diag_ent_normalized_joint_score).sum(
dim=2))[batch_inputs['joint_label_matrix_mask'][..., 0]].mean()
# relation_entities = batch_normalized_joint_score[..., self.ent_label[1]].diagonal(0, 1, 2)
relation_entity_mask = batch_inputs['joint_label_matrix'].diagonal(0, 1, 2)
relation_entity_mask = torch.eq(relation_entity_mask, self.ent_label[1])
batch_row_subject_normalized_joint_score = torch.max(batch_normalized_joint_score[..., self.rel_label[0]], dim=-1).values
batch_column_subject_normalized_joint_score = torch.max(batch_normalized_joint_score.transpose(1, 2)[..., self.rel_label[0]], dim=-1).values
batch_row_object_normalized_joint_score = torch.max(batch_normalized_joint_score[..., self.rel_label[1]], dim=-1).values
batch_column_object_normalized_joint_score = torch.max(batch_normalized_joint_score.transpose(1, 2)[..., self.rel_label[1]], dim=-1).values
results['triple_loss'] = (
(torch.relu(batch_row_object_normalized_joint_score - batch_row_subject_normalized_joint_score) +
torch.relu(batch_column_object_normalized_joint_score - batch_column_subject_normalized_joint_score)) / 2
)[relation_entity_mask].mean()
return results
def hard_joint_decoding(self, batch_normalized_joint_score, batch_seq_tokens_lens):
"""hard_joint_decoding extracts entity and relaition at the same time,
and consider the interconnection of entity and relation.
Args:
batch_normalized_joint_score (tensor): batch joint pred
batch_seq_tokens_lens (list): batch sequence length
Returns:
tuple: predicted entity and relation
"""
separate_position_preds = []
ent_preds = []
rel_preds = []
joint_label_n = self.vocab.get_vocab_size('ent_rel_id')
batch_joint_pred = torch.argmax(batch_normalized_joint_score, dim=-1).cpu().numpy()
ent_label = np.append(self.ent_label.cpu().numpy(), self.none_idx)
rel_label = np.append(self.rel_label.cpu().numpy(), self.none_idx)
for idx, seq_len in enumerate(batch_seq_tokens_lens):
separate_position_preds.append([])
ent_pred = {}
rel_pred = {}
ents = []
joint_pred = batch_joint_pred[idx]
ent_pos = [0] * seq_len
for l in range(self.max_span_length, 0, -1):
for st in range(0, seq_len - l + 1):
pred_cnt = np.array([0] * joint_label_n)
if any(ent_pos[st:st + l]):
continue
for i in range(st, st + l):
for j in range(st, st + l):
pred_cnt[joint_pred[i][j]] += 1
pred = int(ent_label[np.argmax(pred_cnt[ent_label])])
pred_label = self.vocab.get_token_from_index(pred, 'ent_rel_id')
if pred_label == 'None':
continue
ents.append((st, st + l))
for i in range(st, st + l):
ent_pos[i] = 1
ent_pred[(st, st + l)] = pred_label
for idx1 in range(len(ents)):
for idx2 in range(len(ents)):
if idx1 == idx2:
continue
pred_cnt = np.array([0] * joint_label_n)
for i in range(ents[idx1][0], ents[idx1][1]):
for j in range(ents[idx2][0], ents[idx2][1]):
pred_cnt[joint_pred[i][j]] += 1
pred = int(rel_label[np.argmax(pred_cnt[rel_label])])
pred_label = self.vocab.get_token_from_index(pred, 'ent_rel_id')
h = ents[idx1][1] - ents[idx1][0]
w = ents[idx2][1] - ents[idx2][0]
if pred_label == 'None':
continue
rel_pred[(ents[idx1], ents[idx2])] = pred_label
ent_preds.append(ent_pred)
rel_preds.append(rel_pred)
return separate_position_preds, ent_preds, rel_preds
def soft_joint_decoding(self, batch_normalized_joint_score, batch_tokens, batch_seq_tokens_lens):
"""soft_joint_decoding extracts entity and relation at the same time,
and consider the interconnection of entity and relation. This is used for measuring the ability of
joint table filling model in generating Open-format extractions.
