fix ut

Author: iProzd

deepmd/dpmodel/atomic_model/dp_atomic_model.py

@@ -184,7 +184,12 @@ def forward_atomic(
         nframes, nloc, nnei = nlist.shape
         atype = xp_take_first_n(extended_atype, 1, nloc)
 
-        if self.fitting_net.get_dim_fparam() > 0 and fparam is None:
+        # Handle default fparam if fitting net supports it
+        if (
+            hasattr(self.fitting_net, "get_dim_fparam")
+            and self.fitting_net.get_dim_fparam() > 0
+            and fparam is None
+        ):
             # use default fparam
             from deepmd.dpmodel.array_api import (
                 array_api_compat,
@@ -193,8 +198,11 @@ def forward_atomic(
             default_fparam = self.fitting_net.get_default_fparam()
             assert default_fparam is not None
             xp = array_api_compat.array_namespace(extended_coord)
+            default_fparam_array = xp.asarray(
+                default_fparam, dtype=extended_coord.dtype
+            )
             fparam_input_for_des = xp.tile(
-                xp.reshape(default_fparam, (1, -1)), (nframes, 1)
+                xp.reshape(default_fparam_array, (1, -1)), (nframes, 1)
             )
         else:
             fparam_input_for_des = fparam

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -274,7 +274,12 @@ def forward_atomic(
         if self.do_grad_r() or self.do_grad_c():
             extended_coord.requires_grad_(True)
 
-        if self.fitting_net.get_dim_fparam() > 0 and fparam is None:
+        # Handle default fparam if fitting net supports it
+        if (
+            hasattr(self.fitting_net, "get_dim_fparam")
+            and self.fitting_net.get_dim_fparam() > 0
+            and fparam is None
+        ):
             # use default fparam
             default_fparam_tensor = self.fitting_net.get_default_fparam()
             assert default_fparam_tensor is not None

source/tests/consistent/descriptor/common.py

@@ -246,13 +246,13 @@ def eval_pd_descriptor(
             pd_obj.get_sel(),
             distinguish_types=(not mixed_types),
         )
-        fparam_pd = (
-            paddle.to_tensor(fparam).to(PD_DEVICE) if fparam is not None else None
-        )
         return [
             x.detach().cpu().numpy() if paddle.is_tensor(x) else x
             for x in pd_obj(
-                ext_coords, ext_atype, nlist=nlist, mapping=mapping, fparam=fparam_pd
+                ext_coords,
+                ext_atype,
+                nlist=nlist,
+                mapping=mapping,
             )
         ]