Autodocument the list of ONNX-supported models (#13884)

docs/source/serialization.rst

@@ -37,12 +37,18 @@ architectures, and are made to be easily extendable to other architectures.
 
 Ready-made configurations include the following models:
 
+..
+    This table is automatically generated by make style, do not fill manually!
+
 - ALBERT
 - BART
 - BERT
 - DistilBERT
-- GPT-2
+- GPT Neo
 - LayoutLM
+- Longformer
+- mBART
+- OpenAI GPT-2
 - RoBERTa
 - T5
 - XLM-RoBERTa

utils/check_table.py

@@ -62,6 +62,15 @@ _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGe
 _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
 
 
+# This is to make sure the transformers module imported is the one in the repo.
+spec = importlib.util.spec_from_file_location(
+    "transformers",
+    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
+    submodule_search_locations=[TRANSFORMERS_PATH],
+)
+transformers_module = spec.loader.load_module()
+
+
 # Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
 def camel_case_split(identifier):
     "Split a camelcased `identifier` into words."
@@ -78,19 +87,11 @@ def _center_text(text, width):
 
 def get_model_table_from_auto_modules():
     """Generates an up-to-date model table from the content of the auto modules."""
-    # This is to make sure the transformers module imported is the one in the repo.
-    spec = importlib.util.spec_from_file_location(
-        "transformers",
-        os.path.join(TRANSFORMERS_PATH, "__init__.py"),
-        submodule_search_locations=[TRANSFORMERS_PATH],
-    )
-    transformers = spec.loader.load_module()
-
     # Dictionary model names to config.
-    config_maping_names = transformers.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
+    config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     model_name_to_config = {
         name: config_maping_names[code]
-        for code, name in transformers.MODEL_NAMES_MAPPING.items()
+        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()}
@@ -103,7 +104,7 @@ def get_model_table_from_auto_modules():
     flax_models = collections.defaultdict(bool)
 
     # Let's lookup through all transformers object (once).
-    for attr_name in dir(transformers):
+    for attr_name in dir(transformers_module):
         lookup_dict = None
         if attr_name.endswith("Tokenizer"):
             lookup_dict = slow_tokenizers
@@ -178,9 +179,56 @@ def check_model_table(overwrite=False):
             )
 
 
+def has_onnx(model_type):
+    """
+    Returns whether `model_type` is supported by ONNX (by checking if there is an ONNX config) or not.
+    """
+    config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING
+    if model_type not in config_mapping:
+        return False
+    config = config_mapping[model_type]
+    config_module = config.__module__
+    module = transformers_module
+    for part in config_module.split(".")[1:]:
+        module = getattr(module, part)
+    config_name = config.__name__
+    onnx_config_name = config_name.replace("Config", "OnnxConfig")
+    return hasattr(module, onnx_config_name)
+
+
+def get_onnx_model_list():
+    """
+    Return the list of models supporting ONNX.
+    """
+    config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING
+    model_names = config_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING
+    onnx_model_types = [model_type for model_type in config_mapping.keys() if has_onnx(model_type)]
+    onnx_model_names = [model_names[model_type] for model_type in onnx_model_types]
+    onnx_model_names.sort(key=lambda x: x.upper())
+    return "\n".join([f"- {name}" for name in onnx_model_names]) + "\n"
+
+
+def check_onnx_model_list(overwrite=False):
+    """Check the model list in the serialization.rst is consistent with the state of the lib and maybe `overwrite`."""
+    current_list, start_index, end_index, lines = _find_text_in_file(
+        filename=os.path.join(PATH_TO_DOCS, "serialization.rst"),
+        start_prompt="    This table is automatically generated by make style, do not fill manually!",
+        end_prompt="This conversion is handled with the PyTorch version of models ",
+    )
+    new_list = get_onnx_model_list()
+
+    if current_list != new_list:
+        if overwrite:
+            with open(os.path.join(PATH_TO_DOCS, "serialization.rst"), "w", encoding="utf-8", newline="\n") as f:
+                f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
+        else:
+            raise ValueError("The list of ONNX-supported models needs an update. Run `make fix-copies` to fix this.")
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
     args = parser.parse_args()
 
     check_model_table(args.fix_and_overwrite)
+    check_onnx_model_list(args.fix_and_overwrite)