Make sure all submodules are properly registered (#15144)

* Make sure all submodules are properly registered

* Try to fix tests

* Fix tests

src/transformers/__init__.py

@@ -51,7 +51,12 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 # Base objects, independent of any specific backend
 _import_structure = {
+    "benchmark": [],
+    "commands": [],
     "configuration_utils": ["PretrainedConfig"],
+    "convert_graph_to_onnx": [],
+    "convert_slow_tokenizers_checkpoints_to_fast": [],
+    "convert_tf_hub_seq_to_seq_bert_to_pytorch": [],
     "data": [
         "DataProcessor",
         "InputExample",
@@ -84,6 +89,11 @@ _import_structure = {
         "DefaultDataCollator",
         "default_data_collator",
     ],
+    "data.metrics": [],
+    "data.processors": [],
+    "debug_utils": [],
+    "dependency_versions_check": [],
+    "dependency_versions_table": [],
     "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"],
     "feature_extraction_utils": ["BatchFeature"],
     "file_utils": [
@@ -179,6 +189,7 @@ _import_structure = {
         "BlenderbotSmallConfig",
         "BlenderbotSmallTokenizer",
     ],
+    "models.bort": [],
     "models.byt5": ["ByT5Tokenizer"],
     "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"],
     "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"],
@@ -196,6 +207,7 @@ _import_structure = {
     "models.deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"],
     "models.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"],
     "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"],
+    "models.dialogpt": [],
     "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"],
     "models.dpr": [
         "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP",
@@ -236,6 +248,7 @@ _import_structure = {
     "models.mbart": ["MBartConfig"],
     "models.mbart50": [],
     "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
+    "models.megatron_gpt2": [],
     "models.mluke": [],
     "models.mmbt": ["MMBTConfig"],
     "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"],
@@ -316,6 +329,7 @@ _import_structure = {
     "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
     "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"],
     "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"],
+    "onnx": [],
     "pipelines": [
         "AudioClassificationPipeline",
         "AutomaticSpeechRecognitionPipeline",
@@ -343,6 +357,7 @@ _import_structure = {
         "ZeroShotClassificationPipeline",
         "pipeline",
     ],
+    "testing_utils": [],
     "tokenization_utils": ["PreTrainedTokenizer"],
     "tokenization_utils_base": [
         "AddedToken",
@@ -567,6 +582,7 @@ else:
 
 # PyTorch-backed objects
 if is_torch_available():
+    _import_structure["activations"] = []
     _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"]
     _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"]
     _import_structure["data.datasets"] = [
@@ -580,6 +596,7 @@ if is_torch_available():
         "TextDataset",
         "TextDatasetForNextSentencePrediction",
     ]
+    _import_structure["deepspeed"] = []
     _import_structure["generation_beam_search"] = ["BeamScorer", "BeamSearchScorer"]
     _import_structure["generation_logits_process"] = [
         "ForcedBOSTokenLogitsProcessor",
@@ -1455,6 +1472,7 @@ if is_torch_available():
         "get_polynomial_decay_schedule_with_warmup",
         "get_scheduler",
     ]
+    _import_structure["sagemaker"] = []
     _import_structure["trainer"] = ["Trainer"]
     _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"]
     _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"]
@@ -1465,6 +1483,7 @@ else:
 
 # TensorFlow-backed objects
 if is_tf_available():
+    _import_structure["activations_tf"] = []
     _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"]
     _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"]
     _import_structure["generation_tf_utils"] = ["tf_top_k_top_p_filtering"]
@@ -2129,6 +2148,7 @@ else:
         name for name in dir(dummy_flax_objects) if not name.startswith("_")
     ]
 
+
 # Direct imports for type-checking
 if TYPE_CHECKING:
     # Configuration

utils/check_inits.py

@@ -14,8 +14,10 @@
 # limitations under the License.
 
 import collections
+import importlib.util
 import os
 import re
+from pathlib import Path
 
 
 PATH_TO_TRANSFORMERS = "src/transformers"
@@ -202,5 +204,58 @@ def check_all_inits():
         raise ValueError("\n\n".join(failures))
 
 
+def get_transformers_submodules():
+    """
+    Returns the list of Transformers submodules.
+    """
+    submodules = []
+    for path, directories, files in os.walk(PATH_TO_TRANSFORMERS):
+        for folder in directories:
+            if folder.startswith("_"):
+                directories.remove(folder)
+                continue
+            short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS))
+            submodule = short_path.replace(os.path.sep, ".")
+            submodules.append(submodule)
+        for fname in files:
+            if fname == "__init__.py":
+                continue
+            short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS))
+            submodule = short_path.replace(os.path.sep, ".").replace(".py", "")
+            if len(submodule.split(".")) == 1:
+                submodules.append(submodule)
+    return submodules
+
+
+IGNORE_SUBMODULES = [
+    "convert_pytorch_checkpoint_to_tf2",
+    "modeling_flax_pytorch_utils",
+]
+
+
+def check_submodules():
+    # 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()
+
+    module_not_registered = [
+        module
+        for module in get_transformers_submodules()
+        if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
+    ]
+    if len(module_not_registered) > 0:
+        list_of_modules = "\n".join(f"- {module}" for module in module_not_registered)
+        raise ValueError(
+            "The following submodules are not properly registed in the main init of Transformers:\n"
+            f"{list_of_modules}\n"
+            "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."
+        )
+
+
 if __name__ == "__main__":
     check_all_inits()
+    check_submodules()