Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix setting up of ReduceLROnPlateau learning rate scheduler #5444

Merged
merged 2 commits into from
Nov 17, 2022
Merged

Fix setting up of ReduceLROnPlateau learning rate scheduler #5444

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1fe1528
Fix tests
PeganovAnton Aug 10, 2022
e77411a
Add accidentally lost changes
PeganovAnton Aug 10, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions nemo/core/config/schedulers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -284,4 +284,5 @@ def get_scheduler_config(name: str, **kwargs: Optional[Dict[str, Any]]) -> Sched
'WarmupAnnealingParams': WarmupAnnealingParams,
'PolynomialDecayAnnealingParams': PolynomialDecayAnnealingParams,
'PolynomialHoldDecayAnnealingParams': PolynomialHoldDecayAnnealingParams,
'ReduceLROnPlateauParams': ReduceLROnPlateauParams,
}
45 changes: 45 additions & 0 deletions tests/core/test_optimizers_schedulers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -309,6 +309,51 @@ def test_sched_config_parse_from_cls(self):
scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)

@pytest.mark.unit
def test_sched_config_parse_reduce_on_plateau(self):
model = TempModel()
opt_cls = optim.get_optimizer('novograd')
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
reduce_on_plateau_parameters = {
'mode': 'min',
'factor': 0.5,
'patience': 1,
'threshold': 1e-4,
'threshold_mode': 'rel',
'min_lr': 1e-6,
'eps': 1e-7,
'verbose': True,
'cooldown': 1,
}
basic_sched_config = {
'name': 'ReduceLROnPlateau',
'monitor': 'val_loss',
'reduce_on_plateau': True,
'max_steps': self.MAX_STEPS,
}
basic_sched_config.update(reduce_on_plateau_parameters)
scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
assert isinstance(scheduler_setup['scheduler'], torch.optim.lr_scheduler.ReduceLROnPlateau)
for k, v in reduce_on_plateau_parameters.items():
if k == 'min_lr':
k += 's'
v = [v]
found_v = getattr(scheduler_setup['scheduler'], k)
assert (
found_v == v
), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."
dict_config = omegaconf.OmegaConf.create(basic_sched_config)
scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
assert isinstance(scheduler_setup['scheduler'], torch.optim.lr_scheduler.ReduceLROnPlateau)
for k, v in reduce_on_plateau_parameters.items():
if k == 'min_lr':
k += 's'
v = [v]
found_v = getattr(scheduler_setup['scheduler'], k)
assert (
found_v == v
), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."

@pytest.mark.unit
def test_WarmupPolicy(self):
model = TempModel()
Expand Down