feat: add plugin mode for data modifier

deepmd/dpmodel/modifier/__init__.py

@@ -0,0 +1,8 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from .base_modifier import (
+    make_base_modifier,
+)
+
+__all__ = [
+    "make_base_modifier",
+]

deepmd/dpmodel/modifier/base_modifier.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import inspect
+from abc import (
+    ABC,
+    abstractmethod,
+)
+
+from deepmd.utils.plugin import (
+    PluginVariant,
+    make_plugin_registry,
+)
+
+
+def make_base_modifier() -> type[object]:
+    class BaseModifier(ABC, PluginVariant, make_plugin_registry("modifier")):
+        """Base class for data modifier."""
+
+        def __new__(cls, *args, **kwargs):
+            if cls is BaseModifier:
+                cls = cls.get_class_by_type(kwargs["type"])
+            return super().__new__(cls)
+
+        @abstractmethod
+        def serialize(self) -> dict:
+            """Serialize the modifier.
+
+            Returns
+            -------
+            dict
+                The serialized data
+            """
+            pass
+
+        @classmethod
+        def deserialize(cls, data: dict) -> "BaseModifier":
+            """Deserialize the modifier.
+
+            Parameters
+            ----------
+            data : dict
+                The serialized data
+
+            Returns
+            -------
+            BaseModel
+                The deserialized modifier
+            """
+            if inspect.isabstract(cls):
+                return cls.get_class_by_type(data["type"]).deserialize(data)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
+
+        @classmethod
+        def get_modifier(cls, modifier_params: dict) -> "BaseModifier":
+            """Get the modifier by the parameters.
+
+            By default, all the parameters are directly passed to the constructor.
+            If not, override this method.
+
+            Parameters
+            ----------
+            modifier_params : dict
+                The modifier parameters
+
+            Returns
+            -------
+            BaseModifier
+                The modifier
+            """
+            modifier_params = modifier_params.copy()
+            modifier_params.pop("type", None)
+            modifier = cls(**modifier_params)
+            return modifier
+
+    return BaseModifier

deepmd/tf/__init__.py

@@ -21,7 +21,7 @@
     DeepEval,
     DeepPotential,
 )
-from .infer.data_modifier import (
+from .modifier import (
     DipoleChargeModifier,
 )
 

deepmd/tf/entrypoints/train.py

@@ -4,6 +4,7 @@
 Can handle local or distributed training.
 """
 
+import copy
 import json
 import logging
 import time
@@ -20,12 +21,12 @@
     reset_default_tf_session_config,
     tf,
 )
-from deepmd.tf.infer.data_modifier import (
-    DipoleChargeModifier,
-)
 from deepmd.tf.model.model import (
     Model,
 )
+from deepmd.tf.modifier import (
+    BaseModifier,
+)
 from deepmd.tf.train.run_options import (
     RunOptions,
 )
@@ -275,18 +276,13 @@
 
 
 def get_modifier(modi_data=None):
-    modifier: Optional[DipoleChargeModifier]
+    modifier: Optional[BaseModifier]
     if modi_data is not None:
-        if modi_data["type"] == "dipole_charge":
-            modifier = DipoleChargeModifier(
-                modi_data["model_name"],
-                modi_data["model_charge_map"],
-                modi_data["sys_charge_map"],
-                modi_data["ewald_h"],
-                modi_data["ewald_beta"],
-            )
-        else:
-            raise RuntimeError("unknown modifier type " + str(modi_data["type"]))
+        modifier_params = copy.deepcopy(modi_data)
+        modifier_type = modifier_params.pop("type")
+        modifier = BaseModifier.get_class_by_type(modifier_type).get_modifier(
+            modifier_params
+        )
     else:
         modifier = None
     return modifier

deepmd/tf/infer/__init__.py

@@ -8,9 +8,6 @@
     DeepEval,
 )
 
