transformers/utils/check_repo.py

# coding=utf-8
# Copyright 2020 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
import warnings
from pathlib import Path

from transformers import is_flax_available, is_tf_available, is_torch_available
from transformers.file_utils import ENV_VARS_TRUE_VALUES
from transformers.models.auto import get_values


# 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"

# 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 = [
    # models to ignore for not tested
    "M2M100Encoder",  # Building part of bigger (tested) model.
    "M2M100Decoder",  # Building part of bigger (tested) model.
    "Speech2TextEncoder",  # Building part of bigger (tested) model.
    "Speech2TextDecoder",  # Building part of bigger (tested) model.
    "LEDEncoder",  # Building part of bigger (tested) model.
    "LEDDecoder",  # Building part of bigger (tested) model.
    "BartDecoderWrapper",  # Building part of bigger (tested) model.
    "BartEncoder",  # Building part of bigger (tested) model.
    "BertLMHeadModel",  # Needs to be setup as decoder.
    "BlenderbotSmallEncoder",  # Building part of bigger (tested) model.
    "BlenderbotSmallDecoderWrapper",  # Building part of bigger (tested) model.
    "BlenderbotEncoder",  # Building part of bigger (tested) model.
    "BlenderbotDecoderWrapper",  # Building part of bigger (tested) model.
    "MBartEncoder",  # Building part of bigger (tested) model.
    "MBartDecoderWrapper",  # Building part of bigger (tested) model.
    "MegatronBertLMHeadModel",  # Building part of bigger (tested) model.
    "MegatronBertEncoder",  # Building part of bigger (tested) model.
    "MegatronBertDecoder",  # Building part of bigger (tested) model.
    "MegatronBertDecoderWrapper",  # Building part of bigger (tested) model.
    "PegasusEncoder",  # Building part of bigger (tested) model.
    "PegasusDecoderWrapper",  # Building part of bigger (tested) model.
    "DPREncoder",  # Building part of bigger (tested) model.
    "DPRSpanPredictor",  # Building part of bigger (tested) model.
    "ProphetNetDecoderWrapper",  # Building part of bigger (tested) model.
    "ReformerForMaskedLM",  # Needs to be setup as decoder.
    "T5Stack",  # Building part of bigger (tested) model.
    "TFDPREncoder",  # Building part of bigger (tested) model.
    "TFDPRSpanPredictor",  # Building part of bigger (tested) model.
    "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
    "TFRobertaForMultipleChoice",  # TODO: fix
    "SeparableConv1D",  # Building part of bigger (tested) model.
]

# 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_flax_bert.py",
    "test_modeling_flax_roberta.py",
    "test_modeling_mbart.py",
    "test_modeling_mt5.py",
    "test_modeling_pegasus.py",
    "test_modeling_tf_camembert.py",
    "test_modeling_tf_mt5.py",
    "test_modeling_tf_xlm_roberta.py",
    "test_modeling_xlm_prophetnet.py",
    "test_modeling_xlm_roberta.py",
]

# 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 = [
    # models to ignore for model xxx mapping
    "DPRReader",
    "DPRSpanPredictor",
    "FlaubertForQuestionAnswering",
    "GPT2DoubleHeadsModel",
    "LukeForEntityClassification",
    "LukeForEntityPairClassification",
    "LukeForEntitySpanClassification",
    "OpenAIGPTDoubleHeadsModel",
    "RagModel",
    "RagSequenceForGeneration",
    "RagTokenForGeneration",
    "T5Stack",
    "TFDPRReader",
    "TFDPRSpanPredictor",
    "TFGPT2DoubleHeadsModel",
    "TFOpenAIGPTDoubleHeadsModel",
    "TFRagModel",
    "TFRagSequenceForGeneration",
    "TFRagTokenForGeneration",
    "Wav2Vec2ForCTC",
    "XLMForQuestionAnswering",
    "XLNetForQuestionAnswering",
    "SeparableConv1D",
]

