def regenerate_length_beam(self, decoder_out, beam_size):
output_tokens = decoder_out.output_tokens
length_tgt = output_tokens.ne(self.pad).sum(1)
length_tgt = length_tgt[:, None] + utils.new_arange(length_tgt, 1, beam_size) - beam_size // 2
length_tgt = length_tgt.view(-1).clamp_(min=2)
max_length = length_tgt.max()
idx_length = utils.new_arange(length_tgt, max_length)
initial_output_tokens = output_tokens.new_zeros(
length_tgt.size(0), max_length
).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk
)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(decoder_out.output_scores)
return decoder_out._replace(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores
)
def _skeptical_unmasking(output_scores, output_masks, p):
""" Non-consecutive Decoding """
if p >= 0.5:
sorted_index = output_scores.sort(-1)[1]
boundary_len = (
(output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) *
p).long()
skeptical_mask = new_arange(output_masks) < boundary_len
return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
mask_ratio = p
# large number gauranteed to be sorted to last
_large_num = output_scores.shape[1] * 10
# we don't want to mask pad or eos
target_masks = output_masks
# create random sampled scores, which is later used for selecting mask locations
target_score = output_scores
# get length for each example in the batch
target_length = target_masks.sum(1).float()
# get num of masks & unmasks for each example in the batch
mask_nums = (target_length * mask_ratio).long() + 1
unmask_nums = target_length - mask_nums + 1
# create a binary mask where 1 means unmasks
unmask_cutoff = new_arange(target_score) < unmask_nums[:, None]
# make indices larger than B be sorted last -> we will only sample from 0~B
target_score.masked_fill_(~unmask_cutoff, _large_num)
# sorting for the top locations
_, target_rank = target_score.sort(1)
# create a binary mask where 1 means masks
mask_cutoff = new_arange(target_score) < mask_nums[:, None]
# sort the desired indices while discarding the rest
mask_pre, _ = target_rank.masked_fill(~mask_cutoff, _large_num).sort(1)
# add the cumsum to indices to transform to sequence indices
mask_mid = mask_pre + new_arange(mask_pre)
# replace the discarded part with duplicated first column -> ensures correctness.
duped = mask_mid[:, :1].expand(*mask_mid.shape)
mask_fin = mask_mid * mask_cutoff + duped * (~mask_cutoff)
# import pdb
# pdb.set_trace()
# scatter 1 to locations indicated by mask_fin, then fill
skeptical_mask = mask_cutoff.new_zeros(mask_cutoff.size())
return skeptical_mask.scatter(1, mask_fin, 1)
def initialize_output_tokens(self, encoder_out, src_tokens):
# length prediction
length_tgt = self.decoder.forward_length_prediction(
self.decoder.forward_length(normalize=True,
encoder_out=encoder_out),
encoder_out=encoder_out,
)
max_length = length_tgt.clamp_(min=2).max()
idx_length = utils.new_arange(src_tokens, max_length)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()).type_as(encoder_out.encoder_out)
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None,
)
def rerank(self, reranker, finalized, encoder_input, beam_size):
def rebuild_batch(finalized):
finalized_tokens = [f[0]['tokens'] for f in finalized]
finalized_maxlen = max(f.size(0) for f in finalized_tokens)
final_output_tokens = finalized_tokens[0].new_zeros(len(finalized_tokens), finalized_maxlen).fill_(self.pad)
for i, f in enumerate(finalized_tokens):
final_output_tokens[i, :f.size(0)] = f
return final_output_tokens
final_output_tokens = rebuild_batch(finalized)
final_output_tokens[:, 0] = self.eos # autoregressive model assumes starting with EOS
reranker_encoder_out = reranker.encoder(*encoder_input)
length_beam_order = utils.new_arange(
final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1)).t().reshape(-1)
reranker_encoder_out = reranker.encoder.reorder_encoder_out(reranker_encoder_out, length_beam_order)
reranking_scores = reranker.get_normalized_probs(
reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out), True, None)
reranking_scores = reranking_scores.gather(2, final_output_tokens[:, 1:, None])
reranking_masks = final_output_tokens[:, 1:].ne(self.pad)
reranking_scores = reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1)
reranking_scores = reranking_scores / reranking_masks.sum(1).type_as(reranking_scores)
for i in range(len(finalized)):
finalized[i][0]['score'] = reranking_scores[i]
return finalized
def _apply_del_words(
in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx
):
# apply deletion to a tensor
in_masks = in_tokens.ne(padding_idx)
bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx)
max_len = in_tokens.size(1)
word_del_pred.masked_fill_(~in_masks, 1)
word_del_pred.masked_fill_(bos_eos_masks, 0)
reordering = (
new_arange(in_tokens)
.masked_fill_(word_del_pred, max_len)
.sort(1)[1]
)
out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering)
out_scores = None
if in_scores is not None:
out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
out_attn = None
if in_attn is not None:
_mask = word_del_pred[:, :, None].expand_as(in_attn)
_reordering = reordering[:, :, None].expand_as(in_attn)
out_attn = in_attn.masked_fill(_mask, 0.).gather(1, _reordering)
return out_tokens, out_scores, out_attn
def _apply_ins_masks(
in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx
):
in_masks = in_tokens.ne(padding_idx)
in_lengths = in_masks.sum(1)
