Add hallucination filter (#18675)

* Add hallucination penalty

* Make quality changes

* Inverse penalty

* Fix imports & quality

* Fix name spelling issue

* set encoder_repetition_penalty and fix quality

* Fix failing test

* Add to config_common_kwargs

* Fix modelling_rag error

* Update src/transformers/generation_logits_process.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Remove breakpoint

* Make style fixes

* Update encoder_repetition_penalty default value

* Merge latest main changes

* Make fixup changes

* Add EncoderRepetitionPenaltyLogitsProcessor to generation/__init__.py

* Fix repo-inconsistency

* Remove venv

* Remove tensorflow-macos & add tests

* Add documentation

* Fix quality issues

* move encoder_repetition_penalty to config

* Update src/transformers/configuration_utils.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/generation/configuration_utils.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Remove encoder_repetition_penalty from tests

* Fix type error

* Fix format error

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

tests/generation/test_logits_process.py

@@ -28,6 +28,7 @@ if is_torch_available():
 
     from transformers.generation import (
         EncoderNoRepeatNGramLogitsProcessor,
+        EncoderRepetitionPenaltyLogitsProcessor,
         EpsilonLogitsWarper,
         EtaLogitsWarper,
         ExponentialDecayLengthPenalty,
@@ -175,6 +176,31 @@ class LogitsProcessorTest(unittest.TestCase):
         self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2)
         self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2)
 
+    def test_encoder_repetition_penalty_dist_process(self):
+        input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long)
+        vocab_size = 10
+
+        scores = self._get_uniform_logits(batch_size=2, length=vocab_size)
+
+        # give values special values
+        scores[0, 0] = -(1 / vocab_size)
+        scores[1, 5] = 4 / vocab_size
+
+        rep_penalty_proc = EncoderRepetitionPenaltyLogitsProcessor(penalty=2.0, encoder_input_ids=input_ids)
+
+        scores = rep_penalty_proc(input_ids, scores.clone())
+
+        # check that values were correctly changed
+        self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) / 2)
+        self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) * 2)
+
+        self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) * 2)
+        self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) * 2)
+
+        # check that values not in the encoder ids were NOT changed
+        self.assertAlmostEqual(scores[0, 2].item(), (1 / vocab_size))
+        self.assertAlmostEqual(scores[1, 2].item(), (1 / vocab_size))
+
     def test_top_k_dist_warper(self):
         input_ids = None
         vocab_size = 10