def forward(
self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
sentence_spans: torch.IntTensor = None,
sent_labels: torch.IntTensor = None,
evd_chain_labels: torch.IntTensor = None,
q_type: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
if self._sent_labels_src == 'chain':
batch_size, num_spans = sent_labels.size()
sent_labels_mask = (sent_labels >= 0).float()
print("chain:", evd_chain_labels)
# we use the chain as the label to supervise the gate
# In this model, we only take the first chain in ``evd_chain_labels`` for supervision,
# right now the number of chains should only be one too.
evd_chain_labels = evd_chain_labels[:, 0].long()
# build the gate labels. The dim is set to 1 + num_spans to account for the end embedding
# shape: (batch_size, 1+num_spans)
sent_labels = sent_labels.new_zeros((batch_size, 1 + num_spans))
sent_labels.scatter_(1, evd_chain_labels, 1.)
# remove the column for end embedding
# shape: (batch_size, num_spans)
sent_labels = sent_labels[:, 1:].float()
# make the padding be -1
sent_labels = sent_labels * sent_labels_mask + -1. * (
1 - sent_labels_mask)
embedded_question = self._text_field_embedder(question)
# shape: [batch_size, num_para, max_para_len, embedding_dim]
embedded_passage = self._text_field_embedder(passage)
# mask
ques_mask = util.get_text_field_mask(question,
num_wrapping_dims=0).float()
context_mask = util.get_text_field_mask(passage,
num_wrapping_dims=1).float()
# extract word embeddings for each sentence
batch_size, num_para, max_para_len, emb_size = embedded_passage.size()
batch_size, num_para, max_num_sent, _ = sentence_spans.size()
# Shape(spans_rep): (batch_size*num_para*max_num_sent, max_batch_span_width, embedding_dim)
# Shape(spans_mask): (batch_size*num_para, max_num_sent, max_batch_span_width)
spans_rep_sp, spans_mask = convert_sequence_to_spans(
embedded_passage.view(batch_size * num_para, max_para_len,
emb_size),
sentence_spans.view(batch_size * num_para, max_num_sent, 2))
_, _, max_batch_span_width = spans_mask.size()
# flatten out the num_para dimension
# shape: (batch_size, num_para, max_num_sent), specify which sent is not pad(i.e. all tok in the sent is not pad)
sentence_mask = (spans_mask.sum(-1) > 0).float().view(
batch_size, num_para, max_num_sent)
# the maximum total number of sentences for each example
max_num_global_sent = torch.max(sentence_mask.sum([1,
2])).long().item()
num_spans = max_num_global_sent
# shape: (batch_size, num_spans, max_batch_span_width*embedding_dim),
# where num_spans equals to num_para * num_sent(no max bc para paddings are removed)
# and also equals to max_num_global_sent
spans_rep_sp = convert_span_to_sequence(
spans_rep_sp.new_zeros((batch_size, max_num_global_sent)),
spans_rep_sp.view(batch_size * num_para, max_num_sent,
max_batch_span_width * emb_size), sentence_mask)
# shape: (batch_size * num_spans, max_batch_span_width, embedding_dim),
spans_rep_sp = spans_rep_sp.view(batch_size * max_num_global_sent,
max_batch_span_width, emb_size)
# shape: (batch_size, num_spans, max_batch_span_width),
spans_mask = convert_span_to_sequence(
spans_mask.new_zeros((batch_size, max_num_global_sent)),
spans_mask, sentence_mask)
# gate prediction
# Shape(gate_logit): (batch_size * num_spans, 2)
# Shape(gate): (batch_size * num_spans, 1)
# Shape(pred_sent_probs): (batch_size * num_spans, 2)
# Shape(gate_mask): (batch_size, num_spans)
#gate_logit, gate, pred_sent_probs = self._span_gate(spans_rep_sp, spans_mask)
gate_logit, gate, pred_sent_probs, gate_mask, g_att_score = self._span_gate(
spans_rep_sp, spans_mask, self._gate_self_attention_layer,
self._gate_sent_encoder)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
loss = F.nll_loss(F.log_softmax(gate_logit,
dim=-1).view(batch_size * num_spans,
-1),
sent_labels.long().view(batch_size * num_spans),
ignore_index=-1)
gate = (gate >= 0.3).long()
gate = gate.view(batch_size, num_spans)
print(pred_sent_probs.shape)
output_dict = {
"pred_sent_labels": gate, #[B, num_span]
"gate_probs":
pred_sent_probs[:, 1].view(batch_size, num_spans), #[B, num_span]
}
if self._output_att_scores:
if not g_att_score is None:
output_dict['evd_self_attention_score'] = g_att_score
# Compute the loss for training.
try:
#loss = strong_sup_loss
self._loss_trackers['loss'](loss)
output_dict["loss"] = loss
except RuntimeError:
print('\n meta_data:', metadata)
print("sent label:")
for b_label in np.array(sent_labels.cpu()):
b_label = b_label == 1
indices = np.arange(len(b_label))
print(indices[b_label] + 1)