# 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_flax_auto",
        "modeling_flax_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 model in dir(transformers.models):
        # There are some magic dunder attributes in the dir, we ignore them
        if not model.startswith("__"):
            model_module = getattr(transformers.models, model)
            for submodule in dir(model_module):
                if submodule.startswith("modeling") and submodule not in _ignore_modules:
                    modeling_module = getattr(model_module, submodule)
                    if inspect.ismodule(modeling_module):
                        modules.append(modeling_module)
    return modules


def get_models(module):
    """Get the objects in module that are models."""
    models = []
    model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel)
    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_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


# 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"""
    # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class
    with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f:
        content = f.read()
    all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content)
    # Check with one less parenthesis as well
    all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content)
    if len(all_models) > 0:
        model_tested = []
        for entry in all_models:
            for line in entry.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 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.
    if is_torch_available():
        for attr_name in dir(transformers.models.auto.modeling_auto):
            if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING"):
                result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name)))
    if is_tf_available():
        for attr_name in dir(transformers.models.auto.modeling_tf_auto):
            if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING"):
                result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name)))
    if is_flax_available():
        for attr_name in dir(transformers.models.auto.modeling_flax_auto):
            if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING"):
                result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name)))
    return [cls.__name__ for cls in result]


def ignore_unautoclassed(model_name):
    """Rules to determine if `name` should be in an auto class."""
    # Special white list
    if model_name in IGNORE_NON_AUTO_CONFIGURED:
        return True
    # Encoder and Decoder should be ignored
    if "Encoder" in model_name or "Decoder" in model_name:
        return True
    return False


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 not ignore_unautoclassed(model_name):
            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."""
    missing_backends = []
    if not is_torch_available():
        missing_backends.append("PyTorch")
    if not is_tf_available():
        missing_backends.append("TensorFlow")
    if not is_flax_available():
        missing_backends.append("Flax")
    if len(missing_backends) > 0:
        missing = ", ".join(missing_backends)
        if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
            raise Exception(
                "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the "
                f"Transformers repo, the following are missing: {missing}."
            )
        else:
            warnings.warn(
                "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the "
                f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you "
                "didn't make any change in one of those backends modeling files, you should probably execute the "
                "command above to be on the safe side."
            )
    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+$")


def check_decorator_order(filename):
    """Check that in the test file `filename` the slow decorator is always last."""
    with open(filename, "r", encoding="utf-8", newline="\n") as f:
        lines = f.readlines()
    decorator_before = None
    errors = []
    for i, line in enumerate(lines):
        search = _re_decorator.search(line)
        if search is not None:
            decorator_name = search.groups()[0]
            if decorator_before is not None and decorator_name.startswith("parameterized"):
                errors.append(i)
            decorator_before = decorator_name
        elif decorator_before is not None:
            decorator_before = None
    return errors


def check_all_decorator_order():
    """Check that in all test files, the slow decorator is always last."""
    errors = []
    for fname in os.listdir(PATH_TO_TESTS):
        if fname.endswith(".py"):
            filename = os.path.join(PATH_TO_TESTS, fname)
            new_errors = check_decorator_order(filename)
            errors += [f"- {filename}, line {i}" for i in new_errors]
    if len(errors) > 0:
        msg = "\n".join(errors)
        raise ValueError(
            f"The parameterized decorator (and its variants) should always be first, but this is not the case in the following files:\n{msg}"
        )


def find_all_documented_objects():
    """Parse the content of all doc files to detect which classes and functions it documents"""
    documented_obj = []
    for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"):
        with open(doc_file, "r", encoding="utf-8", newline="\n") as f:
            content = f.read()
        raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content)
        documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs]
    return documented_obj


