Add a script to check all models are tested and documented (#6298)

* Add a script to check all models are tested and documented

* Apply suggestions from code review

Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

* Address comments

Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

Makefile

@@ -6,6 +6,7 @@ quality:
 	black --check --line-length 119 --target-version py35 examples templates tests src utils
 	isort --check-only --recursive examples templates tests src utils
 	flake8 examples templates tests src utils
+	python utils/check_repo.py
 
 # Format source code automatically
 

docs/source/model_doc/bert.rst

@@ -78,6 +78,13 @@ BertForPreTraining
     :members:
 
 
+BertModelLMHeadModel
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertLMHeadModel
+    :members:
+
+
 BertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -134,6 +141,13 @@ TFBertForPreTraining
     :members:
 
 
+TFBertModelLMHeadModel
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertLMHeadModel
+    :members:
+
+
 TFBertForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/camembert.rst

@@ -105,6 +105,13 @@ TFCamembertForSequenceClassification
     :members:
 
 
+TFCamembertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFCamembertForMultipleChoice
+    :members:
+
+
 TFCamembertForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/flaubert.rst

@@ -61,6 +61,13 @@ FlaubertForSequenceClassification
     :members:
 
 
+FlaubertForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaubertForMultipleChoice
+    :members:
+
+
 FlaubertForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/reformer.rst

@@ -121,6 +121,13 @@ ReformerForMaskedLM
     :members:
 
 
+ReformerForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.ReformerForSequenceClassification
+    :members:
+
+
 ReformerForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/xlm.rst

@@ -75,6 +75,20 @@ XLMForSequenceClassification
     :members:
 
 
+XLMForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMForMultipleChoice
+    :members:
+
+
+XLMForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLMForTokenClassification
+    :members:
+
+
 XLMForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/modeling_electra.py

@@ -879,6 +879,7 @@ class ElectraForMultipleChoice(ElectraPreTrainedModel):
         inputs_embeds=None,
         labels=None,
         output_attentions=None,
+        output_hidden_states=None,
         return_dict=None,
     ):
         r"""
@@ -908,6 +909,7 @@ class ElectraForMultipleChoice(ElectraPreTrainedModel):
             head_mask=head_mask,
             inputs_embeds=inputs_embeds,
             output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
             return_dict=return_dict,
         )
 

src/transformers/modeling_tf_albert.py

@@ -1260,7 +1260,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
             head_mask = inputs.get("head_mask", head_mask)
             inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
             output_attentions = inputs.get("output_attentions", output_attentions)
-            output_hidden_states = inputs.get("output_hidden_states", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
             return_dict = inputs.get("return_dict", return_dict)
             labels = inputs.get("labels", labels)
             assert len(inputs) <= 10, "Too many inputs."
@@ -1279,6 +1279,11 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
         flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
         flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
         flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+        flat_inputs_embeds = (
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
+            else None
+        )
 
         outputs = self.albert(
             flat_input_ids,
@@ -1286,7 +1291,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
             flat_token_type_ids,
             flat_position_ids,
             head_mask,
-            inputs_embeds,
+            flat_inputs_embeds,
             output_attentions,
             output_hidden_states,
             return_dict=return_dict,

tests/test_modeling_electra.py

@@ -275,6 +275,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
             ElectraModel,
             ElectraForPreTraining,
             ElectraForMaskedLM,
+            ElectraForMultipleChoice,
             ElectraForTokenClassification,
             ElectraForSequenceClassification,
             ElectraForQuestionAnswering,

tests/test_modeling_tf_albert.py

@@ -24,10 +24,12 @@ from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
 
 
 if is_tf_available():
+    import tensorflow as tf
     from transformers.modeling_tf_albert import (
         TFAlbertModel,
         TFAlbertForPreTraining,
         TFAlbertForMaskedLM,
+        TFAlbertForMultipleChoice,
         TFAlbertForSequenceClassification,
         TFAlbertForQuestionAnswering,
         TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -180,6 +182,22 @@ class TFAlbertModelTester:
         self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
         self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])
 
+    def create_and_check_albert_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = TFAlbertForMultipleChoice(config=config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -229,6 +247,10 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs)
 
