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

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* 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>
This commit is contained in:
Yih-Dar
2021-11-01 11:52:36 +01:00
committed by GitHub
parent 9fc1951711
commit 9450bfcc6c
4 changed files with 57 additions and 14 deletions
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7
src/transformers/models/__init__.py
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@@ -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,

28
src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
View File

@@ -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 = None
logits = extract_features
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 (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return (loss, logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return (logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return UniSpeechSatForPreTrainingOutput(
loss=loss,

11
tests/test_modeling_unispeech_sat.py
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@@ -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]):

25
utils/check_repo.py
View File

@@ -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.")