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import os
from typing import Optional, Union
from transformers import BertModel, PreTrainedModel, AutoConfig, BertModel
from transformers.modeling_outputs import TokenClassifierOutput
from torch import nn
from torch.nn import CrossEntropyLoss
from typing import List, Optional
import torch
from itertools import islice
from .configuration_bionextextractor import BioNExtExtractorConfig
import torch
from transformers import AutoModel, PreTrainedModel, AutoConfig, BertConfig
from transformers.modeling_outputs import TokenClassifierOutput, SequenceClassifierOutput
from torch.nn import CrossEntropyLoss
import math
class RelationLossMixin:
def model_loss(self, logits, labels, novel=None, reduction=None):
if reduction is None:
return torch.nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
else:
return torch.nn.functional.cross_entropy(logits.view(-1,self.num_labels), labels.view(-1), reduction=reduction)
class RelationAndNovelLossMixin(RelationLossMixin):
def model_loss(self, logits, labels, novel=None):
relation_logits, novel_logits = logits
relation_loss = super().model_loss(relation_logits, labels, reduction="none")
novel_loss = torch.nn.functional.cross_entropy(novel_logits.view(-1, 2), novel.view(-1), reduction="none")
per_sample_loss = relation_loss + (labels!=8).type(logits[0].dtype)*novel_loss
return per_sample_loss.mean()#relation_loss + (labels!=8).type(logits[0].dtype)*novel_loss(novel_logits.view(-1, 2), novel.view(-1))
#return relation_loss + novel_loss(novel_logits.view(-1, 2), novel.view(-1))
class RelationClassifierBase(PreTrainedModel, RelationLossMixin):
#_keys_to_ignore_on_load_unexpected = [r"pooler"]
config_class=BioNExtExtractorConfig
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
#print(config)
self.bert = BertModel(config, add_pooling_layer=False)
def training_mode(self):
if self.config.update_vocab is not None:
self.bert.resize_token_embeddings(self.config.update_vocab)
def group_embeddings_by_index(self, embeddings, indexes):
assert len(embeddings.shape)==3
batch_size = indexes.shape[0]
max_tokens = embeddings.shape[1]
emb_size = embeddings.shape[2]
# masking padding
mask_index = indexes!=-1
# convert index to 1d of valid index (ignore paddings)
indexes = indexes + mask_index*(torch.arange(batch_size).to(self.device)*max_tokens).view(batch_size,1,1)
indexes = indexes.masked_select(mask_index)
# reshape
embeddings = embeddings.view(batch_size*max_tokens, emb_size)
# get the embeddings by index
selected_embeddings_by_index = torch.index_select(embeddings, 0, indexes)
final_output_shape = (mask_index.shape[0], mask_index.shape[1], emb_size)
group_embeddings = torch.zeros(final_output_shape, dtype=embeddings.dtype).to(self.device).masked_scatter(mask_index, selected_embeddings_by_index)
return group_embeddings, mask_index
def classifier_representation(self, embeddings, mask = None):
raise NotImplementedError("This is base class, pleas extend an implement classifier_representation")
def classifier(self, class_representation, relation_mask = None):
raise NotImplementedError("This is base class, pleas extend an implement classifier")
def forward(self,
input_ids,
indexes=None,
novel=None,
labels=None,
mask=None,
return_dict=None,
**model_kwargs
):
# Default `model.config.use_return_dict´ is `True´
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(input_ids, return_dict=return_dict, **model_kwargs)
assert indexes is not None
embeddings = outputs.last_hidden_state
selected_embeddings, mask_group = self.group_embeddings_by_index(embeddings, indexes)
class_representation = self.classifier_representation(selected_embeddings, mask_group)
logits = self.classifier(class_representation, relation_mask=mask)
loss = None
if labels is not None:
loss = self.model_loss(logits, labels, novel)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class RelationClassifierBiLSTM(RelationClassifierBase):
def __init__(self, config):
super().__init__(config)
self.num_lstm_layers = config.num_lstm_layers
self.lstm = torch.nn.LSTM(config.hidden_size, (config.hidden_size) // 2, self.num_lstm_layers, batch_first=True, bidirectional=True)
self.fc = torch.nn.Linear(config.hidden_size, self.num_labels) # 2 for bidirection
def training_mode(self):
super().training_mode()
self.lstm.reset_parameters()
self.fc.reset_parameters()
def classifier_representation(self, embeddings, mask=None):
out, _ = self.lstm(embeddings)
return out[:, -1, :]
def classifier(self, class_representation, mask=None):
return self.fc(class_representation)
class RelationAndNovelClassifierBiLSTM(RelationClassifierBiLSTM, RelationAndNovelLossMixin):
def __init__(self, config):
super().__init__(config)
self.fc_novel = torch.nn.Linear(config.hidden_size, 2) # 2 for bidirection
def training_mode(self):
super().training_mode()
self.fc_novel.reset_parameters()
def classifier(self, class_representation):
return super().classifier(class_representation), self.fc_novel(class_representation)
class RelationClassifierMHAttention(RelationClassifierBase):
def __init__(self, config):
super().__init__(config)
self.weight = torch.nn.Parameter(torch.Tensor(1,1,config.hidden_size))
torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
self.MHattention_layer = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) # 2 for bidirection
self.fc1 = torch.nn.Linear(config.hidden_size, config.hidden_size//2) # 2 for bidirection
self.fc1_activation = torch.nn.GELU(approximate='none')
self.fc2 = torch.nn.Linear(config.hidden_size//2, self.num_labels) # 2 for bidirection
def training_mode(self):
super().training_mode()
torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
self.MHattention_layer._reset_parameters()
self.fc1.reset_parameters()
self.fc2.reset_parameters()
def classifier_representation(self, embeddings, mask=None):
batch_size = embeddings.shape[0]
weight = self.weight.repeat(batch_size, 1, 1)
if mask is not None:
# flip
mask = mask.squeeze(-1)==False
out_tensors, _ = self.MHattention_layer(weight, embeddings, embeddings, key_padding_mask=mask)
return out_tensors
def classifier(self, class_representation, relation_mask = None):
x = self.fc1(class_representation)
x = self.fc1_activation(x)
logits = self.fc2(x)
if relation_mask is not None:
#print(logits.shape, relation_mask.shape)
logits = logits + relation_mask.view(-1,1,self.num_labels)
return logits
class RelationAndNovelClassifierMHAttention(RelationClassifierMHAttention, RelationAndNovelLossMixin):
def __init__(self, config):
super().__init__(config)
self.fc1_novel = torch.nn.Linear(config.hidden_size, config.hidden_size//2) # 2 for bidirection
self.fc1_novel_activation = torch.nn.GELU(approximate='none')
self.fc2_novel = torch.nn.Linear(config.hidden_size//2, 2) # 2 for bidirection
def training_mode(self):
super().training_mode()
self.fc1_novel.reset_parameters()
self.fc2_novel.reset_parameters()
def classifier(self, class_representation, relation_mask=None):
x = self.fc1_novel(class_representation)
x = self.fc1_novel_activation(x)
return super().classifier(class_representation, relation_mask=relation_mask), self.fc2_novel(x)
ARCH_MAPPING = {"mhawNovelty": RelationAndNovelClassifierMHAttention,
"mha": RelationClassifierMHAttention,
"bilstmwNovelty" : RelationAndNovelClassifierBiLSTM,
"bilstm": RelationClassifierBiLSTM}
## Changing the name to be compatible with HF API
class BioNExtExtractorModel(RelationAndNovelClassifierMHAttention):
config_class=BioNExtExtractorConfig
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