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_albert_for_multiple_choice(*config_and_inputs)
+
     def test_for_sequence_classification(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs)

utils/check_repo.py

@@ -0,0 +1,269 @@
+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_repo.py
+PATH_TO_TRANSFORMERS = "src/transformers"
+PATH_TO_TESTS = "tests"
+PATH_TO_DOC = "docs/source/model_doc"
+
+# Update this list for models that are not tested with a comment explaining the reason it should not be.
+# Being in this list is an exception and should **not** be the rule.
+IGNORE_NON_TESTED = [
+    "BertLMHeadModel",  # Needs to be setup as decoder.
+    "DPREncoder",  # Building part of bigger (tested) model.
+    "DPRSpanPredictor",  # Building part of bigger (tested) model.
+    "ReformerForMaskedLM",  # Needs to be setup as decoder.
+    "T5Stack",  # Building part of bigger (tested) model.
+    "TFAlbertForMultipleChoice",  # TODO: fix
+    "TFAlbertForTokenClassification",  # TODO: fix
+    "TFBertLMHeadModel",  # TODO: fix
+    "TFElectraForMultipleChoice",  # Fix is in #6284
+    "TFElectraForQuestionAnswering",  # TODO: fix
+    "TFElectraForSequenceClassification",  # Fix is in #6284
+    "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
+    "TFRobertaForMultipleChoice",  # TODO: fix
+]
+
+# Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't
+# trigger the common tests.
+TEST_FILES_WITH_NO_COMMON_TESTS = [
+    "test_modeling_camembert.py",
+    "test_modeling_tf_camembert.py",
+    "test_modeling_tf_xlm_roberta.py",
+    "test_modeling_xlm_roberta.py",
+]
+
+# Update this list for models that are not documented with a comment explaining the reason it should not be.
+# Being in this list is an exception and should **not** be the rule.
+IGNORE_NON_DOCUMENTED = [
+    "DPREncoder",  # Building part of bigger (documented) model.
+    "DPRSpanPredictor",  # Building part of bigger (documented) model.
+    "T5Stack",  # Building part of bigger (tested) model.
+    "TFElectraMainLayer",  # Building part of bigger (documented) model (should it be a TFPreTrainedModel ?)
+]
+
+# Update this dict with any special correspondance model name (used in modeling_xxx.py) to doc file.
+MODEL_NAME_TO_DOC_FILE = {
+    "openai": "gpt.rst",
+    "transfo_xl": "transformerxl.rst",
+    "xlm_roberta": "xlmroberta.rst",
+}
+
+# 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()
+
+
+# If some modeling modules should be ignored for all checks, they should be added in the nested list
+# _ignore_modules of this function.
+def get_model_modules():
+    """ Get the model modules inside the transformers library. """
+    _ignore_modules = [
+        "modeling_auto",
+        "modeling_encoder_decoder",
+        "modeling_marian",
+        "modeling_mmbt",
+        "modeling_outputs",
+        "modeling_retribert",
+        "modeling_utils",
+        "modeling_transfo_xl_utilities",
+        "modeling_tf_auto",
+        "modeling_tf_outputs",
+        "modeling_tf_pytorch_utils",
+        "modeling_tf_utils",
+        "modeling_tf_transfo_xl_utilities",
+    ]
+    modules = []
+    for attr_name in dir(transformers):
+        if attr_name.startswith("modeling") and attr_name not in _ignore_modules:
+            module = getattr(transformers, attr_name)
+            if inspect.ismodule(module):
+                modules.append(module)
+    return modules
+
+
+def get_models(module):
+    """ Get the objects in module that are models."""
+    models = []
+    model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel)
+    for attr_name in dir(module):
+        if "Pretrained" in attr_name or "PreTrained" in attr_name:
+            continue
+        attr = getattr(module, attr_name)
+        if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__:
+            models.append((attr_name, attr))
+    return models
+
+
+# If some test_modeling files should be ignored when checking models are all tested, they should be added in the
+# nested list _ignore_files of this function.
+def get_model_test_files():
+    """ Get the model test files."""
+    _ignore_files = [
+        "test_modeling_common",
+        "test_modeling_encoder_decoder",
+        "test_modeling_marian",
+        "test_modeling_mbart",
+        "test_modeling_tf_common",
+    ]
+    test_files = []
+    for filename in os.listdir(PATH_TO_TESTS):
+        if (
+            os.path.isfile(f"{PATH_TO_TESTS}/{filename}")
+            and filename.startswith("test_modeling")
+            and not os.path.splitext(filename)[0] in _ignore_files
+        ):
+            test_files.append(filename)
+    return test_files
+
+
+# If some doc source files should be ignored when checking models are all documented, they should be added in the
+# nested list _ignore_modules of this function.
+def get_model_doc_files():
+    """ Get the model doc files."""
+    _ignore_modules = [
+        "auto",
+        "dialogpt",
+        "marian",
+        "retribert",
+    ]
+    doc_files = []
+    for filename in os.listdir(PATH_TO_DOC):
