Feat: numpy pairtab model

deepmd/dpmodel/model/pair_tab_model.py

@@ -0,0 +1,295 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    Dict,
+    List,
+    Optional,
+    Union,
+)
+
+import numpy as np
+
+from deepmd.dpmodel.output_def import (
+    FittingOutputDef,
+    OutputVariableDef,
+)
+from deepmd.utils.pair_tab import (
+    PairTab,
+)
+
+from .base_atomic_model import (
+    BaseAtomicModel,
+)
+
+
+class PairTabModel(BaseAtomicModel):
+    """Pairwise tabulation energy model.
+
+    This model can be used to tabulate the pairwise energy between atoms for either
+    short-range or long-range interactions, such as D3, LJ, ZBL, etc. It should not
+    be used alone, but rather as one submodel of a linear (sum) model, such as
+    DP+D3.
+
+    Do not put the model on the first model of a linear model, since the linear
+    model fetches the type map from the first model.
+
+    At this moment, the model does not smooth the energy at the cutoff radius, so
+    one needs to make sure the energy has been smoothed to zero.
+
+    Parameters
+    ----------
+    tab_file : str
+        The path to the tabulation file.
+    rcut : float
+        The cutoff radius.
+    sel : int or list[int]
+        The maxmum number of atoms in the cut-off radius.
+    """
+
+    def __init__(
+        self, tab_file: str, rcut: float, sel: Union[int, List[int]], **kwargs
+    ):
+        super().__init__()
+        self.tab_file = tab_file
+        self.rcut = rcut
+
+        self.tab = PairTab(self.tab_file, rcut=rcut)
+
+        if self.tab_file is not None:
+            self.tab_info, self.tab_data = self.tab.get()
+        else:
+            self.tab_info, self.tab_data = None, None
+
+        if isinstance(sel, int):
+            self.sel = sel
+        elif isinstance(sel, list):
+            self.sel = sum(sel)
+        else:
+            raise TypeError("sel must be int or list[int]")
+
+    def fitting_output_def(self) -> FittingOutputDef:
+        return FittingOutputDef(
+            [
+                OutputVariableDef(
+                    name="energy", shape=[1], reduciable=True, differentiable=True
+                )
+            ]
+        )
+
+    def get_rcut(self) -> float:
+        return self.rcut
+
+    def get_sel(self) -> int:
+        return self.sel
+
+    def distinguish_types(self) -> bool:
+        # to match DPA1 and DPA2.
+        return False
+
+    def serialize(self) -> dict:
+        return {"tab": self.tab.serialize(), "rcut": self.rcut, "sel": self.sel}
+
+    @classmethod
+    def deserialize(cls, data) -> "PairTabModel":
+        rcut = data["rcut"]
+        sel = data["sel"]
+        tab = PairTab.deserialize(data["tab"])
+        tab_model = cls(None, rcut, sel)
+        tab_model.tab = tab
+        tab_model.tab_info = tab_model.tab.tab_info
+        tab_model.tab_data = tab_model.tab.tab_data
+        return tab_model
+
+    def forward_atomic(
+        self,
+        extended_coord,
+        extended_atype,
+        nlist,
+        mapping: Optional[np.array] = None,
+        do_atomic_virial: bool = False,
+    ) -> Dict[str, np.array]:
+        self.nframes, self.nloc, self.nnei = nlist.shape
+
+        # this will mask all -1 in the nlist
+        masked_nlist = np.clip(nlist, 0, None)
+
+        atype = extended_atype[:, : self.nloc]  # (nframes, nloc)
+        pairwise_dr = self._get_pairwise_dist(
+            extended_coord
+        )  # (nframes, nall, nall, 3)
+        pairwise_rr = np.sqrt(
+            np.sum(np.power(pairwise_dr, 2), axis=-1)
+        )  # (nframes, nall, nall)
+        self.tab_data = self.tab_data.reshape(
+            self.tab.ntypes, self.tab.ntypes, self.tab.nspline, 4
+        )
+
+        # (nframes, nloc, nnei)
+        j_type = extended_atype[
+            np.arange(extended_atype.shape[0])[:, None, None], masked_nlist
+        ]
+
+        # slice rr to get (nframes, nloc, nnei)
+        rr = np.take_along_axis(pairwise_rr[:, : self.nloc, :], masked_nlist, 2)
+        raw_atomic_energy = self._pair_tabulated_inter(nlist, atype, j_type, rr)
+        atomic_energy = 0.5 * np.sum(
+            np.where(nlist != -1, raw_atomic_energy, np.zeros_like(raw_atomic_energy)),
+            axis=-1,
+        )
+
+        return {"energy": atomic_energy}
+
+    def _pair_tabulated_inter(
+        self,
+        nlist: np.array,
+        i_type: np.array,
+        j_type: np.array,
+        rr: np.array,
+    ) -> np.array:
+        """Pairwise tabulated energy.
+
+        Parameters
+        ----------
+        nlist : np.array
+            The unmasked neighbour list. (nframes, nloc)
+        i_type : np.array
+            The integer representation of atom type for all local atoms for all frames. (nframes, nloc)
+        j_type : np.array
+            The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei)
+        rr : np.array
+            The salar distance vector between two atoms. (nframes, nloc, nnei)