# One good reason for not being documented is to be deprecated. Put in this list deprecated objects.
DEPRECATED_OBJECTS = [
    "AutoModelWithLMHead",
    "BartPretrainedModel",
    "DataCollator",
    "DataCollatorForSOP",
    "GlueDataset",
    "GlueDataTrainingArguments",
    "LineByLineTextDataset",
    "LineByLineWithRefDataset",
    "LineByLineWithSOPTextDataset",
    "PretrainedBartModel",
    "PretrainedFSMTModel",
    "SingleSentenceClassificationProcessor",
    "SquadDataTrainingArguments",
    "SquadDataset",
    "SquadExample",
    "SquadFeatures",
    "SquadV1Processor",
    "SquadV2Processor",
    "TFAutoModelWithLMHead",
    "TFBartPretrainedModel",
    "TextDataset",
    "TextDatasetForNextSentencePrediction",
    "Wav2Vec2ForMaskedLM",
    "Wav2Vec2Tokenizer",
    "glue_compute_metrics",
    "glue_convert_examples_to_features",
    "glue_output_modes",
    "glue_processors",
    "glue_tasks_num_labels",
    "squad_convert_examples_to_features",
    "xnli_compute_metrics",
    "xnli_output_modes",
    "xnli_processors",
    "xnli_tasks_num_labels",
]

# Exceptionally, some objects should not be documented after all rules passed.
# ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT!
UNDOCUMENTED_OBJECTS = [
    "AddedToken",  # This is a tokenizers class.
    "BasicTokenizer",  # Internal, should never have been in the main init.
    "CharacterTokenizer",  # Internal, should never have been in the main init.
    "DPRPretrainedReader",  # Like an Encoder.
    "MecabTokenizer",  # Internal, should never have been in the main init.
    "ModelCard",  # Internal type.
    "SqueezeBertModule",  # Internal building block (should have been called SqueezeBertLayer)
    "TFDPRPretrainedReader",  # Like an Encoder.
    "TransfoXLCorpus",  # Internal type.
    "WordpieceTokenizer",  # Internal, should never have been in the main init.
    "absl",  # External module
    "add_end_docstrings",  # Internal, should never have been in the main init.
    "add_start_docstrings",  # Internal, should never have been in the main init.
    "cached_path",  # Internal used for downloading models.
    "convert_tf_weight_name_to_pt_weight_name",  # Internal used to convert model weights
    "logger",  # Internal logger
    "logging",  # External module
    "requires_backends",  # Internal function
]

# This list should be empty. Objects in it should get their own doc page.
SHOULD_HAVE_THEIR_OWN_PAGE = [
    # Benchmarks
    "PyTorchBenchmark",
    "PyTorchBenchmarkArguments",
    "TensorFlowBenchmark",
    "TensorFlowBenchmarkArguments",
]


def ignore_undocumented(name):
    """Rules to determine if `name` should be undocumented."""
    # NOT DOCUMENTED ON PURPOSE.
    # Constants uppercase are not documented.
    if name.isupper():
        return True
    # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented.
    if (
        name.endswith("PreTrainedModel")
        or name.endswith("Decoder")
        or name.endswith("Encoder")
        or name.endswith("Layer")
        or name.endswith("Embeddings")
        or name.endswith("Attention")
    ):
        return True
    # Submodules are not documented.
    if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile(
        os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py")
    ):
        return True
    # All load functions are not documented.
    if name.startswith("load_tf") or name.startswith("load_pytorch"):
        return True
    # is_xxx_available functions are not documented.
    if name.startswith("is_") and name.endswith("_available"):
        return True
    # Deprecated objects are not documented.
    if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS:
        return True
    # MMBT model does not really work.
    if name.startswith("MMBT"):
        return True
    if name in SHOULD_HAVE_THEIR_OWN_PAGE:
        return True
    return False


def check_all_objects_are_documented():
    """Check all models are properly documented."""
    documented_objs = find_all_documented_objects()
    modules = transformers._modules
    objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")]
    undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)]
    if len(undocumented_objs) > 0:
        raise Exception(
            "The following objects are in the public init so should be documented:\n - "
            + "\n - ".join(undocumented_objs)
        )


def check_repo_quality():
    """Check all models are properly tested and documented."""
    print("Checking all models are properly tested.")
    check_all_decorator_order()
    check_all_models_are_tested()
    print("Checking all objects are properly documented.")
    check_all_objects_are_documented()
    print("Checking all models are in at least one auto class.")
    check_all_models_are_auto_configured()


if __name__ == "__main__":
    check_repo_quality()