Feat (pt): Expose Linear Ener Model

deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -48,12 +48,15 @@
     type_map : list[str]
         Mapping atom type to the name (str) of the type.
         For example `type_map[1]` gives the name of the type 1.
+    weights : Optional[Union[str,list[float]]]
+        Weights of the models. If str, must be `sum` or `mean`. If list, must be a list of float.
     """
 
     def __init__(
         self,
         models: list[BaseAtomicModel],
         type_map: list[str],
+        weights: Optional[Union[str, list[float]]] = "mean",
         **kwargs,
     ):
         super().__init__(type_map, **kwargs)
@@ -89,6 +92,16 @@
         )
         self.nsels = torch.tensor(self.get_model_nsels(), device=env.DEVICE)  # pylint: disable=no-explicit-dtype
 
+        if isinstance(weights, str):
+            assert weights in ["sum", "mean"]
+        elif isinstance(weights, list):
+            assert len(weights) == len(models)
+        else:
+            raise ValueError(
+                f"'weights' must be a string ('sum' or 'mean') or a list of float of length {len(models)}."
+            )
+        self.weights = weights
+
     def mixed_types(self) -> bool:
         """If true, the model
         1. assumes total number of atoms aligned across frames;
@@ -320,7 +333,7 @@
     @classmethod
     def deserialize(cls, data: dict) -> "LinearEnergyAtomicModel":
         data = copy.deepcopy(data)
-        check_version_compatibility(data.get("@version", 2), 2, 1)
+        check_version_compatibility(data.pop("@version", 2), 2, 1)
         data.pop("@class", None)
         data.pop("type", None)
         models = [
@@ -331,16 +344,42 @@
         return super().deserialize(data)
 
     def _compute_weight(
-        self, extended_coord, extended_atype, nlists_
+        self,
+        extended_coord: torch.Tensor,
+        extended_atype: torch.Tensor,
+        nlists_: list[torch.Tensor],
     ) -> list[torch.Tensor]:
         """This should be a list of user defined weights that matches the number of models to be combined."""
         nmodels = len(self.models)
         nframes, nloc, _ = nlists_[0].shape
-        return [
-            torch.ones((nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE)
-            / nmodels
-            for _ in range(nmodels)
-        ]
+        if isinstance(self.weights, str):
+            if self.weights == "sum":
+                return [
+                    torch.ones(
+                        (nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE
+                    )
+                    for _ in range(nmodels)
+                ]
+            elif self.weights == "mean":
+                return [
+                    torch.ones(
+                        (nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE
+                    )
+                    / nmodels
+                    for _ in range(nmodels)
+                ]
+            else:
+                raise ValueError(
+                    "`weights` must be 'sum' or 'mean' when provided as a string."
+                )
+        elif isinstance(self.weights, list):
+            return [
+                torch.ones((nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE)
+                * w
+                for w in self.weights
+            ]
+        else:
+            raise NotImplementedError
 
     def get_dim_fparam(self) -> int:
         """Get the number (dimension) of frame parameters of this atomic model."""
@@ -365,7 +404,9 @@
         return torch.unique(
             torch.cat(
                 [
-                    torch.as_tensor(model.get_sel_type(), dtype=torch.int32)
+                    torch.as_tensor(
+                        model.get_sel_type(), dtype=torch.int64, device=env.DEVICE
+                    )
                     for model in self.models
                 ]
             )

deepmd/pt/model/model/__init__.py

@@ -36,6 +36,9 @@
 from .dos_model import (
     DOSModel,
 )
+from .dp_linear_model import (
+    LinearEnergyModel,
+)
 from .dp_model import (
     DPModelCommon,
 )
@@ -105,6 +108,62 @@
     return SpinEnergyModel(backbone_model=backbone_model, spin=spin)
 
