Add more missing models to models/__init__.py (#14177)

* Add missing models to models/__init__.py

* Fix issues previously undetected

* Add UniSpeechSatForPreTraining to all_model_classes

* fix unispeech sat

* fix

* Add check_model_list() to check_repo.py

* Remove _ignore_models = ["bort"]

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

src/transformers/models/__init__.py

@@ -31,6 +31,7 @@ from . import (
     bigbird_pegasus,
     blenderbot,
     blenderbot_small,
+    byt5,
     camembert,
     canine,
     clip,
@@ -67,6 +68,7 @@ from . import (
     mbart,
     mbart50,
     megatron_bert,
+    megatron_gpt2,
     mmbt,
     mobilebert,
     mpnet,
@@ -84,12 +86,17 @@ from . import (
     segformer,
     sew,
     sew_d,
+    speech_encoder_decoder,
     speech_to_text,
+    speech_to_text_2,
     splinter,
     squeezebert,
     t5,
     tapas,
     transfo_xl,
+    trocr,
+    unispeech,
+    unispeech_sat,
     vision_encoder_decoder,
     visual_bert,
     vit,

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -1157,6 +1157,7 @@ class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel):
 
         self.speaker_proj = nn.Linear(config.hidden_size, config.codevector_dim)
         self.label_embeddings_concat = nn.Parameter(torch.FloatTensor(config.num_clusters, config.codevector_dim))
+        self.label_embeddings_concat.data.zero_()
 
         self.layer_norm_for_extract = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         if self.config.do_stable_layer_norm:
@@ -1268,21 +1269,24 @@ class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel):
         # quantize all (unmasked) extracted features and project to final vq dim
         extract_features = self.dropout_features(outputs[1])
 
-        # layer normalization (has no effect when `config.do_stable_layer_norm == False`)
-        extract_features = self.layer_norm_for_extract(extract_features)
-        quantized_features, codevector_perplexity = self.quantizer(extract_features)
-
-        # project quantized features twice
-        quantized_features = self.project_q(quantized_features)
-        quantized_features = self.project_hid(quantized_features)
-
         # TODO(PVP) - add pretraining logic and add to tests
-        loss = None
+        logits = extract_features
-        logits = None
+        loss = quantized_features = codevector_perplexity = None
+
+        # layer normalization (has no effect when `config.do_stable_layer_norm == False`)
+        #        extract_features = self.layer_norm_for_extract(extract_features)
+        #        quantized_features, codevector_perplexity = self.quantizer(extract_features)
+        #
+        # project quantized features twice
+        #        quantized_features = self.project_q(quantized_features)
+        #        quantized_features = self.project_hid(quantized_features)
+        #
+        #        loss = None
+        #        logits = quantized_features
         if not return_dict:
             if loss is not None:
-                return (loss, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
+                return (loss, logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
-            return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
+            return (logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
 
         return UniSpeechSatForPreTrainingOutput(
             loss=loss,

tests/test_modeling_unispeech_sat.py

@@ -34,6 +34,7 @@ if is_torch_available():
 
     from transformers import (
         UniSpeechSatForCTC,
+        UniSpeechSatForPreTraining,
         UniSpeechSatForSequenceClassification,
         UniSpeechSatModel,
         Wav2Vec2FeatureExtractor,
@@ -293,7 +294,9 @@ class UniSpeechSatModelTester:
 @require_torch
 class UniSpeechSatModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (UniSpeechSatForCTC, UniSpeechSatModel, UniSpeechSatForSequenceClassification) if is_torch_available() else ()
+        (UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification)
+        if is_torch_available()
+        else ()
     )
     test_pruning = False
     test_headmasking = False
@@ -407,6 +410,7 @@ class UniSpeechSatModelTest(ModelTesterMixin, unittest.TestCase):
                     "project_q.bias",
                     "feature_projection.projection.weight",
                     "feature_projection.projection.bias",
+                    "label_embeddings_concat",
                 ]
                 if param.requires_grad:
                     if any([x in name for x in uniform_init_parms]):
@@ -490,7 +494,9 @@ class UniSpeechSatModelTest(ModelTesterMixin, unittest.TestCase):
 @require_torch
 class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (UniSpeechSatForCTC, UniSpeechSatModel, UniSpeechSatForSequenceClassification) if is_torch_available() else ()
+        (UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification)
+        if is_torch_available()
+        else ()
     )
     test_pruning = False
     test_headmasking = False
@@ -610,6 +616,7 @@ class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase):
                     "project_q.bias",
                     "feature_projection.projection.weight",
                     "feature_projection.projection.bias",
+                    "label_embeddings_concat",
                 ]
                 if param.requires_grad:
                     if any([x in name for x in uniform_init_parms]):

utils/check_repo.py

@@ -73,9 +73,11 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "DPREncoder",  # Building part of bigger (tested) model.
     "ProphetNetDecoderWrapper",  # Building part of bigger (tested) model.
     "ReformerForMaskedLM",  # Needs to be setup as decoder.
+    "Speech2Text2DecoderWrapper",  # Building part of bigger (tested) model.
     "TFDPREncoder",  # Building part of bigger (tested) model.
     "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
     "TFRobertaForMultipleChoice",  # TODO: fix
+    "TrOCRDecoderWrapper",  # Building part of bigger (tested) model.
     "SeparableConv1D",  # Building part of bigger (tested) model.
 ]
 
@@ -148,6 +150,26 @@ spec = importlib.util.spec_from_file_location(
 transformers = spec.loader.load_module()
 
 
+def check_model_list():
+    """Check the model list inside the transformers library."""
+    # Get the models from the directory structure of `src/transformers/models/`
+    models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models")
+    _models = []
+    for model in os.listdir(models_dir):
+        model_dir = os.path.join(models_dir, model)
+        if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir):
+            _models.append(model)
+
+    # Get the models from the directory structure of `src/transformers/models/`
+    models = [model for model in dir(transformers.models) if not model.startswith("__")]
+
+    missing_models = sorted(list(set(_models).difference(models)))
+    if missing_models:
+        raise Exception(
+            f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}."
+        )
+
+
 # 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():
@@ -163,6 +185,7 @@ def get_model_modules():
         "modeling_flax_auto",
         "modeling_flax_encoder_decoder",
         "modeling_flax_utils",
+        "modeling_speech_encoder_decoder",
         "modeling_transfo_xl_utilities",
         "modeling_tf_auto",
         "modeling_tf_encoder_decoder",
@@ -560,6 +583,8 @@ def check_all_objects_are_documented():
 
 def check_repo_quality():
     """Check all models are properly tested and documented."""
+    print("Checking all models are included.")
+    check_model_list()
     print("Checking all models are public.")
     check_models_are_in_init()
     print("Checking all models are properly tested.")