Args:
batch_normalized_joint_score (tensor): batch normalized joint score
batch_seq_tokens_lens (list): batch sequence length
Returns:
tuple: predicted entity and relation
"""
separate_position_preds = []
ent_preds = []
rel_preds = []
batch_normalized_joint_score = batch_normalized_joint_score.cpu().numpy()
symmetric_label = self.symmetric_label.cpu().numpy()
ent_label = self.ent_label.cpu().numpy()
rel_label = self.rel_label.cpu().numpy()
for idx, seq_len in enumerate(batch_seq_tokens_lens):
# print(" ".join([self.vocab.get_token_from_index(token.item(), 'tokens') for token in batch_tokens[idx][:seq_len]]))
ent_pred = {}
rel_pred = {}
joint_score = batch_normalized_joint_score[idx][:seq_len, :seq_len, :]
pred_label_tensors = copy.copy(joint_score)
joint_score[..., symmetric_label] = (joint_score[..., symmetric_label] +
joint_score[..., symmetric_label].transpose((1, 0, 2))) / 2
joint_score_feature = joint_score.reshape(seq_len, -1)
transposed_joint_score_feature = joint_score.transpose((1, 0, 2)).reshape(seq_len, -1)
separate_pos = (
(np.linalg.norm(joint_score_feature[0:seq_len - 1] - joint_score_feature[1:seq_len], axis=1) +
np.linalg.norm(
transposed_joint_score_feature[0:seq_len - 1] - transposed_joint_score_feature[1:seq_len], axis=1))
* 0.5 > self.separate_threshold).nonzero()[0]
separate_position_preds.append([pos.item() for pos in separate_pos])
if len(separate_pos) > 0:
spans = [(0, separate_pos[0].item() + 1)] + [(separate_pos[idx].item() + 1, separate_pos[idx + 1].item() + 1)
for idx in range(len(separate_pos) - 1)] + [(separate_pos[-1].item() + 1, seq_len)]
else:
spans = [(0, seq_len)]
# merged_spans = self.merge_similar_spans(spans, joint_score)
merged_spans = [(span, ) for span in spans]
ents = []
index2span = {}
for span in merged_spans:
target_indices = []
for sp in span:
target_indices += [idx for idx in range(sp[0], sp[1])]
score = np.mean(joint_score[target_indices, :, :][:, target_indices, :], axis=(0, 1))
if not (np.max(score[ent_label]) < score[self.none_idx]):
pred = ent_label[np.argmax(score[ent_label])].item()
pred_label = self.vocab.get_token_from_index(pred, 'ent_rel_id')
ents.append(target_indices)
index2span[tuple(target_indices)] = span
ent_pred[span] = pred_label
for ent1 in ents:
for ent2 in ents:
if ent1 == ent2:
continue
score = np.mean(joint_score[ent1, :, :][:, ent2, :], axis=(0, 1))
if not (np.max(score[rel_label]) < score[self.none_idx]):
pred = rel_label[np.argmax(score[rel_label])].item()
pred_label = self.vocab.get_token_from_index(pred, 'ent_rel_id')
rel_pred[(index2span[tuple(ent1)], index2span[tuple(ent2)])] = pred_label
elif (ent_pred[index2span[tuple(ent1)]] == "Relation" and
ent_pred[index2span[tuple(ent2)]] == "Argument") or\
(ent_pred[index2span[tuple(ent1)]] == "Argument"
and ent_pred[index2span[tuple(ent2)]] == "Relation"):
joint_score_tensor = torch.from_numpy(pred_label_tensors[ent1, :, :][:, ent2, :])
batch_joint_pred_sorted = torch.argsort(joint_score_tensor, dim=-1, descending=True)
most_possible_label = torch.argmax(joint_score_tensor, dim=-1)
# assert most_possible_label == batch_joint_pred_sorted[...,0]
second_possible_label = batch_joint_pred_sorted[..., 1]
subj_indices = torch.nonzero(most_possible_label == 3)
obj_indices = torch.nonzero(most_possible_label == 4)
if subj_indices.nelement() != 0 and obj_indices.nelement() == 0:
second_possible_label = second_possible_label[(most_possible_label != 3).nonzero(as_tuple=True)]
second_possible_label = (second_possible_label * 1.0).mean()
if 2.7 < second_possible_label <= 3.3:
rel_pred[(index2span[tuple(ent1)], index2span[tuple(ent2)])] = "Subject"
elif obj_indices.nelement() != 0 and subj_indices.nelement() == 0:
second_possible_label = second_possible_label[(most_possible_label != 4).nonzero(as_tuple=True)]
second_possible_label = (second_possible_label * 1.0).mean()
if 3.5 < second_possible_label <= 4:
rel_pred[(index2span[tuple(ent1)], index2span[tuple(ent2)])] = "Object"
else:
if 1 <= (ent2[0] - ent1[-1]) < 3:
second_possible_label = (second_possible_label * 1.0).mean().item()
if ent_pred[index2span[tuple(ent1)]] == "Relation" or \
(ent_pred[index2span[tuple(ent2)]] == "Relation" and ent2[0] == seq_len - 6):
# if 3.5 < second_possible_label <= 4:
rel_pred[(index2span[tuple(ent1)], index2span[tuple(ent2)])] = "Object"
else:
if 2.7 < second_possible_label <= 3.3:
rel_pred[(index2span[tuple(ent1)], index2span[tuple(ent2)])] = "Subject"
ent_preds.append(ent_pred)
rel_preds.append(rel_pred)
return separate_position_preds, ent_preds, rel_preds
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