# Compute the EM and F1 on SQuAD and add the tokenized input to the output.
if metadata is not None:
output_dict['answer_texts'] = []
question_tokens = []
passage_tokens = []
#token_spans_sp = []
#token_spans_sent = []
sent_labels_list = []
evd_possible_chains = []
ans_sent_idxs = []
ids = []
for i in range(batch_size):
question_tokens.append(metadata[i]['question_tokens'])
passage_tokens.append(metadata[i]['passage_tokens'])
#token_spans_sp.append(metadata[i]['token_spans_sp'])
#token_spans_sent.append(metadata[i]['token_spans_sent'])
sent_labels_list.append(metadata[i]['sent_labels'])
ids.append(metadata[i]['_id'])
passage_str = metadata[i]['original_passage']
#offsets = metadata[i]['token_offsets']
answer_texts = metadata[i].get('answer_texts', [])
output_dict['answer_texts'].append(answer_texts)
# shift sentence indice back
evd_possible_chains.append([
s_idx - 1
for s_idx in metadata[i]['evd_possible_chains'][0]
if s_idx > 0
])
ans_sent_idxs.append(
[s_idx - 1 for s_idx in metadata[i]['ans_sent_idxs']])
if len(metadata[i]['ans_sent_idxs']) > 0:
pred_sent_gate = gate[i].detach().cpu().numpy()
if any([
pred_sent_gate[s_idx - 1] > 0
for s_idx in metadata[i]['ans_sent_idxs']
]):
self.evd_ans_metric(1)
else:
self.evd_ans_metric(0)
self._f1_metrics(pred_sent_probs, sent_labels.view(-1),
gate_mask.view(-1))
output_dict['question_tokens'] = question_tokens
output_dict['passage_tokens'] = passage_tokens
#output_dict['token_spans_sp'] = token_spans_sp
#output_dict['token_spans_sent'] = token_spans_sent
output_dict['sent_labels'] = sent_labels_list
output_dict['evd_possible_chains'] = evd_possible_chains
output_dict['ans_sent_idxs'] = ans_sent_idxs
output_dict['_id'] = ids
return output_dict
def forward(
self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
sentence_spans: torch.IntTensor = None,
sent_labels: torch.IntTensor = None,
evd_chain_labels: torch.IntTensor = None,
q_type: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
if self._sent_labels_src == 'chain':
batch_size, num_spans = sent_labels.size()
sent_labels_mask = (sent_labels >= 0).float()
print("chain:", evd_chain_labels)
# we use the chain as the label to supervise the gate
# In this model, we only take the first chain in ``evd_chain_labels`` for supervision,
# right now the number of chains should only be one too.
evd_chain_labels = evd_chain_labels[:, 0].long()
# build the gate labels. The dim is set to 1 + num_spans to account for the end embedding
# shape: (batch_size, 1+num_spans)
sent_labels = sent_labels.new_zeros((batch_size, 1 + num_spans))
sent_labels.scatter_(1, evd_chain_labels, 1.)
# remove the column for end embedding
# shape: (batch_size, num_spans)
sent_labels = sent_labels[:, 1:].float()
# make the padding be -1
sent_labels = sent_labels * sent_labels_mask + -1. * (
1 - sent_labels_mask)
# word + char embedding
embedded_question = self._text_field_embedder(question)
embedded_passage = self._text_field_embedder(passage)
# mask
ques_mask = util.get_text_field_mask(question).float()
context_mask = util.get_text_field_mask(passage).float()
# BiDAF for answer predicion
ques_output = self._dropout(
self._phrase_layer(embedded_question, ques_mask))
context_output = self._dropout(
self._phrase_layer(embedded_passage, context_mask))
modeled_passage, _, qc_score = self.qc_att(context_output, ques_output,
ques_mask)
modeled_passage = self._modeling_layer(modeled_passage, context_mask)
# BiDAF for gate prediction
ques_output_sp = self._dropout(
self._phrase_layer_sp(embedded_question, ques_mask))
context_output_sp = self._dropout(
self._phrase_layer_sp(embedded_passage, context_mask))
modeled_passage_sp, _, qc_score_sp = self.qc_att_sp(
context_output_sp, ques_output_sp, ques_mask)
modeled_passage_sp = self._modeling_layer_sp(modeled_passage_sp,
context_mask)
# gate prediction
# Shape(spans_rep): (batch_size * num_spans, max_batch_span_width, embedding_dim)
# Shape(spans_mask): (batch_size, num_spans, max_batch_span_width)
spans_rep_sp, spans_mask = convert_sequence_to_spans(
modeled_passage_sp, sentence_spans)
spans_rep, _ = convert_sequence_to_spans(modeled_passage,
sentence_spans)
# Shape(gate_logit): (batch_size * num_spans, 2)
# Shape(gate): (batch_size * num_spans, 1)
# Shape(pred_sent_probs): (batch_size * num_spans, 2)
# Shape(gate_mask): (batch_size, num_spans)
#gate_logit, gate, pred_sent_probs = self._span_gate(spans_rep_sp, spans_mask)
gate_logit, gate, pred_sent_probs, gate_mask, g_att_score = self._span_gate(
spans_rep_sp, spans_mask, self._gate_self_attention_layer,
self._gate_sent_encoder)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
strong_sup_loss = F.nll_loss(
F.log_softmax(gate_logit, dim=-1).view(batch_size * num_spans, -1),
sent_labels.long().view(batch_size * num_spans),
ignore_index=-1)
gate = (gate >= 0.3).long()
