def __init__(self, hparams, num_labels):
super().__init__()
self.hparams = hparams
model_name_or_path = "monologg/koelectra-base-discriminator"
config = AutoConfig.from_pretrained(model_name_or_path,
num_labels=num_labels)
self.model = AutoModelForSequenceClassification.from_pretrained(
model_name_or_path, config=config)
self.num_labels = num_labels
def test_conversion_adaptive_model_classification():
farm_model = Converter.convert_from_transformers(
"deepset/bert-base-german-cased-hatespeech-GermEval18Coarse",
device="cpu")
transformer_model = farm_model.convert_to_transformers()[0]
transformer_model2 = AutoModelForSequenceClassification.from_pretrained(
"deepset/bert-base-german-cased-hatespeech-GermEval18Coarse")
# compare weights
for p1, p2 in zip(transformer_model.parameters(),
transformer_model2.parameters()):
assert (p1.data.ne(p2.data).sum() == 0)
def convert_to_transformers(self):
if len(self.prediction_heads) != 1:
raise ValueError(
f"Currently conversion only works for models with a SINGLE prediction head. "
f"Your model has {len(self.prediction_heads)}")
#TODO add more infos to config
if self.prediction_heads[0].model_type == "span_classification":
# init model
transformers_model = AutoModelForQuestionAnswering.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.qa_outputs.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
elif self.prediction_heads[0].model_type == "text_classification":
# add more info to config
self.language_model.model.config.id2label = {
id: label
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.label2id = {
label: id
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.finetuning_task = "text_classification"
self.language_model.model.config.language = self.language_model.language
# init model
transformers_model = AutoModelForSequenceClassification.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.classifier.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
else:
raise NotImplementedError(
f"FARM -> Transformers conversion is not supported yet for"
f" prediction heads of type {self.prediction_heads[0].model_type}"
)
pass
return transformers_model
def convert_to_transformers(self):
if len(self.prediction_heads) != 1:
raise ValueError(
f"Currently conversion only works for models with a SINGLE prediction head. "
f"Your model has {len(self.prediction_heads)}")
elif len(self.prediction_heads[0].layer_dims) != 2:
raise ValueError(
f"Currently conversion only works for PredictionHeads that are a single layer Feed Forward NN with dimensions [LM_output_dim, number_classes].\n"
f" Your PredictionHead has {str(self.prediction_heads[0].layer_dims)} dimensions."
)
#TODO add more infos to config
if self.prediction_heads[0].model_type == "span_classification":
# init model
transformers_model = AutoModelForQuestionAnswering.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.qa_outputs.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
elif self.prediction_heads[0].model_type == "language_modelling":
# init model
transformers_model = AutoModelWithLMHead.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
ph_state_dict = self.prediction_heads[0].state_dict()
ph_state_dict["transform.dense.weight"] = ph_state_dict.pop(
"dense.weight")
ph_state_dict["transform.dense.bias"] = ph_state_dict.pop(
"dense.bias")
ph_state_dict["transform.LayerNorm.weight"] = ph_state_dict.pop(
"LayerNorm.weight")
ph_state_dict["transform.LayerNorm.bias"] = ph_state_dict.pop(
"LayerNorm.bias")
transformers_model.cls.predictions.load_state_dict(ph_state_dict)
logger.warning(
"Currently only the Masked Language Modeling component of the prediction head is converted, "
"not the Next Sentence Prediction or Sentence Order Prediction components"
)
elif self.prediction_heads[0].model_type == "text_classification":
if self.language_model.model.base_model_prefix == "roberta":
# Classification Heads in transformers have different architecture across Language Model variants
# The RobertaClassificationhead has components: input2dense, dropout, tanh, dense2output
# The tanh function cannot be mapped to current FARM style linear Feed Forward ClassificationHeads.
# So conversion for this type cannot work. We would need a compatible FARM RobertaClassificationHead
logger.error(
"Conversion for Text Classification with Roberta or XLMRoberta not possible at the moment."
)
raise NotImplementedError
# add more info to config
self.language_model.model.config.id2label = {
id: label
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.label2id = {
label: id
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.finetuning_task = "text_classification"
self.language_model.model.config.language = self.language_model.language
self.language_model.model.config.num_labels = self.prediction_heads[
0].num_labels
# init model
transformers_model = AutoModelForSequenceClassification.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.classifier.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
elif self.prediction_heads[0].model_type == "token_classification":
# add more info to config
self.language_model.model.config.id2label = {
id: label
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.label2id = {
label: id
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.finetuning_task = "token_classification"
self.language_model.model.config.language = self.language_model.language
self.language_model.model.config.num_labels = self.prediction_heads[
0].num_labels
# init model
transformers_model = AutoModelForTokenClassification.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.classifier.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
else:
raise NotImplementedError(
f"FARM -> Transformers conversion is not supported yet for"
f" prediction heads of type {self.prediction_heads[0].model_type}"
)
pass
return transformers_model
def convert_to_transformers(self):
if len(self.prediction_heads) != 1:
raise ValueError(
f"Currently conversion only works for models with a SINGLE prediction head. "
f"Your model has {len(self.prediction_heads)}")
#TODO add more infos to config
if self.prediction_heads[0].model_type == "span_classification":
# init model
transformers_model = AutoModelForQuestionAnswering.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.qa_outputs.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
elif self.prediction_heads[0].model_type == "language_modelling":
# init model
transformers_model = AutoModelWithLMHead.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
ph_state_dict = self.prediction_heads[0].state_dict()
ph_state_dict["transform.dense.weight"] = ph_state_dict.pop(
"dense.weight")
ph_state_dict["transform.dense.bias"] = ph_state_dict.pop(
"dense.bias")
ph_state_dict["transform.LayerNorm.weight"] = ph_state_dict.pop(
"LayerNorm.weight")
ph_state_dict["transform.LayerNorm.bias"] = ph_state_dict.pop(
"LayerNorm.bias")
transformers_model.cls.predictions.load_state_dict(ph_state_dict)
logger.warning(
"Currently only the Masked Language Modeling component of the prediction head is converted, "
"not the Next Sentence Prediction or Sentence Order Prediction components"
)
elif self.prediction_heads[0].model_type == "text_classification":
# add more info to config
self.language_model.model.config.id2label = {
id: label
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.label2id = {
label: id
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.finetuning_task = "text_classification"
self.language_model.model.config.language = self.language_model.language
self.language_model.model.config.num_labels = self.prediction_heads[
0].num_labels
# init model
transformers_model = AutoModelForSequenceClassification.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.classifier.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
elif self.prediction_heads[0].model_type == "token_classification":
# add more info to config
self.language_model.model.config.id2label = {
id: label
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.label2id = {
label: id
for id, label in enumerate(self.prediction_heads[0].label_list)
}
self.language_model.model.config.finetuning_task = "token_classification"
self.language_model.model.config.language = self.language_model.language
self.language_model.model.config.num_labels = self.prediction_heads[
0].num_labels
# init model
transformers_model = AutoModelForTokenClassification.from_config(
self.language_model.model.config)
# transfer weights for language model + prediction head
setattr(transformers_model, transformers_model.base_model_prefix,
self.language_model.model)
transformers_model.classifier.load_state_dict(
self.prediction_heads[0].feed_forward.feed_forward[0].
state_dict())
else:
raise NotImplementedError(
f"FARM -> Transformers conversion is not supported yet for"
f" prediction heads of type {self.prediction_heads[0].model_type}"
)
pass
return transformers_model
