Unverified Commit 56be5e80 authored by Saibo-creator's avatar Saibo-creator Committed by GitHub
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Fix: Raise informative exception when `prefix_allowed_tokens_fn` return empty...


Fix: Raise informative exception when `prefix_allowed_tokens_fn` return empty set of tokens (#27797)
Co-authored-by: default avatarArthur <48595927+ArthurZucker@users.noreply.github.com>
parent 307a7d0b
...@@ -1229,7 +1229,14 @@ class PrefixConstrainedLogitsProcessor(LogitsProcessor): ...@@ -1229,7 +1229,14 @@ class PrefixConstrainedLogitsProcessor(LogitsProcessor):
mask = torch.full_like(scores, -math.inf) mask = torch.full_like(scores, -math.inf)
for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])): for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
for beam_id, sent in enumerate(beam_sent): for beam_id, sent in enumerate(beam_sent):
mask[batch_id * self._num_beams + beam_id, self._prefix_allowed_tokens_fn(batch_id, sent)] = 0 prefix_allowed_tokens = self._prefix_allowed_tokens_fn(batch_id, sent)
if len(prefix_allowed_tokens) == 0:
raise ValueError(
f"`prefix_allowed_tokens_fn` returned an empty list for batch ID {batch_id}."
f"This means that the constraint is unsatisfiable. Please check your implementation"
f"of `prefix_allowed_tokens_fn` "
)
mask[batch_id * self._num_beams + beam_id, prefix_allowed_tokens] = 0
return scores + mask return scores + mask
......
...@@ -610,6 +610,13 @@ class LogitsProcessorTest(unittest.TestCase): ...@@ -610,6 +610,13 @@ class LogitsProcessorTest(unittest.TestCase):
torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]] torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]]
) )
def empty_prefix_allowed_tokens_fn(batch_id, inputs_ids):
return []
prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(empty_prefix_allowed_tokens_fn, 1)
self.assertRaises(ValueError, prefix_constrained_logits_proc, input_ids, scores.clone())
def test_hamming_diversity(self): def test_hamming_diversity(self):
vocab_size = 4 vocab_size = 4
num_beams = 2 num_beams = 2
......
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