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import unittest
from onmt.translate.greedy_search import GreedySearch
import torch
class GlobalScorerStub(object):
alpha = 0
beta = 0
def __init__(self):
self.length_penalty = lambda x, alpha: 1.0
self.cov_penalty = lambda cov, beta: torch.zeros(
(1, cov.shape[-2]), device=cov.device, dtype=torch.float
)
self.has_cov_pen = False
self.has_len_pen = False
def update_global_state(self, beam):
pass
def score(self, beam, scores):
return scores
class TestGreedySearch(unittest.TestCase):
BATCH_SZ = 3
INP_SEQ_LEN = 53
DEAD_SCORE = -1e20
BLOCKED_SCORE = -10e20
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([6.0, 5.0]), dim=0)
min_length = 5
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0,
1,
2,
3,
1,
batch_sz,
GlobalScorerStub(),
min_length,
False,
set(),
False,
30,
1.0,
1,
0,
1,
False,
)
samp.initialize(torch.zeros((1, 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 [1.0, 3.0]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(
torch.tensor([6.0, 5.0, 4.0, 3.0, 2.0, 1.0]), dim=0
)
valid_score_dist_2 = torch.log_softmax(torch.tensor([6.0, 1.0]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0,
1,
2,
3,
1,
batch_sz,
GlobalScorerStub(),
0,
False,
set(),
False,
30,
temp,
1,
0,
1,
False,
)
samp.initialize(torch.zeros((1, 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(
[score for score, _, _ in samp.hypotheses[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(
[score for score, _, _ in samp.hypotheses[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()
self.assertTrue(samp.done)
def test_returns_correct_scores_non_deterministic(self):
for batch_sz in [1, 13]:
for temp in [1.0, 3.0]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(
torch.tensor([6.0, 5.0, 4.0, 3.0, 2.0, 1.0]), dim=0
)
valid_score_dist_2 = torch.log_softmax(torch.tensor([6.0, 1.0]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0,
1,
2,
3,
1,
batch_sz,
GlobalScorerStub(),
0,
False,
set(),
False,
30,
temp,
2,
0,
1,
False,
)
samp.initialize(torch.zeros((1, 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.topk_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(
[score for score, _, _ in samp.hypotheses[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."
)
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 [1.0, 3.0]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(
torch.tensor([6.0, 5.0, 4.0, 3.0, 2.0, 1.0]), dim=0
)
valid_score_dist_2 = torch.log_softmax(torch.tensor([6.0, 1.0]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0,
1,
2,
3,
1,
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_topp(self):
for batch_sz in [1, 13]:
for temp in [1.0, 0.3]:
n_words = 100
_non_eos_idxs = [47, 51, 13, 88, 99]
valid_score_dist_1 = torch.log_softmax(
torch.tensor([6.0, 5.0, 4.0, 3.0, 2.0, 1.0]), dim=0
)
valid_score_dist_2 = torch.log_softmax(torch.tensor([6.0, 1.0]), dim=0)
eos_idx = 2
lengths = torch.randint(0, 30, (batch_sz,))
samp = GreedySearch(
0,
1,
2,
3,
1,
batch_sz,
GlobalScorerStub(),
0,
False,
set(),
False,
-1,
temp,
50,
0.5,
1,
False,
)
samp.initialize(torch.zeros((1, 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(
[score for score, _, _ in samp.hypotheses[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(200):
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(
[score for score, _, _ in samp.hypotheses[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."
)
self.assertTrue(samp.done)
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