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import torch
from src.linearize import LinearizedImageEncoder
from src.modeling import ImageEncoder
from src.attention_only_finetune import AttentionOnlyFinetuneEncoder
class _TaskVector(abc.ABC):
def __init__(
self, pretrained_checkpoint=None, finetuned_checkpoint=None, vector=None
):
"""
Initializes the task vector from a pretrained and a finetuned checkpoints.
This can either be done by passing two state dicts (one corresponding to the
pretrained model, and another to the finetuned model), or by directly passing in
the task vector state dict.
"""
if vector is not None:
self.vector = vector
else:
assert (
pretrained_checkpoint is not None and finetuned_checkpoint is not None
)
with torch.no_grad():
pretrained_obj = self._load_checkpoint(pretrained_checkpoint)
finetuned_obj = self._load_checkpoint(finetuned_checkpoint)
if hasattr(pretrained_obj, 'state_dict'):
pretrained_state_dict = pretrained_obj.state_dict()
else:
pretrained_state_dict = pretrained_obj
if hasattr(finetuned_obj, 'state_dict'):
finetuned_state_dict = finetuned_obj.state_dict()
else:
finetuned_state_dict = finetuned_obj
self.vector = {}
for key in pretrained_state_dict:
if pretrained_state_dict[key].dtype not in [torch.float32, torch.float16, torch.bfloat16]:
continue
if key in finetuned_state_dict:
self.vector[key] = (
finetuned_state_dict[key] - pretrained_state_dict[key]
)
@abc.abstractmethod
def _load_checkpoint(self, checkpoint):
raise NotImplementedError
@abc.abstractmethod
def _cast_to_same_type(self, other):
raise NotImplementedError
def __add__(self, other):
other = self._cast_to_same_type(other)
with torch.no_grad():
new_vector = {}
for key in self.vector:
if key not in other.vector:
print(f"Warning, key {key} is not present in both task vectors.")
continue
new_vector[key] = self.vector[key] + other.vector[key]
return self.__class__(vector=new_vector)
def __sub__(self, other):
return self.__add__(-other)
def __radd__(self, other):
if other is None or isinstance(other, int):
return self
return self.__add__(other)
def __neg__(self):
with torch.no_grad():
new_vector = {}
for key in self.vector:
new_vector[key] = -self.vector[key]
return self.__class__(vector=new_vector)
def __pow__(self, power):
with torch.no_grad():
new_vector = {}
for key in self.vector:
new_vector[key] = self.vector[key] ** power
return self.__class__(vector=new_vector)
def __mul__(self, other):
with torch.no_grad():
new_vector = {}
for key in self.vector:
new_vector[key] = other * self.vector[key]
return self.__class__(vector=new_vector)
def dot(self, other):
other = self._cast_to_same_type(other)
with torch.no_grad():
dot_product = 0.0
for key in self.vector:
if key not in other.vector:
print(f"Warning, key {key} is not present in both task vectors.")
continue
dot_product += torch.sum(self.vector[key] * other.vector[key])
return dot_product
def norm(self):
return torch.sqrt(self.dot(self))
def apply_to(self, pretrained_checkpoint, scaling_coef=1.0):
"""Apply a task vector to a pretrained model."""
with torch.no_grad():
pretrained_model = self._load_checkpoint(pretrained_checkpoint)
if hasattr(pretrained_model, 'state_dict'):
new_state_dict = pretrained_model.state_dict()
else:
new_state_dict = pretrained_model.copy()
pretrained_state_dict = new_state_dict.copy()
for key in pretrained_state_dict:
if key in self.vector:
new_state_dict[key] = (
pretrained_state_dict[key] + scaling_coef * self.vector[key]
)
if hasattr(pretrained_model, 'state_dict'):
pretrained_model.load_state_dict(new_state_dict)
return pretrained_model
else:
from src.args import parse_arguments
args = parse_arguments()
if isinstance(self, NonLinearTaskVector):
encoder = self._build_model_from_checkpoint(pretrained_checkpoint, args)
encoder.load_state_dict(new_state_dict)
return encoder
else:
pretrained_model.load_state_dict(new_state_dict)
return pretrained_model
class NonLinearTaskVector(_TaskVector):
"""A task vector for nonlinear models."""
def _load_checkpoint(self, checkpoint):
return torch.load(checkpoint, map_location="cpu")
def _build_model_from_checkpoint(self, checkpoint_path, args):
mode = args.finetuning_mode
if mode in ["linear-2", "linear-2_ortho"]:
return AttentionOnlyFinetuneEncoder(args)
return ImageEncoder(args)
def apply_to(self, pretrained_checkpoint, scaling_coef=1.0):
with torch.no_grad():
from src.args import parse_arguments
args = parse_arguments()
pretrained_model = self._build_model_from_checkpoint(pretrained_checkpoint, args)
pretrained_state_dict = torch.load(pretrained_checkpoint, map_location='cpu')
if hasattr(pretrained_state_dict, 'state_dict'):
pretrained_state_dict = pretrained_state_dict.state_dict()
new_state_dict = pretrained_state_dict.copy()
for key in pretrained_state_dict:
if key in self.vector:
new_state_dict[key] += scaling_coef * self.vector[key]
pretrained_model.load_state_dict(new_state_dict)
return pretrained_model
def _cast_to_same_type(self, other):
if isinstance(other, LinearizedTaskVector):
return linear_to_nonlinear(other, self.vector.keys())
return other
class LinearizedTaskVector(_TaskVector):
"""A task vector for linearized models."""
def _load_checkpoint(self, checkpoint):
return LinearizedImageEncoder.load(checkpoint)
def apply_to(self, pretrained_checkpoint, scaling_coef=1.0):
with torch.no_grad():
pretrained_model = self._load_checkpoint(pretrained_checkpoint)
new_state_dict = pretrained_model.state_dict()
pretrained_state_dict = new_state_dict.copy()
for key in pretrained_state_dict:
if key in self.vector:
new_state_dict[key] += scaling_coef * self.vector[key]
pretrained_model.load_state_dict(new_state_dict)
return pretrained_model
def get_named_parameters(self, param_names):
params = {k: v for k, v in self.vector.items() if "model.params0" not in k}
return {k: v for k, v in zip(param_names, params.values())}
def _cast_to_same_type(self, other):
if isinstance(other, NonLinearTaskVector):
return nonlinear_to_linear(other)
return other
def nonlinear_to_linear(nonlinear_task_vector):
if isinstance(nonlinear_task_vector, LinearizedTaskVector):
return nonlinear_task_vector
else:
linear_params = {
f"model.params.{i}": v
for i, v in enumerate(nonlinear_task_vector.vector.values())
}
linear_params.update({
f"model.params0.{i}": torch.zeros_like(v)
for i, v in enumerate(nonlinear_task_vector.vector.values())
})
return LinearizedTaskVector(vector=linear_params)
def linear_to_nonlinear(linear_task_vector, param_names):
if isinstance(linear_task_vector, NonLinearTaskVector):
return linear_task_vector
else:
return NonLinearTaskVector(
vector=linear_task_vector.get_named_parameters(param_names)
)
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