faster by calculating R in updown and using cached R in forward

extensions-builtin/Lora/network_oft.py

@@ -58,17 +58,18 @@ class NetworkModuleOFT(network.NetworkModule):
 
     def calc_updown(self, orig_weight):
         # this works
-        R = self.R
+        # R = self.R
+        self.R = self.get_weight(self.multiplier())
 
-        # this causes major deepfrying i.e. just doesn't work
+        # sending R to device causes major deepfrying i.e. just doesn't work
         # R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
 
-        if orig_weight.dim() == 4:
-            weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
-        else:
-            weight = torch.einsum("oi, op -> pi", orig_weight, R)
+        # if orig_weight.dim() == 4:
+        #     weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
+        # else:
+        #     weight = torch.einsum("oi, op -> pi", orig_weight, R)
 
-        updown = orig_weight @ R
+        updown = orig_weight @ self.R
         output_shape = self.oft_blocks.shape
 
         ## this works