feat(pt): support loss plugin for external package

deepmd/pt/loss/loss.py

@@ -9,9 +9,12 @@
 from deepmd.utils.data import (
     DataRequirementItem,
 )
+from deepmd.utils.plugin import (
+    make_plugin_registry,
+)
 
 
-class TaskLoss(torch.nn.Module, ABC):
+class TaskLoss(torch.nn.Module, ABC, make_plugin_registry("loss")):
     def __init__(self, **kwargs):
         """Construct loss."""
         super().__init__()
@@ -38,3 +41,23 @@ def display_if_exist(loss: torch.Tensor, find_property: float) -> torch.Tensor:
             whether the property is found
         """
         return loss if bool(find_property) else torch.nan
+
+    @classmethod
+    def get_loss(cls, loss_params: dict) -> "TaskLoss":
+        """Get the loss module by the parameters.
+
+        By default, all the parameters are directly passed to the constructor.
+        If not, override this method.
+
+        Parameters
+        ----------
+        loss_params : dict
+            The loss parameters
+
+        Returns
+        -------
+        TaskLoss
+            The loss module
+        """
+        loss = cls(**loss_params)
+        return loss

deepmd/pt/train/training.py

@@ -28,6 +28,7 @@
     EnergySpinLoss,
     EnergyStdLoss,
     PropertyLoss,
+    TaskLoss,
     TensorLoss,
 )
 from deepmd.pt.model.model import (
@@ -1260,7 +1261,8 @@ def get_loss(loss_params, start_lr, _ntypes, _model):
         loss_params["task_dim"] = task_dim
         return PropertyLoss(**loss_params)
     else:
-        raise NotImplementedError
+        loss_params["starter_learning_rate"] = start_lr
+        return TaskLoss.get_class_by_type(loss_type).get_loss(loss_params)
 
 
 def get_single_model(