spans_rep = spans_rep * gate.unsqueeze(-1).float()
attended_sent_embeddings = convert_span_to_sequence(
modeled_passage_sp, spans_rep, spans_mask)
modeled_passage = attended_sent_embeddings + modeled_passage
self_att_passage = self._self_attention_layer(modeled_passage,
mask=context_mask)
modeled_passage = modeled_passage + self_att_passage[0]
self_att_score = self_att_passage[2]
output_start = self._span_start_encoder(modeled_passage, context_mask)
span_start_logits = self.linear_start(output_start).squeeze(
2) - 1e30 * (1 - context_mask)
output_end = torch.cat([modeled_passage, output_start], dim=2)
output_end = self._span_end_encoder(output_end, context_mask)
span_end_logits = self.linear_end(output_end).squeeze(
2) - 1e30 * (1 - context_mask)
output_type = torch.cat([modeled_passage, output_end, output_start],
dim=2)
output_type = torch.max(output_type, 1)[0]
# output_type = torch.max(self.rnn_type(output_type, context_mask), 1)[0]
predict_type = self.linear_type(output_type)
type_predicts = torch.argmax(predict_type, 1)
best_span = self.get_best_span(span_start_logits, span_end_logits)
output_dict = {
"span_start_logits": span_start_logits,
"span_end_logits": span_end_logits,
"best_span": best_span,
"pred_sent_labels": gate.view(batch_size,
num_spans), #[B, num_span]
"gate_probs":
pred_sent_probs[:, 1].view(batch_size, num_spans), #[B, num_span]
}
if self._output_att_scores:
if not qc_score is None:
output_dict['qc_score'] = qc_score
if not qc_score_sp is None:
output_dict['qc_score_sp'] = qc_score_sp
if not self_att_score is None:
output_dict['self_attention_score'] = self_att_score
if not g_att_score is None:
output_dict['evd_self_attention_score'] = g_att_score
print("sent label:")
for b_label in np.array(sent_labels.cpu()):
b_label = b_label == 1
indices = np.arange(len(b_label))
print(indices[b_label] + 1)
# Compute the loss for training.
if span_start is not None:
try:
start_loss = nll_loss(
util.masked_log_softmax(span_start_logits, None),
span_start.squeeze(-1))
end_loss = nll_loss(
util.masked_log_softmax(span_end_logits, None),
span_end.squeeze(-1))
type_loss = nll_loss(
util.masked_log_softmax(predict_type, None), q_type)
loss = start_loss + end_loss + type_loss + strong_sup_loss
self._loss_trackers['loss'](loss)
self._loss_trackers['start_loss'](start_loss)
self._loss_trackers['end_loss'](end_loss)
self._loss_trackers['type_loss'](type_loss)
self._loss_trackers['strong_sup_loss'](strong_sup_loss)
output_dict["loss"] = loss
except RuntimeError:
print('\n meta_data:', metadata)
print(span_start_logits.shape)
# Compute the EM and F1 on SQuAD and add the tokenized input to the output.
if metadata is not None:
output_dict['best_span_str'] = []
output_dict['answer_texts'] = []
question_tokens = []
passage_tokens = []
token_spans_sp = []
token_spans_sent = []
sent_labels_list = []
evd_possible_chains = []
ans_sent_idxs = []
ids = []
count_yes = 0
count_no = 0
for i in range(batch_size):
question_tokens.append(metadata[i]['question_tokens'])
passage_tokens.append(metadata[i]['passage_tokens'])
token_spans_sp.append(metadata[i]['token_spans_sp'])
token_spans_sent.append(metadata[i]['token_spans_sent'])
sent_labels_list.append(metadata[i]['sent_labels'])
ids.append(metadata[i]['_id'])
passage_str = metadata[i]['original_passage']
offsets = metadata[i]['token_offsets']
if type_predicts[i] == 1:
best_span_string = 'yes'
count_yes += 1
elif type_predicts[i] == 2:
best_span_string = 'no'
count_no += 1
else:
predicted_span = tuple(best_span[i].detach().cpu().numpy())
start_offset = offsets[predicted_span[0]][0]
end_offset = offsets[predicted_span[1]][1]
best_span_string = passage_str[start_offset:end_offset]
output_dict['best_span_str'].append(best_span_string)
answer_texts = metadata[i].get('answer_texts', [])
output_dict['answer_texts'].append(answer_texts)
if answer_texts:
self._squad_metrics(best_span_string.lower(), answer_texts)
# shift sentence indice back
evd_possible_chains.append([
s_idx - 1
for s_idx in metadata[i]['evd_possible_chains'][0]
if s_idx > 0
])
ans_sent_idxs.append(
[s_idx - 1 for s_idx in metadata[i]['ans_sent_idxs']])
self._f1_metrics(pred_sent_probs, sent_labels.view(-1),
gate_mask.view(-1))
output_dict['question_tokens'] = question_tokens
output_dict['passage_tokens'] = passage_tokens
output_dict['token_spans_sp'] = token_spans_sp
output_dict['token_spans_sent'] = token_spans_sent
output_dict['sent_labels'] = sent_labels_list
output_dict['evd_possible_chains'] = evd_possible_chains
output_dict['ans_sent_idxs'] = ans_sent_idxs
output_dict['_id'] = ids
return output_dict
def forward(
self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
sentence_spans: torch.IntTensor = None,
sent_labels: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None,
