Chore: refactor atomic bias

deepmd/dpmodel/fitting/dipole_fitting.py

@@ -36,8 +36,6 @@ class DipoleFitting(GeneralFitting):
 
     Parameters
     ----------
-    var_name
-            The name of the output variable.
     ntypes
             The number of atom types.
     dim_descrpt
@@ -86,7 +84,6 @@ class DipoleFitting(GeneralFitting):
 
     def __init__(
         self,
-        var_name: str,
         ntypes: int,
         dim_descrpt: int,
         embedding_width: int,
@@ -124,7 +121,7 @@ def __init__(
         self.r_differentiable = r_differentiable
         self.c_differentiable = c_differentiable
         super().__init__(
-            var_name=var_name,
+            var_name="dipole",
             ntypes=ntypes,
             dim_descrpt=dim_descrpt,
             neuron=neuron,
@@ -161,6 +158,8 @@ def serialize(self) -> dict:
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = copy.deepcopy(data)
         check_version_compatibility(data.pop("@version", 1), 1, 1)
+        var_name = data.pop("var_name", None)
+        assert var_name == "dipole"
         return super().deserialize(data)
 
     def output_def(self):

deepmd/dpmodel/fitting/polarizability_fitting.py

@@ -39,8 +39,6 @@ class PolarFitting(GeneralFitting):
 
     Parameters
     ----------
-    var_name
-            The name of the output variable.
     ntypes
             The number of atom types.
     dim_descrpt
@@ -88,7 +86,6 @@ class PolarFitting(GeneralFitting):
 
     def __init__(
         self,
-        var_name: str,
         ntypes: int,
         dim_descrpt: int,
         embedding_width: int,
@@ -145,7 +142,7 @@ def __init__(
         self.shift_diag = shift_diag
         self.constant_matrix = np.zeros(ntypes, dtype=GLOBAL_NP_FLOAT_PRECISION)
         super().__init__(
-            var_name=var_name,
+            var_name="polar",
             ntypes=ntypes,
             dim_descrpt=dim_descrpt,
             neuron=neuron,
@@ -201,6 +198,8 @@ def serialize(self) -> dict:
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = copy.deepcopy(data)
         check_version_compatibility(data.pop("@version", 1), 2, 1)
+        var_name = data.pop("var_name", None)
+        assert var_name == "polar"
         return super().deserialize(data)
 
     def output_def(self):

deepmd/pt/model/task/dipole.py

@@ -39,8 +39,6 @@ class DipoleFittingNet(GeneralFitting):
 
     Parameters
     ----------
-    var_name : str
-        The atomic property to fit, 'dipole'.
     ntypes : int
         Element count.
     dim_descrpt : int
@@ -97,7 +95,7 @@ def __init__(
         self.r_differentiable = r_differentiable
         self.c_differentiable = c_differentiable
         super().__init__(
-            var_name=kwargs.pop("var_name", "dipole"),
+            var_name="dipole",
             ntypes=ntypes,
             dim_descrpt=dim_descrpt,
             neuron=neuron,
@@ -131,6 +129,7 @@ def serialize(self) -> dict:
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = copy.deepcopy(data)
         check_version_compatibility(data.pop("@version", 1), 1, 1)
+        data.pop("var_name", None)
         return super().deserialize(data)
 
     def output_def(self) -> FittingOutputDef:

deepmd/pt/model/task/polarizability.py

@@ -47,8 +47,6 @@ class PolarFittingNet(GeneralFitting):
 
     Parameters
     ----------
-    var_name : str
-        The atomic property to fit, 'polar'.
     ntypes : int
         Element count.
     dim_descrpt : int
@@ -127,7 +125,7 @@ def __init__(
             ntypes, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=env.DEVICE
         )
         super().__init__(
-            var_name=kwargs.pop("var_name", "polar"),
+            var_name="polar",
             ntypes=ntypes,
             dim_descrpt=dim_descrpt,
             neuron=neuron,
@@ -180,6 +178,7 @@ def serialize(self) -> dict:
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = copy.deepcopy(data)
         check_version_compatibility(data.pop("@version", 1), 2, 1)
+        data.pop("var_name", None)
         return super().deserialize(data)
 
     def output_def(self) -> FittingOutputDef: