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import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import (
AlbertForSequenceClassification as SeqClassification,
AlbertPreTrainedModel,
AlbertModel,
AlbertConfig
)
from .modeling_outputs import (
QuestionAnsweringModelOutput,
QuestionAnsweringNaModelOutput
)
class AlbertForSequenceClassification(SeqClassification):
model_type = "albert"
class AlbertForQuestionAnsweringAVPool(AlbertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
model_type = "albert"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
# The `has_ans` module is a linear layer with dropout and a linear layer.
# The purpose of this module is to predict whether the question can be
# answered with a "yes" or "no" given the context. It is trained to output
# a probability distribution over the two classes.
#
# In other words, it predicts the probability of the existence of an
# answer given the context.
#
# If the model predicts a high probability of "yes", it means the model
# thinks the question can be answered. If the model predicts a high
# probability of "no", it means the model thinks the question cannot be
# answered.
#
# The output of this module is used in the loss computation to
# encourage the model to output a probability distribution over the two
# classes.
#
# The input to the module is the first word of the sequence (the
# [CLS] token).
#
# The output of the module is a tensor of shape (batch_size,
# num_labels) where each element is a probability.
# Initialize weights
self.albert = AlbertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.has_ans = nn.Sequential(
nn.Dropout(p=config.hidden_dropout_prob),
nn.Linear(config.hidden_size, self.num_labels)
)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
is_impossibles=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# outputs shape: (loss(optional, returned when labels is provided, else None), logits, hidden states, attentions)
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
# sequence_output shape: (batch_size, sequence_length, hidden_size)
sequence_output = outputs[0]
# logits shape: (batch_size, sequence_length, 2)
logits = self.qa_outputs(sequence_output)
# Split logits to start_logits and end_logits
start_logits, end_logits = logits.split(1, dim=-1)
# Note that we use .contiguous() to ensure that the tensor is stored in a contiguous block of memory
# start_logits shape: (batch_size, sequence_length, 1)
# end_logits shape: (batch_size, sequence_length, 1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
# Get the index of the first word
first_word = sequence_output[:, 0, :].contiguous()
has_logits = self.has_ans(first_word)
total_loss = None
if (start_positions is not None and
end_positions is not None and
is_impossibles is not None):
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size() > 1):
end_positions = end_positions.squeeze(-1)
if len(is_impossibles.size()) > 1:
is_impossibles = is_impossibles.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
# clamping the values in the tensor to be within the range of 0 to ignored_index.
# This means that any value less than 0 or greater than or equal to ignored_index will be set to 0.
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
is_impossibles.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
span_loss = start_loss + end_loss
# Internal Front Verification (I-FV)
# alpha1 = 1.0, alpha2 = 0.5
choice_loss = loss_fct(has_logits, is_impossibles.long())
total_loss = (span_loss + choice_loss) / 3
if not return_dict:
output = (
start_logits,
end_logits,
has_logits,
) + outputs[2:] # add hidden states and attention if they are here
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringNaModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
has_logits=has_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class AlbertForQuestionAnsweringAVPoolBCEv3(AlbertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
model_type = "albert"
def __init__(self, config):
super.__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.has_ans1 = nn.Sequential(
nn.Dropout(p=config.hidden_dropout_prob),
nn.Linear(config.hidden_size, 2),
)
self.has_ans2 = nn.Sequential(
nn.Dropout(p=config.hidden_dropout_prob),
nn.Linear(config.hidden_size, 1),
)
# Initialize weights
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
is_impossibles=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
first_word = sequence_output[:, 0, :]
has_logits1 = self.has_ans1(first_word).squeeze(-1)
has_logits2 = self.has_ans2(first_word).squeeze(-1)
total_loss = None
if (
start_positions is not None and
end_positions is not None and
is_impossibles is not None
):
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
if len(is_impossibles.size()) > 1:
is_impossibles = is_impossibles.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
is_impossibles.clamp_(0, ignored_index)
is_impossibles = is_impossibles.to(
dtype=next(self.parameters()).dtype) # fp16 compatibility
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
span_loss = start_loss + end_loss
# Internal Front Verification (I-FV)
choice_fct = nn.BCEWithLogitsLoss()
mse_loss_fct = nn.MSELoss()
choice_loss1 = loss_fct(has_logits1, is_impossibles.long())
choice_loss2 = choice_fct(has_logits2, is_impossibles)
choice_loss3 = mse_loss_fct(has_logits2.view(-1), is_impossibles.view(-1))
choice_loss = choice_loss1 + choice_loss2 + choice_loss3
total_loss = (span_loss + choice_loss) / 5
if not return_dict:
output = (
start_logits,
end_logits,
has_logits1,
) + outputs[2:] # hidden_states, attentions
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringNaModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
has_logits=has_logits1,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) |