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models.py
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from torch import nn
from transformers import BertPreTrainedModel, BertModel
from utils import load_vocabs, merge_subword_tokens
import config
import torch
class BertForTagging(BertPreTrainedModel):
def __init__(self, model_config):
super().__init__(model_config)
self.bert = BertModel(model_config)
self.dropout = nn.Dropout(config.last_layer_dropout)
self.hidden_size = model_config.hidden_size
self.vocabs = load_vocabs(config.vocabs_path)
self.criterion = nn.CrossEntropyLoss()
self.num_upos = len(self.vocabs['upos'])
self.num_xpos = len(self.vocabs['xpos'])
self.num_feats = len(self.vocabs['feats'])
self.classifier_upos = nn.Linear(self.hidden_size, self.num_upos)
self.classifier_xpos = nn.Linear(self.hidden_size, self.num_xpos)
self.classifier_feats = nn.Linear(self.hidden_size, self.num_feats)
def forward(self, batch=None, labels=None):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
word_start_positions = batch['word_start_positions']
outputs = self.bert(input_ids,
attention_mask=attention_mask)
sequence_output = outputs[0] # Last hidden state
sequence_output = self.dropout(sequence_output)
word_outputs = merge_subword_tokens(
sequence_output, word_start_positions, self.hidden_size)
upos_scores = self.classifier_upos(word_outputs)
xpos_scores = self.classifier_xpos(word_outputs)
feats_scores = self.classifier_feats(word_outputs)
loss = None
if labels is not None:
loss = self.get_loss(upos_scores, xpos_scores,
feats_scores, labels)
return (loss, upos_scores, xpos_scores, feats_scores)
def get_loss(self, upos_scores, xpos_scores, feats_scores, labels):
mask = labels['upos'].ne(config.pad_value)
upos_scores, upos_labels = upos_scores[mask], labels['upos'][mask]
xpos_scores, xpos_labels = xpos_scores[mask], labels['xpos'][mask]
feats_scores, feats_labels = feats_scores[mask], labels['feats'][mask]
upos_loss = self.criterion(upos_scores, upos_labels)
xpos_loss = self.criterion(xpos_scores, xpos_labels)
feats_loss = self.criterion(feats_scores, feats_labels)
return upos_loss + xpos_loss + feats_loss
class BertForParsing(BertPreTrainedModel):
def __init__(self, model_config, joint=False):
super().__init__(model_config)
self.bert = BertModel(model_config)
self.dropout = nn.Dropout(config.last_layer_dropout)
self.hidden_size = model_config.hidden_size
self.vocabs = load_vocabs(config.vocabs_path)
self.criterion = nn.CrossEntropyLoss()
self.num_deprel = len(self.vocabs['deprel'])
self.classifier_head = Biaffine(
n_in=self.hidden_size, n_out=1, bias_x=True, bias_y=False)
self.classifier_deprel = Biaffine(
n_in=self.hidden_size, n_out=self.num_deprel, bias_x=True, bias_y=True)
def forward(self, batch, labels=None):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
word_start_positions = batch['word_start_positions']
outputs = self.bert(input_ids,
attention_mask=attention_mask)
sequence_output = outputs[0] # Last hidden state
sequence_output = self.dropout(sequence_output)
word_outputs = merge_subword_tokens(
sequence_output, word_start_positions, self.hidden_size)
word_outputs_heads = torch.cat(
[outputs[1].unsqueeze(1), word_outputs], dim=1)
head_scores = self.classifier_head(word_outputs, word_outputs_heads)
head_scores = head_scores.squeeze()
deprel_scores = self.classifier_deprel(
word_outputs, word_outputs_heads)
deprel_scores = deprel_scores.permute(0, 2, 3, 1)
loss = None
if labels is not None:
loss = self.get_loss(head_scores, deprel_scores, labels)
return (loss, head_scores, deprel_scores)
def get_loss(self, head_scores, deprel_scores, labels):
if len(head_scores.shape) == 2:
head_scores = head_scores.unsqueeze(0)
mask = labels['head'].ne(config.pad_value)
head_scores, head_labels = head_scores[mask], labels['head'][mask]
deprel_scores, deprel_labels = deprel_scores[mask], labels['deprel'][mask]
deprel_scores = deprel_scores[torch.arange(len(head_labels)), head_labels]
