fix: Ensure proper tensor conversion for numpy solver in Eigenvalues class

Author: AsymmetryChou

dptb/nn/energy.py

@@ -90,16 +90,19 @@ def forward(self,
                     chklowtinv = torch.linalg.inv(chklowt)
                     data[self.h_out_field] = (chklowtinv @ data[self.h_out_field] @ torch.transpose(chklowtinv,dim0=1,dim1=2).conj())
                 elif eig_solver == 'numpy':
-                    chklowt = np.linalg.cholesky(data[self.s_out_field].detach().numpy())
+                    chklowt = np.linalg.cholesky(data[self.s_out_field].detach().cpu().numpy())
                     chklowtinv = np.linalg.inv(chklowt)
-                    data[self.h_out_field] = (chklowtinv @ data[self.h_out_field].detach().numpy() @ np.transpose(chklowtinv,(0,2,1)).conj())
-            else:
-                data[self.h_out_field] = data[self.h_out_field]
-            
+                    data[self.h_out_field] = (chklowtinv @ data[self.h_out_field].detach().cpu().numpy() @ np.transpose(chklowtinv,(0,2,1)).conj())
+            elif eig_solver == 'numpy':
+                # Convert to numpy when using numpy solver without overlap
+                data[self.h_out_field] = data[self.h_out_field].detach().cpu().numpy()
+
             if eig_solver == 'torch':
                 eigvals.append(torch.linalg.eigvalsh(data[self.h_out_field]))
             elif eig_solver == 'numpy':
-                eigvals.append(torch.from_numpy(np.linalg.eigvalsh(a=data[self.h_out_field])))
+                eigvals_np = np.linalg.eigvalsh(a=data[self.h_out_field])
+                # Preserve dtype by converting to the Hamiltonian's original dtype
+                eigvals.append(torch.from_numpy(eigvals_np).to(dtype=self.h2k.dtype))
 
         data[self.out_field] = torch.nested.as_nested_tensor([torch.cat(eigvals, dim=0)])
         if nested: