Fix decoding score comparison when using logits processors or warpers (…

…huggingface#10638)

* Normalize using a logits warper

* Add a flag in `generate` to support the logit renormalization

* Add in RAG

src/transformers/generation_logits_process.py

@@ -679,3 +679,16 @@ def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Float
                 self.regulation_factor, cur_len - self.regulation_start
             )
         return scores
+
+
+class LogitNormalization(LogitsProcessor, LogitsWarper):
+    r"""
+    [`LogitsWarper`] and [`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
+    the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
+    this library doesn't do it (it only does it before, but they may need re-normalization) but it still supposes that
+    the scores are normalized when comparing the hypotheses.
+    """
+
+    def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
+        scores = scores.log_softmax(dim=-1)
+        return scores

src/transformers/generation_utils.py

@@ -32,6 +32,7 @@
     ForcedEOSTokenLogitsProcessor,
     HammingDiversityLogitsProcessor,
     InfNanRemoveLogitsProcessor,
+    LogitNormalization,
     LogitsProcessorList,
     MinLengthLogitsProcessor,
     NoBadWordsLogitsProcessor,
@@ -636,6 +637,7 @@ def _get_logits_warper(
         typical_p: Optional[float] = None,
         temperature: Optional[float] = None,
         num_beams: Optional[int] = None,
+        renormalize_logits: Optional[bool] = None,
     ) -> LogitsProcessorList:
         """
         This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`] instances
@@ -660,6 +662,9 @@ def _get_logits_warper(
             warpers.append(TopPLogitsWarper(top_p=top_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
         if typical_p is not None and typical_p < 1.0:
             warpers.append(TypicalLogitsWarper(mass=typical_p, min_tokens_to_keep=(2 if num_beams > 1 else 1)))
+        # `LogitNormalization` should always be the last logit processor, when present
+        if renormalize_logits is True:
+            warpers.append(LogitNormalization())
         return warpers
 
     def _get_logits_processor(
@@ -682,6 +687,7 @@ def _get_logits_processor(
         remove_invalid_values: bool,
         exponential_decay_length_penalty: Tuple,
         logits_processor: Optional[LogitsProcessorList],
+        renormalize_logits: Optional[bool],
     ) -> LogitsProcessorList:
         """
         This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
@@ -754,6 +760,9 @@ def _get_logits_processor(
                 ExponentialDecayLengthPenalty(exponential_decay_length_penalty, eos_token_id, input_ids_seq_length)
             )
         processors = self._merge_criteria_processor_list(processors, logits_processor)
+        # `LogitNormalization` should always be the last logit processor, when present
+        if renormalize_logits is True:
+            processors.append(LogitNormalization())
         return processors
 
     def _get_stopping_criteria(
@@ -858,6 +867,7 @@ def generate(
         diversity_penalty: Optional[float] = None,
         prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
         logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
+        renormalize_logits: Optional[bool] = None,
         stopping_criteria: Optional[StoppingCriteriaList] = StoppingCriteriaList(),
         constraints: Optional[List[Constraint]] = None,
         output_attentions: Optional[bool] = None,
@@ -986,6 +996,10 @@ def generate(
                  Custom logits processors that complement the default logits processors built from arguments and a
                  model's config. If a logit processor is passed that is already created with the arguments or a model's
                  config an error is thrown. This feature is intended for advanced users.
+            renormalize_logits: (`bool`, *optional*, defaults to `False`):
+                Whether to renormalize the logits after applying all the logits processors or warpers (including the
+                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
+                score logits are normalized but some logit processors or warpers break the normalization.
             stopping_criteria (`StoppingCriteriaList`, *optional*):
                  Custom stopping criteria that complement the default stopping criteria built from arguments and a
                  model's config. If a stopping criteria is passed that is already created with the arguments or a
@@ -1241,6 +1255,7 @@ def generate(
             remove_invalid_values=remove_invalid_values,
             exponential_decay_length_penalty=exponential_decay_length_penalty,
             logits_processor=logits_processor,
+            renormalize_logits=renormalize_logits,
         )
 
         # 8. prepare stopping criteria
@@ -1271,7 +1286,12 @@ def generate(
         elif is_sample_gen_mode:
             # 10. prepare logits warper
             logits_warper = self._get_logits_warper(
-                top_k=top_k, top_p=top_p, typical_p=typical_p, temperature=temperature, num_beams=num_beams
+                top_k=top_k,
+                top_p=top_p,
+                typical_p=typical_p,
+                temperature=temperature,
+                num_beams=num_beams,
+                renormalize_logits=renormalize_logits,
             )
 
             # 11. expand input_ids with `num_return_sequences` additional sequences per batch
@@ -1333,7 +1353,12 @@ def generate(
         elif is_beam_sample_gen_mode:
             # 10. prepare logits warper
             logits_warper = self._get_logits_warper(
-                top_k=top_k, top_p=top_p, typical_p=typical_p, temperature=temperature, num_beams=num_beams
+                top_k=top_k,
+                top_p=top_p,
+                typical_p=typical_p,
+                temperature=temperature,
+                num_beams=num_beams,
+                renormalize_logits=renormalize_logits,
             )
 
             if stopping_criteria.max_length is None:

src/transformers/models/rag/modeling_rag.py

@@ -1400,6 +1400,7 @@ def generate(
         n_docs: Optional[int] = None,
         prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]] = None,
         logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
+        renormalize_logits: Optional[bool] = None,
         stopping_criteria: Optional[StoppingCriteriaList] = StoppingCriteriaList(),
         forced_bos_token_id: Optional[int] = None,
         forced_eos_token_id: Optional[int] = None,
@@ -1624,6 +1625,7 @@ def extend_enc_output(tensor, num_beams=None):
             remove_invalid_values=remove_invalid_values,
             exponential_decay_length_penalty=exponential_decay_length_penalty,
             logits_processor=logits_processor,
+            renormalize_logits=renormalize_logits,
         )
 
         if num_beams == 1:

tests/generation/test_generation_logits_process.py

@@ -33,6 +33,7 @@
         ForcedEOSTokenLogitsProcessor,
         HammingDiversityLogitsProcessor,
         InfNanRemoveLogitsProcessor,
+        LogitNormalization,
         LogitsProcessorList,
         MinLengthLogitsProcessor,
         NoBadWordsLogitsProcessor,
@@ -537,3 +538,18 @@ def test_exponential_decay_length_penalty(self):
                 scores_after_start[penalty_start + 1 :, eos_token_id], scores[penalty_start + 1 :, eos_token_id]
             ).all()
         )
+
+    def test_normalization(self):
+        input_ids = None
+
+        scores = torch.tensor(
+            [[-23.18, -29.96, -43.54, 47.77], [-33.58, -26.87, -32.96, 22.51]], device=torch_device, dtype=torch.float
+        )
+
+        logit_normalization = LogitNormalization()
+        normalized_scores = logit_normalization(input_ids, scores).exp()
+
+        ones = torch.ones(scores.shape[0], device=torch_device, dtype=torch.float)
+        self.assertTrue(normalized_scores.sum(dim=-1).allclose(ones))
+
+        self.assertTrue(normalized_scores.allclose(scores.softmax(dim=-1)))