add BaseModel; store type in serialization

deepmd/dpmodel/atomic_model/dp_atomic_model.py

@@ -130,6 +130,8 @@ def forward_atomic(
 
     def serialize(self) -> dict:
         return {
+            "@class": "Model",
+            "type": "standard",
             "type_map": self.type_map,
             "descriptor": self.descriptor.serialize(),
             "fitting": self.fitting.serialize(),
@@ -138,6 +140,8 @@ def serialize(self) -> dict:
     @classmethod
     def deserialize(cls, data) -> "DPAtomicModel":
         data = copy.deepcopy(data)
+        data.pop("@class")
+        data.pop("type")
         descriptor_obj = BaseDescriptor.deserialize(data["descriptor"])
         fitting_obj = BaseFitting.deserialize(data["fitting"])
         obj = cls(descriptor_obj, fitting_obj, type_map=data["type_map"])

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import copy
 import sys
 from abc import (
     abstractmethod,
@@ -182,12 +183,17 @@ def fitting_output_def(self) -> FittingOutputDef:
     @staticmethod
     def serialize(models) -> dict:
         return {
+            "@class": "Model",
+            "type": "linear",
             "models": [model.serialize() for model in models],
             "model_name": [model.__class__.__name__ for model in models],
         }
 
     @staticmethod
     def deserialize(data) -> List[BaseAtomicModel]:
+        data = copy.deepcopy(data)
+        data.pop("@class")
+        data.pop("type")
         model_names = data["model_name"]
         models = [
             getattr(sys.modules[__name__], name).deserialize(model)
@@ -263,6 +269,8 @@ def __init__(
 
     def serialize(self) -> dict:
         return {
+            "@class": "Model",
+            "type": "zbl",
             "models": LinearAtomicModel.serialize([self.dp_model, self.zbl_model]),
             "sw_rmin": self.sw_rmin,
             "sw_rmax": self.sw_rmax,
@@ -271,6 +279,9 @@ def serialize(self) -> dict:
 
     @classmethod
     def deserialize(cls, data) -> "DPZBLLinearAtomicModel":
+        data = copy.deepcopy(data)
+        data.pop("@class")
+        data.pop("type")
         sw_rmin = data["sw_rmin"]
         sw_rmax = data["sw_rmax"]
         smin_alpha = data["smin_alpha"]

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -160,6 +160,10 @@ def do_grad_(self, var_name: str, base: str) -> bool:
                 return self.fitting_output_def()[var_name].c_differentiable
             return self.fitting_output_def()[var_name].r_differentiable
 
+        def get_model_def_script(self) -> str:
+            # TODO: implement this method; saved to model
+            raise NotImplementedError
+
     setattr(BAM, fwd_method_name, BAM.fwd)
     delattr(BAM, "fwd")
 

deepmd/dpmodel/atomic_model/pairtab_atomic_model.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import copy
 from typing import (
     Dict,
     List,
@@ -105,10 +106,19 @@ def mixed_types(self) -> bool:
         return True
 
     def serialize(self) -> dict:
-        return {"tab": self.tab.serialize(), "rcut": self.rcut, "sel": self.sel}
+        return {
+            "@class": "Model",
+            "type": "pairtab",
+            "tab": self.tab.serialize(),
+            "rcut": self.rcut,
+            "sel": self.sel,
+        }
 
     @classmethod
     def deserialize(cls, data) -> "PairTabAtomicModel":
+        data = copy.deepcopy(data)
+        data.pop("@class")
+        data.pop("type")
         rcut = data["rcut"]
         sel = data["sel"]
         tab = PairTab.deserialize(data["tab"])

deepmd/dpmodel/infer/deep_eval.py

@@ -13,8 +13,8 @@
 
 import numpy as np
 
-from deepmd.dpmodel.model.dp_model import (
-    DPModel,
+from deepmd.dpmodel.model.base_model import (
+    BaseModel,
 )
 from deepmd.dpmodel.output_def import (
     ModelOutputDef,
@@ -85,7 +85,7 @@ def __init__(
         self.model_path = model_file
 
