def forward(self, input_ids, token_type_ids=None, attention_mask=None, answer_choice=None,
sentence_span_list=None, sentence_ids=None):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
# check_sentence_id_class_num(sentence_mask, sentence_ids)
batch, max_sen, doc_len = doc_sen_mask.size()
que_vec = layers.weighted_avg(que, self.que_self_attn(que, que_mask)).view(batch, 1, -1)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
# [batch, max_sen, doc_len] -> [batch * max_sen, doc_len]
word_sim = self.word_similarity(que_vec, doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
# [batch * max_sen, doc_len] -> [batch * max_sen, 1, doc_len] -> [batch * max_sen, 1, h]
word_hidden = masked_softmax(word_sim, 1 - doc_mask, dim=1).unsqueeze(1).bmm(doc)
word_hidden = word_hidden.view(batch, max_sen, -1)
doc_vecs = layers.weighted_avg(doc, self.doc_sen_self_attn(doc, doc_mask)).view(batch, max_sen, -1)
# [batch, 1, h]
# sentence_hidden = self.vector_similarity(que_vec, doc_vecs, x2_mask=sentence_mask, x3=word_hidden).squeeze(1)
# [batch, 1, max_sen]
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
sentence_hidden = masked_softmax(sentence_sim, 1 - sentence_mask).bmm(word_hidden).squeeze(1)
if self.freeze_predictor:
sentence_loss = self.get_batch_evidence_loss(sentence_sim, 1 - sentence_mask, sentence_ids)
return {'loss': sentence_loss}
yesno_logits = self.yesno_predictor(torch.cat([sentence_hidden, que_vec.squeeze(1)], dim=1))
sentence_scores = masked_softmax(sentence_sim, 1 - sentence_mask, dim=-1).squeeze_(1)
output_dict = {'yesno_logits': yesno_logits,
'sentence_logits': sentence_scores,
'max_weight_index': sentence_scores.max(dim=1)[1],
'max_weight': sentence_scores.max(dim=1)[0]}
loss = 0
if answer_choice is not None:
choice_loss = F.cross_entropy(yesno_logits, answer_choice, ignore_index=-1)
loss += choice_loss
if sentence_ids is not None:
sentence_loss = self.get_batch_evidence_loss(sentence_sim, 1 - sentence_mask, sentence_ids)
loss += self.evidence_lam * sentence_loss
output_dict['loss'] = loss
output_dict['sentence_sim'] = sentence_sim.detach().cpu().float()
output_dict['sentence_mask'] = (1 - sentence_mask).detach().cpu().float()
return output_dict
def forward(self, input_ids, token_type_ids=None, attention_mask=None, answer_choice=None,
sentence_span_list=None, sentence_ids=None):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
batch, max_sen, doc_len = doc_sen_mask.size()
que_vec = layers.weighted_avg(que, self.que_self_attn(que, que_mask)).view(batch, 1, -1)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
doc_vecs = layers.weighted_avg(doc, self.doc_sen_self_attn(doc, doc_mask)).view(batch, max_sen, -1)
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
sentence_hidden = masked_softmax(sentence_sim, 1 - sentence_mask).bmm(doc_vecs).squeeze(1)
yesno_logits = self.yesno_predictor(torch.cat([sentence_hidden, que_vec.squeeze(1)], dim=1))
sentence_scores = masked_softmax(sentence_sim, 1 - sentence_mask, dim=-1).squeeze_(1)
output_dict = {'yesno_logits': yesno_logits,
'sentence_logits': sentence_scores,
'max_weight_index': sentence_scores.max(dim=1)[1],
'max_weight': sentence_scores.max(dim=1)[0]}
loss = 0
if answer_choice is not None:
choice_loss = F.cross_entropy(yesno_logits, answer_choice, ignore_index=-1)
loss += choice_loss
if sentence_ids is not None:
log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1), 1 - sentence_mask, dim=-1)
sentence_loss = self.evidence_lam * F.nll_loss(log_sentence_sim, sentence_ids, ignore_index=-1)
loss += sentence_loss
output_dict['loss'] = loss
return output_dict
def forward(self,
input_ids,
token_type_ids=None,
attention_mask=None,
answer_choice=None,