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
q_type: torch.IntTensor = None,
sp_mask: torch.IntTensor = None,
coref_mask: torch.FloatTensor = None) -> Dict[str, torch.Tensor]:
embedded_question = self._text_field_embedder(question)
embedded_passage = self._text_field_embedder(passage)
decoupled_passage, spans_mask = convert_sequence_to_spans(
embedded_passage, sentence_spans)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
encodeded_decoupled_passage = \
self._phrase_layer_sp(
decoupled_passage, spans_mask.view(batch_size * num_spans, -1))
context_output_sp = convert_span_to_sequence(
embedded_passage, encodeded_decoupled_passage, spans_mask)
ques_mask = util.get_text_field_mask(question).float()
context_mask = util.get_text_field_mask(passage).float()
ques_output_sp = self._phrase_layer_sp(embedded_question, ques_mask)
modeled_passage_sp = self.qc_att_sp(context_output_sp, ques_output_sp,
ques_mask)
modeled_passage_sp = self.linear_2(modeled_passage_sp)
modeled_passage_sp = self._modeling_layer_sp(modeled_passage_sp,
context_mask)
# Shape(spans_rep): (batch_size * num_spans, max_batch_span_width, embedding_dim)
# Shape(spans_mask): (batch_size, num_spans, max_batch_span_width)
spans_rep_sp, spans_mask = convert_sequence_to_spans(
modeled_passage_sp, sentence_spans)
# Shape(gate_logit): (batch_size * num_spans, 2)
# Shape(gate): (batch_size * num_spans, 1)
# Shape(pred_sent_probs): (batch_size * num_spans, 2)
gate_logit = self._span_gate(spans_rep_sp, spans_mask)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
sent_mask = (sent_labels >= 0).long()
sent_labels = sent_labels * sent_mask
# print(sent_labels)
# print(gate_logit.shape)
# print(gate_logit)
strong_sup_loss = torch.mean(-torch.log(
torch.sum(F.softmax(gate_logit) *
sent_labels.float().view(batch_size, num_spans),
dim=-1) + 1e-10))
# strong_sup_loss = F.nll_loss(F.log_softmax(gate_logit, dim=-1).view(batch_size * num_spans, -1),
# sent_labels.long().view(batch_size * num_spans), ignore_index=-1)
gate = torch.argmax(gate_logit.view(batch_size, num_spans), -1)
# gate = (gate >= 0.5).long().view(batch_size, num_spans)
output_dict = {"gate": gate}
loss = strong_sup_loss
output_dict["loss"] = loss
if metadata is not None:
question_tokens = []
passage_tokens = []
sent_labels_list = []
ids = []
for i in range(batch_size):
question_tokens.append(metadata[i]['question_tokens'])
passage_tokens.append(metadata[i]['passage_tokens'])
sent_labels_list.append(metadata[i]['sent_labels'])
ids.append(metadata[i]['_id'])
self._sent_metrics(gate, sent_labels)
# print(self.get_prediction(gate, sent_labels).item())
# print(self.get_prediction(gate, sent_labels).data)
output_dict['predict'] = [
self.get_prediction(gate, sent_labels).data
]
output_dict['question_tokens'] = question_tokens
output_dict['passage_tokens'] = passage_tokens
output_dict['sent_labels'] = sent_labels_list
output_dict['_id'] = ids
# print(ids)
return output_dict
def forward(self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
sentence_spans: torch.IntTensor = None,
sent_labels: torch.IntTensor = None,
evd_chain_labels: torch.IntTensor = None,
q_type: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# In this model, we only take the first chain in ``evd_chain_labels`` for supervision
evd_chain_labels = evd_chain_labels[:, 0]
# there may be some instances that we can't find any evd chain for training
# In that case, use the mask to ignore those instances
evd_instance_mask = (evd_chain_labels[:, 0] != 0).float() if not evd_chain_labels is None else None
# word + char embedding
embedded_question = self._text_field_embedder(question)
embedded_passage = self._text_field_embedder(passage)
# mask
ques_mask = util.get_text_field_mask(question).float()
context_mask = util.get_text_field_mask(passage).float()
# BiDAF for answer prediction (no dropout since the answer f1 is used as the reward)
ques_output = self._phrase_layer(embedded_question, ques_mask)
context_output = self._phrase_layer(embedded_passage, context_mask)
modeled_passage, _, qc_score = self.qc_att(context_output, ques_output, ques_mask)
modeled_passage = self._modeling_layer(modeled_passage, context_mask)
# BiDAF for chain prediction
ques_output_sp = self._dropout(self._phrase_layer_sp(embedded_question, ques_mask))
context_output_sp = self._dropout(self._phrase_layer_sp(embedded_passage, context_mask))
modeled_passage_sp, _, qc_score_sp = self.qc_att_sp(context_output_sp, ques_output_sp, ques_mask)
modeled_passage_sp = self._modeling_layer_sp(modeled_passage_sp, context_mask)
# chain prediciton
# Shape(spans_rep): (batch_size * num_spans, max_batch_span_width, embedding_dim)
# Shape(spans_mask): (batch_size, num_spans, max_batch_span_width)
spans_rep_sp, spans_mask = convert_sequence_to_spans(modeled_passage_sp, sentence_spans)
