Whisper: fix prompted max length (#24666)

src/transformers/generation/stopping_criteria.py

@@ -6,7 +6,10 @@ from typing import Optional
 
 import torch
 
-from ..utils import add_start_docstrings
+from ..utils import add_start_docstrings, logging
+
+
+logger = logging.get_logger(__name__)
 
 
 STOPPING_CRITERIA_INPUTS_DOCSTRING = r"""
@@ -46,14 +49,25 @@ class MaxLengthCriteria(StoppingCriteria):
     Args:
         max_length (`int`):
             The maximum length that the output sequence can have in number of tokens.
+        max_position_embeddings (`int`, `optional`):
+            The maximum model length, as defined by the model's `config.max_position_embeddings` attribute.
     """
 
-    def __init__(self, max_length: int):
+    def __init__(self, max_length: int, max_position_embeddings: Optional[int] = None):
         self.max_length = max_length
+        self.max_position_embeddings = max_position_embeddings
 
     @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        return input_ids.shape[-1] >= self.max_length
+        cur_len = input_ids.shape[-1]
+        is_done = cur_len >= self.max_length
+        if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
+            logger.warning_once(
+                "This is a friendly reminder - the current text generation call will exceed the model's predefined "
+                f"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
+                "exceptions, performance degradation, or nothing at all."
+            )
+        return is_done
 
 
 class MaxNewTokensCriteria(StoppingCriteria):

src/transformers/generation/utils.py

@@ -954,7 +954,13 @@ class GenerationMixin:
     ) -> StoppingCriteriaList:
         criteria = StoppingCriteriaList()
         if generation_config.max_length is not None:
-            criteria.append(MaxLengthCriteria(max_length=generation_config.max_length))
+            max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
+            criteria.append(
+                MaxLengthCriteria(
+                    max_length=generation_config.max_length,
+                    max_position_embeddings=max_position_embeddings,
+                )
+            )
         if generation_config.max_time is not None:
             criteria.append(MaxTimeCriteria(max_time=generation_config.max_time))
         criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)

src/transformers/models/whisper/modeling_whisper.py

@@ -1715,11 +1715,9 @@ class WhisperForConditionalGeneration(WhisperPreTrainedModel):
             # Set the decoder_start_token_id to <|startofprev|>
             kwargs.update({"decoder_start_token_id": decoder_start_token_id})
 
-            # Update the max generation length to include the prompt
-            specified_max_length = kwargs.pop("max_new_tokens", None) or kwargs.pop("max_length", None)
-            default_max_length = generation_config.max_new_tokens or generation_config.max_length
-            non_prompt_max_length = specified_max_length or default_max_length
-            kwargs["max_new_tokens"] = non_prompt_max_length + len(text_prompt_ids)
+            # If the user passes `max_new_tokens`, increase its number to account for the prompt
+            if kwargs.get("max_new_tokens", None) is not None:
+                kwargs["max_new_tokens"] += len(text_prompt_ids)
 
             # Reformat the forced_decoder_ids to incorporate the prompt
             non_prompt_forced_decoder_ids = (