def add_enc_adapters(bert_model: BertModel,
config: AdapterConfig) -> BertModel:
# Replace specific layer with adapter-added layer
bert_encoder = bert_model.encoder
for i in range(len(bert_model.encoder.layer)):
bert_encoder.layer[i].attention.output = adapt_bert_self_output(
config)(bert_encoder.layer[i].attention.output)
bert_encoder.layer[i].output = adapt_bert_output(config)(
bert_encoder.layer[i].output)
# Freeze all parameters
for param in bert_model.parameters():
param.requires_grad = False
# Unfreeze trainable parts — layer norms and adapters
for name, sub_module in bert_model.named_modules():
if isinstance(sub_module, (Adapter_func, BertLayerNorm)):
for param_name, param in sub_module.named_parameters():
param.requires_grad = True
return bert_model
