feat(pt/dp): support case embedding and sharable fitting

deepmd/dpmodel/atomic_model/dp_atomic_model.py

@@ -65,6 +65,13 @@ def get_sel(self) -> list[int]:
         """Get the neighbor selection."""
         return self.descriptor.get_sel()
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        self.fitting.set_caseid(case_idx)
+
     def mixed_types(self) -> bool:
         """If true, the model
         1. assumes total number of atoms aligned across frames;

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -134,6 +134,14 @@ def get_model_rcuts(self) -> list[float]:
     def get_sel(self) -> list[int]:
         return [max([model.get_nsel() for model in self.models])]
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        for model in self.models:
+            model.set_caseid(case_idx)
+
     def get_model_nsels(self) -> list[int]:
         """Get the processed sels for each individual models. Not distinguishing types."""
         return [model.get_nsel() for model in self.models]

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -68,6 +68,14 @@ def get_sel(self) -> list[int]:
             """Returns the number of selected atoms for each type."""
             pass
 
+        @abstractmethod
+        def set_caseid(self, case_idx) -> None:
+            """
+            Set the case identification of this atomic model by the given case_idx,
+            typically concatenated with the output of the descriptor and fed into the fitting net.
+            """
+            pass
+
         def get_nsel(self) -> int:
             """Returns the total number of selected neighboring atoms in the cut-off radius."""
             return sum(self.get_sel())

deepmd/dpmodel/atomic_model/pairtab_atomic_model.py

@@ -120,6 +120,15 @@ def get_type_map(self) -> list[str]:
     def get_sel(self) -> list[int]:
         return [self.sel]
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        raise NotImplementedError(
+            "Case identification not supported for PairTabAtomicModel!"
+        )
+
     def get_nsel(self) -> int:
         return self.sel
 

deepmd/dpmodel/fitting/dipole_fitting.py

@@ -95,6 +95,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         rcond: Optional[float] = None,
         tot_ener_zero: bool = False,
         trainable: Optional[list[bool]] = None,
@@ -130,6 +131,7 @@ def __init__(
             resnet_dt=resnet_dt,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             tot_ener_zero=tot_ener_zero,
             trainable=trainable,
@@ -159,7 +161,7 @@ def serialize(self) -> dict:
     @classmethod
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = data.copy()
-        check_version_compatibility(data.pop("@version", 1), 2, 1)
+        check_version_compatibility(data.pop("@version", 1), 3, 1)
         var_name = data.pop("var_name", None)
         assert var_name == "dipole"
         return super().deserialize(data)

deepmd/dpmodel/fitting/dos_fitting.py

@@ -36,6 +36,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         bias_dos: Optional[np.ndarray] = None,
         rcond: Optional[float] = None,
         trainable: Union[bool, list[bool]] = True,
@@ -60,6 +61,7 @@ def __init__(
             bias_atom=bias_dos,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             trainable=trainable,
             activation_function=activation_function,
@@ -73,7 +75,7 @@ def __init__(
     @classmethod
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = data.copy()
-        check_version_compatibility(data.pop("@version", 1), 2, 1)
+        check_version_compatibility(data.pop("@version", 1), 3, 1)
         data["numb_dos"] = data.pop("dim_out")
         data.pop("tot_ener_zero", None)
         data.pop("var_name", None)

deepmd/dpmodel/fitting/ener_fitting.py

@@ -32,6 +32,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         rcond: Optional[float] = None,
         tot_ener_zero: bool = False,
         trainable: Optional[list[bool]] = None,
@@ -55,6 +56,7 @@ def __init__(
             resnet_dt=resnet_dt,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             tot_ener_zero=tot_ener_zero,
             trainable=trainable,
@@ -73,7 +75,7 @@ def __init__(
     @classmethod
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = data.copy()
-        check_version_compatibility(data.pop("@version", 1), 2, 1)
+        check_version_compatibility(data.pop("@version", 1), 3, 1)
         data.pop("var_name")
         data.pop("dim_out")
         return super().deserialize(data)

