CI: fix efficientnet pipeline timeout and prevent future similar issues due to large image size (#33123)

* fix param not being passed in tested; add exceptions

* better source of model name

* Update utils/create_dummy_models.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

tests/models/efficientnet/test_modeling_efficientnet.py

@@ -83,6 +83,7 @@ class EfficientNetModelTester:
 
     def get_config(self):
         return EfficientNetConfig(
+            image_size=self.image_size,
             num_channels=self.num_channels,
             kernel_sizes=self.kernel_sizes,
             in_channels=self.in_channels,

tests/utils/tiny_model_summary.json

@@ -1718,7 +1718,7 @@
         "model_classes": [
             "EfficientNetForImageClassification"
         ],
-        "sha": "6ed195ee636d2c0b885139da8c7b45d57ebaeee0"
+        "sha": "993d088cf937b8a90b61f68677cd8f261321c745"
     },
     "EfficientNetModel": {
         "tokenizer_classes": [],
@@ -7243,4 +7243,4 @@
         ],
         "sha": "e144d9f1fe39c21eda1177702640e126892605ce"
     }
-}
+}

utils/create_dummy_models.py

@@ -504,6 +504,27 @@ def convert_feature_extractor(feature_extractor, tiny_config):
     if to_convert:
         feature_extractor = feature_extractor.__class__(**kwargs)
 
+    # Sanity check: on tiny image feature extractors, a large image size results in slow CI -- up to the point where it
+    # can result in timeout issues.
+    if (
+        isinstance(feature_extractor, BaseImageProcessor)
+        and hasattr(feature_extractor, "size")
+        and isinstance(feature_extractor.size, dict)
+    ):
+        largest_image_size = max(feature_extractor.size.values())
+        if largest_image_size > 64:
+            # hardcoded exceptions
+            models_with_large_image_size = ("deformable_detr", "flava", "grounding_dino", "mgp_str", "swiftformer")
+            if any(model_name in tiny_config.model_type for model_name in models_with_large_image_size):
+                pass
+            else:
+                raise ValueError(
+                    f"Image size of {tiny_config.model_type} is too large ({feature_extractor.size}). "
+                    "Please reduce it to 64 or less on each dimension. The following steps are usually the "
+                    "easiest solution: 1) confirm that you're setting `image_size` in your ModelTester class; "
+                    "2) ensure that it gets passed to the tester config init, `get_config()`."
+                )
+
     return feature_extractor
 
 
@@ -526,14 +547,14 @@ def convert_processors(processors, tiny_config, output_folder, result):
         # sanity check 1: fast and slow tokenizers should be compatible (vocab_size)
         if fast_tokenizer is not None and slow_tokenizer is not None:
             if fast_tokenizer.vocab_size != slow_tokenizer.vocab_size:
-                warning_messagae = (
+                warning_message = (
                     "The fast/slow tokenizers "
                     f"({fast_tokenizer.__class__.__name__}/{slow_tokenizer.__class__.__name__}) have different "
                     "vocabulary size: "
                     f"fast_tokenizer.vocab_size = {fast_tokenizer.vocab_size} and "
                     f"slow_tokenizer.vocab_size = {slow_tokenizer.vocab_size}."
                 )
-                result["warnings"].append(warning_messagae)
+                result["warnings"].append(warning_message)
                 if not keep_fast_tokenizer:
                     fast_tokenizer = None
                 slow_tokenizer = None
@@ -541,12 +562,12 @@ def convert_processors(processors, tiny_config, output_folder, result):
         # sanity check 2: fast and slow tokenizers should be compatible (length)
         if fast_tokenizer is not None and slow_tokenizer is not None:
             if len(fast_tokenizer) != len(slow_tokenizer):
-                warning_messagae = (
+                warning_message = (
                     f"The fast/slow tokenizers () have different length: "
                     f"len(fast_tokenizer) = {len(fast_tokenizer)} and "
                     f"len(slow_tokenizer) = {len(slow_tokenizer)}."
                 )
-                result["warnings"].append(warning_messagae)
+                result["warnings"].append(warning_message)
                 if not keep_fast_tokenizer:
                     fast_tokenizer = None
                 slow_tokenizer = None
@@ -1395,7 +1416,7 @@ def create_tiny_models(
         raise ValueError(f"This script should be run from the root of the clone of `transformers` {clone_path}")
 
     report_path = os.path.join(output_path, "reports")
-    os.makedirs(report_path)
+    os.makedirs(report_path, exist_ok=True)
 
     _pytorch_arch_mappings = [
         x

utils/get_test_info.py

@@ -53,7 +53,15 @@ def get_module_path(test_file):
 def get_test_module(test_file):
     """Get the module of a model test file."""
     test_module_path = get_module_path(test_file)
-    test_module = importlib.import_module(test_module_path)
+    try:
+        test_module = importlib.import_module(test_module_path)
+    except AttributeError as exc:
+        # e.g. if you have a `tests` folder in `site-packages`, created by another package, when trying to import
+        # `tests.models...`
+        raise ValueError(
+            f"Could not import module {test_module_path}. Confirm that you don't have a package with the same root "
+            "name installed or in your environment's `site-packages`."
+        ) from exc
 
     return test_module