spans_rep, _ = convert_sequence_to_spans(modeled_passage, sentence_spans)
# Shape(all_predictions): (batch_size, K, num_decoding_steps)
# Shape(all_logprobs): (batch_size, K, num_decoding_steps)
# Shape(seq_logprobs): (batch_size, K)
# Shape(gate): (batch_size * K * num_spans, 1)
# Shape(gate_probs): (batch_size * K * num_spans, 1)
# Shape(gate_mask): (batch_size, num_spans)
# Shape(g_att_score): (batch_size, num_heads, num_spans, num_spans)
# Shape(orders): (batch_size, K, num_spans)
all_predictions, \
all_logprobs, \
seq_logprobs, \
gate, \
gate_probs, \
gate_mask, \
g_att_score, \
orders = self._span_gate(spans_rep_sp, spans_mask,
ques_output_sp, ques_mask,
evd_chain_labels,
self._gate_self_attention_layer,
self._gate_sent_encoder,
get_all_beam=True)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
beam_size = all_predictions.size(1)
# expand all the tensor to fit the beam size
num_toks = modeled_passage.size(1)
emb_dim = spans_rep.size(-1)
spans_rep = spans_rep.reshape(batch_size, num_spans, max_batch_span_width, emb_dim)
spans_rep = spans_rep.unsqueeze(1).expand(batch_size, beam_size, num_spans, max_batch_span_width, emb_dim)
spans_rep = spans_rep.reshape(batch_size * beam_size * num_spans, max_batch_span_width, emb_dim)
spans_mask = spans_mask[:, None, :, :].expand(batch_size, beam_size, num_spans, max_batch_span_width)
spans_mask = spans_mask.reshape(batch_size * beam_size, num_spans, max_batch_span_width)
context_mask = context_mask.unsqueeze(1).expand(batch_size, beam_size, num_toks)
context_mask = context_mask.reshape(batch_size * beam_size, num_toks)
se_mask = gate.expand(batch_size * beam_size * num_spans, max_batch_span_width).unsqueeze(-1)
se_mask = convert_span_to_sequence(modeled_passage_sp, se_mask, spans_mask).squeeze(-1)
spans_rep = spans_rep * gate.unsqueeze(-1)
attended_sent_embeddings = convert_span_to_sequence(modeled_passage_sp, spans_rep, spans_mask)
modeled_passage = attended_sent_embeddings
self_att_passage = self._self_attention_layer(modeled_passage, mask=context_mask)
modeled_passage = modeled_passage + self_att_passage[0]
self_att_score = self_att_passage[2]
output_start = self._span_start_encoder(modeled_passage, context_mask)
span_start_logits = self.linear_start(output_start).squeeze(2) - 1e30 * (1 - context_mask * se_mask)
output_end = torch.cat([modeled_passage, output_start], dim=2)
output_end = self._span_end_encoder(output_end, context_mask)
span_end_logits = self.linear_end(output_end).squeeze(2) - 1e30 * (1 - context_mask * se_mask)
output_type = torch.cat([modeled_passage, output_end, output_start], dim=2)
output_type = torch.max(output_type, 1)[0]
# output_type = torch.max(self.rnn_type(output_type, context_mask), 1)[0]
predict_type = self.linear_type(output_type)
type_predicts = torch.argmax(predict_type, 1)
best_span = self.get_best_span(span_start_logits, span_end_logits)
output_dict = {
"span_start_logits": span_start_logits.view(batch_size, beam_size, num_toks)[:, 0, :],
"span_end_logits": span_end_logits.view(batch_size, beam_size, num_toks)[:, 0, :],
"best_span": best_span.view(batch_size, beam_size, 2)[:, 0, :],
"pred_sent_labels": gate.squeeze(1).view(batch_size, beam_size, num_spans)[:, 0, :], #[B, num_span]
"gate_probs": gate_probs.squeeze(1).view(batch_size, beam_size, num_spans)[:, 0, :], #[B, num_span]
"pred_sent_orders": orders, #[B, K, num_span]
}
if self._output_att_scores:
if not qc_score is None:
output_dict['qc_score'] = qc_score
if not qc_score_sp is None:
output_dict['qc_score_sp'] = qc_score_sp
if not self_att_score is None:
output_dict['self_attention_score'] = self_att_score
if not g_att_score is None:
output_dict['evd_self_attention_score'] = g_att_score
# compute evd rl training metric, rewards, and loss
print("sent label:")
for b_label in np.array(sent_labels.cpu()):
b_label = b_label == 1
indices = np.arange(len(b_label))
print(indices[b_label] + 1)
evd_TP, evd_NP, evd_NT = self._f1_metrics(gate.squeeze(1).view(batch_size, beam_size, num_spans)[:, 0, :],
sent_labels,
mask=gate_mask,
instance_mask=evd_instance_mask if self.training else None,
sum=False)
print("TP:", evd_TP)
print("NP:", evd_NP)
print("NT:", evd_NT)
evd_ps = np.array(evd_TP) / (np.array(evd_NP) + 1e-13)
evd_rs = np.array(evd_TP) / (np.array(evd_NT) + 1e-13)
evd_f1s = 2. * ((evd_ps * evd_rs) / (evd_ps + evd_rs + 1e-13))
#print("evd_f1s:", evd_f1s)
predict_mask = get_evd_prediction_mask(all_predictions[:, :1, :], eos_idx=0)[0]
gold_mask = get_evd_prediction_mask(evd_chain_labels, eos_idx=0)[0]
# ChainAccuracy defaults to take multiple predicted chains, so unsqueeze dim 1
self.evd_sup_acc_metric(predictions=all_predictions[:, :1, :], gold_labels=evd_chain_labels,
predict_mask=predict_mask, gold_mask=gold_mask, instance_mask=evd_instance_mask)
print("gold chain:", evd_chain_labels)