deepmd/dpmodel/fitting/general_fitting.py

@@ -105,6 +105,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         bias_atom_e: Optional[np.ndarray] = None,
         rcond: Optional[float] = None,
         tot_ener_zero: bool = False,
@@ -127,6 +128,7 @@ def __init__(
         self.resnet_dt = resnet_dt
         self.numb_fparam = numb_fparam
         self.numb_aparam = numb_aparam
+        self.numb_caseid = numb_caseid
         self.rcond = rcond
         self.tot_ener_zero = tot_ener_zero
         self.trainable = trainable
@@ -171,11 +173,16 @@ def __init__(
             self.aparam_inv_std = np.ones(self.numb_aparam, dtype=self.prec)
         else:
             self.aparam_avg, self.aparam_inv_std = None, None
+        if self.numb_caseid > 0:
+            self.caseid = np.zeros(self.numb_caseid, dtype=self.prec)
+        else:
+            self.caseid = None
         # init networks
         in_dim = (
             self.dim_descrpt
             + self.numb_fparam
             + (0 if self.use_aparam_as_mask else self.numb_aparam)
+            + self.numb_caseid
         )
         self.nets = NetworkCollection(
             1 if not self.mixed_types else 0,
@@ -222,6 +229,13 @@ def get_type_map(self) -> list[str]:
         """Get the name to each type of atoms."""
         return self.type_map
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this fitting net by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        self.caseid = np.eye(self.numb_caseid, dtype=self.prec)[case_idx]
+
     def change_type_map(
         self, type_map: list[str], model_with_new_type_stat=None
     ) -> None:
@@ -255,6 +269,8 @@ def __setitem__(self, key, value) -> None:
             self.aparam_avg = value
         elif key in ["aparam_inv_std"]:
             self.aparam_inv_std = value
+        elif key in ["caseid"]:
+            self.caseid = value
         elif key in ["scale"]:
             self.scale = value
         else:
@@ -271,6 +287,8 @@ def __getitem__(self, key):
             return self.aparam_avg
         elif key in ["aparam_inv_std"]:
             return self.aparam_inv_std
+        elif key in ["caseid"]:
+            return self.caseid
         elif key in ["scale"]:
             return self.scale
         else:
@@ -287,14 +305,15 @@ def serialize(self) -> dict:
         """Serialize the fitting to dict."""
         return {
             "@class": "Fitting",
-            "@version": 2,
+            "@version": 3,
             "var_name": self.var_name,
             "ntypes": self.ntypes,
             "dim_descrpt": self.dim_descrpt,
             "neuron": self.neuron,
             "resnet_dt": self.resnet_dt,
             "numb_fparam": self.numb_fparam,
             "numb_aparam": self.numb_aparam,
+            "numb_caseid": self.numb_caseid,
             "rcond": self.rcond,
             "activation_function": self.activation_function,
             "precision": self.precision,
@@ -303,6 +322,7 @@ def serialize(self) -> dict:
             "nets": self.nets.serialize(),
             "@variables": {
                 "bias_atom_e": to_numpy_array(self.bias_atom_e),
+                "caseid": to_numpy_array(self.caseid),
                 "fparam_avg": to_numpy_array(self.fparam_avg),
                 "fparam_inv_std": to_numpy_array(self.fparam_inv_std),
                 "aparam_avg": to_numpy_array(self.aparam_avg),
@@ -423,6 +443,19 @@ def _call_common(
                     axis=-1,
                 )
 
+        if self.numb_caseid > 0:
+            assert self.caseid is not None
+            caseid = xp.tile(xp.reshape(self.caseid, [1, 1, -1]), [nf, nloc, 1])
+            xx = xp.concat(
+                [xx, caseid],
+                axis=-1,
+            )
+            if xx_zeros is not None:
+                xx_zeros = xp.concat(
+                    [xx_zeros, caseid],
+                    axis=-1,
+                )
+
         # calculate the prediction
         if not self.mixed_types:
             outs = xp.zeros(

deepmd/dpmodel/fitting/invar_fitting.py

@@ -123,6 +123,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         bias_atom: Optional[np.ndarray] = None,
         rcond: Optional[float] = None,
         tot_ener_zero: bool = False,
@@ -155,6 +156,7 @@ def __init__(
             resnet_dt=resnet_dt,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             bias_atom_e=bias_atom,
             tot_ener_zero=tot_ener_zero,
@@ -183,7 +185,7 @@ def serialize(self) -> dict:
     @classmethod
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = data.copy()
-        check_version_compatibility(data.pop("@version", 1), 2, 1)
+        check_version_compatibility(data.pop("@version", 1), 3, 1)
         return super().deserialize(data)
 
     def _net_out_dim(self):

