def test_doesnt_predict_eos_if_shorter_than_min_len(self):
# batch 0 will always predict EOS. The other batches will predict
# non-eos scores.
for batch_sz in [1, 3]:
n_words = 100
_non_eos_idxs = [47]
valid_score_dist = torch.log_softmax(torch.tensor(
[6., 5.]), dim=0)
min_length = 5
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0, 1, 2, batch_sz, min_length,
False, set(), False, 30, 1., 1)
samp.initialize(torch.zeros(1), lengths)
all_attns = []
for i in range(min_length + 4):
word_probs = torch.full(
(batch_sz, n_words), -float('inf'))
# "best" prediction is eos - that should be blocked
word_probs[0, eos_idx] = valid_score_dist[0]
# include at least one prediction OTHER than EOS
# that is greater than -1e20
word_probs[0, _non_eos_idxs[0]] = valid_score_dist[1]
word_probs[1:, _non_eos_idxs[0] + i] = 0
attns = torch.randn(1, batch_sz, 53)
all_attns.append(attns)
samp.advance(word_probs, attns)
if i < min_length:
self.assertTrue(
samp.topk_scores[0].allclose(valid_score_dist[1]))
self.assertTrue(
samp.topk_scores[1:].eq(0).all())
elif i == min_length:
# now batch 0 has ended and no others have
self.assertTrue(samp.is_finished[0, :].eq(1).all())
self.assertTrue(samp.is_finished[1:, 1:].eq(0).all())
else: # i > min_length
break
def test_returns_correct_scores_deterministic(self):
for batch_sz in [1, 13]:
for temp in [1., 3.]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(torch.tensor(
[6., 5., 4., 3., 2., 1.]), dim=0)
valid_score_dist_2 = torch.log_softmax(torch.tensor(
[6., 1.]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0, 1, 2, batch_sz, 0,
False, set(), False, 30, temp, 1)
samp.initialize(torch.zeros(1), lengths)
# initial step
i = 0
word_probs = torch.full(
(batch_sz, n_words), -float('inf'))
# batch 0 dies on step 0
word_probs[0, eos_idx] = valid_score_dist_1[0]
# include at least one prediction OTHER than EOS
# that is greater than -1e20
word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]
word_probs[1:, _non_eos_idxs[0] + i] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
self.assertTrue(samp.is_finished[0].eq(1).all())
samp.update_finished()
self.assertEqual(
samp.scores[0], [valid_score_dist_1[0] / temp])
if batch_sz == 1:
self.assertTrue(samp.done)
continue
else:
self.assertFalse(samp.done)
# step 2
i = 1
word_probs = torch.full(
(batch_sz - 1, n_words), -float('inf'))
# (old) batch 8 dies on step 1
word_probs[7, eos_idx] = valid_score_dist_2[0]
word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2
word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
self.assertTrue(samp.is_finished[7].eq(1).all())
samp.update_finished()
self.assertEqual(
samp.scores[8], [valid_score_dist_2[0] / temp])
# step 3
i = 2
word_probs = torch.full(
(batch_sz - 2, n_words), -float('inf'))
# everything dies
word_probs[:, eos_idx] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
self.assertTrue(samp.is_finished.eq(1).all())
samp.update_finished()
for b in range(batch_sz):
if b != 0 and b != 8:
self.assertEqual(samp.scores[b], [0])
self.assertTrue(samp.done)
def test_returns_correct_scores_non_deterministic_beams(self):
beam_size = 10
for batch_sz in [1, 13]:
for temp in [1., 3.]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(torch.tensor(
[6., 5., 4., 3., 2., 1.]),
dim=0)
valid_score_dist_2 = torch.log_softmax(torch.tensor([6., 1.]),
dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz, ))
samp = GreedySearch(0, 1, 2, 3,
batch_sz, GlobalScorerStub(), 0, False,
set(), False, 30, temp, 50, 0, beam_size,
False)
samp.initialize(torch.zeros((1, 1)), lengths)
# initial step
# finish one beam
i = 0
for _ in range(100):
word_probs = torch.full((batch_sz * beam_size, n_words),
-float('inf'))
word_probs[beam_size - 2, eos_idx] = valid_score_dist_1[0]
# include at least one prediction OTHER than EOS
# that is greater than -1e20
word_probs[beam_size - 2,
_non_eos_idxs] = valid_score_dist_1[1:]
word_probs[beam_size - 2 + 1:, _non_eos_idxs[0] + i] = 0
word_probs[:beam_size - 2, _non_eos_idxs[0] + i] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if samp.is_finished[beam_size - 2].eq(1).all():
self.assertFalse(samp.is_finished[:beam_size -
2].eq(1).any())
self.assertFalse(samp.is_finished[beam_size - 2 +
1].eq(1).any())
break
else:
self.fail("Batch 0 never ended (very unlikely but maybe "
"due to stochasticisty. If so, please increase "
"the range of the for-loop.")
samp.update_finished()
self.assertEqual([samp.topk_scores[beam_size - 2]],
[valid_score_dist_1[0] / temp])
# step 2
# finish example in last batch
i = 1
for _ in range(100):
word_probs = torch.full(
(batch_sz * beam_size - 1, n_words), -float('inf'))
# (old) batch 8 dies on step 1
word_probs[(batch_sz - 1) * beam_size + 7,
eos_idx] = valid_score_dist_2[0]
word_probs[:(batch_sz - 1) * beam_size + 7,
_non_eos_idxs[:2]] = valid_score_dist_2
word_probs[(batch_sz - 1) * beam_size + 8:,
_non_eos_idxs[:2]] = valid_score_dist_2
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if (samp.is_finished[(batch_sz - 1) * beam_size +
7].eq(1).all()):
break
else:
self.fail("Batch 8 never ended (very unlikely but maybe "
"due to stochasticisty. If so, please increase "
"the range of the for-loop.")
samp.update_finished()
self.assertEqual([
score for score, _, _ in samp.hypotheses[batch_sz - 1][-1:]
], [valid_score_dist_2[0] / temp])
# step 3
i = 2
for _ in range(250):
word_probs = torch.full((samp.alive_seq.shape[0], n_words),
-float('inf'))
# everything dies
word_probs[:, eos_idx] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if samp.is_finished.any():
samp.update_finished()
if samp.is_finished.eq(1).all():
break
else:
self.fail("All batches never ended (very unlikely but "
"maybe due to stochasticisty. If so, please "
"increase the range of the for-loop.")
self.assertTrue(samp.done)
def test_returns_correct_scores_non_deterministic(self):
for batch_sz in [1, 13]:
for temp in [1., 3.]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(torch.tensor(
[6., 5., 4., 3., 2., 1.]), dim=0)
valid_score_dist_2 = torch.log_softmax(torch.tensor(
[6., 1.]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0, 1, 2, batch_sz, 0,
False, set(), False, 30, temp, 2)
samp.initialize(torch.zeros(1), lengths)
# initial step
i = 0
for _ in range(100):
word_probs = torch.full(
(batch_sz, n_words), -float('inf'))
# batch 0 dies on step 0
word_probs[0, eos_idx] = valid_score_dist_1[0]
# include at least one prediction OTHER than EOS
# that is greater than -1e20
word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]
word_probs[1:, _non_eos_idxs[0] + i] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if samp.is_finished[0].eq(1).all():
break
else:
self.fail("Batch 0 never ended (very unlikely but maybe "
"due to stochasticisty. If so, please increase "
"the range of the for-loop.")
samp.update_finished()
self.assertEqual(
samp.scores[0], [valid_score_dist_1[0] / temp])
if batch_sz == 1:
self.assertTrue(samp.done)
continue
else:
self.assertFalse(samp.done)
# step 2
i = 1
for _ in range(100):
word_probs = torch.full(
(batch_sz - 1, n_words), -float('inf'))
# (old) batch 8 dies on step 1
word_probs[7, eos_idx] = valid_score_dist_2[0]
word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2
word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if samp.is_finished[7].eq(1).all():
break
else:
self.fail("Batch 8 never ended (very unlikely but maybe "
"due to stochasticisty. If so, please increase "
"the range of the for-loop.")
samp.update_finished()
self.assertEqual(
samp.scores[8], [valid_score_dist_2[0] / temp])
# step 3
i = 2
for _ in range(250):
word_probs = torch.full(
(samp.alive_seq.shape[0], n_words), -float('inf'))
# everything dies
word_probs[:, eos_idx] = 0
attns = torch.randn(1, batch_sz, 53)
samp.advance(word_probs, attns)
if samp.is_finished.any():
samp.update_finished()
if samp.is_finished.eq(1).all():
break
else:
self.fail("All batches never ended (very unlikely but "
"maybe due to stochasticisty. If so, please "
"increase the range of the for-loop.")
for b in range(batch_sz):
if b != 0 and b != 8:
self.assertEqual(samp.scores[b], [0])
self.assertTrue(samp.done)
def _sample_batch_with_strategy(self, batch,
decode_strategy: GreedySearch):
usr_src_map = False
parallel_paths = decode_strategy.parallel_paths
src, enc_states, memory_bank, src_lengths = self._run_encoder(
batch=batch)
self.model.decoder.init_state(src, memory_bank, enc_states)
results = {
"scores": None,
"predictions": None,
"attention": None,
}
src_map = batch.src_map if usr_src_map else None
fn_map_state, memory_bank, memory_lengths, src_map = decode_strategy.initialize(
memory_bank, src_lengths, src_map)
if fn_map_state is not None:
self.model.decoder.map_state(fn_map_state)
for step in range(decode_strategy.max_length):
decoder_input = decode_strategy.current_predictions.view(1, -1, 1)
log_probs, attn = self._decoder_and_generate(
decoder_input,
memory_bank,
batch,
memory_lengths=memory_lengths,
src_map=src_map,
step=step,
batch_offset=decode_strategy.batch_offset)
decode_strategy.advance(log_probs, attn)
any_finished = decode_strategy.is_finished.any()
if any_finished:
decode_strategy.update_finished()
if decode_strategy.done:
break
select_indices = decode_strategy.select_indices
if any_finished:
# Reorder states.
if isinstance(memory_bank, tuple):
memory_bank = tuple(
x.index_select(1, select_indices) for x in memory_bank)
else:
memory_bank = memory_bank.index_select(1, select_indices)
memory_lengths = memory_lengths.index_select(0, select_indices)
if src_map is not None:
src_map = src_map.index_select(1, select_indices)
if parallel_paths > 1 or any_finished:
self.model.decoder.map_state(
lambda state, dim: state.index_select(dim, select_indices))
# results['log_probs'] = log_probs
results['scores'] = decode_strategy.scores
results['predictions'] = decode_strategy.predictions
# predictions is a list which its len is same as batch_size
results['attention'] = decode_strategy.attention
return results