-from .data_modifier import (
-    DipoleChargeModifier,
-)
 from .deep_dipole import (
     DeepDipole,
 )
@@ -43,7 +40,6 @@
     "DeepPot",
     "DeepPotential",
     "DeepWFC",
-    "DipoleChargeModifier",
     "EwaldRecp",
     "calc_model_devi",
 ]

deepmd/tf/infer/deep_eval.py

@@ -139,7 +139,7 @@
 
         # looks ugly...
         if self.modifier_type == "dipole_charge":
-            from deepmd.tf.infer.data_modifier import (
+            from deepmd.tf.modifier import (
                 DipoleChargeModifier,
             )
 

deepmd/tf/modifier/__init__.py

@@ -0,0 +1,12 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from .base_modifier import (
+    BaseModifier,
+)
+from .dipole_charge import (
+    DipoleChargeModifier,
+)
+
+__all__ = [
+    "BaseModifier",
+    "DipoleChargeModifier",
+]

deepmd/tf/modifier/base_modifier.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from deepmd.dpmodel.modifier.base_modifier import (
+    make_base_modifier,
+)
+from deepmd.tf.infer import (
+    DeepPot,
+)
+
+
+class BaseModifier(DeepPot, make_base_modifier()):
+    def __init__(self, *args, **kwargs) -> None:
+        """Construct a basic model for different tasks."""
+        DeepPot.__init__(self, *args, **kwargs)

deepmd/tf/infer/data_modifier.py → deepmd/tf/modifier/dipole_charge.py

@@ -17,6 +17,9 @@
 from deepmd.tf.infer.ewald_recp import (
     EwaldRecp,
 )
+from deepmd.tf.modifier.base_modifier import (
+    BaseModifier,
+)
 from deepmd.tf.utils.data import (
     DeepmdData,
 )
@@ -25,7 +28,8 @@
 )
 
 
-class DipoleChargeModifier(DeepDipole):
+@BaseModifier.register("dipole_charge")
+class DipoleChargeModifier(DeepDipole, BaseModifier):
     """Parameters
     ----------
     model_name
@@ -40,6 +44,9 @@
             Splitting parameter of the Ewald sum. Unit: A^{-1}
     """
 
+    def __new__(cls, *args, model_name=None, **kwargs):
+        return super().__new__(cls, model_name)
+
     def __init__(
         self,
         model_name: str,
@@ -82,6 +89,44 @@
         self.force = None
         self.ntypes = len(self.sel_a)
 
+    def serialize(self) -> dict:
+        """Serialize the modifier.
+
+        Returns
+        -------
+        dict
+            The serialized data
+        """
+        data = {
+            "@class": "Modifier",
+            "type": self.modifier_prefix,
+            "@version": 3,
+            "model_name": self.model_name,
+            "model_charge_map": self.model_charge_map,
+            "sys_charge_map": self.sys_charge_map,
+            "ewald_h": self.ewald_h,
+            "ewald_beta": self.ewald_beta,
+        }
+        return data
+
+    @classmethod
+    def deserialize(cls, data: dict) -> "BaseModifier":
+        """Deserialize the modifier.
+
+        Parameters
+        ----------
+        data : dict
+            The serialized data
+
+        Returns
+        -------
+        BaseModel
+            The deserialized modifier
+        """
+        data = data.copy()
+        modifier = cls(**data)
+        return modifier
+
     def build_fv_graph(self) -> tf.Tensor:
         """Build the computational graph for the force and virial inference."""
         with tf.variable_scope("modifier_attr"):

deepmd/utils/argcheck.py

@@ -55,6 +55,8 @@
 doc_dos = "Fit a density of states model. The total density of states / site-projected density of states labels should be provided by `dos.npy` or `atom_dos.npy` in each data system. The file has number of frames lines and number of energy grid columns (times number of atoms in `atom_dos.npy`). See `loss` parameter."
 doc_dipole = "Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter."
 doc_polar = "Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file with has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter."
+# modifier
+doc_dipole_charge = "Use WFCC to model the electronic structure of the system. Correct the long-range interaction."
 
 
 def list_to_doc(xx):
@@ -2015,6 +2017,10 @@ def fitting_variant_type_args():
 
