def forward(self, src_emb, batch):
# encoding
src_mask_inv = (batch["src_mask"] == 0).to(device) # [batch, seq]
# src_mask_inv = batch["src_mask_inv"]
# print('src_emb:', src_emb.size())
# assert len(src_emb.size()) == 3, 'Invalid input size:{}, should be 3!!'.format(' x '.join([str(itm) for itm in src_emb.size()]))
structure_emb = self.relation_embedding(
batch["wr"].to(device)) # [batch, seq, seq, s_dim]
assert len(structure_emb.size(
)) == 4, 'Invalid input size:{}, should be 3!!'.format(' x '.join(
[str(itm) for itm in structure_emb.size()]))
assert src_emb.size(1) == structure_emb.size(1) and src_emb.size(
1) == structure_emb.size(2)
# print('structure size:', structure_emb.size())
# print("input_size", src_emb.size())
if self.use_pe:
src_emb = self.position_encoder(src_emb.transpose(0, 1)).transpose(
0, 1) # [batch, seq, dim]
# print('src_emb_pos:', src_emb.size())
src_emb = self.encoder(src_emb,
src_key_padding_mask=src_mask_inv,
structure=structure_emb) # [batch, seq, dim]
# exit()
assert has_nan(src_emb) is False
return src_emb
def forward(self, batch):
# encoding
src_mask_inv = (batch["src_mask"] == 0).to(device) # [batch, seq]
# batch["src_mask_inv"] = src_mask_inv
src_emb = self.embedding(batch["src"].to(device)) # [batch, seq, dim]
# print("input_size", history_emb.size())
src_emb = self.position_encoder(src_emb.transpose(0, 1)).transpose(
0, 1) # [batch, seq, dim]
src_emb = self.encoder(
src_emb, src_key_padding_mask=src_mask_inv) # [batch, seq, dim]
assert has_nan(src_emb) is False
return src_emb
def forward(self, batch):
# encoding
src_mask_inv = batch["con_mask"] == 0 # [batch, seq]
batch["con_mask_inv"] = src_mask_inv
src_emb = self.embedding(batch["con"]) # [batch, seq, dim]
structure_emb = self.relation_embedding(
batch["rel"]) # [batch, seq, seq, s_dim]
# print('structure size:', structure_emb.size())
# print("input_size", src_emb.size())
if self.use_pe:
src_emb = self.position_encoder(src_emb.transpose(0, 1)).transpose(
0, 1) # [batch, seq, dim]
src_emb = self.encoder(src_emb,
src_key_padding_mask=src_mask_inv,
structure=structure_emb) # [batch, seq, dim]
batch["con_emb"] = src_emb
assert has_nan(src_emb) is False
return src_emb
def forward(self, src_emb, src_length, relations):
# src_emb: [batch, seq, dim]
# print("input_size", src_emb.size())
bsz, max_seq_len, hid_size = src_emb.size()
# print('max_seq_len:', max_seq_len)
src_mask = torch.from_numpy(len_to_mask(src_length, max_seq_len)).to(
src_emb.device)
src_mask_inv = src_mask == 0
relations = relations.squeeze(1)
structure_emb = self.relation_embedding(
relations) # [batch, seq, seq, s_dim]
# print('structure size:', structure_emb.size())
if self.use_pe:
src_emb = self.position_encoder(src_emb.transpose(0, 1)).transpose(
0, 1) # [batch, seq, dim]
src_emb = self.encoder(src_emb,
src_key_padding_mask=src_mask_inv,
structure=structure_emb) # [batch, seq, dim]
assert has_nan(src_emb) is False
return src_emb
def decode(
self,
sent_memory_emb,
graph_memory_emb,
sent_memory_mask,
graph_memory_mask,
beamsize,
max_step,
): # [batch, seq, dim]
batch_size, sent_memory_seq, s_dim = list(sent_memory_emb.shape)
if graph_memory_emb is not None:
_, graph_memory_seq, g_dim = list(graph_memory_emb.shape)
beam = [[BeamInstance(ids=[self.BOS], neg_logp=0.0, is_finish=False)]
for i in range(batch_size)]
cur_beamsize = 1
for step in range(max_step):
cur_seq = step + 1
target_input = [[beam[i][j].ids for j in range(cur_beamsize)]
for i in range(batch_size)
] # [batch, beam, cur_seq]
target_input = (torch.tensor(target_input).to(device).view(
batch_size * cur_beamsize, cur_seq)) # [batch*beam, cur_seq]
target_emb = self.dec_word_embedding(target_input).transpose(