         model_data = load_dp_model(model_file)
-        self.dp = DPModel.deserialize(model_data["model"])
+        self.dp = BaseModel.deserialize(model_data["model"])
         self.rcut = self.dp.get_rcut()
         self.type_map = self.dp.get_type_map()
         if isinstance(auto_batch_size, bool):

deepmd/dpmodel/model/base_model.py

@@ -0,0 +1,158 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import inspect
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    Any,
+    List,
+    Type,
+)
+
+from deepmd.utils.plugin import (
+    make_plugin_registry,
+)
+
+
+def make_base_model() -> Type[object]:
+    class BaseBaseModel(ABC, make_plugin_registry("model")):
+        """Base class for final exported model that will be directly used for inference.
+
+        The class defines some abstractmethods that will be directly called by the
+        inference interface. If the final model class inherits some of those methods
+        from other classes, `BaseModel` should be inherited as the last class to ensure
+        the correct method resolution order.
+
+        This class is backend-indepedent.
+
+        See Also
+        --------
+        deepmd.dpmodel.model.base_model.BaseModel
+            BaseModel class for DPModel backend.
+        """
+
+        def __new__(cls, *args, **kwargs):
+            if inspect.isabstract(cls):
+                cls = cls.get_class_by_type(kwargs.get("type", "standard"))
+            return super().__new__(cls)
+
+        @abstractmethod
+        def __call__(self, *args: Any, **kwds: Any) -> Any:
+            """Inference method.
+
+            Parameters
+            ----------
+            *args : Any
+                The input data for inference.
+            **kwds : Any
+                The input data for inference.
+
+            Returns
+            -------
+            Any
+                The output of the inference.
+            """
+            pass
+
+        @abstractmethod
+        def get_type_map(self) -> List[str]:
+            """Get the type map."""
+
+        @abstractmethod
+        def get_rcut(self):
+            """Get the cut-off radius."""
+
+        @abstractmethod
+        def get_dim_fparam(self):
+            """Get the number (dimension) of frame parameters of this atomic model."""
+
+        @abstractmethod
+        def get_dim_aparam(self):
+            """Get the number (dimension) of atomic parameters of this atomic model."""
+
+        @abstractmethod
+        def get_sel_type(self) -> List[int]:
+            """Get the selected atom types of this model.
+
+            Only atoms with selected atom types have atomic contribution
+            to the result of the model.
+            If returning an empty list, all atom types are selected.
+            """
+
+        @abstractmethod
+        def is_aparam_nall(self) -> bool:
+            """Check whether the shape of atomic parameters is (nframes, nall, ndim).
+
+            If False, the shape is (nframes, nloc, ndim).
+            """
+
+        @abstractmethod
+        def model_output_type(self) -> str:
+            """Get the output type for the model."""
+
+        @abstractmethod
+        def serialize(self) -> dict:
+            """Serialize the model.
+
+            Returns
+            -------
+            dict
+                The serialized data
+            """
+            pass
+
+        @classmethod
+        def deserialize(cls, data: dict) -> "BaseBaseModel":
+            """Deserialize the model.
+
+            Parameters
+            ----------
+            data : dict
+                The serialized data
+
+            Returns
+            -------
+            BaseModel
+                The deserialized model
+            """
+            if inspect.isabstract(cls):
+                return cls.get_class_by_type(data["type"]).deserialize(data)
+            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+
+        model_def_script: str
+
+        @abstractmethod
+        def get_model_def_script(self) -> str:
+            """Get the model definition script."""
+            pass
+
+        @abstractmethod
+        def get_nnei(self) -> int:
+            """Returns the total number of selected neighboring atoms in the cut-off radius."""
+            # for C++ interface
+            pass
+
+        @abstractmethod
+        def get_nsel(self) -> int:
+            """Returns the total number of selected neighboring atoms in the cut-off radius."""
+            pass
+
+    return BaseBaseModel
+
+
+class BaseModel(make_base_model()):
+    """Base class for final exported model that will be directly used for inference.
+
+    The class defines some abstractmethods that will be directly called by the
+    inference interface. If the final model class inherbits some of those methods
+    from other classes, `BaseModel` should be inherited as the last class to ensure
+    the correct method resolution order.
+
+    This class is for the DPModel backend.
+
+    See Also
+    --------
+    deepmd.dpmodel.model.base_model.BaseBaseModel
+        Backend-independent BaseModel class.
+    """