+
+        Returns
+        -------
+        np.array
+            The masked atomic energy for all local atoms for all frames. (nframes, nloc, nnei)
+
+        Raises
+        ------
+        Exception
+            If the distance is beyond the table.
+
+        Notes
+        -----
+        This function is used to calculate the pairwise energy between two atoms.
+        It uses a table containing cubic spline coefficients calculated in PairTab.
+        """
+        rmin = self.tab_info[0]
+        hh = self.tab_info[1]
+        hi = 1.0 / hh
+
+        self.nspline = int(self.tab_info[2] + 0.1)
+
+        uu = (rr - rmin) * hi  # this is broadcasted to (nframes,nloc,nnei)
+
+        # if nnei of atom 0 has -1 in the nlist, uu would be 0.
+        # this is to handle the nlist where the mask is set to 0, so that we don't raise exception for those atoms.
+        uu = np.where(nlist != -1, uu, self.nspline + 1)
+
+        if np.any(uu < 0):
+            raise Exception("coord go beyond table lower boundary")
+
+        idx = uu.astype(int)
+
+        uu -= idx
+        table_coef = self._extract_spline_coefficient(
+            i_type, j_type, idx, self.tab_data, self.nspline
+        )
+        table_coef = table_coef.reshape(self.nframes, self.nloc, self.nnei, 4)
+        ener = self._calcualte_ener(table_coef, uu)
+        # here we need to overwrite energy to zero at rcut and beyond.
+        mask_beyond_rcut = rr >= self.rcut
+        # also overwrite values beyond extrapolation to zero
+        extrapolation_mask = rr >= self.tab.rmin + self.nspline * self.tab.hh
+        ener[mask_beyond_rcut] = 0
+        ener[extrapolation_mask] = 0
+
+        return ener
+
+    @staticmethod
+    def _get_pairwise_dist(coords: np.array) -> np.array:
+        """Get pairwise distance `dr`.
+
+        Parameters
+        ----------
+        coords : np.array
+            The coordinate of the atoms shape of (nframes * nall * 3).
+
+        Returns
+        -------
+        np.array
+            The pairwise distance between the atoms (nframes * nall * nall * 3).
+        """
+        return np.expand_dims(coords, 2) - np.expand_dims(coords, 1)
+
+    @staticmethod
+    def _extract_spline_coefficient(
+        i_type: np.array,
+        j_type: np.array,
+        idx: np.array,
+        tab_data: np.array,
+        nspline: int,
+    ) -> np.array:
+        """Extract the spline coefficient from the table.
+
+        Parameters
+        ----------
+        i_type : np.array
+            The integer representation of atom type for all local atoms for all frames. (nframes, nloc)
+        j_type : np.array
+            The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei)
+        idx : np.array
+            The index of the spline coefficient. (nframes, nloc, nnei)
+        tab_data : np.array
+            The table storing all the spline coefficient. (ntype, ntype, nspline, 4)
+        nspline : int
+            The number of splines in the table.
+
+        Returns
+        -------
+        np.array
+            The spline coefficient. (nframes, nloc, nnei, 4), shape may be squeezed.
+        """
+        # (nframes, nloc, nnei)
+        expanded_i_type = np.broadcast_to(
+            i_type[:, :, np.newaxis],
+            (i_type.shape[0], i_type.shape[1], j_type.shape[-1]),
+        )
+
+        # (nframes, nloc, nnei, nspline, 4)
+        expanded_tab_data = tab_data[expanded_i_type, j_type]
+
+        # (nframes, nloc, nnei, 1, 4)
+        expanded_idx = np.broadcast_to(
+            idx[..., np.newaxis, np.newaxis], (*idx.shape, 1, 4)
+        )
+        clipped_indices = np.clip(expanded_idx, 0, nspline - 1).astype(int)
+
+        # (nframes, nloc, nnei, 4)
+        final_coef = np.squeeze(
+            np.take_along_axis(expanded_tab_data, clipped_indices, 3)
+        )
+
+        # when the spline idx is beyond the table, all spline coefficients are set to `0`, and the resulting ener corresponding to the idx is also `0`.
+        final_coef[expanded_idx.squeeze() > nspline] = 0
+        return final_coef
+
+    @staticmethod
+    def _calcualte_ener(coef: np.array, uu: np.array) -> np.array:
+        """Calculate energy using spline coeeficients.
+
+        Parameters
+        ----------
+        coef : np.array
+            The spline coefficients. (nframes, nloc, nnei, 4)
+        uu : np.array
+            The atom displancemnt used in interpolation and extrapolation (nframes, nloc, nnei)
+
+        Returns
+        -------
+        np.array
+            The atomic energy for all local atoms for all frames. (nframes, nloc, nnei)
+        """
+        a3, a2, a1, a0 = coef[..., 0], coef[..., 1], coef[..., 2], coef[..., 3]
+        etmp = (a3 * uu + a2) * uu + a1  # this should be elementwise operations.
+        ener = etmp * uu + a0  # this energy has the extrapolated value when rcut > rmax
+        return ener