0, 1) # [cur_seq, batch*beam, dim]
target_emb = self.position_encoder(target_emb).transpose(
0, 1) # [batch*beam, cur_seq, dim]
cur_sent_memory_emb = (sent_memory_emb.unsqueeze(dim=1).repeat(
1, cur_beamsize, 1,
1).view(batch_size * cur_beamsize, sent_memory_seq,
s_dim)) # [batch*beam, sent_memory_seq, dim]
cur_sent_memory_mask_inv = (sent_memory_mask.unsqueeze(
dim=1).repeat(1, cur_beamsize,
1).view(batch_size * cur_beamsize,
sent_memory_seq) == 0
) # [batch*beam, graph_memory_seq]
if graph_memory_emb is not None:
cur_graph_memory_emb = (
graph_memory_emb.unsqueeze(dim=1).repeat(
1, cur_beamsize, 1,
1).view(batch_size * cur_beamsize, graph_memory_seq,
g_dim)) # [batch*beam, graph_memory_seq, dim]
cur_graph_memory_mask_inv = (
graph_memory_mask.unsqueeze(dim=1).repeat(
1, cur_beamsize, 1).view(batch_size * cur_beamsize,
graph_memory_seq) == 0
) # [batch*beam, graph_memory_seq]
else:
cur_graph_memory_emb = None
cur_graph_memory_mask_inv = None
cur_triu_mask = torch.triu(torch.ones(cur_seq, cur_seq).to(device),
diagonal=1) # [cur_seq, cur_seq]
cur_triu_mask = cur_triu_mask.repeat(batch_size * cur_beamsize, 1,
1)
cur_triu_mask.masked_fill_(cur_triu_mask == 1, -1e20)
target_emb = self.decoder(
target_emb,
cur_sent_memory_emb,
cur_graph_memory_emb,
tgt_mask=cur_triu_mask,
tgt_key_padding_mask=None,
sent_memory_key_padding_mask=cur_sent_memory_mask_inv,
graph_memory_key_padding_mask=cur_graph_memory_mask_inv,
) # [batch*beam, cur_seq, dim]
assert has_nan(target_emb) is False
# generating step outputs
logits = self.projector(target_emb[:, -1, :]).view(
batch_size, cur_beamsize,
self.word_vocab_size) # [batch, beam, vocab]
logits = F.log_softmax(logits, dim=2)
indices = logits.topk(
beamsize + 1, dim=2)[1].cpu().numpy() # [batch, beam, topk]
all_finish = True
next_beam = []
for i in range(batch_size):
cands = []
for j in range(cur_beamsize):
if beam[i][j].is_finish:
cands.append(
(0, beam[i][j].neg_logp, self.EOS,
j)) # to make sure 'finished' are in the front
else:
for nid in indices[i, j]:
neg_logp = beam[i][j].neg_logp - logits[
i, j, nid].item()
cands.append((1, neg_logp, int(nid),
j)) # '0' finished; '1' unfinished
assert len(cands) >= beamsize
assert sum([x[0] == 0 for x in cands]) <= beamsize, cands
cands.sort()
next_beam.append([])
for _, neg_logp, nid, j in cands[:beamsize]:
is_finish = beam[i][j].is_finish or nid == self.EOS
all_finish &= is_finish
next_instance = BeamInstance(ids=beam[i][j].ids + [
nid,
],
neg_logp=neg_logp,
is_finish=is_finish)
next_beam[-1].append(next_instance)
# preparing for the next loop
if all_finish:
break
beam = next_beam
cur_beamsize = beamsize
best = []
for i in range(batch_size):
indices = np.argsort([x.get_logp_norm(self.EOS) for x in beam[i]])
j = indices[0]
best.append(beam[i][j].get_ids(self.EOS))
return best
def forward(self, batch):
# encoding
sent_mask_inv = batch["src_mask"] == 0 # [batch, seq]
graph_mask_inv = batch["con_mask"] == 0
sent_mem = self.word_encoder(batch)
graph_mem = self.graph_encoder(
batch) if self.graph_encoder is not None else None
# decoding, batch['target'] includes both <bos> and <eos>
if batch["tgt_ref"] is not None:
target_input = batch["tgt_input"] # [batch, trg_seq]
target_ref = batch["tgt_ref"] # [batch, trg_seq]
bsz, trg_seq = target_input.size()
triangle_mask = torch.triu(torch.ones(trg_seq, trg_seq).to(device),
diagonal=1) # [trg_seq, trg_seq]
triangle_mask.masked_fill_(triangle_mask == 1, -1e20)
triangle_mask = triangle_mask.repeat(bsz, 1, 1)
target_mask_inv = batch["tgt_mask"] == 0 # [batch, trg_seq]
target_emb = self.dec_word_embedding(target_input).transpose(
0, 1) # [batch, trg_seq, dim]
target_emb = self.position_encoder(target_emb).transpose(
