Feat: support virtual atom

deepmd/dpmodel/atomic_model/base_atomic_model.py

@@ -56,22 +56,19 @@
 
     def atomic_output_def(self) -> FittingOutputDef:
         old_def = self.fitting_output_def()
-        if self.atom_excl is None:
-            return old_def
-        else:
-            old_list = list(old_def.get_data().values())
-            return FittingOutputDef(
-                old_list  # noqa:RUF005
-                + [
-                    OutputVariableDef(
-                        name="mask",
-                        shape=[1],
-                        reduciable=False,
-                        r_differentiable=False,
-                        c_differentiable=False,
-                    )
-                ]
-            )
+        old_list = list(old_def.get_data().values())
+        return FittingOutputDef(
+            old_list  # noqa:RUF005
+            + [
+                OutputVariableDef(
+                    name="mask",
+                    shape=[1],
+                    reduciable=False,
+                    r_differentiable=False,
+                    c_differentiable=False,
+                )
+            ]
+        )
 
     def forward_common_atomic(
         self,
@@ -82,31 +79,66 @@
         fparam: Optional[np.ndarray] = None,
         aparam: Optional[np.ndarray] = None,
     ) -> Dict[str, np.ndarray]:
+        """Common interface for atomic inference.
+
+        This method accept extended coordinates, extended atom typs, neighbor list,
+        and predict the atomic contribution of the fit property.
+
+        Parameters
+        ----------
+        extended_coord
+            extended coodinates, shape: nf x (nall x 3)
+        extended_atype
+            extended atom typs, shape: nf x nall
+            for a type < 0 indicating the atomic is virtual.
+        nlist
+            neighbor list, shape: nf x nloc x nsel
+        mapping
+            extended to local index mapping, shape: nf x nall
+        fparam
+            frame parameters, shape: nf x dim_fparam
+        aparam
+            atomic parameter, shape: nf x nloc x dim_aparam
+
+        Returns
+        -------
+        ret_dict
+            dict of output atomic properties.
+            should implement the definition of `fitting_output_def`.
+            ret_dict["mask"] of shape nf x nloc will be provided.
+            ret_dict["mask"][ff,ii] == 1 indicating the ii-th atom of the ff-th frame is real.
+            ret_dict["mask"][ff,ii] == 0 indicating the ii-th atom of the ff-th frame is virtual.
+
+        """
         _, nloc, _ = nlist.shape
         atype = extended_atype[:, :nloc]
         if self.pair_excl is not None:
             pair_mask = self.pair_excl.build_type_exclude_mask(nlist, extended_atype)
             # exclude neighbors in the nlist
             nlist = np.where(pair_mask == 1, nlist, -1)
 
+        ext_atom_mask = self.make_atom_mask(extended_atype)
         ret_dict = self.forward_atomic(
             extended_coord,
-            extended_atype,
+            np.where(ext_atom_mask, extended_atype, 0),
             nlist,
             mapping=mapping,
             fparam=fparam,
             aparam=aparam,
         )
 
+        # nf x nloc
+        atom_mask = ext_atom_mask[:, :nloc].astype(np.int32)
         if self.atom_excl is not None:
-            atom_mask = self.atom_excl.build_type_exclude_mask(atype)
-            for kk in ret_dict.keys():
-                out_shape = ret_dict[kk].shape
-                ret_dict[kk] = (
-                    ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
-                    * atom_mask[:, :, None]
-                ).reshape(out_shape)
-            ret_dict["mask"] = atom_mask
+            atom_mask *= self.atom_excl.build_type_exclude_mask(atype)
+
+        for kk in ret_dict.keys():
+            out_shape = ret_dict[kk].shape
+            ret_dict[kk] = (
+                ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
+                * atom_mask[:, :, None]
+            ).reshape(out_shape)
+        ret_dict["mask"] = atom_mask
 
         return ret_dict
 

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -136,6 +136,28 @@
         def deserialize(cls, data: dict):
             pass
 
