def test_onecyclelr_with_epoch_interval_warns():
"""Test warning when a OneCycleLR is used and interval is epoch."""
model = BoringModel()
optimizer = optim.Adam(model.parameters())
lr_scheduler = {"scheduler": optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, total_steps=3)}
with pytest.warns(RuntimeWarning, match="Are you sure you didn't mean 'interval': 'step'?"):
_configure_schedulers_automatic_opt([lr_scheduler], None)
def configure_optimizers(self):
"""
Combine architecture optimizers and user's model optimizers.
You can overwrite :meth:`configure_architecture_optimizers` if architecture optimizers are needed in your NAS algorithm.
For now :attr:`model` is tested against evaluators in :mod:`nni.retiarii.evaluator.pytorch.lightning`
and it only returns 1 optimizer.
But for extendibility, codes for other return value types are also implemented.
"""
# pylint: disable=assignment-from-none
arc_optimizers = self.configure_architecture_optimizers()
if arc_optimizers is None:
return self.model.configure_optimizers()
if isinstance(arc_optimizers, optim.Optimizer):
arc_optimizers = [arc_optimizers]
self.arc_optim_count = len(arc_optimizers)
# FIXME: this part uses non-official lightning API.
# The return values ``frequency`` and ``monitor`` are ignored because lightning requires
# ``len(optimizers) == len(frequency)``, and gradient backword is handled manually.
# For data structure of variables below, please see pytorch lightning docs of ``configure_optimizers``.
try:
# above v1.6
from pytorch_lightning.core.optimizer import ( # pylint: disable=import-error
_configure_optimizers, # type: ignore
_configure_schedulers_automatic_opt, # type: ignore
_configure_schedulers_manual_opt # type: ignore
)
w_optimizers, lr_schedulers, self.frequencies, monitor = \
_configure_optimizers(self.model.configure_optimizers()) # type: ignore
lr_schedulers = (_configure_schedulers_automatic_opt(
lr_schedulers, monitor) if self.automatic_optimization else
_configure_schedulers_manual_opt(lr_schedulers))
except ImportError:
# under v1.5
w_optimizers, lr_schedulers, self.frequencies, monitor = \
self.trainer._configure_optimizers(self.model.configure_optimizers()) # type: ignore
lr_schedulers = self.trainer._configure_schedulers(
lr_schedulers, monitor,
not self.automatic_optimization) # type: ignore
if any(sch["scheduler"].optimizer not in w_optimizers
for sch in lr_schedulers): # type: ignore
raise Exception(
"Some schedulers are attached with an optimizer that wasn't returned from `configure_optimizers`."
)
# variables used to handle optimizer frequency
self.cur_optimizer_step = 0
self.cur_optimizer_index = 0
return arc_optimizers + w_optimizers, lr_schedulers