# Compute the EM and F1 on SQuAD and add the tokenized input to the output.
# Compute before loss for rl
best_span = best_span.view(batch_size, beam_size, 2)
if metadata is not None:
output_dict['best_span_str'] = []
output_dict['answer_texts'] = []
question_tokens = []
passage_tokens = []
token_spans_sp = []
token_spans_sent = []
sent_labels_list = []
evd_possible_chains = []
ans_sent_idxs = []
pred_chains_include_ans = []
beam_pred_chains_include_ans = []
beam2_pred_chains_include_ans = []
ids = []
ems = []
f1s = []
rb_ems = []
ch_lens = []
#count_yes = 0
#count_no = 0
for i in range(batch_size):
question_tokens.append(metadata[i]['question_tokens'])
passage_tokens.append(metadata[i]['passage_tokens'])
token_spans_sp.append(metadata[i]['token_spans_sp'])
token_spans_sent.append(metadata[i]['token_spans_sent'])
sent_labels_list.append(metadata[i]['sent_labels'])
ids.append(metadata[i]['_id'])
passage_str = metadata[i]['original_passage']
offsets = metadata[i]['token_offsets']
answer_texts = metadata[i].get('answer_texts', [])
output_dict['answer_texts'].append(answer_texts)
beam_best_span_string = []
beam_f1s = []
beam_ems = []
beam_rb_ems = []
beam_ch_lens = []
for b_idx in range(beam_size):
if type_predicts[i] == 1:
best_span_string = 'yes'
#count_yes += 1
elif type_predicts[i] == 2:
best_span_string = 'no'
#count_no += 1
else:
predicted_span = tuple(best_span[i, b_idx].detach().cpu().numpy())
start_offset = offsets[predicted_span[0]][0]
end_offset = offsets[predicted_span[1]][1]
best_span_string = passage_str[start_offset:end_offset]
beam_best_span_string.append(best_span_string)
if answer_texts:
em, f1 = self._squad_metrics(best_span_string.lower(), answer_texts)
beam_ems.append(em)
beam_f1s.append(f1)
rb_chain = [s_idx-1 for s_idx in metadata[i]['evd_possible_chains'][0] if s_idx > 0]
pd_chain = [s_idx-1 for s_idx in all_predictions[i, b_idx].detach().cpu().numpy() if s_idx > 0]
beam_rb_ems.append(float(rb_chain == pd_chain))
beam_ch_lens.append(float(len(pd_chain)))
ems.append(beam_ems)
f1s.append(beam_f1s)
rb_ems.append(beam_rb_ems)
ch_lens.append(beam_ch_lens)
output_dict['best_span_str'].append(beam_best_span_string[0])
# shift sentence indice back
evd_possible_chains.append([s_idx-1 for s_idx in metadata[i]['evd_possible_chains'][0] if s_idx > 0])
ans_sent_idxs.append([s_idx-1 for s_idx in metadata[i]['ans_sent_idxs']])
print("ans_sent_idxs:", metadata[i]['ans_sent_idxs'])
if len(metadata[i]['ans_sent_idxs']) > 0:
pred_sent_orders = orders[i].detach().cpu().numpy()
if any([pred_sent_orders[0][s_idx-1] >= 0 for s_idx in metadata[i]['ans_sent_idxs']]):
self.evd_ans_metric(1)
pred_chains_include_ans.append(1)
else:
self.evd_ans_metric(0)
pred_chains_include_ans.append(0)
if any([any([pred_sent_orders[beam][s_idx-1] >= 0 for s_idx in metadata[i]['ans_sent_idxs']])
for beam in range(len(pred_sent_orders))]):
self.evd_beam_ans_metric(1)
beam_pred_chains_include_ans.append(1)
else:
self.evd_beam_ans_metric(0)
beam_pred_chains_include_ans.append(0)
if any([any([pred_sent_orders[beam][s_idx-1] >= 0 for s_idx in metadata[i]['ans_sent_idxs']])
for beam in range(2)]):
self.evd_beam2_ans_metric(1)
beam2_pred_chains_include_ans.append(1)
else:
self.evd_beam2_ans_metric(0)
beam2_pred_chains_include_ans.append(0)
output_dict['question_tokens'] = question_tokens
output_dict['passage_tokens'] = passage_tokens
output_dict['token_spans_sp'] = token_spans_sp
output_dict['token_spans_sent'] = token_spans_sent
output_dict['sent_labels'] = sent_labels_list
output_dict['evd_possible_chains'] = evd_possible_chains
output_dict['ans_sent_idxs'] = ans_sent_idxs
output_dict['pred_chains_include_ans'] = pred_chains_include_ans
output_dict['beam_pred_chains_include_ans'] = beam_pred_chains_include_ans
output_dict['_id'] = ids
# Compute the loss for training.
# RL Loss equals ``-log(P) * (R - baseline)``
# Shape: (batch_size, num_decoding_steps)
tot_rs = 0.