+        if os.path.isfile(f"{PATH_TO_DOC}/{filename}") and not os.path.splitext(filename)[0] in _ignore_modules:
+            doc_files.append(filename)
+    return doc_files
+
+
+# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class
+# for the all_model_classes variable.
+def find_tested_models(test_file):
+    """ Parse the content of test_file to detect what's in all_model_classes"""
+    with open(os.path.join(PATH_TO_TESTS, test_file)) as f:
+        content = f.read()
+    all_models = re.search(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content)
+    # Check with one less parenthesis
+    if all_models is None:
+        all_models = re.search(r"all_model_classes\s+=\s+\(([^\)]*)\)", content)
+    if all_models is not None:
+        model_tested = []
+        for line in all_models.groups()[0].split(","):
+            name = line.strip()
+            if len(name) > 0:
+                model_tested.append(name)
+        return model_tested
+
+
+def check_models_are_tested(module, test_file):
+    """ Check models defined in module are tested in test_file."""
+    defined_models = get_models(module)
+    tested_models = find_tested_models(test_file)
+    if tested_models is None:
+        if test_file in TEST_FILES_WITH_NO_COMMON_TESTS:
+            return
+        return [
+            f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. "
+            + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file "
+            + "`utils/check_repo.py`."
+        ]
+    failures = []
+    for model_name, _ in defined_models:
+        if model_name not in tested_models and model_name not in IGNORE_NON_TESTED:
+            failures.append(
+                f"{model_name} is defined in {module.__name__} but is not tested in "
+                + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file."
+                + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`"
+                + "in the file `utils/check_repo.py`."
+            )
+    return failures
+
+
+def check_all_models_are_tested():
+    """ Check all models are properly tested."""
+    modules = get_model_modules()
+    test_files = get_model_test_files()
+    failures = []
+    for module in modules:
+        test_file = f"test_{module.__name__.split('.')[1]}.py"
+        if test_file not in test_files:
+            failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.")
+        new_failures = check_models_are_tested(module, test_file)
+        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))
+
+
+def find_documented_classes(doc_file):
+    """ Parse the content of doc_file to detect which classes it documents"""
+    with open(os.path.join(PATH_TO_DOC, doc_file)) as f:
+        content = f.read()
+    return re.findall(r"autoclass:: transformers.(\S+)\s+", content)
+
+
+def check_models_are_documented(module, doc_file):
+    """ Check models defined in module are documented in doc_file."""
+    defined_models = get_models(module)
+    documented_classes = find_documented_classes(doc_file)
+    failures = []
+    for model_name, _ in defined_models:
+        if model_name not in documented_classes and model_name not in IGNORE_NON_DOCUMENTED:
+            failures.append(
+                f"{model_name} is defined in {module.__name__} but is not documented in "
+                + f"{os.path.join(PATH_TO_DOC, doc_file)}. Add it to that file."
+                + "If this model should not be documented, add its name to `IGNORE_NON_DOCUMENTED`"
+                + "in the file `utils/check_repo.py`."
+            )
+    return failures
+
+
+def _get_model_name(module):
+    """ Get the model name for the module defining it."""
+    splits = module.__name__.split("_")
+    # Secial case for transfo_xl
+    if splits[-1] == "xl":
+        return "_".join(splits[-2:])
+    # Secial case for xlm_roberta
+    if splits[-1] == "roberta" and splits[-2] == "xlm":
+        return "_".join(splits[-2:])
+    return splits[-1]
+
+
+def check_all_models_are_documented():
+    """ Check all models are properly documented."""
+    modules = get_model_modules()
+    doc_files = get_model_doc_files()
+    failures = []
+    for module in modules:
+        model_name = _get_model_name(module)
+        doc_file = MODEL_NAME_TO_DOC_FILE.get(model_name, f"{model_name}.rst")
+        if doc_file not in doc_files:
+            failures.append(
+                f"{module.__name__} does not have its corresponding doc file {doc_file}. "
+                + f"If the doc file exists but isn't named {doc_file}, update `MODEL_NAME_TO_DOC_FILE` "
+                + "in the file `utils/check_repo.py`."
+            )
+        new_failures = check_models_are_documented(module, doc_file)
+        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))
+
+
+def check_repo_quality():
+    """ Check all models are properly tested and documented."""
+    print("Checking all models are properly tested.")
+    check_all_models_are_tested()
+    print("Checking all models are properly documented.")
+    check_all_models_are_documented()
+
+
+if __name__ == "__main__":
+    check_repo_quality()