Skip warning if tracing with dynamo (#25581)

* Ignore warning if tracing with dynamo

* fix import error

* separate to function

* add test

src/transformers/modeling_utils.py

@@ -81,7 +81,12 @@ from .utils import (
     strtobool,
 )
 from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
-from .utils.import_utils import ENV_VARS_TRUE_VALUES, is_sagemaker_mp_enabled, is_torch_fx_proxy
+from .utils.import_utils import (
+    ENV_VARS_TRUE_VALUES,
+    is_sagemaker_mp_enabled,
+    is_torch_fx_proxy,
+    is_torchdynamo_compiling,
+)
 from .utils.quantization_config import BitsAndBytesConfig, GPTQConfig, QuantizationMethod
 from .utils.versions import require_version_core
 
@@ -3799,7 +3804,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         """
 
         # Skip the check during tracing.
-        if is_torch_fx_proxy(input_ids) or torch.jit.is_tracing():
+        if is_torch_fx_proxy(input_ids) or torch.jit.is_tracing() or is_torchdynamo_compiling():
             return
 
         if (attention_mask is not None) or (self.config.pad_token_id is None):

src/transformers/utils/import_utils.py

@@ -463,6 +463,17 @@ def is_torch_compile_available():
     return hasattr(torch, "compile")
 
 
+def is_torchdynamo_compiling():
+    if not is_torch_available():
+        return False
+    try:
+        import torch._dynamo as dynamo  # noqa: F401
+
+        return dynamo.is_compiling()
+    except Exception:
+        return False
+
+
 def is_torch_tensorrt_fx_available():
     if importlib.util.find_spec("torch_tensorrt") is None:
         return False

tests/test_modeling_utils.py

@@ -55,6 +55,7 @@ from transformers.utils import (
     WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
 )
+from transformers.utils.import_utils import is_torchdynamo_available
 
 
 sys.path.append(str(Path(__file__).parent.parent / "utils"))
@@ -1014,6 +1015,25 @@ class ModelUtilsTest(TestCasePlus):
                 model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
             self.assertIn("You may ignore this warning if your `pad_token_id`", cl.out)
 
+        if not is_torchdynamo_available():
+            return
+        with self.subTest("Ensure that the warning code is skipped when compiling with torchdynamo."):
+            logger.warning_once.cache_clear()
+            from torch._dynamo import config, testing
+
+            config = PretrainedConfig()
+            config.pad_token_id = 0
+            model = ModelWithHead(config)
+            input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]])
+
+            def f(input_ids):
+                model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
+
+            compile_counter = testing.CompileCounter()
+            opt_fn = torch.compile(f, dynamic=True, backend=compile_counter)
+            opt_fn(input_ids)
+            self.assertEqual(compile_counter.frame_count, 0)
+
     @require_torch_gpu
     @slow
     def test_pretrained_low_mem_new_config(self):