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modelling.py
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modelling.py
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import torch
from transformers import BertPreTrainedModel
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer, AutoConfig, AutoModel
from torch import nn
class Simple_BERT(BertPreTrainedModel):
def __init__(self, config, actual_model_checkpoint):
super(Simple_BERT, self).__init__(config)
self.bert = AutoModel.from_pretrained(actual_model_checkpoint)
self.classifier = nn.Linear(768, 5)
self.softmax = nn.Softmax(dim=2)
self.criterion = nn.CrossEntropyLoss(reduction='none')
self.token_dropout = nn.Dropout(0.1)
def forward(self, input_ids, attn_mask, labels, lambda_max_loss = 0, lambda_mask_loss = 0):
lhs = self.bert(input_ids, attn_mask).last_hidden_state
logits = self.classifier(self.token_dropout(lhs))
#ypreds = torch.argmax(self.softmax(logits), dim=2)
active_loss = attn_mask.view(-1) == 1
active_logits = logits.view(-1, 5)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = self.criterion(active_logits, active_labels)
loss_crossEntropy = torch.mean(loss)
if lambda_max_loss == 0.0 and lambda_mask_loss == 0.0:
ypreds = torch.argmax(self.softmax(logits), dim=2)
return ypreds, loss_crossEntropy
batch_size = input_ids.shape[0]
max_seq_len = input_ids.shape[1]
active_loss = active_loss.view(batch_size, max_seq_len)
active_max = []
active_mask = []
start_id = 0
for i in range(batch_size):
sent_len = torch.sum(active_loss[i])
# mask-loss
if lambda_mask_loss != 0.0:
active_mask.append((input_ids[i] == 103)[: sent_len]) # id of [MASK] is 103, according to the bertTokenizer
# max-loss
if lambda_max_loss != 0.0:
end_id = start_id + sent_len
active_max.append(torch.max(loss[start_id: end_id]))
start_id = end_id
if lambda_max_loss != 0:
loss_max = torch.mean(torch.stack(active_max))
else:
loss_max = 0.0
if lambda_mask_loss != 0:
active_mask = torch.cat(active_mask)
if sum(active_mask) != 0:
loss_mask = torch.sum(loss[active_mask]) / sum(active_mask)
else:
loss_mask = 0.0
ypreds = torch.argmax(self.softmax(logits), dim=2)
#loss = self.criterion(logits.view(-1, 5), labels.view(-1))
return ypreds, loss_crossEntropy + lambda_max_loss * loss_max + lambda_mask_loss * loss_mask
#loss = self.criterion(logits.view(-1, 5), labels.view(-1))
#return ypreds, loss
class Transform_CharacterBERT(BertPreTrainedModel):
def __init__(self, config, actual_model_checkpoint, char_vocab, embed_matrix, tokenizer, max_word_len, cnn_size):
super(Transform_CharacterBERT, self).__init__(config)
self.bert = AutoModel.from_pretrained(actual_model_checkpoint)
self.classifier = nn.Linear(868, 5)
self.softmax = nn.Softmax(dim=2)
self.criterion = nn.CrossEntropyLoss(reduction='none')
self.char_embeddings = CharEmbeddings(len(char_vocab), 300, embed_matrix,max_word_len, cnn_size, 0.1)
self.token_dropout = nn.Dropout(0.1)
self.tokenizer = tokenizer
self.max_word_len = max_word_len
self.conv_filter_size = cnn_size
self.char_vocab = char_vocab
def get_char_seq(self, words, max_len):
char_seq = list()
for i in range(0, self.max_word_len + self.conv_filter_size - 1): #### CLS
char_seq.append(self.char_vocab['<PAD>'])
for i in range(0, self.conv_filter_size - 1): #### Extra Padding
char_seq.append(self.char_vocab['<PAD>'])
for word in words:
for c in word[0:min(len(word), self.max_word_len)]:
if c in self.char_vocab:
char_seq.append(self.char_vocab[c])
else:
char_seq.append(self.char_vocab['<UNK>'])
pad_len = self.max_word_len - len(word)
for i in range(0, pad_len):
char_seq.append(self.char_vocab['<PAD>'])
for i in range(0, self.conv_filter_size - 1):
char_seq.append(self.char_vocab['<PAD>'])
pad_len = max_len - len(words) - 1
for i in range(0, pad_len):
for i in range(0, self.max_word_len + self.conv_filter_size - 1):
char_seq.append(self.char_vocab['<PAD>'])
return char_seq