if "ans" in self._ft_reward:
ans_rs = seq_logprobs.new_tensor(f1s) # shape: (batch_size, beam_size)
print('ans rs:', ans_rs)
tot_rs = tot_rs + ans_rs
if "rb" in self._ft_reward:
rb_rs = seq_logprobs.new_tensor(rb_ems) # shape: (batch_size, beam_size)
print('rb rs:', rb_rs)
tot_rs = tot_rs + 0.7 * rb_rs
if "len" in self._ft_reward:
len_rs = seq_logprobs.new_tensor(ch_lens) # shape: (batch_size, beam_size)
len_rs = (1. - len_rs / 5.) * (len_rs > 0).float()
print("len rs:", len_rs)
tot_rs = tot_rs + 0.7 * len_rs
#rs_baseline = torch.mean(rs)
rs_baseline = 0#torch.mean(tot_rs)
tot_rs = tot_rs - rs_baseline
rl_loss = -torch.mean(seq_logprobs * tot_rs)
if span_start is not None:
try:
loss = rl_loss
self._loss_trackers['loss'](loss)
self._loss_trackers['rl_loss'](rl_loss)
output_dict["loss"] = loss
except RuntimeError:
print('\n meta_data:', metadata)
print(output_dict['_id'])
print(span_start_logits.shape)
return output_dict
def forward(
self, # type: ignore
question: Dict[str, torch.LongTensor],
passage: Dict[str, torch.LongTensor],
span_start: torch.IntTensor = None,
span_end: torch.IntTensor = None,
sentence_spans: torch.IntTensor = None,
sent_labels: torch.IntTensor = None,
evd_chain_labels: torch.IntTensor = None,
q_type: torch.IntTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# In this model, we only take the first chain in ``evd_chain_labels`` for supervision
evd_chain_labels = evd_chain_labels[:,
0] if not evd_chain_labels is None else None
# there may be some instances that we can't find any evd chain for training
# In that case, use the mask to ignore those instances
evd_instance_mask = (evd_chain_labels[:, 0] != 0).float(
) if not evd_chain_labels is None else None
# bert embedding for answer prediction
# shape: [batch_size, max_q_len, emb_size]
embedded_question = self._text_field_embedder(question)
# shape: [batch_size, num_para, max_para_len, embedding_dim]
embedded_passage = self._text_field_embedder(passage)
# mask
ques_mask = util.get_text_field_mask(question,
num_wrapping_dims=0).float()
context_mask = util.get_text_field_mask(passage,
num_wrapping_dims=1).float()
# extract word embeddings for each sentence
batch_size, num_para, max_para_len, emb_size = embedded_passage.size()
batch_size, num_para, max_num_sent, _ = sentence_spans.size()
# Shape(spans_rep): (batch_size*num_para*max_num_sent, max_batch_span_width, embedding_dim)
# Shape(spans_mask): (batch_size*num_para, max_num_sent, max_batch_span_width)
spans_rep_sp, spans_mask = convert_sequence_to_spans(
embedded_passage.view(batch_size * num_para, max_para_len,
emb_size),
sentence_spans.view(batch_size * num_para, max_num_sent, 2))
_, _, max_batch_span_width = spans_mask.size()
# flatten out the num_para dimension
# shape: (batch_size, num_para, max_num_sent), specify which sent is not pad(i.e. all tok in the sent is not pad)
sentence_mask = (spans_mask.sum(-1) > 0).float().view(
batch_size, num_para, max_num_sent)
# the maximum total number of sentences for each example
max_num_global_sent = torch.max(sentence_mask.sum([1,
2])).long().item()
num_spans = max_num_global_sent
# shape: (batch_size, num_spans, max_batch_span_width*embedding_dim),
# where num_spans equals to num_para * num_sent(no max bc para paddings are removed)
# and also equals to max_num_global_sent
spans_rep_sp = convert_span_to_sequence(
spans_rep_sp.new_zeros((batch_size, max_num_global_sent)),
spans_rep_sp.view(batch_size * num_para, max_num_sent,
max_batch_span_width * emb_size), sentence_mask)
# shape: (batch_size * num_spans, max_batch_span_width, embedding_dim),
spans_rep_sp = spans_rep_sp.view(batch_size * max_num_global_sent,
max_batch_span_width, emb_size)
# shape: (batch_size, num_spans, max_batch_span_width),
spans_mask = convert_span_to_sequence(
spans_mask.new_zeros((batch_size, max_num_global_sent)),
spans_mask, sentence_mask)
# chain prediction
# Shape(all_predictions): (batch_size, num_decoding_steps)
# Shape(all_logprobs): (batch_size, num_decoding_steps)
# Shape(seq_logprobs): (batch_size,)
# Shape(gate): (batch_size * num_spans, 1)
# Shape(gate_probs): (batch_size * num_spans, 1)
# Shape(gate_mask): (batch_size, num_spans)
# Shape(g_att_score): (batch_size, num_heads, num_spans, num_spans)
# Shape(orders): (batch_size, K, num_spans)
all_predictions, \
all_logprobs, \
