pt: improve nlist performance

deepmd/pt/train/training.py

@@ -950,7 +950,7 @@
                     batch_data = next(iter(self.validation_data[task_key]))
 
         for key in batch_data.keys():
-            if key == "sid" or key == "fid":
+            if key == "sid" or key == "fid" or key == "box":
                 continue
             elif not isinstance(batch_data[key], list):
                 if batch_data[key] is not None:

deepmd/pt/utils/nlist.py

@@ -27,14 +27,16 @@
 ):
     nframes, nloc = atype.shape[:2]
     if box is not None:
+        box_gpu = box.to(coord.device, non_blocking=True)
         coord_normalized = normalize_coord(
             coord.view(nframes, nloc, 3),
-            box.reshape(nframes, 3, 3),
+            box_gpu.reshape(nframes, 3, 3),
         )
     else:
+        box_gpu = None
         coord_normalized = coord.clone()
     extended_coord, extended_atype, mapping = extend_coord_with_ghosts(
-        coord_normalized, atype, box, rcut
+        coord_normalized, atype, box_gpu, rcut, box
     )
     nlist = build_neighbor_list(
         extended_coord,
@@ -262,6 +264,7 @@
     atype: torch.Tensor,
     cell: Optional[torch.Tensor],
     rcut: float,
+    cell_cpu: Optional[torch.Tensor] = None,
 ):
     """Extend the coordinates of the atoms by appending peridoc images.
     The number of images is large enough to ensure all the neighbors
@@ -277,6 +280,8 @@
         simulation cell tensor of shape [-1, 9].
     rcut : float
         the cutoff radius
+    cell_cpu : torch.Tensor
+        cell on cpu for performance
 
     Returns
     -------
@@ -299,27 +304,25 @@
     else:
         coord = coord.view([nf, nloc, 3])
         cell = cell.view([nf, 3, 3])
+        cell_cpu = cell_cpu.view([nf, 3, 3]) if cell_cpu is not None else cell
         # nf x 3
-        to_face = to_face_distance(cell)
+        to_face = to_face_distance(cell_cpu)
         # nf x 3
         # *2: ghost copies on + and - directions
         # +1: central cell
         nbuff = torch.ceil(rcut / to_face).to(torch.long)
         # 3
         nbuff = torch.max(nbuff, dim=0, keepdim=False).values
-        xi = torch.arange(-nbuff[0], nbuff[0] + 1, 1, device=device)
-        yi = torch.arange(-nbuff[1], nbuff[1] + 1, 1, device=device)
-        zi = torch.arange(-nbuff[2], nbuff[2] + 1, 1, device=device)
-        xyz = xi.view(-1, 1, 1, 1) * torch.tensor(
-            [1, 0, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=device
-        )
-        xyz = xyz + yi.view(1, -1, 1, 1) * torch.tensor(
-            [0, 1, 0], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=device
-        )
-        xyz = xyz + zi.view(1, 1, -1, 1) * torch.tensor(
-            [0, 0, 1], dtype=env.GLOBAL_PT_FLOAT_PRECISION, device=device
-        )
+        nbuff_cpu = nbuff.cpu()
+        xi = torch.arange(-nbuff_cpu[0], nbuff_cpu[0] + 1, 1, device="cpu")
+        yi = torch.arange(-nbuff_cpu[1], nbuff_cpu[1] + 1, 1, device="cpu")
+        zi = torch.arange(-nbuff_cpu[2], nbuff_cpu[2] + 1, 1, device="cpu")
+        eye_3 = torch.eye(3, dtype=env.GLOBAL_PT_FLOAT_PRECISION, device="cpu")
+        xyz = xi.view(-1, 1, 1, 1) * eye_3[0]
+        xyz = xyz + yi.view(1, -1, 1, 1) * eye_3[1]
+        xyz = xyz + zi.view(1, 1, -1, 1) * eye_3[2]
         xyz = xyz.view(-1, 3)
+        xyz = xyz.to(device=device, non_blocking=True)
         # ns x 3
         shift_idx = xyz[torch.argsort(torch.norm(xyz, dim=1))]
         ns, _ = shift_idx.shape

deepmd/pt/utils/region.py

@@ -21,7 +21,7 @@
         the internal coordinates
 
     """
-    rec_cell = torch.linalg.inv(cell)
+    rec_cell, _ = torch.linalg.inv_ex(cell)
     return torch.matmul(coord, rec_cell)