from transformers import LiltPreTrainedModel, LiltModel
import copy
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
from transformers.utils import ModelOutput
class BiaffineAttention(torch.nn.Module):
"""Implements a biaffine attention operator for binary relation classification.
PyTorch implementation of the biaffine attention operator from "End-to-end neural relation
extraction using deep biaffine attention" (https://arxiv.org/abs/1812.11275) which can be used
as a classifier for binary relation classification.
Args:
in_features (int): The size of the feature dimension of the inputs.
out_features (int): The size of the feature dimension of the output.
Shape:
- x_1: `(N, *, in_features)` where `N` is the batch dimension and `*` means any number of
additional dimensisons.
- x_2: `(N, *, in_features)`, where `N` is the batch dimension and `*` means any number of
additional dimensions.
- Output: `(N, *, out_features)`, where `N` is the batch dimension and `*` means any number
of additional dimensions.
Examples:
>>> batch_size, in_features, out_features = 32, 100, 4
>>> biaffine_attention = BiaffineAttention(in_features, out_features)
>>> x_1 = torch.randn(batch_size, in_features)
>>> x_2 = torch.randn(batch_size, in_features)
>>> output = biaffine_attention(x_1, x_2)
>>> print(output.size())
torch.Size([32, 4])
"""
def __init__(self, in_features, out_features):
super(BiaffineAttention, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.bilinear = torch.nn.Bilinear(in_features, in_features, out_features, bias=False)
self.linear = torch.nn.Linear(2 * in_features, out_features, bias=True)
self.reset_parameters()
def forward(self, x_1, x_2):
return self.bilinear(x_1, x_2) + self.linear(torch.cat((x_1, x_2), dim=-1))
def reset_parameters(self):
self.bilinear.reset_parameters()
self.linear.reset_parameters()
class REDecoder(nn.Module):
def __init__(self, config, input_size):
super().__init__()
self.entity_emb = nn.Embedding(3, input_size, scale_grad_by_freq=True)
projection = nn.Sequential(
nn.Linear(input_size * 2, config.hidden_size),
nn.ReLU(),
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(config.hidden_size, config.hidden_size // 2),
nn.ReLU(),
nn.Dropout(config.hidden_dropout_prob),
)
self.ffnn_head = copy.deepcopy(projection)
self.ffnn_tail = copy.deepcopy(projection)
self.rel_classifier = BiaffineAttention(config.hidden_size // 2, 2)
self.loss_fct = CrossEntropyLoss()
def build_relation(self, relations, entities):
batch_size = len(relations)
new_relations = []
for b in range(batch_size):
if len(entities[b]["start"]) <= 2:
entities[b] = {"end": [1, 1], "label": [0, 0], "start": [0, 0]}
all_possible_relations = set(
[
(i, j)
for i in range(len(entities[b]["label"]))
for j in range(len(entities[b]["label"]))
if entities[b]["label"][i] == 1 and entities[b]["label"][j] == 2
]
)
if len(all_possible_relations) == 0:
all_possible_relations = set([(0, 1)])
positive_relations = set(list(zip(relations[b]["head"], relations[b]["tail"])))
negative_relations = all_possible_relations - positive_relations
positive_relations = set([i for i in positive_relations if i in all_possible_relations])
reordered_relations = list(positive_relations) + list(negative_relations)
relation_per_doc = {"head": [], "tail": [], "label": []}
relation_per_doc["head"] = [i[0] for i in reordered_relations]
relation_per_doc["tail"] = [i[1] for i in reordered_relations]
relation_per_doc["label"] = [1] * len(positive_relations) + [0] * (
len(reordered_relations) - len(positive_relations)
)
assert len(relation_per_doc["head"]) != 0
new_relations.append(relation_per_doc)
return new_relations, entities
def get_predicted_relations(self, logits, relations, entities):
pred_relations = []
for i, pred_label in enumerate(logits.argmax(-1)):
if pred_label != 1:
continue
rel = {}
rel["head_id"] = relations["head"][i]
rel["head"] = (entities["start"][rel["head_id"]], entities["end"][rel["head_id"]])
rel["head_type"] = entities["label"][rel["head_id"]]
rel["tail_id"] = relations["tail"][i]
rel["tail"] = (entities["start"][rel["tail_id"]], entities["end"][rel["tail_id"]])
rel["tail_type"] = entities["label"][rel["tail_id"]]
rel["type"] = 1
pred_relations.append(rel)
return pred_relations
def forward(self, hidden_states, entities, relations):
batch_size, max_n_words, context_dim = hidden_states.size()
device = hidden_states.device
relations, entities = self.build_relation(relations, entities)
loss = 0
all_pred_relations = []
all_logits = []
all_labels = []
for b in range(batch_size):
head_entities = torch.tensor(relations[b]["head"], device=device)
tail_entities = torch.tensor(relations[b]["tail"], device=device)
relation_labels = torch.tensor(relations[b]["label"], device=device)
entities_start_index = torch.tensor(entities[b]["start"], device=device)
entities_labels = torch.tensor(entities[b]["label"], device=device)
head_index = entities_start_index[head_entities]
head_label = entities_labels[head_entities]
head_label_repr = self.entity_emb(head_label)
tail_index = entities_start_index[tail_entities]
tail_label = entities_labels[tail_entities]
tail_label_repr = self.entity_emb(tail_label)
head_repr = torch.cat(
(hidden_states[b][head_index], head_label_repr),
dim=-1,
)
tail_repr = torch.cat(
(hidden_states[b][tail_index], tail_label_repr),
dim=-1,
)
heads = self.ffnn_head(head_repr)
tails = self.ffnn_tail(tail_repr)
logits = self.rel_classifier(heads, tails)
pred_relations = self.get_predicted_relations(logits, relations[b], entities[b])
all_pred_relations.append(pred_relations)
all_logits.append(logits)
all_labels.append(relation_labels)
all_logits = torch.cat(all_logits, 0)
all_labels = torch.cat(all_labels, 0)
loss = self.loss_fct(all_logits, all_labels)
return loss, all_pred_relations
@dataclass
class ReOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
entities: Optional[Dict] = None
relations: Optional[Dict] = None
pred_relations: Optional[Dict] = None
class REHead(nn.Module):
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.extractor = REDecoder(config, config.hidden_size)
def forward(self,sequence_output, entities, relations):
sequence_output = self.dropout(sequence_output)
loss, pred_relations = self.extractor(sequence_output, entities, relations)
return ReOutput(
loss=loss,
entities=entities,
relations=relations,
pred_relations=pred_relations,
)
class LiLTRobertaLikeForRelationExtraction(LiltPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.lilt = LiltModel(config, add_pooling_layer=False)
# self.dropout = nn.Dropout(config.hidden_dropout_prob)
# self.extractor = REDecoder(config, config.hidden_size)
self.rehead = REHead(config)
self.init_weights()
def forward(
self,
input_ids=None,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
entities=None,
relations=None,
):
# for param in self.lilt.parameters():
# param.requires_grad = False
outputs = self.lilt(
input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
seq_length = input_ids.size(1)
sequence_output = outputs[0]
re_output = self.rehead(sequence_output, entities, relations)
return re_output