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# -*- coding: utf-8 -*-
# @Time : 2022/4/16 12:10 下午
# @Author : JianingWang
# @File : multiple_choice.py
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
# from transformers import MegatronBertPreTrainedModel, MegatronBertModel
from transformers.models.megatron_bert import MegatronBertPreTrainedModel, MegatronBertModel
from transformers.modeling_outputs import MultipleChoiceModelOutput
class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
# classifier_dropout = (
# config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
# )
classifier_dropout = 0.2
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
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,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
pseudo=None
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.bert(
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,
return_dict=return_dict,
)
pooled_output = outputs[1] # [batch_size, num_choices, hidden_dim]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output) # [batch_size, num_choices, 1]
reshaped_logits = logits.view(-1, num_choices) # [batch_size, num_choices]
loss = None
if labels is not None:
if pseudo is None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
else:
loss_fct = CrossEntropyLoss(reduction="none")
loss = loss_fct(reshaped_logits, labels)
weight = 1 - pseudo * 0.9
loss *= weight
loss = loss.mean()
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class MegatronBertRDropForMultipleChoice(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
# classifier_dropout = (
# config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
# )
classifier_dropout = 0.2
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
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,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
logits_list = []
for i in range(2):
outputs = self.bert(
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,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
logits_list.append(logits.view(-1, num_choices))
loss = None
alpha = 1.0
for logits in logits_list:
if labels is not None:
loss_fct = CrossEntropyLoss()
l = loss_fct(logits, labels)
if loss:
loss += alpha * l
else:
loss = alpha * l
if loss is not None:
p = torch.log_softmax(logits_list[0], dim=-1)
p_tec = torch.exp(p)
q = torch.log_softmax(logits_list[-1], dim=-1)
q_tec = torch.exp(q)
kl_loss = F.kl_div(p, q_tec, reduction="none").sum()
reverse_kl_loss = F.kl_div(q, p_tec, reduction="none").sum()
loss += 0.5 * (kl_loss + reverse_kl_loss) / 2.
return MultipleChoiceModelOutput(
loss=loss,
logits=logits_list[0],
hidden_states=None,
attentions=None
)
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