Fix setting up of ReduceLROnPlateau learning rate scheduler

nemo/core/config/schedulers.py

@@ -284,4 +284,5 @@ def get_scheduler_config(name: str, **kwargs: Optional[Dict[str, Any]]) -> Sched
     'WarmupAnnealingParams': WarmupAnnealingParams,
     'PolynomialDecayAnnealingParams': PolynomialDecayAnnealingParams,
     'PolynomialHoldDecayAnnealingParams': PolynomialHoldDecayAnnealingParams,
+    'ReduceLROnPlateauParams': ReduceLROnPlateauParams,
 }

tests/core/test_optimizers_schedulers.py

@@ -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()