Fix 'L2Clipping' algorithm.

declearn/optimizer/modules/_clipping.py

@@ -30,11 +30,12 @@ class L2Clipping(OptiModule):
 
     This module implements the following algorithm:
 
-        Init(max_norm):
-        Step(max_norm):
-            norm = euclidean_norm(grads)
-            clip = min(norm, max_norm)
-            grads *= clip / max_norm
+        Init(max_norm, per_grad):
+            assign hyper-parameters
+        Step(grads):
+            norm = euclidean_norm(grads)  # parameter-wise
+            clip = max(max_norm / norm, 1.0)
+            grads *= clip
 
     In other words, (batch-averaged) gradients are clipped
     based on their L2 (euclidean) norm, based on a single,
@@ -67,7 +68,7 @@ class L2Clipping(OptiModule):
         gradients: Vector,
     ) -> Vector:
         l2_norm = (gradients**2).sum() ** 0.5
-        c_scale = (l2_norm / self.max_norm).minimum(1.0)
+        c_scale = (self.max_norm / l2_norm).minimum(1.0)
         return gradients * c_scale
 
     def get_config(