sentence_span_list=None,
sentence_ids=None,
sentence_label=None):
sequence_output, _ = self.bert(input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
# check_sentence_id_class_num(sentence_mask, sentence_ids)
batch, max_sen, doc_len = doc_sen_mask.size()
# que_len = que_mask.size(1)
que_vec = layers.weighted_avg(que, self.que_self_attn(que,
que_mask)).view(
batch, 1, -1)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
# [batch, max_sen, doc_len] -> [batch * max_sen, doc_len]
word_sim = self.word_similarity(que_vec,
doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
# [batch * max_sen, doc_len] -> [batch * max_sen, 1, doc_len] -> [batch * max_sen, 1, h]
word_hidden = masked_softmax(word_sim, 1 - doc_mask,
dim=1).unsqueeze(1).bmm(doc)
word_hidden = word_hidden.view(batch, max_sen, -1)
doc_vecs = layers.weighted_avg(doc,
self.doc_sen_self_attn(doc,
doc_mask)).view(
batch,
max_sen, -1)
# [batch, 1, max_sen]
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
sentence_scores = masked_softmax(sentence_sim, 1 - sentence_mask)
sentence_hidden = sentence_scores.bmm(word_hidden).squeeze(1)
yesno_logits = self.yesno_predictor(
torch.cat([sentence_hidden, que_vec.squeeze(1)], dim=1))
sentence_scores = sentence_scores.squeeze(1)
max_sentence_score = sentence_scores.max(dim=-1)
output_dict = {
'yesno_logits': yesno_logits,
'sentence_logits': sentence_scores,
'max_weight': max_sentence_score[0],
'max_weight_index': max_sentence_score[1]
}
loss = 0
if answer_choice is not None:
choice_loss = F.cross_entropy(yesno_logits,
answer_choice,
ignore_index=-1)
loss += choice_loss
if sentence_ids is not None:
log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1),
1 - sentence_mask,
dim=-1)
sentence_loss = self.evidence_lam * F.nll_loss(
log_sentence_sim, sentence_ids, ignore_index=-1)
loss += sentence_loss
if self.add_entropy:
no_evidence_mask = (sentence_ids != -1)
entropy = layers.get_masked_entropy(sentence_scores,
mask=no_evidence_mask)
loss += self.evidence_lam * entropy
if sentence_label is not None:
# sentence_label: batch * List[k]
# [batch, max_sen]
# log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1), 1 - sentence_mask, dim=-1)
sentence_prob = 1 - sentence_scores
log_sentence_sim = -torch.log(sentence_prob + 1e-15)
negative_loss = 0
for b in range(batch):
for sen_id, k in enumerate(sentence_label[b]):
negative_loss += k * log_sentence_sim[b][sen_id]
negative_loss /= batch
loss += self.negative_lam * negative_loss
output_dict['loss'] = loss
return output_dict
def forward(self,
input_ids,
token_type_ids=None,
attention_mask=None,
answer_choice=None,
sentence_span_list=None,
sentence_ids=None):
sequence_output, _ = self.bert(input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
batch, max_sen, doc_len = doc_sen_mask.size()
# que_len = que_mask.size(1)
que_vec = layers.weighted_avg(que, self.que_self_attn(que,
que_mask)).view(
batch, 1, -1)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
# [batch, max_sen, doc_len] -> [batch * max_sen, doc_len]
word_sim = self.word_similarity(que_vec,
doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
# [batch * max_sen, doc_len] -> [batch * max_sen, 1, doc_len] -> [batch * max_sen, 1, h]
word_hidden = masked_softmax(word_sim, 1 - doc_mask,
dim=1).unsqueeze(1).bmm(doc)
word_hidden = word_hidden.view(batch, max_sen, -1)
doc_vecs = layers.weighted_avg(doc,
self.doc_sen_self_attn(doc,
doc_mask)).view(
batch,
max_sen, -1)
# [batch, 1, h]
# sentence_hidden = self.vector_similarity(que_vec, doc_vecs, x2_mask=sentence_mask, x3=word_hidden).squeeze(1)
# [batch, 1, max_sen]
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
