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from typing import Generator
from trainer.trainer_utils import get_optimizer
class CapacitronOptimizer:
"""Double optimizer class for the Capacitron model."""
def __init__(self, config: dict, model_params: Generator) -> None:
self.primary_params, self.secondary_params = self.split_model_parameters(model_params)
optimizer_names = list(config.optimizer_params.keys())
optimizer_parameters = list(config.optimizer_params.values())
self.primary_optimizer = get_optimizer(
optimizer_names[0],
optimizer_parameters[0],
config.lr,
parameters=self.primary_params,
)
self.secondary_optimizer = get_optimizer(
optimizer_names[1],
self.extract_optimizer_parameters(optimizer_parameters[1]),
optimizer_parameters[1]["lr"],
parameters=self.secondary_params,
)
self.param_groups = self.primary_optimizer.param_groups
def first_step(self):
self.secondary_optimizer.step()
self.secondary_optimizer.zero_grad()
self.primary_optimizer.zero_grad()
def step(self):
# Update param groups to display the correct learning rate
self.param_groups = self.primary_optimizer.param_groups
self.primary_optimizer.step()
def zero_grad(self, set_to_none=False):
self.primary_optimizer.zero_grad(set_to_none)
self.secondary_optimizer.zero_grad(set_to_none)
def load_state_dict(self, state_dict):
self.primary_optimizer.load_state_dict(state_dict[0])
self.secondary_optimizer.load_state_dict(state_dict[1])
def state_dict(self):
return [self.primary_optimizer.state_dict(), self.secondary_optimizer.state_dict()]
@staticmethod
def split_model_parameters(model_params: Generator) -> list:
primary_params = []
secondary_params = []
for name, param in model_params:
if param.requires_grad:
if name == "capacitron_vae_layer.beta":
secondary_params.append(param)
else:
primary_params.append(param)
return [iter(primary_params), iter(secondary_params)]
@staticmethod
def extract_optimizer_parameters(params: dict) -> dict:
"""Extract parameters that are not the learning rate"""
return {k: v for k, v in params.items() if k != "lr"}
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