Hybrid descriptor

deepmd/dpmodel/descriptor/__init__.py

@@ -1,4 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+from .hybrid import (
+    DescrptHybrid,
+)
 from .make_base_descriptor import (
     make_base_descriptor,
 )
@@ -12,5 +15,6 @@
 __all__ = [
     "DescrptSeA",
     "DescrptSeR",
+    "DescrptHybrid",
     "make_base_descriptor",
 ]

deepmd/dpmodel/descriptor/hybrid.py

@@ -0,0 +1,186 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    List,
+    Optional,
+)
+
+import numpy as np
+
+from deepmd.dpmodel.common import (
+    NativeOP,
+)
+from deepmd.dpmodel.descriptor.base_descriptor import (
+    BaseDescriptor,
+)
+from deepmd.utils.path import (
+    DPPath,
+)
+from deepmd.utils.version import (
+    check_version_compatibility,
+)
+
+
+@BaseDescriptor.register("hybrid")
+class DescrptHybrid(BaseDescriptor, NativeOP):
+    """Concate a list of descriptors to form a new descriptor.
+
+    Parameters
+    ----------
+    list : list
+            Build a descriptor from the concatenation of the list of descriptors.
+    """
+
+    def __init__(
+        self,
+        list: list,
+    ) -> None:
+        super().__init__()
+        # warning: list is conflict with built-in list
+        descrpt_list = list
+        if descrpt_list == [] or descrpt_list is None:
+            raise RuntimeError(
+                "cannot build descriptor from an empty list of descriptors."
+            )
+        formatted_descript_list = []
+        for ii in descrpt_list:
+            if isinstance(ii, BaseDescriptor):
+                formatted_descript_list.append(ii)
+            elif isinstance(ii, dict):
+                formatted_descript_list.append(BaseDescriptor(**ii))
+            else:
+                raise NotImplementedError
+        self.descrpt_list = formatted_descript_list
+        self.numb_descrpt = len(self.descrpt_list)
+        for ii in range(1, self.numb_descrpt):
+            assert (
+                self.descrpt_list[ii].get_ntypes() == self.descrpt_list[0].get_ntypes()
+            ), f"number of atom types in {ii}th descrptor does not match others"
+        # if hybrid sel is larger than sub sel, the nlist needs to be cut for each type
+        hybrid_sel = self.get_sel()
+        self.nlist_cut_idx: List[np.ndarray] = []
+        for ii in range(self.numb_descrpt):
+            sub_sel = self.descrpt_list[ii].get_sel()
+            start_idx = np.cumsum(np.pad(hybrid_sel, (1, 0), "constant"))[:-1]
+            end_idx = start_idx + np.array(sub_sel)
+            cut_idx = np.concatenate(
+                [range(ss, ee) for ss, ee in zip(start_idx, end_idx)]
+            )
+            self.nlist_cut_idx.append(cut_idx)
+
+    def get_rcut(self) -> float:
+        """Returns the cut-off radius."""
+        return np.max([descrpt.get_rcut() for descrpt in self.descrpt_list]).item()
+
+    def get_sel(self) -> List[int]:
+        """Returns the number of selected atoms for each type."""
+        return np.max(
+            [descrpt.get_sel() for descrpt in self.descrpt_list], axis=0
+        ).tolist()
+
+    def get_ntypes(self) -> int:
+        """Returns the number of element types."""
+        return self.descrpt_list[0].get_ntypes()
+
+    def get_dim_out(self) -> int:
+        """Returns the output dimension."""
+        return np.sum([descrpt.get_dim_out() for descrpt in self.descrpt_list]).item()
+
+    def get_dim_emb(self) -> int:
+        """Returns the output dimension."""
+        return np.sum([descrpt.get_dim_emb() for descrpt in self.descrpt_list]).item()
+
+    def mixed_types(self):
+        """Returns if the descriptor requires a neighbor list that distinguish different
+        atomic types or not.
+        """
+        return all(descrpt.mixed_types() for descrpt in self.descrpt_list)
+
+    def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None):
+        """Update mean and stddev for descriptor elements."""
+        for descrpt in self.descrpt_list:
+            descrpt.compute_input_stats(merged, path)
+
+    def call(
+        self,
+        coord_ext,
+        atype_ext,
+        nlist,
+        mapping: Optional[np.ndarray] = None,
+    ):
+        """Compute the descriptor.
+
+        Parameters
+        ----------
+        coord_ext
+            The extended coordinates of atoms. shape: nf x (nallx3)
+        atype_ext
+            The extended aotm types. shape: nf x nall
+        nlist
+            The neighbor list. shape: nf x nloc x nnei
+        mapping
+            The index mapping, not required by this descriptor.
+
+        Returns
+        -------
+        descriptor
+            The descriptor. shape: nf x nloc x (ng x axis_neuron)
+        gr
+            The rotationally equivariant and permutationally invariant single particle
+            representation. shape: nf x nloc x ng x 3. This descriptor returns None
+        g2
+            The rotationally invariant pair-partical representation.
+            this descriptor returns None
+        h2
+            The rotationally equivariant pair-partical representation.
+            this descriptor returns None
+        sw
+            The smooth switch function. this descriptor returns None
+        """
+        out_descriptor = []
+        for descrpt, nci in zip(self.descrpt_list, self.nlist_cut_idx):
+            # cut the nlist to the correct length
+            odescriptor, _, _, _, _ = descrpt(
+                coord_ext, atype_ext, nlist[:, :, nci], mapping
+            )
+            out_descriptor.append(odescriptor)
+        out_descriptor = np.concatenate(out_descriptor, axis=-1)
+        return out_descriptor, None, None, None, None
+
+    @classmethod
+    def update_sel(cls, global_jdata: dict, local_jdata: dict) -> dict:
+        """Update the selection and perform neighbor statistics.
+
+        Parameters
+        ----------
+        global_jdata : dict
+            The global data, containing the training section
+        local_jdata : dict
+            The local data refer to the current class
+        """
+        local_jdata_cpy = local_jdata.copy()
+        local_jdata_cpy["list"] = [
+            BaseDescriptor.update_sel(global_jdata, sub_jdata)
+            for sub_jdata in local_jdata["list"]
+        ]
+        return local_jdata_cpy
+
+    def serialize(self) -> dict:
+        return {
+            "@class": "Descriptor",
+            "type": "hybrid",
+            "@version": 1,
+            "list": [descrpt.serialize() for descrpt in self.descrpt_list],
+        }
+
+    @classmethod
+    def deserialize(cls, data: dict) -> "DescrptHybrid":
+        data = data.copy()
+        class_name = data.pop("@class")
+        assert class_name == "Descriptor"
+        class_type = data.pop("type")
+        assert class_type == "hybrid"
+        check_version_compatibility(data.pop("@version"), 1, 1)
+        obj = cls(
+            list=[BaseDescriptor.deserialize(ii) for ii in data["list"]],
+        )
+        return obj

deepmd/pt/model/descriptor/__init__.py

@@ -18,6 +18,7 @@
 )
 from .hybrid import (
     DescrptBlockHybrid,
+    DescrptHybrid,
 )
 from .repformers import (
     DescrptBlockRepformers,
@@ -39,6 +40,7 @@
     "DescrptSeR",
     "DescrptDPA1",
     "DescrptDPA2",
+    "DescrptHybrid",
     "prod_env_mat",
     "DescrptGaussianLcc",
     "DescrptBlockHybrid",