Generate: fix default max length warning (#25539)

src/transformers/generation/flax_utils.py

@@ -377,7 +377,7 @@ class FlaxGenerationMixin:
         # Prepare `max_length` depending on other stopping criteria.
         input_ids_seq_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:
+        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}) "

src/transformers/generation/tf_utils.py

@@ -829,7 +829,7 @@ class TFGenerationMixin:
         # 7. Prepare `max_length` depending on other stopping criteria.
         input_ids_seq_length = shape_list(input_ids)[-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:
+        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}) "

src/transformers/generation/utils.py

@@ -1249,7 +1249,7 @@ class GenerationMixin:
         """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:
+        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"

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1300,7 +1300,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
         # 5. Prepare `max_length` depending on other stopping criteria.
         input_ids_seq_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:
+        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
             logger.warning(
                 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.",

tests/generation/test_utils.py

@@ -16,6 +16,7 @@
 
 import inspect
 import unittest
+import warnings
 
 import numpy as np
 
@@ -2844,3 +2845,28 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         with self.assertRaises(TypeError):
             # FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo"
             bart_model.generate(input_ids, foo="bar")
+
+    def test_default_max_length_warning(self):
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
+        model.config.pad_token_id = tokenizer.eos_token_id
+
+        text = "Hello world"
+        tokenized_inputs = tokenizer([text], return_tensors="pt")
+        input_ids = tokenized_inputs.input_ids.to(torch_device)
+
+        # Default generation config value of 20 -> emits warning
+        with self.assertWarns(UserWarning):
+            model.generate(input_ids)
+
+        # Explicitly setting max_length to 20 -> no warning
+        with warnings.catch_warnings(record=True) as warning_list:
+            model.generate(input_ids, max_length=20)
+            self.assertEqual(len(warning_list), 0)
+
+        # Generation config max_length != 20 -> no warning
+        with warnings.catch_warnings(record=True) as warning_list:
+            model.generation_config.max_length = 10
+            model.generation_config._from_model_config = False  # otherwise model.config.max_length=20 takes precedence
+            model.generate(input_ids)
+            self.assertEqual(len(warning_list), 0)