fixes act_order for group-wise convolutions

src/brevitas/graph/gpfq.py

@@ -234,22 +234,25 @@ def single_layer_update(self):
         for t in range(weight.shape[-1]):
             for group_index in range(self.groups):
                 U[group_index] += torch.matmul(
-                    weight[group_index, :, perm[t]].unsqueeze(1),
-                    self.float_input[group_index, :,
-                                     perm[t]].unsqueeze(0))  #[OC/Groups, 1] * [1, INSHAPE[1]]
-                norm = torch.linalg.norm(self.quantized_input[group_index, :, perm[t]], 2) ** 2
+                    weight[group_index, :, permutation_list[group_index][t]].unsqueeze(1),
+                    self.float_input[group_index, :, permutation_list[group_index][t]].unsqueeze(
+                        0))  #[OC/Groups, 1] * [1, INSHAPE[1]]
+                norm = torch.linalg.norm(
+                    self.quantized_input[group_index, :, permutation_list[group_index][t]], 2) ** 2
                 if norm > 0:
                     q_arg = U[group_index].matmul(
-                        self.quantized_input[group_index, :, perm[t]]) / norm
+                        self.quantized_input[group_index, :,
+                                             permutation_list[group_index][t]]) / norm
                 else:
                     q_arg = torch.zeros_like(U[group_index, :, 0])
 
-                weight[group_index, :, perm[t]] = q_arg
+                weight[group_index, :, permutation_list[group_index][t]] = q_arg
             q = self.get_quant_weights(t, 0, permutation_list)
             for group_index in range(self.groups):
                 U[group_index] -= torch.matmul(
                     q[group_index].unsqueeze(1),
-                    self.quantized_input[group_index, :, perm[t]].unsqueeze(0))
+                    self.quantized_input[group_index, :,
+                                         permutation_list[group_index][t]].unsqueeze(0))
 
         del self.float_input
         del self.quantized_input