 
+def get_linear_model(model_params):
+    model_params = copy.deepcopy(model_params)
+    weights = model_params.get("weights", "mean")
+    list_of_models = []
+    ntypes = len(model_params["type_map"])
+    for sub_model_params in model_params["models"]:
+        if "descriptor" in sub_model_params:
+            # descriptor
+            sub_model_params["descriptor"]["ntypes"] = ntypes
+            sub_model_params["descriptor"]["type_map"] = copy.deepcopy(
+                model_params["type_map"]
+            )
+            descriptor = BaseDescriptor(**sub_model_params["descriptor"])
+            # fitting
+            fitting_net = sub_model_params.get("fitting_net", {})
+            fitting_net["type"] = fitting_net.get("type", "ener")
+            fitting_net["ntypes"] = descriptor.get_ntypes()
+            fitting_net["type_map"] = copy.deepcopy(model_params["type_map"])
+            fitting_net["mixed_types"] = descriptor.mixed_types()
+            if fitting_net["type"] in ["dipole", "polar"]:
+                fitting_net["embedding_width"] = descriptor.get_dim_emb()
+            fitting_net["dim_descrpt"] = descriptor.get_dim_out()
+            grad_force = "direct" not in fitting_net["type"]
+            if not grad_force:
+                fitting_net["out_dim"] = descriptor.get_dim_emb()
+                if "ener" in fitting_net["type"]:
+                    fitting_net["return_energy"] = True
+            fitting = BaseFitting(**fitting_net)
+            list_of_models.append(
+                DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"])
+            )
+
+        else:  # must be pairtab
+            assert (
+                "type" in sub_model_params and sub_model_params["type"] == "pairtab"
+            ), "Sub-models in LinearEnergyModel must be a DPModel or a PairTable Model"
+            list_of_models.append(
+                PairTabAtomicModel(
+                    sub_model_params["tab_file"],
+                    sub_model_params["rcut"],
+                    sub_model_params["sel"],
+                    type_map=model_params["type_map"],
+                )
+            )
+
+    atom_exclude_types = model_params.get("atom_exclude_types", [])
+    pair_exclude_types = model_params.get("pair_exclude_types", [])
+    return LinearEnergyModel(
+        models=list_of_models,
+        type_map=model_params["type_map"],
+        weights=weights,
+        atom_exclude_types=atom_exclude_types,
+        pair_exclude_types=pair_exclude_types,
+    )
+
+
 def get_zbl_model(model_params):
     model_params = copy.deepcopy(model_params)
     ntypes = len(model_params["type_map"])
@@ -247,6 +306,8 @@
             return get_zbl_model(model_params)
         else:
             return get_standard_model(model_params)
+    elif model_type == "linear_ener":
+        return get_linear_model(model_params)
     else:
         return BaseModel.get_class_by_type(model_type).get_model(model_params)
 
@@ -265,4 +326,5 @@
     "DPZBLModel",
     "make_model",
     "make_hessian_model",
+    "LinearEnergyModel",
 ]