deepmd/dpmodel/fitting/polarizability_fitting.py

@@ -101,6 +101,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         rcond: Optional[float] = None,
         tot_ener_zero: bool = False,
         trainable: Optional[list[bool]] = None,
@@ -150,6 +151,7 @@ def __init__(
             resnet_dt=resnet_dt,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             tot_ener_zero=tot_ener_zero,
             trainable=trainable,
@@ -187,7 +189,7 @@ def __getitem__(self, key):
     def serialize(self) -> dict:
         data = super().serialize()
         data["type"] = "polar"
-        data["@version"] = 3
+        data["@version"] = 4
         data["embedding_width"] = self.embedding_width
         data["fit_diag"] = self.fit_diag
         data["shift_diag"] = self.shift_diag
@@ -198,7 +200,7 @@ def serialize(self) -> dict:
     @classmethod
     def deserialize(cls, data: dict) -> "GeneralFitting":
         data = data.copy()
-        check_version_compatibility(data.pop("@version", 1), 3, 1)
+        check_version_compatibility(data.pop("@version", 1), 4, 1)
         var_name = data.pop("var_name", None)
         assert var_name == "polar"
         return super().deserialize(data)

deepmd/dpmodel/fitting/property_fitting.py

@@ -78,6 +78,7 @@ def __init__(
         resnet_dt: bool = True,
         numb_fparam: int = 0,
         numb_aparam: int = 0,
+        numb_caseid: int = 0,
         activation_function: str = "tanh",
         precision: str = DEFAULT_PRECISION,
         mixed_types: bool = True,
@@ -99,6 +100,7 @@ def __init__(
             resnet_dt=resnet_dt,
             numb_fparam=numb_fparam,
             numb_aparam=numb_aparam,
+            numb_caseid=numb_caseid,
             rcond=rcond,
             trainable=trainable,
             activation_function=activation_function,
@@ -111,7 +113,7 @@ def __init__(
     @classmethod
     def deserialize(cls, data: dict) -> "PropertyFittingNet":
         data = data.copy()
-        check_version_compatibility(data.pop("@version"), 2, 1)
+        check_version_compatibility(data.pop("@version"), 3, 1)
         data.pop("dim_out")
         data.pop("var_name")
         data.pop("tot_ener_zero")

deepmd/dpmodel/model/make_model.py

@@ -552,6 +552,9 @@ def serialize(self) -> dict:
         def deserialize(cls, data) -> "CM":
             return cls(atomic_model_=T_AtomicModel.deserialize(data))
 
+        def set_caseid(self, case_idx):
+            self.atomic_model.set_caseid(case_idx)
+
         def get_dim_fparam(self) -> int:
             """Get the number (dimension) of frame parameters of this atomic model."""
             return self.atomic_model.get_dim_fparam()

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -93,6 +93,13 @@ def get_sel(self) -> list[int]:
         """Get the neighbor selection."""
         return self.sel
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        self.fitting_net.set_caseid(case_idx)
+
     def mixed_types(self) -> bool:
         """If true, the model
         1. assumes total number of atoms aligned across frames;

deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -158,6 +158,14 @@ def get_model_rcuts(self) -> list[float]:
     def get_sel(self) -> list[int]:
         return [max([model.get_nsel() for model in self.models])]
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        for model in self.models:
+            model.set_caseid(case_idx)
+
     def get_model_nsels(self) -> list[int]:
         """Get the processed sels for each individual models. Not distinguishing types."""
         return [model.get_nsel() for model in self.models]

deepmd/pt/model/atomic_model/pairtab_atomic_model.py

@@ -141,6 +141,15 @@ def get_type_map(self) -> list[str]:
     def get_sel(self) -> list[int]:
         return [self.sel]
 
+    def set_caseid(self, case_idx):
+        """
+        Set the case identification of this atomic model by the given case_idx,
+        typically concatenated with the output of the descriptor and fed into the fitting net.
+        """
+        raise NotImplementedError(
+            "Case identification not supported for PairTabAtomicModel!"
+        )
+
     def get_nsel(self) -> int:
         return self.sel
 

deepmd/pt/model/model/make_model.py

@@ -514,6 +514,9 @@ def serialize(self) -> dict:
         def deserialize(cls, data) -> "CM":
             return cls(atomic_model_=T_AtomicModel.deserialize(data))
 
+        def set_caseid(self, case_idx):
+            self.atomic_model.set_caseid(case_idx)
+
         @torch.jit.export
         def get_dim_fparam(self) -> int:
             """Get the number (dimension) of frame parameters of this atomic model."""