Optimizer lr scheduler interval (#545)

* Make tensor dtypes `np.float32` for MPS devices

numpy defaults to numpy.float64 when they should be numpy.float32

This caused training to fail on MPS devices but it works on my M1 with
this.

* Add lr scheduler interval config

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Manu Joseph V <manujosephv@gmail.com>

Author: 3 people

src/pytorch_tabular/config/config.py

@@ -677,6 +677,9 @@ class OptimizerConfig:
         lr_scheduler_monitor_metric (Optional[str]): Used with ReduceLROnPlateau, where the plateau is
                 decided based on this metric
 
+        lr_scheduler_interval (Optional[str]): Interval at which to step the LR Scheduler, one of "epoch"
+                or "step". Defaults to `epoch`.
+
     """
 
     optimizer: str = field(
@@ -709,6 +712,11 @@ class OptimizerConfig:
         metadata={"help": "Used with ReduceLROnPlateau, where the plateau is decided based on this metric"},
     )
 
+    lr_scheduler_interval: Optional[str] = field(
+        default="epoch",
+        metadata={"help": "Interval at which to step the LR Scheduler, one of `epoch` or `step`. Defaults to `epoch`."},
+    )
+
     @staticmethod
     def read_from_yaml(filename: str = "config/optimizer_config.yml"):
         config = _read_yaml(filename)

src/pytorch_tabular/models/base_model.py

@@ -588,8 +588,11 @@ def configure_optimizers(self):
                 }
             return {
                 "optimizer": opt,
-                "lr_scheduler": self._lr_scheduler(opt, **self.hparams.lr_scheduler_params),
-                "monitor": self.hparams.lr_scheduler_monitor_metric,
+                "lr_scheduler": {
+                    "scheduler": self._lr_scheduler(opt, **self.hparams.lr_scheduler_params),
+                    "monitor": self.hparams.lr_scheduler_monitor_metric,
+                    "interval": self.hparams.lr_scheduler_interval,
+                },
             }
         else:
             return opt

src/pytorch_tabular/ssl_models/base_model.py

@@ -181,7 +181,7 @@ def test_step(self, batch, batch_idx):
     def on_validation_epoch_end(self) -> None:
         if hasattr(self.hparams, "log_logits") and self.hparams.log_logits:
             warnings.warn(
-                "Logging Logits is disabled for SSL tasks. Set `log_logits` to False" " to turn off this warning"
+                "Logging Logits is disabled for SSL tasks. Set `log_logits` to False to turn off this warning"
             )
         super().on_validation_epoch_end()
 
@@ -219,8 +219,11 @@ def configure_optimizers(self):
                 }
             return {
                 "optimizer": opt,
-                "lr_scheduler": self._lr_scheduler(opt, **self.hparams.lr_scheduler_params),
-                "monitor": self.hparams.lr_scheduler_monitor_metric,
+                "lr_scheduler": {
+                    "scheduler": self._lr_scheduler(opt, **self.hparams.lr_scheduler_params),
+                    "monitor": self.hparams.lr_scheduler_monitor_metric,
+                    "interval": self.hparams.lr_scheduler_interval,
+                },
             }
         else:
             return opt