import jittor as jit
from jittor import Module
from typing import List, Callable, Dict
from jboat.higher_jit.patch import _MonkeyPatchBase
from jboat.utils.op_utils import update_tensor_grads
from jboat.operation_registry import register_class
from jboat.na_ol.hyper_gradient import HyperGradient
[docs]
@register_class
class IAD(HyperGradient):
"""
Computes the hyper-gradient of the upper-level variables using Initialization-based Auto Differentiation (IAD) [1].
Parameters
----------
ll_objective : Callable
The lower-level objective function of the BLO problem.
ul_objective : Callable
The upper-level objective function of the BLO problem.
ll_model : jittor.Module
The lower-level model of the BLO problem.
ul_model : jittor.Module
The upper-level model of the BLO problem.
ll_var : List[jittor.Var]
List of variables optimized with the lower-level objective.
ul_var : List[jittor.Var]
List of variables optimized with the upper-level objective.
solver_config : Dict[str, Any]
Dictionary containing solver configurations.
References
----------
[1] Finn C., Abbeel P., Levine S., "Model-agnostic meta-learning for fast adaptation of deep networks", in ICML, 2017.
"""
def __init__(
self,
ll_objective: Callable,
ul_objective: Callable,
ll_model: Module,
ul_model: Module,
ll_var: List,
ul_var: List,
solver_config: Dict,
):
super(IAD, self).__init__(
ll_objective,
ul_objective,
ul_model,
ll_model,
ll_var,
ul_var,
solver_config,
)
[docs]
def compute_gradients(
self,
ll_feed_dict: Dict,
ul_feed_dict: Dict,
auxiliary_model: _MonkeyPatchBase,
max_loss_iter: int = 0,
hyper_gradient_finished: bool = False,
next_operation: str = None,
**kwargs
):
"""
Compute the hyper-gradients of the upper-level variables with the data from feed_dict and patched models.
Parameters
----------
ll_feed_dict : Dict
Dictionary containing the lower-level data used for optimization. It typically includes training data, targets, and other information required to compute the LL objective.
ul_feed_dict : Dict
Dictionary containing the upper-level data used for optimization. It typically includes validation data, targets, and other information required to compute the UL objective.
auxiliary_model : _MonkeyPatchBase
A patched lower model wrapped by the `higher` library. It serves as the lower-level model for optimization.
max_loss_iter : int
The number of iterations used for backpropagation.
next_operation : str
The next operator for the calculation of the hypergradient.
hyper_gradient_finished : bool
A boolean flag indicating whether the hypergradient computation is finished.
Returns
-------
Dict
A dictionary containing the upper-level objective and the status of hypergradient computation.
"""
if next_operation is not None:
lower_model_params = kwargs.get(
"lower_model_params", list(auxiliary_model.parameters())
)
hparams = list(auxiliary_model.parameters(time=0))
return {
"ll_feed_dict": ll_feed_dict,
"ul_feed_dict": ul_feed_dict,
"auxiliary_model": auxiliary_model,
"max_loss_iter": max_loss_iter,
"hyper_gradient_finished": hyper_gradient_finished,
"hparams": hparams,
"lower_model_params": lower_model_params,
**kwargs,
}
else:
lower_model_params = kwargs.get(
"lower_model_params", list(auxiliary_model.parameters())
)
ul_loss = self.ul_objective(
ul_feed_dict, self.ul_model, auxiliary_model, params=lower_model_params
)
grads_upper = jit.grad(ul_loss, list(auxiliary_model.parameters(time=0)))
update_tensor_grads(self.ul_var, grads_upper)
return {"upper_loss": ul_loss.item(), "hyper_gradient_finished": True}