# HACK: hacky way to shift all the paddings to eos first.
in_tokens.masked_fill_(~in_masks, eos_idx)
mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0)
out_lengths = in_lengths + mask_ins_pred.sum(1)
out_max_len = out_lengths.max()
out_masks = (
new_arange(out_lengths, out_max_len)[None, :]
< out_lengths[:, None]
)
reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
out_tokens = (
in_tokens.new_zeros(in_tokens.size(0), out_max_len)
.fill_(padding_idx)
.masked_fill_(out_masks, unk_idx)
)
out_tokens[:, 0] = in_tokens[:, 0]
out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
out_scores = None
if in_scores is not None:
in_scores.masked_fill_(~in_masks, 0)
out_scores = in_scores.new_zeros(*out_tokens.size())
out_scores[:, 0] = in_scores[:, 0]
out_scores.scatter_(1, reordering, in_scores[:, 1:])
return out_tokens, out_scores
def _skeptical_unmasking(output_scores, output_masks, p):
sorted_index = output_scores.sort(-1)[1]
boundary_len = (
(output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
).long()
skeptical_mask = new_arange(output_masks) < boundary_len
return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
def _random_mask(target_tokens):
pad = self.tgt_dict.pad()
bos = self.tgt_dict.bos()
eos = self.tgt_dict.eos()
unk = self.tgt_dict.unk()
en_tag = self.tgt_dict.index("<en>")
ch_tag = self.tgt_dict.index("<ch>")
target_masks = target_tokens.ne(pad) & \
target_tokens.ne(bos) & \
target_tokens.ne(eos) & \
target_tokens.ne(en_tag) & \
target_tokens.ne(ch_tag)
target_score = target_tokens.clone().float().uniform_()
target_score.masked_fill_(~target_masks, 2.0)
target_length = target_masks.sum(1).float()
target_length = target_length * target_length.clone().uniform_()
target_length = target_length + 1 # make sure to mask at least one token.
_, target_rank = target_score.sort(1)
target_cutoff = new_arange(
target_rank) < target_length[:, None].long()
prev_target_tokens = target_tokens.masked_fill(
target_cutoff.scatter(1, target_rank, target_cutoff), unk)
return prev_target_tokens
def initialize_output_tokens(self, encoder_out, src_tokens):
# length prediction
enc_feats = encoder_out.encoder_out # T x B x C
src_masks = encoder_out.encoder_padding_mask # B x T or None
if src_masks is None:
src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_(
enc_feats.size(0))
else:
src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0)
src_lengs = src_lengs.long()
length_tgt = src_lengs * 2
max_length = length_tgt.clamp_(min=2).max()
idx_length = utils.new_arange(src_tokens, max_length)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()).type_as(encoder_out.encoder_out)
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None,
)
def _uniform_assignment(src_lens, trg_lens):
max_trg_len = trg_lens.max()
steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size
# max_trg_len
index_t = utils.new_arange(trg_lens, max_trg_len).float()
index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len
index_t = torch.round(index_t).long().detach()
return index_t
def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx):
padding_masks = in_tokens[:, 1:].eq(padding_idx)
word_ins_scores.masked_fill_(padding_masks, 0.0)
word_ins_pred.masked_fill_(padding_masks, padding_idx)
in_coords = new_arange(in_tokens).type_as(in_scores)
# shift all padding predictions to infinite
out_coords = (in_coords[:, 1:] - 0.5).masked_fill(
word_ins_pred.eq(padding_idx), float("inf")
)
out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1]
out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords)
out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords)
return out_tokens, out_scores
def _random_mask(target_tokens):
target_masks = target_tokens.ne(pad) & \
target_tokens.ne(bos) & \
target_tokens.ne(eos)
target_score = target_tokens.clone().float().uniform_()
target_score.masked_fill_(~target_masks, 2.0)
target_length = target_masks.sum(1).float()
target_length = target_length * target_length.clone().uniform_()
# make sure to mask at least one token.
target_length = target_length + 1
_, target_rank = target_score.sort(1)
target_cutoff = new_arange(
target_rank) < target_length[:, None].long()
prev_target_tokens = target_tokens.masked_fill(
target_cutoff.scatter(1, target_rank, target_cutoff), unk)
return prev_target_tokens
def initialize_output_tokens(self, encoder_out, src_tokens):
# length prediction
_, length_tgt = self.decoder.forward_length_prediction(encoder_out)
max_length = length_tgt.max()
idx_length = utils.new_arange(src_tokens, max_length)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()).type_as(encoder_out["encoder_out"])
return {
"output_tokens": initial_output_tokens,
"output_scores": initial_output_scores,
"attn": None
}
def initialize_output_tokens(self, encoder_out, src_tokens, tgt_lang):
# length prediction
length_tgt = self.decoder.forward_length_prediction(
self.decoder.forward_length(normalize=True,
encoder_out=encoder_out),
tgt_lengths=None)
print("predict tgt_lengths: ", length_tgt)
max_length = length_tgt.clamp_(min=2).max()
idx_length = utils.new_arange(src_tokens, max_length)
positions = torch.arange(1, max_length + 1)[None, :].repeat(
src_tokens.size(0), 1).to(src_tokens.device)
positions.masked_fill_(idx_length[None, :] + 1 > length_tgt[:, None],
0)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length).long().fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk)
initial_output_tokens[:, 0] = self.bos
if tgt_lang == "en":
initial_output_tokens[:, 1] = self.en_tag
langs = src_tokens.new_zeros(src_tokens.size(0), max_length).long()
elif tgt_lang == "ch":
initial_output_tokens[:, 1] = self.ch_tag
langs = src_tokens.new_ones(src_tokens.size(0), max_length).long()
else:
assert tgt_lang == ("en", "ch")
pass
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()).type_as(encoder_out.encoder_out)
return langs, positions, DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None)
def generate(self, models, sample, prefix_tokens=None):
# TODO: iterative refinement generator does not support ensemble for now.
if not self.retain_dropout:
for model in models:
model.eval()
model, reranker = models[0], None
if self.reranking:
assert len(models) > 1, "Assuming the last checkpoint is the reranker"
assert self.beam_size > 1, "Reranking requires multiple translation for each example"
reranker = models[-1]
models = models[:-1]
if len(models) > 1 and hasattr(model, 'enable_ensemble'):
assert model.allow_ensemble, "{} does not support ensembling".format(model.__class__.__name__)
model.enable_ensemble(models)
# TODO: better encoder inputs?
src_tokens = sample["net_input"]["src_tokens"]
src_lengths = sample["net_input"]["src_lengths"]
bsz, src_len = src_tokens.size()
# initialize
encoder_out = model.forward_encoder([src_tokens, src_lengths])
prev_decoder_out = model.initialize_output_tokens(encoder_out, src_tokens)
if self.beam_size > 1:
assert model.allow_length_beam, \
"{} does not support decoding with length beam.".format(model.__class__.__name__)
# regenerate data based on length-beam
length_beam_order = utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1)
encoder_out = model.encoder.reorder_encoder_out(encoder_out, length_beam_order)
prev_decoder_out = model.regenerate_length_beam(prev_decoder_out, self.beam_size)
bsz = bsz * self.beam_size
sent_idxs = torch.arange(bsz)
prev_output_tokens = prev_decoder_out.output_tokens.clone()
if self.retain_history:
prev_decoder_out = prev_decoder_out._replace(history=[prev_output_tokens])
finalized = [[] for _ in range(bsz)]
def is_a_loop(x, y, s, a):
b, l_x, l_y = x.size(0), x.size(1), y.size(1)
if l_x > l_y:
y = torch.cat([y, x.new_zeros(b, l_x - l_y).fill_(self.pad)], 1)
s = torch.cat([s, s.new_zeros(b, l_x - l_y)], 1)
if a is not None:
a = torch.cat([a, a.new_zeros(b, l_x - l_y, a.size(2))], 1)
elif l_x < l_y:
x = torch.cat([x, y.new_zeros(b, l_y - l_x).fill_(self.pad)], 1)
return (x == y).all(1), y, s, a
def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn):
cutoff = prev_out_token.ne(self.pad) & prev_out_token.ne(self.bos) & prev_out_token.ne(self.eos)
tokens = prev_out_token[cutoff]
if prev_out_score is None:
scores, score = None, None
else:
scores = prev_out_score[cutoff]
score = scores.mean()
if prev_out_attn is None:
hypo_attn, alignment = None, None
else:
hypo_attn = prev_out_attn[cutoff]
alignment = hypo_attn.max(dim=1)[1]
return {
"steps": step,
"tokens": tokens,
"positional_scores": scores,
"score": score,
"hypo_attn": hypo_attn,
"alignment": alignment,
}
for step in range(self.max_iter + 1):
decoder_options = {
"eos_penalty": self.eos_penalty,
"max_ratio": self.max_ratio,
"decoding_format": self.decoding_format,
}
prev_decoder_out = prev_decoder_out._replace(
step=step,
max_step=self.max_iter + 1,
)
decoder_out = model.forward_decoder(
prev_decoder_out, encoder_out, **decoder_options
)
if self.adaptive:
# terminate if there is a loop
terminated, out_tokens, out_scores, out_attn = is_a_loop(
prev_output_tokens, decoder_out.output_tokens, decoder_out.output_scores, decoder_out.attn
)
if self.decoding_format == 'easy-first':
