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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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96
tests/kit/model_zoo/transformers/albert.py
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@@ -19,44 +19,52 @@ def data_gen_fn():
def data_gen_for_pretrain():
inputs = data_gen_fn()
inputs['labels'] = inputs['input_ids'].clone()
inputs['sentence_order_label'] = torch.zeros(BATCH_SIZE, dtype=torch.int64)
inputs["labels"] = inputs["input_ids"].clone()
inputs["sentence_order_label"] = torch.zeros(BATCH_SIZE, dtype=torch.int64)
return inputs
output_transform_fn = lambda x: x
config = transformers.AlbertConfig(embedding_size=128,
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=256)
config = transformers.AlbertConfig(
embedding_size=128, hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256
)
model_zoo.register(name='transformers_albert',
model_fn=lambda: transformers.AlbertModel(config, add_pooling_layer=False),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_pretraining',
model_fn=lambda: transformers.AlbertForPreTraining(config),
data_gen_fn=data_gen_for_pretrain,
output_transform_fn=lambda x: dict(loss=x.loss),
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_masked_lm',
model_fn=lambda: transformers.AlbertForMaskedLM(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_sequence_classification',
model_fn=lambda: transformers.AlbertForSequenceClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_token_classification',
model_fn=lambda: transformers.AlbertForTokenClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="transformers_albert",
model_fn=lambda: transformers.AlbertModel(config, add_pooling_layer=False),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_albert_for_pretraining",
model_fn=lambda: transformers.AlbertForPreTraining(config),
data_gen_fn=data_gen_for_pretrain,
output_transform_fn=lambda x: dict(loss=x.loss),
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_albert_for_masked_lm",
model_fn=lambda: transformers.AlbertForMaskedLM(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_albert_for_sequence_classification",
model_fn=lambda: transformers.AlbertForSequenceClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_albert_for_token_classification",
model_fn=lambda: transformers.AlbertForTokenClassification(config),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
# ===============================
# Register multi-sentence ALBERT
@@ -80,13 +88,17 @@ def data_gen_for_mcq():
return encoding
model_zoo.register(name='transformers_albert_for_question_answering',
model_fn=lambda: transformers.AlbertForQuestionAnswering(config),
data_gen_fn=data_gen_for_qa,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_multiple_choice',
model_fn=lambda: transformers.AlbertForMultipleChoice(config),
data_gen_fn=data_gen_for_mcq,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="transformers_albert_for_question_answering",
model_fn=lambda: transformers.AlbertForQuestionAnswering(config),
data_gen_fn=data_gen_for_qa,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(
name="transformers_albert_for_multiple_choice",
model_fn=lambda: transformers.AlbertForMultipleChoice(config),
data_gen_fn=data_gen_for_mcq,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)