model.py

from abc import ABCMeta
import numpy as np
import torch
from transformers.pytorch_utils import nn
import torch.nn.functional as F
from src.configuration import BertABSAConfig
from transformers import BertModel, BertForSequenceClassification, PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput


class BertBaseForSequenceClassification(PreTrainedModel, metaclass=ABCMeta):
 config_class = BertABSAConfig

 def __init__(self, config):
 super(BertBaseForSequenceClassification, self).__init__(config)
 self.num_classes = config.num_classes
 self.embed_dim = config.embed_dim
 self.dropout = nn.Dropout(config.dropout_rate)

 self.bert = BertForSequenceClassification.from_pretrained('bert-base-uncased', # noqa
 output_hidden_states=False, # noqa
 output_attentions=False, # noqa
 num_labels=self.num_classes) # noqa
 print("BERT Model Loaded")

 def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
 out = self.bert(input_ids=input_ids, attention_mask=attention_mask,
 token_type_ids=token_type_ids, labels=labels)
 return out


class BertLSTMForSequenceClassification(PreTrainedModel, metaclass=ABCMeta):
 config_class = BertABSAConfig

 def __init__(self, config):
 super(BertLSTMForSequenceClassification, self).__init__(config)
 self.num_classes = config.num_classes
 self.embed_dim = config.embed_dim
 self.num_layers = config.num_layers
 self.hidden_dim_lstm = config.hidden_dim_lstm
 self.dropout = nn.Dropout(config.dropout_rate)

 self.bert = BertModel.from_pretrained('bert-base-uncased',
 output_hidden_states=True,
 output_attentions=False)
 print("BERT Model Loaded")
 self.lstm = nn.LSTM(self.embed_dim, self.hidden_dim_lstm, batch_first=True) # noqa
 self.fc = nn.Linear(self.hidden_dim_lstm, self.num_classes)

 def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
 bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
 hidden_states = bert_output["hidden_states"]

 hidden_states = torch.stack([hidden_states[layer_i][:, 0].squeeze()
 for layer_i in range(0, self.num_layers)], dim=-1) # noqa
 hidden_states = hidden_states.view(-1, self.num_layers, self.embed_dim)
 out, _ = self.lstm(hidden_states, None)
 out = self.dropout(out[:, -1, :])
 logits = self.fc(out)
 loss = None
 if labels is not None:
 loss = F.cross_entropy(logits, labels)
 out = SequenceClassifierOutput(
 loss=loss,
 logits=logits,
 hidden_states=bert_output.hidden_states,
 attentions=bert_output.attentions,
 )
 return out


class BertAttentionForSequenceClassification(PreTrainedModel, metaclass=ABCMeta):
 config_class = BertABSAConfig

 def __init__(self, config):
 super(BertAttentionForSequenceClassification, self).__init__(config)
 self.num_classes = config.num_classes
 self.embed_dim = config.embed_dim
 self.num_layers = config.num_layers
 self.fc_hidden = config.fc_hidden
 self.dropout = nn.Dropout(config.dropout_rate)

 self.bert = BertModel.from_pretrained('bert-base-uncased',
 output_hidden_states=True,
 output_attentions=False)
 print("BERT Model Loaded")

 q_t = np.random.normal(loc=0.0, scale=0.1, size=(1, self.embed_dim))
 self.q = nn.Parameter(torch.from_numpy(q_t)).float().to(self.device)
 w_ht = np.random.normal(loc=0.0, scale=0.1, size=(self.embed_dim, self.fc_hidden)) # noqa
 self.w_h = nn.Parameter(torch.from_numpy(w_ht)).float().to(self.device)

 self.fc = nn.Linear(self.fc_hidden, self.num_classes)

 def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
 bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
 hidden_states = bert_output["hidden_states"]

 hidden_states = torch.stack([hidden_states[layer_i][:, 0].squeeze()
 for layer_i in range(0, self.num_layers)], dim=-1) # noqa
 hidden_states = hidden_states.view(-1, self.num_layers, self.embed_dim)
 out = self.attention(hidden_states)
 out = self.dropout(out)
 logits = self.fc(out)
 loss = None
 if labels is not None:
 loss = F.cross_entropy(logits, labels)
 out = SequenceClassifierOutput(
 loss=loss,
 logits=logits,
 hidden_states=bert_output.hidden_states,
 attentions=bert_output.attentions,
 )
 return out

 def attention(self, h):
 v = torch.matmul(self.q, h.transpose(-2, -1)).squeeze(1)
 v = F.softmax(v, -1)
 v_temp = torch.matmul(v.unsqueeze(1), h).transpose(-2, -1)
 v = torch.matmul(self.w_h.transpose(1, 0), v_temp).squeeze(2)
 return v