deepmd/dpmodel/model/dp_model.py

@@ -2,12 +2,16 @@
 from deepmd.dpmodel.atomic_model import (
     DPAtomicModel,
 )
+from deepmd.dpmodel.model.base_model import (
+    BaseModel,
+)
 
 from .make_model import (
     make_model,
 )
 
 
 # use "class" to resolve "Variable not allowed in type expression"
-class DPModel(make_model(DPAtomicModel)):
+@BaseModel.register("standard")
+class DPModel(make_model(DPAtomicModel), BaseModel):
     pass

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -93,6 +93,8 @@ def mixed_types(self) -> bool:
 
     def serialize(self) -> dict:
         return {
+            "@class": "Model",
+            "type": "standard",
             "type_map": self.type_map,
             "descriptor": self.descriptor.serialize(),
             "fitting": self.fitting_net.serialize(),

deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -207,6 +207,8 @@ def fitting_output_def(self) -> FittingOutputDef:
     @staticmethod
     def serialize(models) -> dict:
         return {
+            "@class": "Model",
+            "type": "linear",
             "models": [model.serialize() for model in models],
             "model_name": [model.__class__.__name__ for model in models],
         }
@@ -301,6 +303,8 @@ def __init__(
 
     def serialize(self) -> dict:
         return {
+            "@class": "Model",
+            "type": "zbl",
             "models": LinearAtomicModel.serialize([self.dp_model, self.zbl_model]),
             "sw_rmin": self.sw_rmin,
             "sw_rmax": self.sw_rmax,

deepmd/pt/model/atomic_model/pairtab_atomic_model.py

@@ -124,7 +124,13 @@ def mixed_types(self) -> bool:
         return True
 
     def serialize(self) -> dict:
-        return {"tab": self.tab.serialize(), "rcut": self.rcut, "sel": self.sel}
+        return {
+            "@class": "Model",
+            "type": "pairtab",
+            "tab": self.tab.serialize(),
+            "rcut": self.rcut,
+            "sel": self.sel,
+        }
 
     @classmethod
     def deserialize(cls, data) -> "PairTabAtomicModel":

deepmd/pt/model/model/dp_model.py

@@ -2,9 +2,45 @@
 from deepmd.pt.model.atomic_model import (
     DPAtomicModel,
 )
+from deepmd.pt.model.model.model import (
+    BaseModel,
+)
+from deepmd.pt.model.task.dipole import (
+    DipoleFittingNet,
+)
+from deepmd.pt.model.task.ener import (
+    EnergyFittingNet,
+)
+from deepmd.pt.model.task.polarizability import (
+    PolarFittingNet,
+)
 
 from .make_model import (
     make_model,
 )
 
-DPModel = make_model(DPAtomicModel)
+
+@BaseModel.register("standard")
+class DPModel(make_model(DPAtomicModel), BaseModel):
+    def __new__(cls, descriptor, fitting, *args, **kwargs):
+        from deepmd.pt.model.model.dipole_model import (
+            DipoleModel,
+        )
+        from deepmd.pt.model.model.ener_model import (
+            EnergyModel,
+        )
+        from deepmd.pt.model.model.polar_model import (
+            PolarModel,
+        )
+
+        # according to the fitting network to decide the type of the model
+        if cls is DPModel:
+            # map fitting to model
+            if isinstance(fitting, EnergyFittingNet):
+                cls = EnergyModel
+            elif isinstance(fitting, DipoleFittingNet):
+                cls = DipoleModel
+            elif isinstance(fitting, PolarFittingNet):
+                cls = PolarModel
+            # else: unknown fitting type, fall back to DPModel
+        return super().__new__(cls)