avoid errors when the size of input_ids passed to PrefixConstrainedLogitsProcessor is zero

[HiDolen]

What does this PR do?

PrefixConstrainedLogitsProcessor.__call__() does not account for the case where the size of the input input_ids is zero.

It can lead to an error when executing input_ids.view(-1, self._num_beams, input_ids.shape[-1]):

RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 1, 0] because the unspecified dimension size -1 can be any value and is ambiguous

Fixes noamgat/lm-format-enforcer#132

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@gante

[github-actions]

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[gante]

Hi @HiDolen 👋 Thank you for opening the PR!

Technically, we might want to apply prefix_allowed_tokens_fn() if input_ids is empty. Instead of providing an early return in that case, can we change how we iterate over the batch and the beam items so as to not hit this exception?

e.g.

for row_id in range(input_ids.shape[0]):
    batch_id = row_id // self._num_beams
    beam_id = row_id % self._num_beams
    sent = input_ids[row_id, ...]
    (...)

[HiDolen]

Hi @HiDolen 👋 Thank you for opening the PR!

Technically, we might want to apply prefix_allowed_tokens_fn() if input_ids is empty. Instead of providing an early return in that case, can we change how we iterate over the batch and the beam items so as to not hit this exception?

e.g.

for row_id in range(input_ids.shape[0]):
    batch_id = row_id // self._num_beams
    beam_id = row_id % self._num_beams
    sent = input_ids[row_id, ...]
    (...)

How adout this? Sorry for the late reply.

@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
    mask = torch.full_like(scores, -math.inf)
-   if input_ids.shape[-1] == 0:
-       return scores + mask
+   batch_size = input_ids.shape[0] // self._num_beams

-   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 batch_id in range(batch_size):
+       for beam_id in range(self._num_beams):
+           sent = input_ids[batch_id * self._num_beams + beam_id]
            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

    scores_processed = scores + mask
    return scores_processed

[gante]

Perfect, thank you for iterating and for improving transformers 🙌