Reorder the System methods

lighter/system.py

@@ -87,46 +87,6 @@ def __init__(
         self.mode = None
         self.batch_size = 0
 
-    def forward(self, input: Any) -> Any:  # pylint: disable=arguments-differ
-        """
-        Forward pass through the model. Supports multi-input models.
-
-        Args:
-            input: The input data.
-
-        Returns:
-            Any: The model's output.
-        """
-
-        # Keyword arguments to pass to the forward method
-        kwargs = {}
-        # Add `epoch` argument if forward accepts it
-        if hasarg(self.model.forward, Data.EPOCH):
-            kwargs[Data.EPOCH] = self.current_epoch
-        # Add `step` argument if forward accepts it
-        if hasarg(self.model.forward, Data.STEP):
-            kwargs[Data.STEP] = self.global_step
-
-        # Use the inferer if specified and in val/test/predict mode
-        if self.inferer and self.mode in [Mode.VAL, Mode.TEST, Mode.PREDICT]:
-            return self.inferer(input, self.model, **kwargs)
-        else:
-            return self.model(input, **kwargs)
-
-    def configure_optimizers(self) -> dict:
-        """
-        Configures the optimizers and learning rate schedulers.
-
-        Returns:
-            dict: A dictionary containing the optimizer and scheduler.
-        """
-        if self.optimizer is None:
-            raise ValueError("Please specify 'system.optimizer' in the config.")
-        if self.scheduler is None:
-            return {"optimizer": self.optimizer}
-        else:
-            return {"optimizer": self.optimizer, "lr_scheduler": self.scheduler}
-
     def _step(self, batch: dict, batch_idx: int) -> dict[str, Any] | Any:
         """
         Performs a step in the specified mode, processing the batch and calculating loss and metrics.
@@ -154,6 +114,32 @@ def _prepare_batch(self, batch: dict) -> tuple[Any, Any, Any]:
         input, target, identifier = adapters.batch(batch)
         return input, target, identifier
 
+    def forward(self, input: Any) -> Any:  # pylint: disable=arguments-differ
+        """
+        Forward pass through the model. Supports multi-input models.
+
+        Args:
+            input: The input data.
+
+        Returns:
+            Any: The model's output.
+        """
+
+        # Keyword arguments to pass to the forward method
+        kwargs = {}
+        # Add `epoch` argument if forward accepts it
+        if hasarg(self.model.forward, Data.EPOCH):
+            kwargs[Data.EPOCH] = self.current_epoch
+        # Add `step` argument if forward accepts it
+        if hasarg(self.model.forward, Data.STEP):
+            kwargs[Data.STEP] = self.global_step
+
+        # Use the inferer if specified and in val/test/predict mode
+        if self.inferer and self.mode in [Mode.VAL, Mode.TEST, Mode.PREDICT]:
+            return self.inferer(input, self.model, **kwargs)
+        else:
+            return self.model(input, **kwargs)
+
     def _calculate_loss(self, input: Any, target: Any, pred: Any) -> Tensor | dict[str, Tensor] | None:
         adapters = getattr(self.adapters, self.mode)
         loss = None
@@ -238,26 +224,6 @@ def _prepare_output(
             Data.EPOCH: self.current_epoch,
         }
 
-    def on_train_start(self) -> None:
-        """Called when the train begins."""
-        self.mode = Mode.TRAIN
-        self.batch_size = self.train_dataloader().batch_size
-
-    def on_validation_start(self) -> None:
-        """Called when the validation loop begins."""
-        self.mode = Mode.VAL
-        self.batch_size = self.val_dataloader().batch_size
-
-    def on_test_start(self) -> None:
-        """Called when the test begins."""
-        self.mode = Mode.TEST
-        self.batch_size = self.test_dataloader().batch_size
-
-    def on_predict_start(self) -> None:
-        """Called when the prediction begins."""
-        self.mode = Mode.PREDICT
-        self.batch_size = self.predict_dataloader().batch_size
-
     @property
     def learning_rate(self) -> float:
         """
@@ -281,3 +247,37 @@ def learning_rate(self, value: float) -> None:
         if len(self.optimizer.param_groups) > 1:
             raise ValueError("The learning rate is not available when there are multiple optimizer parameter groups.")
         self.optimizer.param_groups[0]["lr"] = value
+
+    def configure_optimizers(self) -> dict:
+        """
+        Configures the optimizers and learning rate schedulers.
+
+        Returns:
+            dict: A dictionary containing the optimizer and scheduler.
+        """
+        if self.optimizer is None:
+            raise ValueError("Please specify 'system.optimizer' in the config.")
+        if self.scheduler is None:
+            return {"optimizer": self.optimizer}
+        else:
+            return {"optimizer": self.optimizer, "lr_scheduler": self.scheduler}
+
+    def on_train_start(self) -> None:
+        """Called when the train begins."""
+        self.mode = Mode.TRAIN
+        self.batch_size = self.train_dataloader().batch_size
+
+    def on_validation_start(self) -> None:
+        """Called when the validation loop begins."""
+        self.mode = Mode.VAL
+        self.batch_size = self.val_dataloader().batch_size
+
+    def on_test_start(self) -> None:
+        """Called when the test begins."""
+        self.mode = Mode.TEST
+        self.batch_size = self.test_dataloader().batch_size
+
+    def on_predict_start(self) -> None:
+        """Called when the prediction begins."""
+        self.mode = Mode.PREDICT
+        self.batch_size = self.predict_dataloader().batch_size