fix(stat): Caculate correct fitting stat when using default fparam and using share fitting.

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -325,11 +325,22 @@ def wrapped_sampler() -> list[dict]:
                 atom_exclude_types = self.atom_excl.get_exclude_types()
                 for sample in sampled:
                     sample["atom_exclude_types"] = list(atom_exclude_types)
+            if (
+                "find_fparam" not in sampled[0]
+                and "fparam" not in sampled[0]
+                and self.has_default_fparam()
+            ):
+                default_fparam = self.get_default_fparam()
+                for sample in sampled:
+                    nframe = sample["atype"].shape[0]
+                    sample["fparam"] = default_fparam.repeat(nframe, 1)
             return sampled
 
         self.descriptor.compute_input_stats(wrapped_sampler, stat_file_path)
         self.fitting_net.compute_input_stats(
-            wrapped_sampler, protection=self.data_stat_protect
+            wrapped_sampler,
+            protection=self.data_stat_protect,
+            stat_file_path=stat_file_path,
         )
         if compute_or_load_out_stat:
             self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
@@ -342,6 +353,9 @@ def has_default_fparam(self) -> bool:
         """Check if the model has default frame parameters."""
         return self.fitting_net.has_default_fparam()
 
+    def get_default_fparam(self) -> Optional[torch.Tensor]:
+        return self.fitting_net.get_default_fparam()
+
     def get_dim_aparam(self) -> int:
         """Get the number (dimension) of atomic parameters of this atomic model."""
         return self.fitting_net.get_dim_aparam()

deepmd/pt/model/model/make_model.py

@@ -530,6 +530,9 @@ def has_default_fparam(self) -> bool:
             """Check if the model has default frame parameters."""
             return self.atomic_model.has_default_fparam()
 
+        def get_default_fparam(self) -> Optional[torch.Tensor]:
+            return self.atomic_model.get_default_fparam()
+
         @torch.jit.export
         def get_dim_aparam(self) -> int:
             """Get the number (dimension) of atomic parameters of this atomic model."""