+        def make_atom_mask(
+            self,
+            atype: t_tensor,
+        ) -> t_tensor:
+            """The atoms with type < 0 are treated as virutal atoms,
+            which serves as place-holders for multi-frame calculations
+            with different number of atoms in different frames.
+
+            Parameters
+            ----------
+            atype
+                Atom types. >= 0 for real atoms <0 for virtual atoms.
+
+            Returns
+            -------
+            mask
+                True for real atoms and False for virutal atoms.
+
+            """
+            # supposed to be supported by all backends
+            return atype >= 0
+
         def do_grad_r(
             self,
             var_name: Optional[str] = None,

deepmd/dpmodel/utils/nlist.py

@@ -15,7 +15,7 @@
 
 ## translated from torch implemantation by chatgpt
 def build_neighbor_list(
-    coord1: np.ndarray,
+    coord: np.ndarray,
     atype: np.ndarray,
     nloc: int,
     rcut: float,
@@ -26,10 +26,11 @@
 
     Parameters
     ----------
-    coord1 : np.ndarray
+    coord : np.ndarray
         exptended coordinates of shape [batch_size, nall x 3]
     atype : np.ndarray
         extended atomic types of shape [batch_size, nall]
+        type < 0 the atom is treat as virtual atoms.
     nloc : int
         number of local atoms.
     rcut : float
@@ -54,11 +55,20 @@
         if distinguish_types==True and we have two types
         |---- nsel[0] -----| |---- nsel[1] -----|
         xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1
+        For virtual atoms all neighboring positions are filled with -1.
 
     """
-    batch_size = coord1.shape[0]
-    coord1 = coord1.reshape(batch_size, -1)
-    nall = coord1.shape[1] // 3
+    batch_size = coord.shape[0]
+    coord = coord.reshape(batch_size, -1)
+    nall = coord.shape[1] // 3
+    # fill virtual atoms with large coords so they are not neighbors of any
+    # real atom.
+    xmax = np.max(coord) + 2.0 * rcut
+    # nf x nall
+    is_vir = atype < 0
+    coord1 = np.where(is_vir[:, :, None], xmax, coord.reshape(-1, nall, 3)).reshape(
+        -1, nall * 3
+    )
     if isinstance(sel, int):
         sel = [sel]
     nsel = sum(sel)
@@ -88,7 +98,7 @@
             axis=-1,
         )
     assert list(nlist.shape) == [batch_size, nloc, nsel]
-    nlist = np.where((rr > rcut), -1, nlist)
+    nlist = np.where(np.logical_or((rr > rcut), is_vir[:, :nloc, None]), -1, nlist)
 
     if distinguish_types:
         return nlist_distinguish_types(nlist, atype, sel)

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -58,24 +58,44 @@
         else:
             self.pair_excl = PairExcludeMask(self.get_ntypes(), self.pair_exclude_types)
 
+    # to make jit happy...
+    def make_atom_mask(
+        self,
+        atype: torch.Tensor,
+    ) -> torch.Tensor:
+        """The atoms with type < 0 are treated as virutal atoms,
+        which serves as place-holders for multi-frame calculations
+        with different number of atoms in different frames.
+
+        Parameters
+        ----------
+        atype
+            Atom types. >= 0 for real atoms <0 for virtual atoms.
+
+        Returns
+        -------
+        mask
+            True for real atoms and False for virutal atoms.
+
+        """
+        # supposed to be supported by all backends
+        return atype >= 0
+
     def atomic_output_def(self) -> FittingOutputDef:
         old_def = self.fitting_output_def()
-        if self.atom_excl is None:
-            return old_def
-        else:
-            old_list = list(old_def.get_data().values())
-            return FittingOutputDef(
-                old_list  # noqa:RUF005
-                + [
-                    OutputVariableDef(
-                        name="mask",
-                        shape=[1],
-                        reduciable=False,
-                        r_differentiable=False,
-                        c_differentiable=False,
-                    )
-                ]
-            )
+        old_list = list(old_def.get_data().values())
+        return FittingOutputDef(
+            old_list  # noqa:RUF005
+            + [
+                OutputVariableDef(
+                    name="mask",
+                    shape=[1],
+                    reduciable=False,
+                    r_differentiable=False,
+                    c_differentiable=False,
+                )
+            ]
+        )
 
