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import torch
from transformers.models.bert.modeling_bert import BertModel, BertPreTrainedModel
from torch import nn
from itertools import chain
from torch.nn import MSELoss, CrossEntropyLoss
from cleantext import clean
from num2words import num2words
import re
import string
punct_chars = list((set(string.punctuation) | {'’', '‘', '–', '—', '~', '|', '“', '”', '…', "'", "`", '_'}))
punct_chars.sort()
punctuation = ''.join(punct_chars)
replace = re.compile('[%s]' % re.escape(punctuation))
def get_num_words(text):
if not isinstance(text, str):
print("%s is not a string" % text)
text = replace.sub(' ', text)
text = re.sub(r'\s+', ' ', text)
text = text.strip()
text = re.sub(r'\[.+\]', " ", text)
return len(text.split())
def number_to_words(num):
try:
return num2words(re.sub(",", "", num))
except:
return num
clean_str = lambda s: clean(s,
fix_unicode=True, # fix various unicode errors
to_ascii=True, # transliterate to closest ASCII representation
lower=True, # lowercase text
no_line_breaks=True, # fully strip line breaks as opposed to only normalizing them
no_urls=True, # replace all URLs with a special token
no_emails=True, # replace all email addresses with a special token
no_phone_numbers=True, # replace all phone numbers with a special token
no_numbers=True, # replace all numbers with a special token
no_digits=False, # replace all digits with a special token
no_currency_symbols=False, # replace all currency symbols with a special token
no_punct=False, # fully remove punctuation
replace_with_url="<URL>",
replace_with_email="<EMAIL>",
replace_with_phone_number="<PHONE>",
replace_with_number=lambda m: number_to_words(m.group()),
replace_with_digit="0",
replace_with_currency_symbol="<CUR>",
lang="en"
)
clean_str_nopunct = lambda s: clean(s,
fix_unicode=True, # fix various unicode errors
to_ascii=True, # transliterate to closest ASCII representation
lower=True, # lowercase text
no_line_breaks=True, # fully strip line breaks as opposed to only normalizing them
no_urls=True, # replace all URLs with a special token
no_emails=True, # replace all email addresses with a special token
no_phone_numbers=True, # replace all phone numbers with a special token
no_numbers=True, # replace all numbers with a special token
no_digits=False, # replace all digits with a special token
no_currency_symbols=False, # replace all currency symbols with a special token
no_punct=True, # fully remove punctuation
replace_with_url="<URL>",
replace_with_email="<EMAIL>",
replace_with_phone_number="<PHONE>",
replace_with_number=lambda m: number_to_words(m.group()),
replace_with_digit="0",
replace_with_currency_symbol="<CUR>",
lang="en"
)
class MultiHeadModel(BertPreTrainedModel):
"""Pre-trained BERT model that uses our loss functions"""
def __init__(self, config, head2size):
super(MultiHeadModel, self).__init__(config, head2size)
config.num_labels = 1
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
module_dict = {}
for head_name, num_labels in head2size.items():
module_dict[head_name] = nn.Linear(config.hidden_size, num_labels)
self.heads = nn.ModuleDict(module_dict)
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None,
head2labels=None, return_pooler_output=False, head2mask=None,
nsp_loss_weights=None):
device = "cuda" if torch.cuda.is_available() else "cpu"
# Get logits
output = self.bert(
input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask,
output_attentions=False, output_hidden_states=False, return_dict=True)
pooled_output = self.dropout(output["pooler_output"]).to(device)
head2logits = {}
return_dict = {}
for head_name, head in self.heads.items():
head2logits[head_name] = self.heads[head_name](pooled_output)
head2logits[head_name] = head2logits[head_name].float()
return_dict[head_name + "_logits"] = head2logits[head_name]
if head2labels is not None:
for head_name, labels in head2labels.items():
num_classes = head2logits[head_name].shape[1]
# Regression (e.g. for politeness)
if num_classes == 1:
# Only consider positive examples
if head2mask is not None and head_name in head2mask:
num_positives = head2labels[head2mask[head_name]].sum() # use certain labels as mask
if num_positives == 0:
return_dict[head_name + "_loss"] = torch.tensor([0]).to(device)
else:
loss_fct = MSELoss(reduction='none')
loss = loss_fct(head2logits[head_name].view(-1), labels.float().view(-1))
return_dict[head_name + "_loss"] = loss.dot(head2labels[head2mask[head_name]].float().view(-1)) / num_positives
else:
loss_fct = MSELoss()
return_dict[head_name + "_loss"] = loss_fct(head2logits[head_name].view(-1), labels.float().view(-1))
else:
loss_fct = CrossEntropyLoss(weight=nsp_loss_weights.float())
return_dict[head_name + "_loss"] = loss_fct(head2logits[head_name], labels.view(-1))
if return_pooler_output:
return_dict["pooler_output"] = output["pooler_output"]
return return_dict
class InputBuilder(object):
"""Base class for building inputs from segments."""
def __init__(self, tokenizer):
self.tokenizer = tokenizer
self.mask = [tokenizer.mask_token_id]
def build_inputs(self, history, reply, max_length):
raise NotImplementedError
def mask_seq(self, sequence, seq_id):
sequence[seq_id] = self.mask
return sequence
@classmethod
def _combine_sequence(self, history, reply, max_length, flipped=False):
# Trim all inputs to max_length
history = [s[:max_length] for s in history]
reply = reply[:max_length]
if flipped:
return [reply] + history
return history + [reply]
class BertInputBuilder(InputBuilder):
"""Processor for BERT inputs"""
def __init__(self, tokenizer):
InputBuilder.__init__(self, tokenizer)
self.cls = [tokenizer.cls_token_id]
self.sep = [tokenizer.sep_token_id]
self.model_inputs = ["input_ids", "token_type_ids", "attention_mask"]
self.padded_inputs = ["input_ids", "token_type_ids"]
self.flipped = False
def build_inputs(self, history, reply, max_length, input_str=True):
"""See base class."""
if input_str:
history = [self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(t)) for t in history]
reply = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(reply))
sequence = self._combine_sequence(history, reply, max_length, self.flipped)
sequence = [s + self.sep for s in sequence]
sequence[0] = self.cls + sequence[0]
instance = {}
instance["input_ids"] = list(chain(*sequence))
last_speaker = 0
other_speaker = 1
seq_length = len(sequence)
instance["token_type_ids"] = [last_speaker if ((seq_length - i) % 2 == 1) else other_speaker
for i, s in enumerate(sequence) for _ in s]
return instance |