deepmd/pt/model/model/dp_linear_model.py

@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from copy import (
+    deepcopy,
+)
+from typing import (
+    Optional,
+)
+
+import torch
+
+from deepmd.pt.model.atomic_model import (
+    LinearEnergyAtomicModel,
+)
+from deepmd.pt.model.model.model import (
+    BaseModel,
+)
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
+
+from .dp_model import (
+    DPModelCommon,
+)
+from .make_model import (
+    make_model,
+)
+
+DPLinearModel_ = make_model(LinearEnergyAtomicModel)
+
+
+@BaseModel.register("linear_ener")
+class LinearEnergyModel(DPLinearModel_):
+    model_type = "ener"
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+    def translated_output_def(self):
+        out_def_data = self.model_output_def().get_data()
+        output_def = {
+            "atom_energy": deepcopy(out_def_data["energy"]),
+            "energy": deepcopy(out_def_data["energy_redu"]),
+        }
+        if self.do_grad_r("energy"):
+            output_def["force"] = deepcopy(out_def_data["energy_derv_r"])
+            output_def["force"].squeeze(-2)
+        if self.do_grad_c("energy"):
+            output_def["virial"] = deepcopy(out_def_data["energy_derv_c_redu"])
+            output_def["virial"].squeeze(-2)
+            output_def["atom_virial"] = deepcopy(out_def_data["energy_derv_c"])
+            output_def["atom_virial"].squeeze(-3)
+        if "mask" in out_def_data:
+            output_def["mask"] = deepcopy(out_def_data["mask"])
+        return output_def
+
+    def forward(
+        self,
+        coord,
+        atype,
+        box: Optional[torch.Tensor] = None,
+        fparam: Optional[torch.Tensor] = None,
+        aparam: Optional[torch.Tensor] = None,
+        do_atomic_virial: bool = False,
+    ) -> dict[str, torch.Tensor]:
+        model_ret = self.forward_common(
+            coord,
+            atype,
+            box,
+            fparam=fparam,
+            aparam=aparam,
+            do_atomic_virial=do_atomic_virial,
+        )
+
+        model_predict = {}
+        model_predict["atom_energy"] = model_ret["energy"]
+        model_predict["energy"] = model_ret["energy_redu"]
+        if self.do_grad_r("energy"):
+            model_predict["force"] = model_ret["energy_derv_r"].squeeze(-2)
+        if self.do_grad_c("energy"):
+            model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2)
+            if do_atomic_virial:
+                model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze(-3)
+        else:
+            model_predict["force"] = model_ret["dforce"]
+        if "mask" in model_ret:
+            model_predict["mask"] = model_ret["mask"]
+        return model_predict
+
+    @torch.jit.export
+    def forward_lower(
+        self,
+        extended_coord,
+        extended_atype,
+        nlist,
+        mapping: Optional[torch.Tensor] = None,
+        fparam: Optional[torch.Tensor] = None,
+        aparam: Optional[torch.Tensor] = None,
+        do_atomic_virial: bool = False,
+    ):
+        model_ret = self.forward_common_lower(
+            extended_coord,
+            extended_atype,
+            nlist,
+            mapping=mapping,
+            fparam=fparam,
+            aparam=aparam,
+            do_atomic_virial=do_atomic_virial,
+            extra_nlist_sort=self.need_sorted_nlist_for_lower(),
+        )
+
+        model_predict = {}
+        model_predict["atom_energy"] = model_ret["energy"]
+        model_predict["energy"] = model_ret["energy_redu"]
+        if self.do_grad_r("energy"):
+            model_predict["extended_force"] = model_ret["energy_derv_r"].squeeze(-2)
+        if self.do_grad_c("energy"):
+            model_predict["virial"] = model_ret["energy_derv_c_redu"].squeeze(-2)
+            if do_atomic_virial:
+                model_predict["extended_virial"] = model_ret["energy_derv_c"].squeeze(
+                    -3
+                )
+        else:
+            assert model_ret["dforce"] is not None
+            model_predict["dforce"] = model_ret["dforce"]
+        return model_predict
+
+    @classmethod
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[list[str]],
+        local_jdata: dict,
+    ) -> tuple[dict, Optional[float]]:
+        """Update the selection and perform neighbor statistics.
+
+        Parameters
+        ----------
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
+        local_jdata : dict
+            The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
+        """
+        local_jdata_cpy = local_jdata.copy()
+        type_map = local_jdata_cpy["type_map"]
+        min_nbor_dist = None
+        for idx, sub_model in enumerate(local_jdata_cpy["models"]):
+            if "tab_file" not in sub_model:
+                sub_model, temp_min = DPModelCommon.update_sel(
+                    train_data, type_map, local_jdata["models"][idx]
+                )
+                if min_nbor_dist is None or temp_min <= min_nbor_dist:
+                    min_nbor_dist = temp_min
+        return local_jdata_cpy, min_nbor_dist

doc/model/linear.md

@@ -1,7 +1,7 @@
-## Linear model {{ tensorflow_icon }}
+## Linear model {{ tensorflow_icon }} {{ pytorch_icon }}
 
 :::{note}
-**Supported backends**: TensorFlow {{ tensorflow_icon }}
+**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}
 :::
 
 One can linearly combine existing models with arbitrary coefficients:

examples/water/d3/dftd3.txt

@@ -97,4 +97,4 @@
 9.700000000000001066e+00 -1.186747936398473687e-05 -7.637113677130612127e-06 -5.528293849956352819e-06
 9.800000000000000711e+00 -1.114523618469756001e-05 -7.174288601187318493e-06 -5.194401230658985063e-06
 9.900000000000000355e+00 -1.047381249252528874e-05 -6.743886368019750717e-06 -4.883815978498405921e-06
-1.000000000000000000e+01  0.000000000000000e00e+00  0.000000000000000e00e+00  0.000000000000000e00e+00
+1.000000000000000000e+01  0.000000000000000000e+00  0.000000000000000000e+00  0.000000000000000000e+00