# Parallel Easy-First that exits early.
# If the best sequence terminated in beam, terminated all.
new_bsz, seq_len = out_tokens.size()
nb_sents = int(new_bsz/self.beam_size)
out_tokens_beam = out_tokens.view(nb_sents, self.beam_size, seq_len)
out_scores_beam = out_scores.view(nb_sents, self.beam_size, seq_len)
score_mask = out_tokens_beam.ne(self.pad) & out_tokens_beam.ne(
self.bos) & out_tokens_beam.ne(self.eos)
out_scores_avg_beam = (score_mask*out_scores_beam).sum(2) /(score_mask.sum(2))
_, max_idxes = out_scores_avg_beam.max(1)
terminated_beam = terminated.view(nb_sents, self.beam_size)
terminated = terminated_beam[torch.arange(
nb_sents, device=terminated_beam.device), max_idxes]
# [nb_sents]
if terminated.any():
new_out_scores_beam = score_mask*(torch.ones_like(out_scores_beam)*(-1000))
new_out_scores_beam[terminated, max_idxes[terminated]] = out_scores_beam[terminated, max_idxes[terminated]]
out_scores_beam = new_out_scores_beam
out_scores = out_scores_beam.view(new_bsz, seq_len)
terminated = terminated.unsqueeze(1).repeat([1, self.beam_size]).view(new_bsz)
decoder_out = decoder_out._replace(
output_tokens=out_tokens,
output_scores=out_scores,
attn=out_attn,
)
else:
terminated = decoder_out.output_tokens.new_zeros(decoder_out.output_tokens.size(0)).bool()
if step == self.max_iter: # reach last iteration, terminate
terminated.fill_(1)
# collect finalized sentences
finalized_idxs = sent_idxs[terminated]
finalized_tokens = decoder_out.output_tokens[terminated]
finalized_scores = decoder_out.output_scores[terminated]
finalized_attn = (
None if (decoder_out.attn is None or decoder_out.attn.size(0) == 0) else decoder_out.attn[terminated]
)
if self.retain_history:
finalized_history_tokens = [h[terminated] for h in decoder_out.history]
for i in range(finalized_idxs.size(0)):
finalized[finalized_idxs[i]] = [
finalized_hypos(
step,
finalized_tokens[i],
finalized_scores[i],
None if finalized_attn is None else finalized_attn[i],
)
]
if self.retain_history:
finalized[finalized_idxs[i]][0]['history'] = []
for j in range(len(finalized_history_tokens)):
finalized[finalized_idxs[i]][0]['history'].append(
finalized_hypos(
step,
finalized_history_tokens[j][i],
None, None
)
)
# check if all terminated
if terminated.sum() == terminated.size(0):
break
# for next step
not_terminated = ~terminated
prev_decoder_out = decoder_out._replace(
output_tokens=decoder_out.output_tokens[not_terminated],
output_scores=decoder_out.output_scores[not_terminated],
attn=decoder_out.attn[not_terminated]
if (decoder_out.attn is not None and decoder_out.attn.size(0) > 0)
else None,
history=[h[not_terminated] for h in decoder_out.history]
if decoder_out.history is not None
else None,
)
encoder_out = model.encoder.reorder_encoder_out(encoder_out, not_terminated.nonzero().squeeze())
sent_idxs = sent_idxs[not_terminated]
prev_output_tokens = prev_decoder_out.output_tokens.clone()
if self.beam_size > 1:
if reranker is not None:
finalized = self.rerank(
reranker, finalized, [src_tokens, src_lengths], self.beam_size
)
# aggregate information from length beam
finalized = [
finalized[np.argmax(
[finalized[self.beam_size * i + j][0]['score'] for j in range(self.beam_size)]
) + self.beam_size * i] for i in range(len(finalized) // self.beam_size)
]
return finalized
