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# -*- coding: utf-8 -*-
# @Time : 2023/02/18 02:07 p.m.
# @Author : JianingWang
# @File : parameter_freeze.py
import torch
"""
This is use for parameter fixing and unfreezing, which can be viewed as parameter-efficient settings.
"""
class ParameterFreeze():
# freeze all parameters
def freeze_lm(self, model: torch.nn.Module):
for name, param in model.named_parameters():
param.requires_grad = False
return model
# freeze all parameters without cls / mlm head
def freeze_lm_encoder(self, model: torch.nn.Module):
for name, param in model.named_parameters():
if "lm_head" in name or ("cls" in name):
print(name)
continue
param.requires_grad = False
return model
# freeze all parameters without bias
def freeze_lm_finetune_bias(self, model: torch.nn.Module):
for name, param in model.named_parameters():
if "bias" in name:
print(name)
continue
param.requires_grad = False
return model
# freeze the component that user defined
def freeze_lm_component(self, model: torch.nn.Module, component: str):
if "attention" in component:
for name, param in model.named_parameters():
if "attention" in name:
if "output" in component:
if "output" in name:
continue
else:
continue
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif "feedforward" in component:
for name, param in model.named_parameters():
if "dense" in name and "attention" not in name:
if "output" in component:
if "output" in name:
continue
else:
if "intermediate" in component:
if "intermediate" in name:
continue
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif component == "adapter":
for name, param in model.named_parameters():
if "adapter" in name:
continue
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif "embedding" in component:
for name, param in model.named_parameters():
if "embedding" in name:
continue
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif "bias" in component:
for name, param in model.named_parameters():
if "bias" in name:
continue
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif "head" in component:
for name, param in model.named_parameters():
param.requires_grad = False
model = self.unfreeze_classification_head(model)
elif "prompt_emb" in component:
for name, param in model.named_parameters():
if "prompt_emb" in name:
continue
param.requires_grad = False
return model
# unfreeze cls head
def unfreeze_classification_head(self, model: torch.nn.Module):
for name, param in model.named_parameters():
if "lm_head" in name or ("cls" in name) or ("classifier" in name):
param.requires_grad = True
return model
# freeze k layers
def freeze_lm_k_layers(self, model: torch.nn.Module, k):
keep_layers = []
update_parameters = []
for i in range(k):
keep_layers.append("layer."+str(23-i))
for name, param in model.named_parameters():
update = False
for layer_num in keep_layers:
if layer_num in name:
if "dense" in name and "attention" not in name:
if "output" in name:
print(name)
update_parameters.append(name)
update = True
if not update:
param.requires_grad = False
model = self.unfreeze_classification_head(model)
return model
def unfreeze_lm(self, model: torch.nn.Module):
for param in model.parameters():
param.requires_grad = True
return model
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