     def forward_common_atomic(
         self,
@@ -86,6 +106,37 @@
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
     ) -> Dict[str, torch.Tensor]:
+        """Common interface for atomic inference.
+
+        This method accept extended coordinates, extended atom typs, neighbor list,
+        and predict the atomic contribution of the fit property.
+
+        Parameters
+        ----------
+        extended_coord
+            extended coodinates, shape: nf x (nall x 3)
+        extended_atype
+            extended atom typs, shape: nf x nall
+            for a type < 0 indicating the atomic is virtual.
+        nlist
+            neighbor list, shape: nf x nloc x nsel
+        mapping
+            extended to local index mapping, shape: nf x nall
+        fparam
+            frame parameters, shape: nf x dim_fparam
+        aparam
+            atomic parameter, shape: nf x nloc x dim_aparam
+
+        Returns
+        -------
+        ret_dict
+            dict of output atomic properties.
+            should implement the definition of `fitting_output_def`.
+            ret_dict["mask"] of shape nf x nloc will be provided.
+            ret_dict["mask"][ff,ii] == 1 indicating the ii-th atom of the ff-th frame is real.
+            ret_dict["mask"][ff,ii] == 0 indicating the ii-th atom of the ff-th frame is virtual.
+
+        """
         _, nloc, _ = nlist.shape
         atype = extended_atype[:, :nloc]
 
@@ -94,24 +145,28 @@
             # exclude neighbors in the nlist
             nlist = torch.where(pair_mask == 1, nlist, -1)
 
+        ext_atom_mask = self.make_atom_mask(extended_atype)
         ret_dict = self.forward_atomic(
             extended_coord,
-            extended_atype,
+            torch.where(ext_atom_mask, extended_atype, 0),
             nlist,
             mapping=mapping,
             fparam=fparam,
             aparam=aparam,
         )
 
+        # nf x nloc
+        atom_mask = ext_atom_mask[:, :nloc].to(torch.int32)
         if self.atom_excl is not None:
-            atom_mask = self.atom_excl(atype)
-            for kk in ret_dict.keys():
-                out_shape = ret_dict[kk].shape
-                ret_dict[kk] = (
-                    ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
-                    * atom_mask[:, :, None]
-                ).reshape(out_shape)
-            ret_dict["mask"] = atom_mask
+            atom_mask *= self.atom_excl(atype)
+
+        for kk in ret_dict.keys():
+            out_shape = ret_dict[kk].shape
+            ret_dict[kk] = (
+                ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
+                * atom_mask[:, :, None]
+            ).view(out_shape)
+        ret_dict["mask"] = atom_mask
 
         return ret_dict
 

deepmd/pt/utils/nlist.py

@@ -51,7 +51,7 @@
 
 
 def build_neighbor_list(
-    coord1: torch.Tensor,
+    coord: torch.Tensor,
     atype: torch.Tensor,
     nloc: int,
     rcut: float,
@@ -62,10 +62,11 @@
 
     Parameters
     ----------
-    coord1 : torch.Tensor
+    coord : torch.Tensor
         exptended coordinates of shape [batch_size, nall x 3]
     atype : torch.Tensor
         extended atomic types of shape [batch_size, nall]
+        if type < 0 the atom is treat as virtual atoms.
     nloc : int
         number of local atoms.
     rcut : float
@@ -90,11 +91,20 @@
         if distinguish_types==True and we have two types
         |---- nsel[0] -----| |---- nsel[1] -----|
         xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1
+        For virtual atoms all neighboring positions are filled with -1.
 
     """
-    batch_size = coord1.shape[0]
-    coord1 = coord1.view(batch_size, -1)
-    nall = coord1.shape[1] // 3
+    batch_size = coord.shape[0]
+    coord = coord.view(batch_size, -1)
+    nall = coord.shape[1] // 3
+    # fill virtual atoms with large coords so they are not neighbors of any
+    # real atom.
+    xmax = torch.max(coord) + 2.0 * rcut
+    # nf x nall
+    is_vir = atype < 0
+    coord1 = torch.where(is_vir[:, :, None], xmax, coord.view(-1, nall, 3)).view(
+        -1, nall * 3
+    )
     if isinstance(sel, int):
         sel = [sel]
     nsel = sum(sel)
@@ -133,7 +143,9 @@
             dim=-1,
         )
     assert list(nlist.shape) == [batch_size, nloc, nsel]
-    nlist = nlist.masked_fill((rr > rcut), -1)
+    nlist = torch.where(
+        torch.logical_or((rr > rcut), is_vir[:, :nloc, None]), -1, nlist
+    )
 
     if distinguish_types:
         return nlist_distinguish_types(nlist, atype, sel)