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Adding schedule free with adam and nadam #672

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38 changes: 38 additions & 0 deletions init2winit/optimizer_lib/optimizers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -394,6 +394,44 @@ def get_optimizer(hps, model=None, batch_axis_name=None):
eps=hps.opt_hparams['eps'],
weight_decay=hps.opt_hparams['weight_decay'],
)
elif hps.optimizer == 'schedule_free_adam':
base_opt = utils.static_inject_hyperparams(optax.adamw)(
learning_rate=0.0,
b1=0.,
b2=hps.opt_hparams['beta2'],
eps=hps.opt_hparams['epsilon'],
weight_decay=hps.opt_hparams['weight_decay'],
)
opt_init, opt_update = utils.static_inject_hyperparams(
optax.contrib.schedule_free
)(
learning_rate=0.0,
base_optimizer=base_opt,
b1=hps.opt_hparams['beta1'],
weight_lr_power=hps.opt_hparams['weight_lr_power'],
)
elif hps.optimizer == 'schedule_free_nadam':
base_opt = utils.static_inject_hyperparams(kitchen_sink.nadamw)(
learning_rate=0.0,
b1=0.0,
b2=hps.opt_hparams['beta2'],
eps=hps.opt_hparams['epsilon'],
eps_root=hps.opt_hparams.get('epsilon_root', 0.0),
debias=hps.opt_hparams.get('debias', True),
weight_decay=weight_decay,
# NOTE(dsuo): we provide this wiring, but specifying a weight decay
# mask in a config file / serializing properly is not completely
# straightforward.
weight_decay_mask=hps.opt_hparams.get('weight_decay_mask', None),
)
opt_init, opt_update = utils.static_inject_hyperparams(
optax.contrib.schedule_free
)(
learning_rate=0.0,
base_optimizer=base_opt,
b1=hps.opt_hparams['beta1'],
weight_lr_power=hps.opt_hparams['weight_lr_power'],
)

elif hps.optimizer == 'kitchen_sink':
opt_init, opt_update = utils.static_inject_hyperparams(
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10 changes: 9 additions & 1 deletion init2winit/trainer_lib/base_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -549,7 +549,15 @@ def _eval(
)

if self._eval_use_ema:
if isinstance(
if (
schedule_free_state := optax.tree_utils.tree_get(
self._optimizer_state, 'ScheduleFreeState'
)
) is not None:
eval_params = optax.contrib.schedule_free_eval_params(
schedule_free_state, self._params
)
elif isinstance(
self._optimizer_state, optax.InjectStatefulHyperparamsState
):
eval_params = self._optimizer_state.inner_state[0][0].ema
Expand Down