head_loss = self.criterion(head_scores, head_labels)
deprel_loss = self.criterion(deprel_scores, deprel_labels)
return head_loss + deprel_loss
class BertForJointTaggingAndParsing(BertPreTrainedModel):
def __init__(self, model_config):
super().__init__(model_config)
self.bert = BertModel(model_config)
self.dropout = nn.Dropout(config.last_layer_dropout)
self.hidden_size = model_config.hidden_size
self.vocabs = load_vocabs(config.vocabs_path)
self.criterion = nn.CrossEntropyLoss()
self.num_upos = len(self.vocabs['upos'])
self.num_xpos = len(self.vocabs['xpos'])
self.num_feats = len(self.vocabs['feats'])
self.num_deprel = len(self.vocabs['deprel'])
self.classifier_upos = nn.Linear(self.hidden_size, self.num_upos)
self.classifier_xpos = nn.Linear(self.hidden_size, self.num_xpos)
self.classifier_feats = nn.Linear(self.hidden_size, self.num_feats)
self.classifier_head = Biaffine(
n_in=self.hidden_size, n_out=1, bias_x=True, bias_y=False)
self.classifier_deprel = Biaffine(
n_in=self.hidden_size, n_out=self.num_deprel, bias_x=True, bias_y=True)
def forward(self, batch, labels=None):
input_ids = batch['input_ids']
attention_mask = batch['attention_mask']
word_start_positions = batch['word_start_positions']
outputs = self.bert(input_ids, attention_mask=attention_mask)
sequence_output = outputs[0] # Last hidden state
sequence_output = self.dropout(sequence_output)
word_outputs = merge_subword_tokens(
sequence_output, word_start_positions, self.hidden_size)
word_outputs_heads = torch.cat(
[outputs[1].unsqueeze(1), word_outputs], dim=1)
upos_scores = self.classifier_upos(word_outputs)
xpos_scores = self.classifier_xpos(word_outputs)
feats_scores = self.classifier_feats(word_outputs)
head_scores = self.classifier_head(word_outputs, word_outputs_heads)
head_scores = head_scores.squeeze()
deprel_scores = self.classifier_deprel(
word_outputs, word_outputs_heads)
deprel_scores = deprel_scores.permute(0, 2, 3, 1)
loss = None
if labels is not None:
loss = self.get_loss(upos_scores, xpos_scores,
feats_scores, head_scores, deprel_scores, labels)
return (loss, upos_scores, xpos_scores, feats_scores, head_scores, deprel_scores)
def get_loss(self, upos_scores, xpos_scores, feats_scores, head_scores, deprel_scores, labels):
if len(head_scores.shape) == 2:
head_scores = head_scores.unsqueeze(0)
mask = labels['upos'].ne(config.pad_value)
upos_scores, upos_labels = upos_scores[mask], labels['upos'][mask]
xpos_scores, xpos_labels = xpos_scores[mask], labels['xpos'][mask]
feats_scores, feats_labels = feats_scores[mask], labels['feats'][mask]
upos_loss = self.criterion(upos_scores, upos_labels)
xpos_loss = self.criterion(xpos_scores, xpos_labels)
feats_loss = self.criterion(feats_scores, feats_labels)
head_scores, head_labels = head_scores[mask], labels['head'][mask]
deprel_scores, deprel_labels = deprel_scores[mask], labels['deprel'][mask]
deprel_scores = deprel_scores[torch.arange(len(head_labels)), head_labels]
head_loss = self.criterion(head_scores, head_labels)
deprel_loss = self.criterion(deprel_scores, deprel_labels)
return upos_loss + xpos_loss + feats_loss + head_loss + deprel_loss
class Biaffine(nn.Module):
# Taken from TowerParse (Glavaš and Vulić 2021a).
# https://github.com/codogogo/towerparse/blob/b55b57f2c9b8f71f7bf61a4d4b6110466b58ee68/biaffine.py#L72
# Original credit: Class taken from https://github.com/yzhangcs/biaffine-parser
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
self.weight = nn.Parameter(torch.Tensor(n_out,
n_in + bias_x,
n_in + bias_y))
self.init_weights()
def extra_repr(self):
s = f"n_in={self.n_in}, n_out={self.n_out}"
if self.bias_x:
s += f", bias_x={self.bias_x}"
if self.bias_y:
s += f", bias_y={self.bias_y}"
return s
def init_weights(self):
nn.init.zeros_(self.weight)
def forward(self, x, y):
if self.bias_x:
x = torch.cat((x, torch.ones_like(x[..., :1])), -1)
if self.bias_y:
y = torch.cat((y, torch.ones_like(y[..., :1])), -1)
# [batch_size, n_out, seq_len, seq_len]
s = torch.einsum('bxi,oij,byj->boxy', x, self.weight, y)
return s