Check all models are in an auto class (huggingface#8425)

src/transformers/modeling_tf_auto.py

@@ -31,6 +31,7 @@
     FunnelConfig,
     GPT2Config,
     LongformerConfig,
+    LxmertConfig,
     MobileBertConfig,
     OpenAIGPTConfig,
     RobertaConfig,
@@ -113,6 +114,7 @@
 )
 from .modeling_tf_gpt2 import TFGPT2LMHeadModel, TFGPT2Model
 from .modeling_tf_longformer import TFLongformerForMaskedLM, TFLongformerForQuestionAnswering, TFLongformerModel
+from .modeling_tf_lxmert import TFLxmertForPreTraining, TFLxmertModel
 from .modeling_tf_marian import TFMarianMTModel
 from .modeling_tf_mbart import TFMBartForConditionalGeneration
 from .modeling_tf_mobilebert import (
@@ -168,6 +170,7 @@
 
 TF_MODEL_MAPPING = OrderedDict(
     [
+        (LxmertConfig, TFLxmertModel),
         (T5Config, TFT5Model),
         (DistilBertConfig, TFDistilBertModel),
         (AlbertConfig, TFAlbertModel),
@@ -192,6 +195,7 @@
 
 TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
     [
+        (LxmertConfig, TFLxmertForPreTraining),
         (T5Config, TFT5ForConditionalGeneration),
         (DistilBertConfig, TFDistilBertForMaskedLM),
         (AlbertConfig, TFAlbertForPreTraining),

utils/check_repo.py

@@ -70,6 +70,34 @@
     "marian": "marian.rst",
 }
 
+# Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and
+# should **not** be the rule.
+IGNORE_NON_AUTO_CONFIGURED = [
+    "DPRContextEncoder",
+    "DPREncoder",
+    "DPRReader",
+    "DPRSpanPredictor",
+    "FlaubertForQuestionAnswering",
+    "FunnelBaseModel",
+    "GPT2DoubleHeadsModel",
+    "OpenAIGPTDoubleHeadsModel",
+    "ProphetNetDecoder",
+    "ProphetNetEncoder",
+    "RagModel",
+    "RagSequenceForGeneration",
+    "RagTokenForGeneration",
+    "T5Stack",
+    "TFBertForNextSentencePrediction",
+    "TFFunnelBaseModel",
+    "TFGPT2DoubleHeadsModel",
+    "TFMobileBertForNextSentencePrediction",
+    "TFOpenAIGPTDoubleHeadsModel",
+    "XLMForQuestionAnswering",
+    "XLMProphetNetDecoder",
+    "XLMProphetNetEncoder",
+    "XLNetForQuestionAnswering",
+]
+
 # This is to make sure the transformers module imported is the one in the repo.
 spec = importlib.util.spec_from_file_location(
     "transformers",
@@ -282,6 +310,45 @@ def check_all_models_are_documented():
         raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))
 
 
+def get_all_auto_configured_models():
+    """ Return the list of all models in at least one auto class."""
+    result = set()  # To avoid duplicates we concatenate all model classes in a set.
+    for attr_name in dir(transformers.modeling_auto):
+        if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING"):
+            result = result | set(getattr(transformers.modeling_auto, attr_name).values())
+    for attr_name in dir(transformers.modeling_tf_auto):
+        if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING"):
+            result = result | set(getattr(transformers.modeling_tf_auto, attr_name).values())
+    return [cls.__name__ for cls in result]
+
+
+def check_models_are_auto_configured(module, all_auto_models):
+    """ Check models defined in module are each in an auto class."""
+    defined_models = get_models(module)
+    failures = []
+    for model_name, _ in defined_models:
+        if model_name not in all_auto_models and model_name not in IGNORE_NON_AUTO_CONFIGURED:
+            failures.append(
+                f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. "
+                "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file "
+                "`utils/check_repo.py`."
+            )
+    return failures
+
+
+def check_all_models_are_auto_configured():
+    """ Check all models are each in an auto class."""
+    modules = get_model_modules()
+    all_auto_models = get_all_auto_configured_models()
+    failures = []
+    for module in modules:
+        new_failures = check_models_are_auto_configured(module, all_auto_models)
+        if new_failures is not None:
+            failures += new_failures
+    if len(failures) > 0:
+        raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))
+
+
 _re_decorator = re.compile(r"^\s*@(\S+)\s+$")
 
 
@@ -325,6 +392,8 @@ def check_repo_quality():
     check_all_models_are_tested()
     print("Checking all models are properly documented.")
     check_all_models_are_documented()
+    print("Checking all models are in at least one auto class.")
+    check_all_models_are_auto_configured()
 
 
 if __name__ == "__main__":