seq_logprobs, \
gate, \
gate_probs, \
gate_mask, \
g_att_score, \
orders = self._span_gate(spans_rep_sp, spans_mask,
embedded_question, ques_mask,
evd_chain_labels,
self._gate_self_attention_layer,
self._gate_sent_encoder)
batch_size, num_spans, max_batch_span_width = spans_mask.size()
output_dict = {
"pred_sent_labels":
gate.squeeze(1).view(batch_size, num_spans), # [B, num_span]
"gate_probs":
gate_probs.squeeze(1).view(batch_size, num_spans), # [B, num_span]
"pred_sent_orders": orders, # [B, K, num_span]
}
if self._output_att_scores:
if not g_att_score is None:
output_dict['evd_self_attention_score'] = g_att_score
# compute evd rl training metric, rewards, and loss
print("sent label:")
for b_label in np.array(sent_labels.cpu()):
b_label = b_label == 1
indices = np.arange(len(b_label))
print(indices[b_label] + 1)
evd_TP, evd_NP, evd_NT = self._f1_metrics(
gate.squeeze(1).view(batch_size, num_spans),
sent_labels,
mask=gate_mask,
instance_mask=evd_instance_mask if self.training else None,
sum=False)
print("TP:", evd_TP)
print("NP:", evd_NP)
print("NT:", evd_NT)
evd_ps = np.array(evd_TP) / (np.array(evd_NP) + 1e-13)
evd_rs = np.array(evd_TP) / (np.array(evd_NT) + 1e-13)
evd_f1s = 2. * ((evd_ps * evd_rs) / (evd_ps + evd_rs + 1e-13))
predict_mask = get_evd_prediction_mask(all_predictions.unsqueeze(1),
eos_idx=0)[0]
gold_mask = get_evd_prediction_mask(evd_chain_labels, eos_idx=0)[0]
# default to take multiple predicted chains, so unsqueeze dim 1
self.evd_sup_acc_metric(predictions=all_predictions.unsqueeze(1),
gold_labels=evd_chain_labels,
predict_mask=predict_mask,
gold_mask=gold_mask,
instance_mask=evd_instance_mask)
print("gold chain:", evd_chain_labels)
predict_mask = predict_mask.float().squeeze(1)
rl_loss = -torch.mean(
torch.sum(all_logprobs * predict_mask * evd_instance_mask[:, None],
dim=1))
# Compute the EM and F1 on SQuAD and add the tokenized input to the output.
# Compute before loss for rl
if metadata is not None:
output_dict['answer_texts'] = []
question_tokens = []
passage_tokens = []
# token_spans_sp = []
token_spans_sent = []
sent_labels_list = []
evd_possible_chains = []
ans_sent_idxs = []
pred_chains_include_ans = []
beam_pred_chains_include_ans = []
ids = []
for i in range(batch_size):
question_tokens.append(metadata[i]['question_tokens'])
passage_tokens.append(metadata[i]['passage_tokens'])
# token_spans_sp.append(metadata[i]['token_spans_sp'])
token_spans_sent.append(metadata[i]['token_spans_sent'])
sent_labels_list.append(metadata[i]['sent_labels'])
ids.append(metadata[i]['_id'])
passage_str = metadata[i]['original_passage']
offsets = metadata[i]['token_offsets']
answer_texts = metadata[i].get('answer_texts', [])
output_dict['answer_texts'].append(answer_texts)
# shift sentence indice back
evd_possible_chains.append([
s_idx - 1
for s_idx in metadata[i]['evd_possible_chains'][0]
if s_idx > 0
])
ans_sent_idxs.append(
[s_idx - 1 for s_idx in metadata[i]['ans_sent_idxs']])
print("ans_sent_idxs:", metadata[i]['ans_sent_idxs'])
if len(metadata[i]['ans_sent_idxs']) > 0:
pred_sent_orders = orders[i].detach().cpu().numpy()
if any([
pred_sent_orders[0][s_idx - 1] >= 0
for s_idx in metadata[i]['ans_sent_idxs']
]):
self.evd_ans_metric(1)
pred_chains_include_ans.append(1)
else:
self.evd_ans_metric(0)
pred_chains_include_ans.append(0)
if any([
any([
pred_sent_orders[beam][s_idx - 1] >= 0
for s_idx in metadata[i]['ans_sent_idxs']
]) for beam in range(len(pred_sent_orders))
]):
self.evd_beam_ans_metric(1)
beam_pred_chains_include_ans.append(1)
else:
self.evd_beam_ans_metric(0)
beam_pred_chains_include_ans.append(0)
output_dict['question_tokens'] = question_tokens
output_dict['passage_tokens'] = passage_tokens
# output_dict['token_spans_sp'] = token_spans_sp
output_dict['token_spans_sent'] = token_spans_sent
output_dict['sent_labels'] = sent_labels_list
output_dict['evd_possible_chains'] = evd_possible_chains
output_dict['ans_sent_idxs'] = ans_sent_idxs
output_dict['pred_chains_include_ans'] = pred_chains_include_ans
output_dict[
'beam_pred_chains_include_ans'] = beam_pred_chains_include_ans
output_dict['_id'] = ids
# Compute the loss for training.
if evd_chain_labels is not None:
loss = rl_loss
self._loss_trackers['loss'](loss)
self._loss_trackers['rl_loss'](rl_loss)
output_dict["loss"] = loss
return output_dict