 
 #  --- Modifier configurations: --- #
+modifier_args_plugin = ArgsPlugin()
+
+
+@modifier_args_plugin.register("dipole_charge", doc=doc_dipole_charge)
 def modifier_dipole_charge():
     doc_model_name = "The name of the frozen dipole model file."
     doc_model_charge_map = f"The charge of the WFCC. The list length should be the same as the {make_link('sel_type', 'model[standard]/fitting_net[dipole]/sel_type')}. "
@@ -2035,14 +2041,9 @@ def modifier_dipole_charge():
 
 def modifier_variant_type_args():
     doc_modifier_type = "The type of modifier."
-    doc_dipole_charge = "Use WFCC to model the electronic structure of the system. Correct the long-range interaction."
     return Variant(
         "type",
-        [
-            Argument(
-                "dipole_charge", dict, modifier_dipole_charge(), doc=doc_dipole_charge
-            ),
-        ],
+        modifier_args_plugin.get_all_argument(),
         optional=False,
         doc=doc_modifier_type,
     )

source/tests/tf/test_data_modifier.py

@@ -7,7 +7,7 @@
     GLOBAL_NP_FLOAT_PRECISION,
     tf,
 )
-from deepmd.tf.infer.data_modifier import (
+from deepmd.tf.modifier import (
     DipoleChargeModifier,
 )
 from deepmd.tf.train.run_options import (
@@ -97,11 +97,11 @@ def test_fv(self) -> None:
 
     def _test_fv(self) -> None:
         dcm = DipoleChargeModifier(
-            str(tests_path / os.path.join(modifier_datapath, "dipole.pb")),
-            [-8],
-            [6, 1],
-            1,
-            0.25,
+            model_name=str(tests_path / os.path.join(modifier_datapath, "dipole.pb")),
+            model_charge_map=[-8],
+            sys_charge_map=[6, 1],
+            ewald_h=1,
+            ewald_beta=0.25,
         )
         data = Data()
         coord, box, atype = data.get_data()

source/tests/tf/test_data_modifier_shuffle.py

@@ -8,12 +8,12 @@
     GLOBAL_NP_FLOAT_PRECISION,
     tf,
 )
-from deepmd.tf.infer.data_modifier import (
-    DipoleChargeModifier,
-)
 from deepmd.tf.infer.deep_dipole import (
     DeepDipole,
 )
+from deepmd.tf.modifier import (
+    DipoleChargeModifier,
+)
 from deepmd.tf.train.run_options import (
     RunOptions,
 )
@@ -197,11 +197,11 @@ def test_z_dipole(self) -> None:
 
     def test_modify(self) -> None:
         dcm = DipoleChargeModifier(
-            os.path.join(modifier_datapath, "dipole.pb"),
-            [-1, -3],
-            [1, 1, 1, 1, 1],
-            1,
-            0.25,
+            model_name=os.path.join(modifier_datapath, "dipole.pb"),
+            model_charge_map=[-1, -3],
+            sys_charge_map=[1, 1, 1, 1, 1],
+            ewald_h=1,
+            ewald_beta=0.25,
         )
         ve0, vf0, vv0 = dcm.eval(self.coords0, self.box0, self.atom_types0)
         ve1, vf1, vv1 = dcm.eval(self.coords1, self.box1, self.atom_types1)

source/tests/tf/test_dipolecharge.py

@@ -7,7 +7,7 @@
 from deepmd.tf.env import (
     GLOBAL_NP_FLOAT_PRECISION,
 )
-from deepmd.tf.infer import (
+from deepmd.tf.modifier import (
     DipoleChargeModifier,
 )
 from deepmd.tf.utils.convert import (
@@ -32,7 +32,11 @@ def setUpClass(cls) -> None:
             "dipolecharge_d.pb",
         )
         cls.dp = DipoleChargeModifier(
-            "dipolecharge_d.pb", [-1.0, -3.0], [1.0, 1.0, 1.0, 1.0, 1.0], 4.0, 0.2
+            model_name="dipolecharge_d.pb",
+            model_charge_map=[-1.0, -3.0],
+            sys_charge_map=[1.0, 1.0, 1.0, 1.0, 1.0],
+            ewald_h=4.0,
+            ewald_beta=0.2,
         )
 
     def setUp(self) -> None: