Generate: length validation (#25384)

src/transformers/generation/utils.py

@@ -1245,6 +1245,52 @@ class GenerationMixin:
                 " generate arguments will also show up in this list)"
             )
 
+    def _validate_generated_length(self, generation_config, input_ids_length, has_default_max_length):
+        """Performs validation related to the resulting generated length"""
+
+        # 1. Max length warnings related to poor parameterization
+        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length != 20:
+            # 20 is the default max_length of the generation config
+            warnings.warn(
+                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) to control the"
+                "generation length. We recommend setting `max_new_tokens` to control the maximum length of the "
+                "generation.",
+                UserWarning,
+            )
+        if input_ids_length >= generation_config.max_length:
+            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            warnings.warn(
+                f"Input length of {input_ids_string} is {input_ids_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_new_tokens`.",
+                UserWarning,
+            )
+
+        # 2. Min length warnings due to unfeasible parameter combinations
+        min_length_error_suffix = (
+            " Generation will stop at the defined maximum length. You should decrease the minimum length and/or "
+            "increase the maximum length."
+        )
+        if has_default_max_length:
+            min_length_error_suffix += (
+                f" Note that `max_length` is set to {generation_config.max_length}, its default value."
+            )
+        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
+            warnings.warn(
+                f"Unfeasible length constraints: `min_length` ({generation_config.min_length}) is larger than"
+                f" the maximum possible length ({generation_config.max_length})." + min_length_error_suffix,
+                UserWarning,
+            )
+        if generation_config.min_new_tokens is not None:
+            min_length = generation_config.min_new_tokens + input_ids_length
+            if min_length > generation_config.max_length:
+                warnings.warn(
+                    f"Unfeasible length constraints: `min_new_tokens` ({generation_config.min_new_tokens}), when "
+                    f"added to the prompt length ({input_ids_length}), is larger than"
+                    f" the maximum possible length ({generation_config.max_length})." + min_length_error_suffix,
+                    UserWarning,
+                )
+
     @torch.no_grad()
     def generate(
         self,
@@ -1458,16 +1504,9 @@ class GenerationMixin:
             streamer.put(input_ids.cpu())
 
         # 6. Prepare `max_length` depending on other stopping criteria.
-        input_ids_seq_length = input_ids.shape[-1]
+        input_ids_length = input_ids.shape[-1]
         has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length != 20:
-            # 20 is the default max_length of the generation config
-            warnings.warn(
-                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
-                "to control the generation length.  recommend setting `max_new_tokens` to control the maximum length of the generation.",
-                UserWarning,
-            )
-        elif generation_config.max_new_tokens is not None:
+        if generation_config.max_new_tokens is not None:
             if not has_default_max_length:
                 logger.warning(
                     f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
@@ -1475,20 +1514,8 @@ class GenerationMixin:
                     "Please refer to the documentation for more information. "
                     "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
                 )
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-
-        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
-            raise ValueError(
-                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
-                f" the maximum length ({generation_config.max_length})"
-            )
-        if input_ids_seq_length >= generation_config.max_length:
-            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
-            logger.warning(
-                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
+            generation_config.max_length = generation_config.max_new_tokens + input_ids_length
+        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
 
         # 7. determine generation mode
         generation_mode = self._get_generation_mode(generation_config, assistant_model)
@@ -1512,7 +1539,7 @@ class GenerationMixin:
         # 8. prepare distribution pre_processing samplers
         logits_processor = self._get_logits_processor(
             generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
+            input_ids_seq_length=input_ids_length,
             encoder_input_ids=inputs_tensor,
             prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
             logits_processor=logits_processor,