Generate: text generation pipeline no longer emits `max_length` warning when it is not set (#23139)

src/transformers/generation/flax_utils.py

@@ -385,7 +385,6 @@ class FlaxGenerationMixin:
                 UserWarning,
             )
         elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
             if not has_default_max_length:
                 logger.warning(
                     f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
@@ -393,6 +392,7 @@ class FlaxGenerationMixin:
                     "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(

src/transformers/generation/tf_utils.py

@@ -858,7 +858,6 @@ class TFGenerationMixin:
                 UserWarning,
             )
         elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
             if not has_default_max_length:
                 logger.warning(
                     f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
@@ -866,6 +865,7 @@ class TFGenerationMixin:
                     "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 the input length is a tensor (i.e. dynamic length), skip length checks
         if not isinstance(input_ids_seq_length, tf.Tensor):

src/transformers/generation/utils.py

@@ -1348,7 +1348,6 @@ class GenerationMixin:
                 UserWarning,
             )
         elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
             if not has_default_max_length:
                 logger.warning(
                     f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
@@ -1356,6 +1355,7 @@ 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(

src/transformers/pipelines/text_generation.py

@@ -1,3 +1,4 @@
+import copy
 import enum
 import warnings
 
@@ -105,17 +106,8 @@ class TextGenerationPipeline(Pipeline):
             prefix_inputs = self.tokenizer(
                 prefix, padding=False, add_special_tokens=False, return_tensors=self.framework
             )
-            prefix_length = prefix_inputs["input_ids"].shape[-1]
+            generate_kwargs["prefix_length"] = prefix_inputs["input_ids"].shape[-1]
 
-            if "max_new_tokens" in generate_kwargs:
-                pass
-            elif "max_length" in generate_kwargs:
-                generate_kwargs["max_length"] += prefix_length
-            else:
-                generate_kwargs["max_length"] = self.model.config.max_length + prefix_length
-
-            if "min_length" in generate_kwargs:
-                generate_kwargs["min_length"] += prefix_length
         if handle_long_generation is not None:
             if handle_long_generation not in {"hole"}:
                 raise ValueError(
@@ -247,6 +239,26 @@ class TextGenerationPipeline(Pipeline):
         else:
             in_b = input_ids.shape[0]
         prompt_text = model_inputs.pop("prompt_text")
+
+        # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
+        # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
+        generate_kwargs = copy.deepcopy(generate_kwargs)
+        prefix_length = generate_kwargs.pop("prefix_length", 0)
+        if prefix_length > 0:
+            has_max_new_tokens = "max_new_tokens" in generate_kwargs or (
+                "generation_config" in generate_kwargs
+                and generate_kwargs["generation_config"].max_new_tokens is not None
+            )
+            if not has_max_new_tokens:
+                generate_kwargs["max_length"] = generate_kwargs.get("max_length") or self.model.config.max_length
+                generate_kwargs["max_length"] += prefix_length
+            has_min_new_tokens = "min_new_tokens" in generate_kwargs or (
+                "generation_config" in generate_kwargs
+                and generate_kwargs["generation_config"].min_new_tokens is not None
+            )
+            if not has_min_new_tokens and "min_length" in generate_kwargs:
+                generate_kwargs["min_length"] += prefix_length
+
         # BS x SL
         generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
         out_b = generated_sequence.shape[0]

tests/pipelines/test_pipelines_text_generation.py

@@ -14,8 +14,15 @@
 
 import unittest
 
-from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
+from transformers import (
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+    TextGenerationPipeline,
+    logging,
+    pipeline,
+)
 from transformers.testing_utils import (
+    CaptureLogger,
     is_pipeline_test,
     require_accelerate,
     require_tf,
@@ -323,3 +330,26 @@ class TextGenerationPipelineTests(unittest.TestCase):
 
         pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto", torch_dtype=torch.float16)
         pipe("This is a test", do_sample=True, top_p=0.5)
+
+    def test_pipeline_length_setting_warning(self):
+        prompt = """Hello world"""
+        text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2")
+        if text_generator.model.framework == "tf":
+            logger = logging.get_logger("transformers.generation.tf_utils")
+        else:
+            logger = logging.get_logger("transformers.generation.utils")
+        logger_msg = "Both `max_new_tokens`"  # The beggining of the message to be checked in this test
+
+        # Both are set by the user -> log warning
+        with CaptureLogger(logger) as cl:
+            _ = text_generator(prompt, max_length=10, max_new_tokens=1)
+        self.assertIn(logger_msg, cl.out)
+
+        # The user only sets one -> no warning
+        with CaptureLogger(logger) as cl:
+            _ = text_generator(prompt, max_new_tokens=1)
+        self.assertNotIn(logger_msg, cl.out)
+
+        with CaptureLogger(logger) as cl:
+            _ = text_generator(prompt, max_length=10)
+        self.assertNotIn(logger_msg, cl.out)