def _get_word_embedding_from_chars(self, emb: FT) -> FT:
"""Get word embeddings based on character embeddings."""
if emb.ndim == 4:
emb = emb.align_to('batch', 'word', 'emb', 'pos')
bs, ws, es, l = emb.shape
# NOTE(j_luo) embedding size might not match hidden size.
emb_3d = emb.rename(None).reshape(bs * ws, es, -1)
ret = self.cnn(emb_3d).max(dim=-1)[0]
return ret.view(bs, ws, -1).rename('batch', 'word', 'emb')
else:
emb = emb.align_to('word', 'emb', 'pos')
ret = self.cnn(emb.rename(None)).max(dim=-1)[0]
return ret.rename('word', 'emb')
return emb.mean(dim='pos')
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 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 _extract_one_span(self, batch: ExtractBatch, extracted: Extracted,
word_repr: FT, unit_repr: FT,
char_log_probs: FT) -> Extracted:
# Propose all span start/end positions.
start_candidates = get_named_range(batch.max_length, 'len_s').align_to(
'batch', 'len_s', 'len_e')
# Range from `min_word_length` to `max_word_length`.
len_candidates = get_named_range(
g.max_word_length + 1 - g.min_word_length,
'len_e') + g.min_word_length
len_candidates = len_candidates.align_to('batch', 'len_s', 'len_e')
# This is inclusive.
end_candidates = start_candidates + len_candidates - 1
# Only keep the viable/valid spans around.
viable = (end_candidates < batch.lengths.align_as(end_candidates))
start_candidates = start_candidates.expand_as(viable)
len_candidates = len_candidates.expand_as(viable)
# NOTE(j_luo) Use `viable` to get the lengths. `len_candidates` has dummy axes.
# IDEA(j_luo) Any better way of handling this? Perhaps persistent names?
len_s = viable.size('len_s')
len_e = viable.size('len_e')
bi = get_named_range(batch.batch_size, 'batch').expand_as(viable)
with NoName(start_candidates, end_candidates, len_candidates, bi,
viable):
viable_starts = start_candidates[viable].rename('viable')
viable_lens = len_candidates[viable].rename('viable')
viable_bi = bi[viable].rename('viable')
# Get the word positions to get the corresponding representations.
viable_starts = viable_starts.align_to('viable', 'len_w')
word_pos_offsets = get_named_range(g.max_word_length,
'len_w').align_as(viable_starts)
word_pos = viable_starts + word_pos_offsets
word_pos = word_pos.clamp(max=batch.max_length - 1)
# Get the corresponding representations.
nh = NameHelper()
viable_bi = viable_bi.expand_as(word_pos)
word_pos = nh.flatten(word_pos, ['viable', 'len_w'], 'viable_X_len_w')
viable_bi = nh.flatten(viable_bi, ['viable', 'len_w'],
'viable_X_len_w')
word_repr = word_repr.align_to('batch', 'length', 'char_emb')
if g.input_format == 'text':
with NoName(word_repr, viable_bi, word_pos, batch.unit_id_seqs):
extracted_word_repr = word_repr[viable_bi, word_pos].rename(
'viable_X_len_w', 'char_emb')
extracted_unit_ids = batch.unit_id_seqs[
viable_bi, word_pos].rename('viable_X_len_w')
else:
with NoName(word_repr, viable_bi, word_pos):
extracted_word_repr = word_repr[viable_bi, word_pos].rename(
'viable_X_len_w', 'char_emb')
extracted_unit_ids = None
extracted_word_repr = nh.unflatten(extracted_word_repr,
'viable_X_len_w',
['viable', 'len_w'])
# Main body: Run DP to find the best matches.
matches = self._get_matches(extracted_word_repr, unit_repr,
viable_lens, extracted_unit_ids,
char_log_probs)
# Revert to the old shape (so that invalid spans are included).
bi = get_named_range(batch.batch_size, 'batch').expand_as(viable)
lsi = get_named_range(len_s, 'len_s').expand_as(viable)
lei = get_named_range(len_e, 'len_e').expand_as(viable)
vs = matches.ll.size('vocab')
# IDEA(j_luo) NoName shouldn't make size() calls unavaiable. Otherwise size() calls have to be moved outside the context. Also the names should be preserved as well.
with NoName(bi, lsi, lei, viable, matches.ll):
v_bi = bi[viable]
v_lsi = lsi[viable]
v_lei = lei[viable]
all_ll = get_zeros(batch.batch_size, len_s, len_e, vs)
all_ll = all_ll.float().fill_(-9999.9)
all_ll[v_bi, v_lsi, v_lei] = matches.ll
matches.ll = all_ll.rename('batch', 'len_s', 'len_e', 'vocab')
new_extracted = Extracted(batch.batch_size, matches, viable,
len_candidates)
return new_extracted