Fix _grad_magnitude_ema_up _grad_magnitude_ema_down getting saved to LoRA

Author: rockerBOO

networks/lora_flux.py

@@ -126,8 +126,8 @@ def __init__(
         self.mgpo_beta = mgpo_beta
 
         # EMA of gradient magnitudes for adaptive normalization
-        self._grad_magnitude_ema_down = torch.nn.Parameter(torch.tensor(1.0), requires_grad=False)
-        self._grad_magnitude_ema_up = torch.nn.Parameter(torch.tensor(1.0), requires_grad=False)
+        self.register_buffer('_grad_magnitude_ema_down', torch.tensor(1.0), persistent=False)
+        self.register_buffer('_grad_magnitude_ema_up', torch.tensor(1.0), persistent=False)
 
         self.optimizer: torch.optim.Optimizer | None = None
 
@@ -337,24 +337,23 @@ def update_grad_norms(self):
     def update_gradient_ema(self):
         """
         Update EMA of gradient magnitudes for adaptive perturbation normalization
-
         Formula: ḡₗ⁽ᵗ⁾ = β * ḡₗ⁽ᵗ⁻¹⁾ + (1 - β) * ||∇ΔWₗL||₂
         """
         if self.mgpo_beta is None:
             return
-
+        
         # Update EMA for lora_down gradient magnitude
         if self.lora_down.weight.grad is not None:
             current_grad_norm = torch.norm(self.lora_down.weight.grad, p=2)
-            self._grad_magnitude_ema_down.data = (
-                self.mgpo_beta * self._grad_magnitude_ema_down.data + (1 - self.mgpo_beta) * current_grad_norm
+            self._grad_magnitude_ema_down.mul_(self.mgpo_beta).add_(
+                current_grad_norm, alpha=(1 - self.mgpo_beta)
             )
-
+        
         # Update EMA for lora_up gradient magnitude
         if self.lora_up.weight.grad is not None:
             current_grad_norm = torch.norm(self.lora_up.weight.grad, p=2)
-            self._grad_magnitude_ema_up.data = (
-                self.mgpo_beta * self._grad_magnitude_ema_up.data + (1 - self.mgpo_beta) * current_grad_norm
+            self._grad_magnitude_ema_up.mul_(self.mgpo_beta).add_(
+                current_grad_norm, alpha=(1 - self.mgpo_beta)
             )
 
     def get_mgpo_output_perturbation(self, x: Tensor) -> Tensor | None: