def gumbel_softmax(logits: FT,
temperature: float,
num_samples: Optional[int] = None) -> Tuple[FT, FT, LT]:
"""Sample from the Gumbel-Softmax distribution and optionally discretize."""
logits = logits.align_to('batch', 'length', 'label')
y = gumbel_softmax_sample(logits, temperature, num_samples)
y = y.align_to('batch', 'length', 'label', ...)
max_values, max_inds = y.max(dim='label')
y_one_hot = (max_values.align_as(y) == y).float()
y_one_hot = (y_one_hot - y).detach() + y
bi = get_named_range(logits.size('batch'), 'batch').align_as(max_inds)
li = get_named_range(logits.size('length'), 'length').align_as(max_inds)
if num_samples is None:
with NoName(max_inds, y_one_hot, bi, li):
probs = y_one_hot[bi, li, max_inds]
probs.rename_('batch', 'length')
else:
si = get_named_range(max_inds.size('sample'),
'sample').align_as(max_inds)
with NoName(max_inds, y_one_hot, bi, li, si):
probs = y_one_hot[bi, li, max_inds, si]
probs.rename_('batch', 'length', 'sample')
seq_probs = (1e-8 + probs).log().sum(dim='length').exp()
return y, y_one_hot, max_inds, seq_probs
def search(self,
sot_id: int,
src_emb: FT,
src_outputs: FT,
src_paddings: BT,
src_lengths: LT,
beam_size: int,
lang_emb: Optional[FT] = None) -> Hypotheses:
if beam_size <= 0:
raise ValueError(f'`beam_size` must be positive.')
batch_size = src_emb.size('batch')
tokens = torch.full([batch_size, beam_size], sot_id,
dtype=torch.long).to(src_emb.device).rename(
'batch', 'beam')
accum_scores = torch.full_like(tokens, -9999.9).float()
accum_scores[:, 0] = 0.0
init_att = None
if g.input_feeding:
init_att = get_zeros(batch_size, beam_size,
g.hidden_size).rename('batch', 'beam',
'hidden')
lstm_state = LstmStatesByLayers.zero_state(
self.cell.num_layers,
batch_size,
beam_size,
self.attn.input_tgt_size,
bidirectional=False,
names=['batch', 'beam', 'hidden'])
def expand_beam(orig, collapse: bool = True):
if collapse:
return torch.repeat_interleave(orig, beam_size, dim='batch')
else:
return duplicate(orig, 'batch', beam_size, 'beam')
src_emb = expand_beam(src_emb)
src_outputs = expand_beam(src_outputs)
src_paddings = expand_beam(src_paddings)
max_lengths = (src_lengths.float() * 1.5).long()
max_lengths = expand_beam(max_lengths, collapse=False)
constants = BeamConstant(src_emb,
src_outputs,
src_paddings,
max_lengths,
lang_emb=lang_emb)
init_beam = Beam(0,
accum_scores,
tokens,
lstm_state,
constants,
prev_att=init_att)
hyps = super().search(init_beam)
return hyps
def extend(self, label_log_probs: FT):
num_labels = label_log_probs.size('label')
label_log_probs = label_log_probs.align_to('batch', 'beam', 'label')
new_hyp_log_probs = self.hyp_log_probs[-1].align_to(
'batch', 'beam', 'label') + label_log_probs
new_hyp_log_probs = new_hyp_log_probs.flatten(['beam', 'label'],
'beam_X_label')
top_values, top_inds = torch.topk(new_hyp_log_probs, g.beam_size,
'beam_X_label')
beam_ids = top_inds // num_labels
label_ids = top_inds % num_labels
self.beam_ids.append(beam_ids.rename(beam_X_label='beam'))
self.hyps.append(label_ids.rename(beam_X_label='beam'))
self.hyp_log_probs.append(top_values.rename(beam_X_label='beam'))
def search_by_probs(self, lengths: LT,
label_log_probs: FT) -> Tuple[LT, FT]:
max_length = lengths.max().item()
bs = label_log_probs.size('batch')
label_log_probs = label_log_probs.align_to('length', 'batch', 'label')
beam = Beam(bs)
for step in range(max_length):
__label_log_probs = label_log_probs[step]
# __lengths = lengths[step]
within_length = (step < lengths).align_as(
__label_log_probs) # __lengths
beam.extend(__label_log_probs * within_length.float())
