def eval_one_batch(self, batch):
enc_batch, enc_lens, enc_pos, enc_padding_mask, enc_batch_extend_vocab, extra_zeros, c_t, coverage = \
get_input_from_batch(batch, use_cuda)
dec_batch, dec_lens, dec_pos, dec_padding_mask, max_dec_len, tgt_batch = \
get_output_from_batch(batch, use_cuda)
enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_lens)
s_t = self.model.reduce_state(enc_h)
step_losses = []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t = dec_batch[:, di] # Teacher forcing
final_dist, s_t, c_t, attn_dist, p_gen, next_coverage = self.model.decoder(
y_t, s_t, enc_out, enc_fea, enc_padding_mask, c_t, extra_zeros,
enc_batch_extend_vocab, coverage, di)
tgt = tgt_batch[:, di]
gold_probs = torch.gather(final_dist, 1,
tgt.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage),
1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
coverage = next_coverage
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_step_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_step_losses / dec_lens
loss = torch.mean(batch_avg_loss)
return loss.item()
def train_one_batch(self, batch):
enc_batch, enc_lens, enc_pos, enc_padding_mask, enc_batch_extend_vocab, \
extra_zeros, c_t, coverage = get_input_from_batch(batch, use_cuda)
dec_batch, dec_lens, dec_pos, dec_padding_mask, max_dec_len, tgt_batch = \
get_output_from_batch(batch, use_cuda)
self.optimizer.zero_grad()
if not config.tran:
enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_lens)
else:
enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_pos)
s_t = self.model.reduce_state(enc_h)
step_losses, cove_losses = [], []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t = dec_batch[:, di] # Teacher forcing
final_dist, s_t, c_t, attn_dist, p_gen, next_coverage = \
self.model.decoder(y_t, s_t, enc_out, enc_fea, enc_padding_mask, c_t,
extra_zeros, enc_batch_extend_vocab, coverage, di)
tgt = tgt_batch[:, di]
step_mask = dec_padding_mask[:, di]
gold_probs = torch.gather(final_dist, 1,
tgt.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage),
1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
cove_losses.append(step_coverage_loss * step_mask)
coverage = next_coverage
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / dec_lens
loss = torch.mean(batch_avg_loss)
loss.backward()
clip_grad_norm_(self.model.encoder.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.decoder.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.reduce_state.parameters(),
config.max_grad_norm)
self.optimizer.step()
if config.is_coverage:
cove_losses = torch.sum(torch.stack(cove_losses, 1), 1)
batch_cove_loss = cove_losses / dec_lens
batch_cove_loss = torch.mean(batch_cove_loss)
return loss.item(), batch_cove_loss.item()
return loss.item(), 0.
def train_one_batch(self, batch):
enc_batch, enc_lens, enc_pos, enc_padding_mask, enc_batch_extend_vocab, \
extra_zeros, c_t, coverage = get_input_from_batch(batch, use_cuda, transformer=True)
dec_batch, dec_lens, dec_pos, dec_padding_mask, max_dec_len, tgt_batch = \
get_output_from_batch(batch, use_cuda, transformer=True)
self.optimizer.zero_grad()
pred = self.model(enc_batch, enc_pos, dec_batch, dec_pos)
gold_probs = torch.gather(pred, -1, tgt_batch.unsqueeze(-1)).squeeze()
batch_loss = -torch.log(gold_probs + config.eps)
batch_loss = batch_loss * dec_padding_mask
sum_losses = torch.sum(batch_loss, 1)
batch_avg_loss = sum_losses / dec_lens
loss = torch.mean(batch_avg_loss)
loss.backward()
# update parameters
self.optimizer.step_and_update_lr()
return loss.item(), 0.
def train_one_batch(self, batch):
enc_batch, enc_lens, enc_pos, enc_padding_mask, enc_batch_extend_vocab, \
extra_zeros, c_t, coverage = get_input_from_batch(batch, use_cuda)
dec_batch, dec_lens, dec_pos, dec_padding_mask, max_dec_len, tgt_batch = \
get_output_from_batch(batch, use_cuda)
self.optimizer.zero_grad()
enc_outs = []
enc_feas = []
enc_hs = []
if not config.tran:
for i, encoder in enumerate(self.model.encoders):
enc_out, enc_fea, enc_h = encoder(enc_batch[i], enc_lens[i])
enc_outs.append(enc_out)
enc_feas.append(enc_fea)
enc_hs.append(enc_h)
# else:
# enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_pos)
s_t = self.model.reduce_state(enc_h)
step_losses, cove_losses = [], []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t = dec_batch[:, di] # Teacher forcing
# modify the original frame for two encoders.
final_dist_0, s_t_0, c_t_0, attn_dist_0, p_gen, next_coverage = \
self.model.decoder(y_t, s_t, enc_outs[0], enc_feas[0], enc_padding_mask[0], c_t,
extra_zeros, enc_batch_extend_vocab[0], coverage, di)
final_dist_1, s_t_1, c_t_1, attn_dist, p_gen, next_coverage = \
self.model.decoder(y_t, s_t, enc_outs[1], enc_feas[1], enc_padding_mask[1], c_t,
extra_zeros, enc_batch_extend_vocab[1], coverage, di)
y_t_emb = self.model.decoder.tgt_word_emb(y_t)
encoders_att = self.model.encoders_att(enc_hs, y_t_emb)
final_dist = torch.stack((final_dist_0, final_dist_1), dim=1)
final_dist = torch.bmm(encoders_att, final_dist).squeeze()
encoders_att_ = encoders_att.transpose(0,
1).contiguous() # 1 x b x 2
h = s_t_0[0] * encoders_att_[:, :, :1] + s_t_1[
0] * encoders_att_[:, :, 1:]
c = s_t_0[1] * encoders_att_[:, :, :1] + s_t_1[
1] * encoders_att_[:, :, 1:]
s_t = (h, c)
tgt = tgt_batch[:, di]
step_mask = dec_padding_mask[:, di]
gold_probs = torch.gather(final_dist, 1,
tgt.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage),
1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
cove_losses.append(step_coverage_loss * step_mask)
coverage = next_coverage
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / dec_lens
loss = torch.mean(batch_avg_loss)
loss.backward()
clip_grad_norm_(self.model.encoders.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.decoder.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.reduce_state.parameters(),
config.max_grad_norm)
self.optimizer.step()
if config.is_coverage:
cove_losses = torch.sum(torch.stack(cove_losses, 1), 1)
batch_cove_loss = cove_losses / dec_lens
batch_cove_loss = torch.mean(batch_cove_loss)
return loss.item(), batch_cove_loss.item()
return loss.item(), 0.
def beam_search(self, batch):
# single example repeated across the batch
enc_batch, enc_lens, enc_pos, enc_padding_mask, enc_batch_extend_vocab, extra_zeros, c_t, coverage = \
get_input_from_batch(batch, use_cuda)
enc_out, enc_fea, enc_h = self.model.encoder(enc_batch, enc_lens)
s_t = self.model.reduce_state(enc_h)
dec_h, dec_c = s_t # b x hidden_dim
dec_h = dec_h.squeeze()
dec_c = dec_c.squeeze()
# decoder batch preparation, it has beam_size example initially everything is repeated
beams = [
Beam(tokens=[self.vocab.word2id(config.BOS_TOKEN)],
log_probs=[0.0],
state=(dec_h[0], dec_c[0]),
context=c_t[0],
coverage=(coverage[0] if config.is_coverage else None))
for _ in range(config.beam_size)
]
steps = 0
results = []
while steps < config.max_dec_steps and len(results) < config.beam_size:
latest_tokens = [h.latest_token for h in beams]
latest_tokens = [t if t < self.vocab.size() else self.vocab.word2id(config.UNK_TOKEN) \
for t in latest_tokens]
y_t = Variable(torch.LongTensor(latest_tokens))
if use_cuda:
y_t = y_t.cuda()
all_state_h = [h.state[0] for h in beams]
all_state_c = [h.state[1] for h in beams]
all_context = [h.context for h in beams]
s_t = (torch.stack(all_state_h,
0).unsqueeze(0), torch.stack(all_state_c,
0).unsqueeze(0))
c_t = torch.stack(all_context, 0)
coverage_t = None
if config.is_coverage:
all_coverage = [h.coverage for h in beams]
coverage_t = torch.stack(all_coverage, 0)
final_dist, s_t, c_t, attn_dist, p_gen, coverage_t = self.model.decoder(
y_t, s_t, enc_out, enc_fea, enc_padding_mask, c_t, extra_zeros,
enc_batch_extend_vocab, coverage_t, steps)
log_probs = torch.log(final_dist)
topk_log_probs, topk_ids = torch.topk(log_probs,
config.beam_size * 2)
dec_h, dec_c = s_t
dec_h = dec_h.squeeze()
dec_c = dec_c.squeeze()
all_beams = []
# On the first step, we only had one original hypothesis (the initial hypothesis). On subsequent steps, all original hypotheses are distinct.
num_orig_beams = 1 if steps == 0 else len(beams)
for i in range(num_orig_beams):
h = beams[i]
state_i = (dec_h[i], dec_c[i])
context_i = c_t[i]
coverage_i = (coverage[i] if config.is_coverage else None)
for j in range(config.beam_size *
2): # for each of the top 2*beam_size hyps:
new_beam = h.extend(token=topk_ids[i, j].item(),
log_prob=topk_log_probs[i, j].item(),
state=state_i,
context=context_i,
coverage=coverage_i)
all_beams.append(new_beam)
beams = []
for h in self.sort_beams(all_beams):
if h.latest_token == self.vocab.word2id(config.EOS_TOKEN):
if steps >= config.min_dec_steps:
results.append(h)
else:
beams.append(h)
if len(beams) == config.beam_size or len(
results) == config.beam_size:
break
steps += 1
if len(results) == 0:
results = beams
beams_sorted = self.sort_beams(results)
return beams_sorted[0]