def get_all_char_seq_tensor(self, input_ids):
all_char_seqs = []
max_len = input_ids.shape[1]
for i in range(len(input_ids)):
i_id = input_ids[i]
all_toks = self.tokenizer.convert_ids_to_tokens(i_id, skip_special_tokens = True)
char_seq = self.get_char_seq(all_toks, max_len)
all_char_seqs.append(char_seq)
return torch.tensor(all_char_seqs)
def forward(self, input_ids, attn_mask, labels, lambda_max_loss = 0, lambda_mask_loss = 0):
lhs = self.bert(input_ids, attn_mask).last_hidden_state
#print('LHS', lhs.device)
all_char_seqs = self.get_all_char_seq_tensor(input_ids)
all_char_seqs = all_char_seqs.to('cuda')
#print('allchar', all_char_seqs.device)
char_token_embs = self.char_embeddings(all_char_seqs)
char_token_embs = char_token_embs.to('cuda')
#print('chartok', char_token_embs.device)
lhs_plus_char = torch.cat([lhs, char_token_embs], dim = -1)
# print("lhs shape", lhs.shape)
logits = self.classifier(self.token_dropout(lhs_plus_char))
active_loss = attn_mask.view(-1) == 1
active_logits = logits.view(-1, 5)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = self.criterion(active_logits, active_labels)
loss_crossEntropy = torch.mean(loss)
if lambda_max_loss == 0.0 and lambda_mask_loss == 0.0:
ypreds = torch.argmax(self.softmax(logits), dim=2)
return ypreds, loss_crossEntropy
batch_size = input_ids.shape[0]
max_seq_len = input_ids.shape[1]
active_loss = active_loss.view(batch_size, max_seq_len)
active_max = []
active_mask = []
start_id = 0
for i in range(batch_size):
sent_len = torch.sum(active_loss[i])
# mask-loss
if lambda_mask_loss != 0.0:
active_mask.append((input_ids[i] == 103)[: sent_len]) # id of [MASK] is 103, according to the bertTokenizer
# max-loss
if lambda_max_loss != 0.0:
end_id = start_id + sent_len
active_max.append(torch.max(loss[start_id: end_id]))
start_id = end_id
if lambda_max_loss != 0:
loss_max = torch.mean(torch.stack(active_max))
else:
loss_max = 0.0
if lambda_mask_loss != 0:
active_mask = torch.cat(active_mask)
if sum(active_mask) != 0:
loss_mask = torch.sum(loss[active_mask]) / sum(active_mask)
else:
loss_mask = 0.0
ypreds = torch.argmax(self.softmax(logits), dim=2)
#loss = self.criterion(logits.view(-1, 5), labels.view(-1))
return ypreds, loss_crossEntropy + lambda_max_loss * loss_max + lambda_mask_loss * loss_mask
# lhs = self.bert(input_ids, attn_mask).last_hidden_state
# #print('LHS', lhs.device)
# all_char_seqs = self.get_all_char_seq_tensor(input_ids)
# all_char_seqs = all_char_seqs.to('cuda')
# #print('allchar', all_char_seqs.device)
# char_token_embs = self.char_embeddings(all_char_seqs)
# char_token_embs = char_token_embs.to('cuda')
# #print('chartok', char_token_embs.device)
# lhs_plus_char = torch.cat([lhs, char_token_embs], dim = -1)
# # print("lhs shape", lhs.shape)
# logits = self.classifier(self.token_dropout(lhs_plus_char))
# ypreds = torch.argmax(self.softmax(logits), dim=2)
# loss = self.criterion(logits.view(-1, 5), labels.view(-1))
# return ypreds, loss
class CharEmbeddings(nn.Module):
def __init__(self, vocab_size, embed_dim, embed_matrix, max_word_len, conv_filter_size, drop_out_rate):
super(CharEmbeddings, self).__init__()
self.embeddings = nn.Embedding(vocab_size, embed_dim, padding_idx=0) ### B * Pad_length * 300
self.embeddings.weight.data.copy_((embed_matrix))
self.embeddings.weight.requires_grad = True
self.conv1d = nn.Conv1d(embed_dim, 100, conv_filter_size)
self.max_pool = nn.MaxPool1d(max_word_len + conv_filter_size - 1, max_word_len + conv_filter_size - 1)
self.dropout = nn.Dropout(drop_out_rate)
def forward(self, char_seq):
char_embeds = self.embeddings(char_seq) ### B * Pad_length * 300
char_embeds = self.dropout(char_embeds) ##same
char_embeds = char_embeds.permute(0, 2, 1) ##B*300_pad_lenght
char_feature = torch.tanh(self.max_pool(self.conv1d(char_embeds))) ##B*100*num_of_tokens
char_feature = char_feature.permute(0, 2, 1) ##B * num_of_tokens * 100
return char_feature