fix device problem of residual weights

pina/solver/physics_informed_solver/rba_pinn.py

@@ -140,6 +140,21 @@ class RBAPINN(PINN):
         # Set the loss function to return non-aggregated losses
         self._loss_fn = type(self._loss_fn)(reduction="none")
 
+    def on_train_start(self):
+        """
+        Ensure that all residual weight buffers registered during initialization
+        are moved to the correct computation device.
+        """
+        # Move all weight buffers to the correct device
+        for cond in self.problem.input_pts:
+
+            # Get the buffer for the current condition
+            weight_buf = getattr(self, f"weight_{cond}")
+
+            # Move the buffer to the correct device
+            weight_buf.data = weight_buf.data.to(self.device)
+            self.weights[cond] = weight_buf
+
     def training_step(self, batch, batch_idx, **kwargs):
         """
         Solver training step. It computes the optimization cycle and aggregates
@@ -235,7 +250,7 @@ class RBAPINN(PINN):
             idx = torch.arange(
                 batch_idx * len_res,
                 (batch_idx + 1) * len_res,
-                device=res.device,
+                device=self.weights[cond].device,
             ) % len(self.problem.input_pts[cond])
 
             losses[cond] = self._apply_reduction(
@@ -271,7 +286,7 @@ class RBAPINN(PINN):
 
             # Compute normalized residuals
             res = residuals[cond]
-            res_abs = res.abs()
+            res_abs = torch.linalg.vector_norm(res, ord=2, dim=1, keepdim=True)
             r_norm = (self.eta * res_abs) / (res_abs.max() + 1e-12)
 
             # Get the correct indices for the weights. Modulus is used according
@@ -280,7 +295,7 @@ class RBAPINN(PINN):
             idx = torch.arange(
                 batch_idx * len_pts,
                 (batch_idx + 1) * len_pts,
-                device=res.device,
+                device=self.weights[cond].device,
             ) % len(self.problem.input_pts[cond])
 
             # Update weights