refactor(pt): reuse dpmodel NeighborStatOP

This class does not need torchscript, so we can reuse dpmodel codes to reduce redundancy.

Author: njzjz

deepmd/dpmodel/utils/neighbor_stat.py

@@ -87,7 +87,7 @@ def call(
         )
         assert list(diff.shape) == [nframes, nloc, nall, 3]
         # remove the diagonal elements
-        mask = xp.eye(nloc, nall, dtype=xp.bool)
+        mask = xp.eye(nloc, nall, dtype=xp.bool, device=array_api_compat.device(diff))
         mask = xp.tile(mask[None, :, :, None], (nframes, 1, 1, 3))
         diff = xp.where(mask, xp.full_like(diff, xp.inf), diff)
         rr2 = xp.sum(xp.square(diff), axis=-1)

deepmd/dpmodel/utils/nlist.py

@@ -117,7 +117,9 @@ def build_neighbor_list(
     assert list(diff.shape) == [batch_size, nloc, nall, 3]
     rr = xp.linalg.vector_norm(diff, axis=-1)
     # if central atom has two zero distances, sorting sometimes can not exclude itself
-    rr -= xp.eye(nloc, nall, dtype=diff.dtype)[xp.newaxis, :, :]
+    rr -= xp.eye(nloc, nall, dtype=diff.dtype, device=array_api_compat.device(diff))[
+        xp.newaxis, :, :
+    ]
     nlist = xp.argsort(rr, axis=-1)
     rr = xp.sort(rr, axis=-1)
     rr = rr[:, :, 1:]
@@ -128,11 +130,26 @@ def build_neighbor_list(
         nlist = nlist[:, :, :nsel]
     else:
         rr = xp.concatenate(
-            [rr, xp.ones([batch_size, nloc, nsel - nnei], dtype=rr.dtype) + rcut],
+            [
+                rr,
+                xp.ones(
+                    [batch_size, nloc, nsel - nnei],
+                    dtype=rr.dtype,
+                    device=array_api_compat.device(rr),
+                )
+                + rcut,
+            ],
             axis=-1,
         )
         nlist = xp.concatenate(
-            [nlist, xp.ones([batch_size, nloc, nsel - nnei], dtype=nlist.dtype)],
+            [
+                nlist,
+                xp.ones(
+                    [batch_size, nloc, nsel - nnei],
+                    dtype=nlist.dtype,
+                    device=array_api_compat.device(nlist),
+                ),
+            ],
             axis=-1,
         )
     assert list(nlist.shape) == [batch_size, nloc, nsel]
@@ -218,7 +235,11 @@ def build_multiple_neighbor_list(
         return {}
     nb, nloc, nsel = nlist.shape
     if nsel < nsels[-1]:
-        pad = -1 * xp.ones((nb, nloc, nsels[-1] - nsel), dtype=nlist.dtype)
+        pad = -1 * xp.ones(
+            (nb, nloc, nsels[-1] - nsel),
+            dtype=nlist.dtype,
+            device=array_api_compat.device(nlist),
+        )
         nlist = xp.concat([nlist, pad], axis=-1)
         nsel = nsels[-1]
     coord1 = xp.reshape(coord, (nb, -1, 3))
@@ -276,7 +297,12 @@ def extend_coord_with_ghosts(
     xp = array_api_compat.array_namespace(coord, atype)
     nf, nloc = atype.shape
     # int64 for index
-    aidx = xp.tile(xp.arange(nloc, dtype=xp.int64)[xp.newaxis, :], (nf, 1))
+    aidx = xp.tile(
+        xp.arange(nloc, dtype=xp.int64, device=array_api_compat.device(atype))[
+            xp.newaxis, :
+        ],
+        (nf, 1),
+    )
     if cell is None:
         nall = nloc
         extend_coord = coord
@@ -288,17 +314,41 @@ def extend_coord_with_ghosts(
         to_face = to_face_distance(cell)
         nbuff = xp.astype(xp.ceil(rcut / to_face), xp.int64)
         nbuff = xp.max(nbuff, axis=0)
-        xi = xp.arange(-int(nbuff[0]), int(nbuff[0]) + 1, 1, dtype=xp.int64)
-        yi = xp.arange(-int(nbuff[1]), int(nbuff[1]) + 1, 1, dtype=xp.int64)
-        zi = xp.arange(-int(nbuff[2]), int(nbuff[2]) + 1, 1, dtype=xp.int64)
-        xyz = xp.linalg.outer(xi, xp.asarray([1, 0, 0]))[:, xp.newaxis, xp.newaxis, :]
+        xi = xp.arange(
+            -int(nbuff[0]),
+            int(nbuff[0]) + 1,
+            1,
+            dtype=xp.int64,
+            device=array_api_compat.device(coord),
+        )
+        yi = xp.arange(
+            -int(nbuff[1]),
+            int(nbuff[1]) + 1,
+            1,
+            dtype=xp.int64,
+            device=array_api_compat.device(coord),
+        )
+        zi = xp.arange(
+            -int(nbuff[2]),
+            int(nbuff[2]) + 1,
+            1,
+            dtype=xp.int64,
+            device=array_api_compat.device(coord),
+        )
+        xyz = xp.linalg.outer(
+            xi, xp.asarray([1, 0, 0], device=array_api_compat.device(xi))
+        )[:, xp.newaxis, xp.newaxis, :]
         xyz = (
             xyz
-            + xp.linalg.outer(yi, xp.asarray([0, 1, 0]))[xp.newaxis, :, xp.newaxis, :]
+            + xp.linalg.outer(
+                yi, xp.asarray([0, 1, 0], device=array_api_compat.device(yi))
+            )[xp.newaxis, :, xp.newaxis, :]
         )
         xyz = (
             xyz
-            + xp.linalg.outer(zi, xp.asarray([0, 0, 1]))[xp.newaxis, xp.newaxis, :, :]
+            + xp.linalg.outer(
+                zi, xp.asarray([0, 0, 1], device=array_api_compat.device(zi))
+            )[xp.newaxis, xp.newaxis, :, :]
         )
         xyz = xp.reshape(xyz, (-1, 3))
         xyz = xp.astype(xyz, coord.dtype)