beam.finish_search(lengths)
samples = beam.samples.rename(beam='sample')
sample_log_probs = beam.sample_log_probs.rename(beam='sample')
return samples, sample_log_probs
def search_by_probs(self, lengths: LT,
label_log_probs: FT) -> Tuple[LT, FT]:
max_length = lengths.max().item()
samples = get_tensor(
torch.LongTensor(list(product([B, I, O], repeat=max_length))))
samples.rename_('sample', 'length')
bs = label_log_probs.size('batch')
samples = samples.align_to('batch', 'sample',
'length').expand(bs, -1, -1)
sample_log_probs = label_log_probs.gather('label', samples)
with NoName(lengths):
length_mask = get_length_mask(lengths, max_length).rename(
'batch', 'length')
length_mask = length_mask.align_to(sample_log_probs)
sample_log_probs = (sample_log_probs *
length_mask.float()).sum(dim='length')
return samples, sample_log_probs
def _get_Wh_s(self, h_s: FT) -> FT:
sl, bs, ds = h_s.size()
with NoName(h_s):
Wh_s = h_s.reshape(sl * bs, -1).mm(self.Wa).view(sl, bs, -1)
return Wh_s
def _get_matches(self, extracted_word_repr: FT, unit_repr: FT,
viable_lens: LT, extracted_unit_ids: LT,
char_log_probs: FT) -> Matches:
ns = extracted_word_repr.size('viable')
len_w = extracted_word_repr.size('len_w')
nt = len(self.vocab_feat_matrix)
msl = extracted_word_repr.size('len_w')
mtl = self.vocab_feat_matrix.size('length')
# Compute cosine distances all at once: for each viable span, compare it against all units.
ctx_logits = extracted_word_repr @ unit_repr.t()
ctx_log_probs = ctx_logits.log_softmax(dim='unit').flatten(
['viable', 'len_w'], 'viable_X_len_w')
with NoName(char_log_probs, extracted_unit_ids):
global_log_probs = char_log_probs[extracted_unit_ids].rename(
'viable_X_len_w', 'unit')
weighted_log_probs = g.context_weight * ctx_log_probs + (
1.0 - g.context_weight) * global_log_probs
costs = -weighted_log_probs
# Name: viable x len_w x unit
costs = costs.unflatten('viable_X_len_w', [('viable', ns),
('len_w', len_w)])
# NOTE(j_luo) Use dictionary to save every state.
fs = dict()
for i in range(msl + 1):
fs[(i, 0)] = get_zeros(ns, nt).fill_(i * self.ins_del_cost)
for j in range(mtl + 1):
fs[(0, j)] = get_zeros(ns, nt).fill_(j * self.ins_del_cost)
# ------------------------ Main body: DP ----------------------- #
# Transition.
with NoName(self.indexed_segments, costs):
for ls in range(1, msl + 1):
min_lt = max(ls - 2, 1)
max_lt = min(ls + 2, mtl + 1)
for lt in range(min_lt, max_lt):
transitions = list()
if (ls - 1, lt) in fs:
transitions.append(fs[(ls - 1, lt)] +
self.ins_del_cost)
if (ls, lt - 1) in fs:
transitions.append(fs[(ls, lt - 1)] +
self.ins_del_cost)
if (ls - 1, lt - 1) in fs:
vocab_inds = self.indexed_segments[:, lt - 1]
sub_cost = costs[:, ls - 1, vocab_inds]
transitions.append(fs[(ls - 1, lt - 1)] + sub_cost)
if transitions:
all_s = torch.stack(transitions, dim=-1)
new_s, _ = all_s.min(dim=-1)
fs[(ls, lt)] = new_s
f_lst = list()
for i in range(msl + 1):
for j in range(mtl + 1):
if (i, j) not in fs:
fs[(i, j)] = get_zeros(ns, nt).fill_(9999.9)
f_lst.append(fs[(i, j)])
f = torch.stack(f_lst, dim=0).view(msl + 1, mtl + 1, -1,
len(self.vocab))
f.rename_('len_w_src', 'len_w_tgt', 'viable', 'vocab')
# Get the values wanted.
with NoName(f, viable_lens, self.vocab_length):
idx_src = viable_lens.unsqueeze(dim=-1)
idx_tgt = self.vocab_length
viable_i = get_range(ns, 2, 0)
vocab_i = get_range(len(self.vocab_length), 2, 1)
nll = f[idx_src, idx_tgt, viable_i, vocab_i]
nll.rename_('viable', 'vocab')
# Get the best spans.
matches = Matches(-nll, f)
return matches