Raise a clear error for empty token lists in bad_words_ids / sequence_bias

src/transformers/generation/logits_process.py

@@ -1376,6 +1376,7 @@ def _validate_arguments(self):
         def all_token_bias_pairs_are_valid(sequence):
             return (
                 isinstance(sequence[0], list)
+                and len(sequence[0]) > 0
                 and all(isinstance(token_id, (int, np.integer)) and token_id > 0 for token_id in sequence[0])
                 and isinstance(sequence[1], float)
             )
@@ -1477,6 +1478,8 @@ def _validate_arguments(self):
             raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
         if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
             raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
+        if any(len(bad_word_ids) == 0 for bad_word_ids in bad_words_ids):
+            raise ValueError(f"`bad_words_ids` has to be a list of non-empty lists, but is {bad_words_ids}.")
         if any(
             any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
             for bad_word_ids in bad_words_ids

tests/generation/test_logits_process.py

@@ -743,6 +743,17 @@ def test_no_bad_words_dist_processor(self):
         filtered_scores = no_bad_words_dist_proc(input_ids, scores)
         torch.testing.assert_close(scores, filtered_scores, rtol=1e-3, atol=1e-3)
 
+    def test_bad_words_and_sequence_bias_reject_empty_token_list(self):
+        # An empty token list (e.g. from `tokenizer("", add_special_tokens=False).input_ids`) must be rejected at
+        # construction with a clear `ValueError`, rather than crashing later with an `IndexError` during generation.
+        eos_token_id = 4
+        with self.assertRaises(ValueError):
+            NoBadWordsLogitsProcessor(bad_words_ids=[[]], eos_token_id=eos_token_id)
+        with self.assertRaises(ValueError):
+            NoBadWordsLogitsProcessor(bad_words_ids=[[], [3]], eos_token_id=eos_token_id)
+        with self.assertRaises(ValueError):
+            SequenceBiasLogitsProcessor(sequence_bias=[[[], 1.0]])
+
     def test_bias_dist_processor(self):
         vocab_size = 5
         batch_size = 2