deepmd/pt/model/model/pair_tab.py → deepmd/pt/model/model/pair_tab_model.py

@@ -11,20 +11,20 @@
     nn,
 )
 
-from deepmd.dpmodel import (
+from deepmd.model_format import (
     FittingOutputDef,
     OutputVariableDef,
 )
 from deepmd.utils.pair_tab import (
     PairTab,
 )
 
-from .base_atomic_model import (
-    BaseAtomicModel,
+from .atomic_model import (
+    AtomicModel,
 )
 
 
-class PairTabModel(nn.Module, BaseAtomicModel):
+class PairTabModel(nn.Module, AtomicModel):
     """Pairwise tabulation energy model.
 
     This model can be used to tabulate the pairwise energy between atoms for either
@@ -54,13 +54,19 @@
         super().__init__()
         self.tab_file = tab_file
         self.rcut = rcut
-
         self.tab = PairTab(self.tab_file, rcut=rcut)
-        self.ntypes = self.tab.ntypes
 
-        tab_info, tab_data = self.tab.get()  # this returns -> Tuple[np.array, np.array]
-        self.tab_info = torch.from_numpy(tab_info)
-        self.tab_data = torch.from_numpy(tab_data)
+        # handle deserialization with no input file
+        if self.tab_file is not None:
+            (
+                tab_info,
+                tab_data,
+            ) = self.tab.get()  # this returns -> Tuple[np.array, np.array]
+            self.tab_info = torch.from_numpy(tab_info)
+            self.tab_data = torch.from_numpy(tab_data)
+        else:
+            self.tab_info = None
+            self.tab_data = None
 
         # self.model_type = "ener"
         # self.model_version = MODEL_VERSION ## this shoud be in the parent class
@@ -72,7 +78,7 @@
         else:
             raise TypeError("sel must be int or list[int]")
 
-    def fitting_output_def(self) -> FittingOutputDef:
+    def get_fitting_output_def(self) -> FittingOutputDef:
         return FittingOutputDef(
             [
                 OutputVariableDef(
@@ -92,12 +98,18 @@
         return False
 
     def serialize(self) -> dict:
-        # place holder, implemantated in future PR
-        raise NotImplementedError
-
-    def deserialize(cls):
-        # place holder, implemantated in future PR
-        raise NotImplementedError
+        return {"tab": self.tab.serialize(), "rcut": self.rcut, "sel": self.sel}
+
+    @classmethod
+    def deserialize(cls, data) -> "PairTabModel":
+        rcut = data["rcut"]
+        sel = data["sel"]
+        tab = PairTab.deserialize(data["tab"])
+        tab_model = cls(None, rcut, sel)
+        tab_model.tab = tab
+        tab_model.tab_info = torch.from_numpy(tab_model.tab.tab_info)
+        tab_model.tab_data = torch.from_numpy(tab_model.tab.tab_data)
+        return tab_model
 
     def forward_atomic(
         self,