# sentence_hidden = self.hard_sample(sentence_sim, use_gumbel=self.use_gumbel, dim=-1,
# hard=True, mask=(1 - sentence_mask)).bmm(word_hidden).squeeze(1)
if self.training:
_sample_prob, _sample_log_prob = self.sample_one_hot(
sentence_sim, 1 - sentence_mask)
loss_and_reward, _ = self.reward_func(word_hidden, que_vec,
answer_choice, _sample_prob,
_sample_log_prob)
output_dict = {'loss': loss_and_reward}
else:
_prob, _ = self.sample_one_hot(sentence_sim, 1 - sentence_mask)
loss, _yesno_logits = self.simple_step(word_hidden, que_vec,
answer_choice, _prob)
sentence_scores = masked_softmax(sentence_sim,
1 - sentence_mask,
dim=-1).squeeze_(1)
output_dict = {
'max_weight': sentence_scores.max(dim=1)[0],
'max_weight_index': sentence_scores.max(dim=1)[1],
'sentence_logits': sentence_scores,
'loss': loss,
'yesno_logits': _yesno_logits
}
return output_dict
def forward(self,
input_ids,
token_type_ids=None,
attention_mask=None,
answer_choice=None,
sentence_span_list=None,
sentence_ids=None,
sentence_label=None):
sequence_output, _ = self.bert(input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask, cls_h = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list, return_cls=True)
batch, max_sen, doc_len = doc_sen_mask.size()
que_mask = 1 - que_mask
doc_sen_mask = 1 - doc_sen_mask
sentence_mask = 1 - sentence_mask
cls_h.unsqueeze_(1)
que_vec = masked_softmax(self.que_word_sum(cls_h, que),
que_mask).bmm(que)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
doc_word_sim = self.doc_word_sum(cls_h,
doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
doc_sen_h = masked_softmax(doc_word_sim, doc_mask,
dim=-1).unsqueeze(1).bmm(doc).view(
batch, max_sen, -1)
sentence_sim = self.doc_sen_sum(que_vec, doc_sen_h)
sentence_scores = masked_softmax(sentence_sim, sentence_mask)
doc_vec = sentence_scores.bmm(doc_sen_h).squeeze(1)
yesno_logits = self.yesno_predictor(
torch.cat([doc_vec, que_vec.squeeze(1)], dim=1))
output_dict = {
'yesno_logits': yesno_logits,
'sentence_scores': sentence_scores
}
loss = 0
if answer_choice is not None:
loss += F.cross_entropy(yesno_logits,
answer_choice,
ignore_index=-1)
if sentence_ids is not None:
log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1),
sentence_mask,
dim=-1)
sentence_loss = self.evidence_lam * F.nll_loss(
log_sentence_sim, sentence_ids, ignore_index=-1)
loss += sentence_loss
if self.cls_sup and answer_choice is not None:
extra_yesno_logits = self.extra_predictor(cls_h.squeeze(1))
extra_choice_loss = self.extra_yesno_lam * F.cross_entropy(
extra_yesno_logits, answer_choice, ignore_index=-1)
loss += extra_choice_loss
output_dict['loss'] = loss
return output_dict
def forward(self, input_ids, token_type_ids=None, attention_mask=None, answer_choice=None,
sentence_span_list=None, sentence_ids=None):
sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
# check_sentence_id_class_num(sentence_mask, sentence_ids)
batch, max_sen, doc_len = doc_sen_mask.size()
# que_len = que_mask.size(1)
que_vec = layers.weighted_avg(que, self.que_self_attn(que, que_mask)).view(batch, 1, -1)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
# [batch, max_sen, doc_len] -> [batch * max_sen, doc_len]
word_sim = self.word_similarity(que_vec, doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
# [batch * max_sen, doc_len] -> [batch * max_sen, 1, doc_len] -> [batch * max_sen, 1, h]
word_hidden = masked_softmax(word_sim, 1 - doc_mask, dim=1).unsqueeze(1).bmm(doc)
word_hidden = word_hidden.view(batch, max_sen, -1)
doc_vecs = layers.weighted_avg(doc, self.doc_sen_self_attn(doc, doc_mask)).view(batch, max_sen, -1)
# [batch, 1, h]
# sentence_hidden = self.vector_similarity(que_vec, doc_vecs, x2_mask=sentence_mask, x3=word_hidden).squeeze(1)
# [batch, 1, max_sen]
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
