def forward(self, v, b, q, e, labels):
"""Forward
v: [batch, num_objs, obj_dim]
b: [batch, num_objs, b_dim]
q: [batch, seq_length]
e: [batch, num_entities]
return: logits, not probs
"""
assert q.size(1) > e.data.max(), 'len(q)=%d > e_pos.max()=%d' % (
q.size(1), e.data.max())
MINUS_INFINITE = -99
if 's' in self.op:
v = torch.cat([v, b], 2)
w_emb = self.w_emb(q)
q_emb = self.q_emb.forward_all(w_emb) # [batch, q_len, q_dim]
# entity positions
q_emb = utils.batched_index_select(q_emb, 1, e)
att = self.v_att.forward_all(v, q_emb, True, True,
MINUS_INFINITE) # b x g x v x q
mask = (e == 0).unsqueeze(1).unsqueeze(2).expand(att.size())
mask[:, :, :, 0].data.fill_(0) # at least one entity per sentence
att.data.masked_fill_(mask.data, MINUS_INFINITE)
return None, att
def forward(self, batch):
embeddings = self.model(
batch['input_ids'],
attention_mask=batch['attention_masks'],
token_type_ids=batch['segment_ids'],
)[0] # [B, L_doc, 768]
cls_embeddings = batched_index_select(embeddings, batch['index_ids'])
cls_embeddings *= (batch['label_ids'] != -1).float().unsqueeze(2)
logits = self.classifier(
self.dropout(cls_embeddings)) # [B, L_cls, 768]
logits = logits.transpose(1, 2) # [B, 2, L_cls]
return logits
def actor_layer(self, batch_state, mask, n_label_sents=None):
"""
Determines which sentences to extract for each of the documents represented by batch_state
:param batch_state: A torch.tensor representing sentence embeddings of each document within the batch.
Shape: (batch_size, n_doc_sentences, embedding_dim)
:param mask: A torch.tensor of booleans indicating whether or not the document within the batch
actually has the sentence. This is necessary because we've batched multiple documents
together of various lengths.
:param n_label_sents: An optional list containing number of extracted sentences in summary labels (oracle)
:return: A tuple containing:
- action_dists: list(Categorical()) containing categorical distributions. Each entry
represents the distribution amongst sentences to extract at a given
step for all batches.
- action_indices: torch.tensor() containing the indicies of extracted sentences
Shape: (batch_size, n_extracted_sentences, embedding_dim)
- ext_sents: A torch.tensor() containing extracted sentence embeddings.
Shape: (batch_size, n_extracted_sentences, embedding_dim)
- n_ext_sents: A torch.tensor() where entries show # of sentences extracted per sample
"""
# Obtain distribution amongst actions
batch_state, mask = self.add_stop_action(batch_state, mask)
# Obtain number of samples in batch
batch_size = batch_state.shape[0]
max_n_doc_sents = batch_state.shape[1]
embedding_dim = batch_state.shape[2]
# Obtain maximum number of sentences to extract
if n_label_sents is None:
n_doc_sents = mask.sum(dim=1)
batch_max_n_ext_sents = torch.tensor([self.max_n_ext_sents] *
batch_size)
batch_max_n_ext_sents = torch.min(batch_max_n_ext_sents.float(),
n_doc_sents)
else:
batch_max_n_ext_sents = n_label_sents
# Create variables to stop extraction loop
max_n_ext_sents = batch_max_n_ext_sents.max(
) # Maximum number of sentences to extract
stop_action_idx = max_n_doc_sents - 1 # Previously appended stop_action embedding
is_stop_action = torch.zeros(batch_size).bool()
src_doc_lengths = torch.sum(mask, dim=1)
batch_state = torch.nn.utils.rnn.pack_padded_sequence(
batch_state,
lengths=src_doc_lengths,
batch_first=True,
enforce_sorted=False)
# Extraction loop
action_indices, ext_sents, action_dists, stop_action_list = list(
), list(), list(), list()
n_ext_sents = 0
is_first_sent = True
extraction_labels = None
while True:
# Obtain distribution amongst sentences to extract
if is_first_sent:
action_probs, __ = self.extraction_model.forward(
batch_state, mask)
is_first_sent = False
else:
action_probs, __ = self.extraction_model.forward(
sent_embeddings=batch_state,
sent_mask=mask,
extraction_indicator=extraction_labels,
use_init_embedding=True)
action_probs = action_probs[:, -1:, :]
action_dist = Categorical(action_probs)
# Sample sentence to extract
ext_sent_indices = action_dist.sample().T
# Embeddings of sentences to extract
ext_sent_embeddings = batched_index_select(batch_state, 1,
ext_sent_indices)
# Collect
action_dists.append(action_dist)
ext_sents.append(ext_sent_embeddings)
action_indices.append(ext_sent_indices)
# Form extraction_labels
extraction_labels = torch.zeros(batch_size, max_n_doc_sents)
already_ext_indices = torch.cat(action_indices)
extraction_labels[torch.arange(batch_size),
already_ext_indices] = 1
# Track number of sentences extracted from article
n_ext_sents = n_ext_sents + 1
# Check to see if should stop extracting sentences
# Todo: Fix this, the mask ALWAYS masks out the stop action...
is_stop_action = is_stop_action | (ext_sent_indices >=
stop_action_idx)
stop_action_list.append(is_stop_action)
all_samples_stop = torch.sum(is_stop_action) >= batch_size
is_long_enough = n_ext_sents >= max_n_ext_sents
if all_samples_stop or is_long_enough:
break
action_indices = torch.stack(action_indices).T.squeeze(1)
n_ext_sents = (~torch.stack(stop_action_list).squeeze(1).T).sum(dim=1)
ext_sents = torch.stack(ext_sents).transpose(0, 1).squeeze()
return action_dists, action_indices, ext_sents, n_ext_sents