Add a check on config classes docstring checkpoints

.circleci/config.yml

@@ -881,6 +881,7 @@ jobs:
  - run: python utils/check_dummies.py
  - run: python utils/check_repo.py
  - run: python utils/check_inits.py
+ - run: python utils/check_config_docstrings.py
  - run: make deps_table_check_updated
  - run: python utils/tests_fetcher.py --sanity_check
 

Makefile

@@ -39,6 +39,7 @@ repo-consistency:
  python utils/check_dummies.py
  python utils/check_repo.py
  python utils/check_inits.py
+ python utils/check_config_docstrings.py
  python utils/tests_fetcher.py --sanity_check
 
 # this target runs checks on all files

docs/source/en/pr_checks.mdx

@@ -108,6 +108,7 @@ This checks that:
 - All objects added to the init are documented (performed by `utils/check_repo.py`)
 - All `__init__.py` files have the same content in their two sections (performed by `utils/check_inits.py`)
 - All code identified as a copy from another module is consistent with the original (performed by `utils/check_copies.py`)
+- All configuration classes have at least one valid checkpoint mentioned in their docstrings (performed by `utils/check_config_docstrings.py`)
 - The translations of the READMEs and the index of the doc have the same model list as the main README (performed by `utils/check_copies.py`)
 - The auto-generated tables in the documentation are up to date (performed by `utils/check_table.py`)
 - The library has all objects available even if not all optional dependencies are installed (performed by `utils/check_dummies.py`)

src/transformers/models/convbert/configuration_convbert.py

@@ -37,9 +37,11 @@ class ConvBertConfig(PretrainedConfig):
  This is the configuration class to store the configuration of a [`ConvBertModel`]. It is used to instantiate an
  ConvBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
  with the defaults will yield a similar configuration to that of the ConvBERT
- [conv-bert-base](https://huggingface.co/YituTech/conv-bert-base) architecture. Configuration objects inherit from
- [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
- for more information.
+ [YituTech/conv-bert-base](https://huggingface.co/YituTech/conv-bert-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
 
  Args:
  vocab_size (`int`, *optional*, defaults to 30522):

src/transformers/models/imagegpt/configuration_imagegpt.py

@@ -32,7 +32,7 @@ class ImageGPTConfig(PretrainedConfig):
  This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
  used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
  Instantiating a configuration with the defaults will yield a similar configuration to that of the ImageGPT
- [small](https://huggingface.co/imagegpt) architecture.
+ [openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture.
 
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.

utils/check_config_docstrings.py

@@ -0,0 +1,84 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib
+import inspect
+import os
+import re
+
+
+# All paths are set with the intent you should run this script from the root of the repo with the command
+# python utils/check_config_docstrings.py
+PATH_TO_TRANSFORMERS = "src/transformers"
+
+
+# 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(PATH_TO_TRANSFORMERS, "__init__.py"),
+ submodule_search_locations=[PATH_TO_TRANSFORMERS],
+)
+transformers = spec.loader.load_module()
+
+CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
+
+# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
+# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
+_re_checkpoint = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
+
+
+CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
+ "CLIPConfig",
+ "DecisionTransformerConfig",
+ "EncoderDecoderConfig",
+ "RagConfig",
+ "SpeechEncoderDecoderConfig",
+ "VisionEncoderDecoderConfig",
+ "VisionTextDualEncoderConfig",
+}
+
+
+def check_config_docstrings_have_checkpoints():
+ configs_without_checkpoint = []
+
+ for config_class in list(CONFIG_MAPPING.values()):
+ checkpoint_found = False
+
+ # source code of `config_class`
+ config_source = inspect.getsource(config_class)
+ checkpoints = _re_checkpoint.findall(config_source)
+
+ for checkpoint in checkpoints:
+ # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
+ # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
+ ckpt_name, ckpt_link = checkpoint
+
+ # verify the checkpoint name corresponds to the checkpoint link
+ ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
+ if ckpt_link == ckpt_link_from_name:
+ checkpoint_found = True
+ break
+
+ name = config_class.__name__
+ if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
+ configs_without_checkpoint.append(name)
+
+ if len(configs_without_checkpoint) > 0:
+ message = "\n".join(sorted(configs_without_checkpoint))
+ raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}")
+
+
+if __name__ == "__main__":
+ check_config_docstrings_have_checkpoints()