# Test performance of only evidence sentences
# if not self.training:
# max_index = masked_softmax(sentence_sim, 1 - sentence_mask).max(dim=-1, keepdim=True)[1]
# one_hot_vec = torch.zeros_like(sentence_sim).scatter_(-1, max_index, 1.0)
# sentence_hidden = one_hot_vec.bmm(word_hidden).squeeze(1)
# else:
# Test performance of only max k evidence sentences
# if not self.training:
# k_max_mask = rep_layers.get_k_max_mask(sentence_sim * (1 - sentence_mask.unsqueeze(1)).to(sentence_sim.dtype), dim=-1, k=2)
# sentence_hidden = masked_softmax(sentence_sim, k_max_mask).bmm(word_hidden).squeeze(1)
# else:
sentence_hidden = masked_softmax(sentence_sim, 1 - sentence_mask).bmm(word_hidden).squeeze(1)
yesno_logits = self.yesno_predictor(torch.cat([sentence_hidden, que_vec.squeeze(1)], dim=1))
sentence_scores = masked_softmax(sentence_sim, 1 - sentence_mask, dim=-1).squeeze_(1)
output_dict = {'yesno_logits': yesno_logits,
'sentence_logits': sentence_scores,
'max_weight_index': sentence_scores.max(dim=1)[1],
'max_weight': sentence_scores.max(dim=1)[0]}
loss = 0
if answer_choice is not None:
choice_loss = F.cross_entropy(yesno_logits, answer_choice, ignore_index=-1)
loss += choice_loss
if sentence_ids is not None:
log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1), 1 - sentence_mask, dim=-1)
sentence_loss = self.evidence_lam * F.nll_loss(log_sentence_sim, sentence_ids, ignore_index=-1)
loss += sentence_loss
output_dict['loss'] = loss
return output_dict
def forward(self,
input_ids,
token_type_ids=None,
attention_mask=None,
answer_choice=None,
sentence_span_list=None,
sentence_ids=None,
sentence_label=None):
sequence_output, _ = self.bert(input_ids,
token_type_ids,
attention_mask,
output_all_encoded_layers=False)
# mask: 1 for masked value and 0 for true value
# doc, que, doc_mask, que_mask = layers.split_doc_que(sequence_output, token_type_ids, attention_mask)
doc_sen, que, doc_sen_mask, que_mask, sentence_mask = \
layers.split_doc_sen_que(sequence_output, token_type_ids, attention_mask, sentence_span_list)
# check_sentence_id_class_num(sentence_mask, sentence_ids)
batch, max_sen, doc_len = doc_sen_mask.size()
# que_len = que_mask.size(1)
que_vec = layers.weighted_avg(que, self.que_self_attn(que,
que_mask)).view(
batch, 1, -1)
doc = doc_sen.reshape(batch, max_sen * doc_len, -1)
# [batch, max_sen, doc_len] -> [batch * max_sen, doc_len]
word_sim = self.word_similarity(que_vec,
doc).view(batch * max_sen, doc_len)
doc = doc_sen.reshape(batch * max_sen, doc_len, -1)
doc_mask = doc_sen_mask.reshape(batch * max_sen, doc_len)
# [batch * max_sen, doc_len] -> [batch * max_sen, 1, doc_len] -> [batch * max_sen, 1, h]
word_hidden = masked_softmax(word_sim, 1 - doc_mask,
dim=1).unsqueeze(1).bmm(doc)
word_hidden = word_hidden.view(batch, max_sen, -1)
doc_vecs = layers.weighted_avg(doc,
self.doc_sen_self_attn(doc,
doc_mask)).view(
batch,
max_sen, -1)
# [batch, 1, max_sen]
sentence_sim = self.vector_similarity(que_vec, doc_vecs)
softmax_sim = masked_softmax(sentence_sim, 1 - sentence_mask, dim=-1)
sentence_hidden = softmax_sim.bmm(word_hidden).squeeze(1)
softmax_sim.squeeze_(dim=1)
# yesno_logits = self.yesno_predictor(torch.cat([sentence_hidden, que_vec.squeeze(1)], dim=1))
output_dict = {
'sentence_logits': softmax_sim,
'max_weight_index': softmax_sim.max(dim=1)[1],
'max_weight': softmax_sim.max(dim=1)[0]
}
if sentence_ids is not None:
log_sentence_sim = masked_log_softmax(sentence_sim.squeeze(1),
1 - sentence_mask,
dim=-1)
sentence_loss = self.evidence_lam * F.nll_loss(
log_sentence_sim, sentence_ids, ignore_index=-1)
output_dict['loss'] = sentence_loss
return output_dict
return output_dict