deepmd/pt/utils/neighbor_stat.py

@@ -12,103 +12,13 @@
 from deepmd.pt.utils.env import (
     DEVICE,
 )
-from deepmd.pt.utils.nlist import (
-    extend_coord_with_ghosts,
-)
 from deepmd.utils.data_system import (
     DeepmdDataSystem,
 )
 from deepmd.utils.neighbor_stat import NeighborStat as BaseNeighborStat
-
-
-class NeighborStatOP(torch.nn.Module):
-    """Class for getting neighbor statistics data information.
-
-    Parameters
-    ----------
-    ntypes
-        The num of atom types
-    rcut
-        The cut-off radius
-    mixed_types : bool, optional
-        If True, treat neighbors of all types as a single type.
-    """
-
-    def __init__(
-        self,
-        ntypes: int,
-        rcut: float,
-        mixed_types: bool,
-    ) -> None:
-        super().__init__()
-        self.rcut = float(rcut)
-        self.ntypes = ntypes
-        self.mixed_types = mixed_types
-
-    def forward(
-        self,
-        coord: torch.Tensor,
-        atype: torch.Tensor,
-        cell: torch.Tensor | None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """Calculate the neareest neighbor distance between atoms, maximum nbor size of
-        atoms and the output data range of the environment matrix.
-
-        Parameters
-        ----------
-        coord
-            The coordinates of atoms.
-        atype
-            The atom types.
-        cell
-            The cell.
-
-        Returns
-        -------
-        torch.Tensor
-            The minimal squared distance between two atoms, in the shape of (nframes,)
-        torch.Tensor
-            The maximal number of neighbors
-        """
-        nframes = coord.shape[0]
-        coord = coord.view(nframes, -1, 3)
-        nloc = coord.shape[1]
-        coord = coord.view(nframes, nloc * 3)
-        extend_coord, extend_atype, _ = extend_coord_with_ghosts(
-            coord, atype, cell, self.rcut
-        )
-
-        coord1 = extend_coord.reshape(nframes, -1)
-        nall = coord1.shape[1] // 3
-        coord0 = coord1[:, : nloc * 3]
-        diff = (
-            coord1.reshape([nframes, -1, 3])[:, None, :, :]
-            - coord0.reshape([nframes, -1, 3])[:, :, None, :]
-        )
-        assert list(diff.shape) == [nframes, nloc, nall, 3]
-        # remove the diagonal elements
-        mask = torch.eye(nloc, nall, dtype=torch.bool, device=diff.device)
-        diff[:, mask] = torch.inf
-        rr2 = torch.sum(torch.square(diff), dim=-1)
-        min_rr2, _ = torch.min(rr2, dim=-1)
-        # count the number of neighbors
-        if not self.mixed_types:
-            mask = rr2 < self.rcut**2
-            nnei = torch.zeros(
-                (nframes, nloc, self.ntypes), dtype=torch.int32, device=mask.device
-            )
-            for ii in range(self.ntypes):
-                nnei[:, :, ii] = torch.sum(
-                    mask & extend_atype.eq(ii)[:, None, :], dim=-1
-                )
-        else:
-            mask = rr2 < self.rcut**2
-            # virtual types (<0) are not counted
-            nnei = torch.sum(mask & extend_atype.ge(0)[:, None, :], dim=-1).view(
-                nframes, nloc, 1
-            )
-        max_nnei, _ = torch.max(nnei, dim=1)
-        return min_rr2, max_nnei
+from deepmd.dpmodel.utils.neighbor_stat import (
+    NeighborStatOP,
+)
 
 
 class NeighborStat(BaseNeighborStat):
@@ -131,8 +41,7 @@ def __init__(
         mixed_type: bool = False,
     ) -> None:
         super().__init__(ntypes, rcut, mixed_type)
-        op = NeighborStatOP(ntypes, rcut, mixed_type)
-        self.op = torch.jit.script(op)
+        self.op = NeighborStatOP(ntypes, rcut, mixed_type)
         self.auto_batch_size = AutoBatchSize()
 
     def iterator(