0, 1) # [batch, trg_seq, dim]
# print('tgt_emb_size:', target_emb.size())
# print('memory_size:', combined_emb.size())
target_emb = self.decoder(
target_emb,
sent_mem,
graph_mem,
tgt_mask=triangle_mask,
tgt_key_padding_mask=target_mask_inv,
sent_memory_key_padding_mask=sent_mask_inv,
graph_memory_key_padding_mask=graph_mask_inv,
) # [batch, trg_seq, dim]
assert has_nan(target_emb) is False
# generating outputs
logits = self.projector(target_emb) # [batch, trg_seq, vocab]
preds = logits.argmax(dim=2) # [batch, trg_seq]
loss = F.cross_entropy(
logits.contiguous().view(-1, self.word_vocab_size),
target_ref.contiguous().view(-1),
ignore_index=0,
)
train_right = ((preds == target_ref).float() *
batch["tgt_mask"]).sum()
train_total = batch["tgt_mask"].sum()
return {
"preds": preds,
"loss": loss,
"counts": (train_right, train_total),
"selected_kn": None,
"trg_selected_kn": None,
}
else:
return {
"sent_memory_emb": sent_mem,
"sent_memory_mask": batch["src_mask"],
"graph_memory_emb": graph_mem,
"graph_memory_mask": batch["con_mask"],
"selected_kn": None,
"trg_selected_kn": None,
}
def inference(
self,
sent_memory_emb,
graph_memory_emb,
sent_memory_mask,
graph_memory_mask,
max_step,
use_sampling=False,
):
batch_size, sent_memory_seq, dim = list(sent_memory_emb.shape)
_, graph_memory_seq, _ = list(graph_memory_emb.shape)
sent_memory_mask_inv = sent_memory_mask == 0 # [batch, sent_memory_seq]
graph_memory_mask_inv = graph_memory_mask == 0 # [batch, sent_memory_seq]
target_ids = [[self.BOS
for i in range(batch_size)]] # [target_seq, batch]
target_mask = [[1.0] for i in range(batch_size)] # [batch, target_seq]
target_prob = [] # [target_seq, batch]
is_finish = [False for _ in range(batch_size)]
rows = torch.arange(batch_size).to(device)
for step in range(max_step):
cur_seq = step + 1
cur_emb = self.dec_word_embedding(
torch.tensor(target_ids).to(device)) # [cur_seq, batch, dim]
cur_emb = self.position_encoder(cur_emb) # [cur_seq, batch, dim]
cur_mask = torch.tensor(target_mask).to(device)
cur_mask_inv = cur_mask == 0.0 # [batch, cur_seq]
cur_triu_mask = torch.triu(torch.ones(cur_seq, cur_seq).to(device),
diagonal=1) # [cur_seq, cur_seq]
cur_triu_mask.masked_fill_(cur_triu_mask == 1, -1e20)
cur_emb = self.decoder(
cur_emb,
sent_memory_emb, # [batch, sent_len, dim]
graph_memory_emb, # [batch, graph_len, dim]
tgt_mask=cur_triu_mask,
tgt_key_padding_mask=cur_mask_inv,
sent_memory_key_padding_mask=sent_memory_mask_inv,
graph_memory_key_padding_mask=graph_memory_mask_inv,
) # [batch, cur_seq, dim]
assert has_nan(cur_emb) is False
# break after the first time when all items are finished
if all(is_finish) or step == max_step - 1:
cur_len = cur_mask.sum(dim=1).long()
target_vec = universal_sentence_embedding(
cur_emb, cur_mask, cur_len)
break
# generating step outputs
logits = self.projector(cur_emb[:, -1, :]).view(
batch_size, self.word_vocab_size) # [batch, vocab]
if use_sampling is False:
indices = logits.argmax(dim=1) # [batch]
else:
indices = Categorical(logits=logits).sample() # [batch]
prob = F.softmax(logits, dim=1)[rows, indices] # [batch]
target_prob.append(prob)
indices = indices.cpu().tolist()
target_ids.append(indices)
for i in range(batch_size):
target_mask[i].append(
0.0 if is_finish[i] else
1.0) # based on if is_finish in the last step
for i in range(batch_size):
is_finish[i] |= indices[i] == self.EOS
target_ids = list(map(list,
zip(*target_ids[1:]))) # [batch, target_seq]
target_mask = torch.tensor([x[1:] for x in target_mask
]).to(device) # [batch, target_seq]
target_prob = torch.stack(target_prob, dim=1) # [batch, target_seq]
return target_vec, target_ids, target_prob, target_mask
