refactor: use forward hook instead of custom forward

extensions-builtin/Lora/network_oft.py

@@ -36,9 +36,11 @@ class NetworkModuleOFT(network.NetworkModule):
     # how do we revert this to unload the weights?
     def apply_to(self):
         self.org_forward = self.org_module[0].forward
-        self.org_module[0].forward = self.forward
+        #self.org_module[0].forward = self.forward
+        self.org_module[0].register_forward_hook(self.forward_hook)
 
     def get_weight(self, oft_blocks, multiplier=None):
+        self.constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)
         block_Q = oft_blocks - oft_blocks.transpose(1, 2)
         norm_Q = torch.norm(block_Q.flatten())
         new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
@@ -66,14 +68,10 @@ class NetworkModuleOFT(network.NetworkModule):
         output_shape = self.oft_blocks.shape
 
         return self.finalize_updown(updown, orig_weight, output_shape)
-
-    def forward(self, x, y=None):
-        x = self.org_forward(x)
-        if self.multiplier() == 0.0:
-            return x
-
-        # calculating R here is excruciatingly slow
-        #R = self.get_weight().to(x.device, dtype=x.dtype)
+    
+    def forward_hook(self, module, args, output):
+        #print(f'Forward hook in {self.network_key} called')
+        x = output
         R = self.R.to(x.device, dtype=x.dtype)
 
         if x.dim() == 4:
@@ -83,3 +81,20 @@ class NetworkModuleOFT(network.NetworkModule):
         else:
             x = torch.matmul(x, R)
         return x
+
+    # def forward(self, x, y=None):
+    #     x = self.org_forward(x)
+    #     if self.multiplier() == 0.0:
+    #         return x
+
+    #     # calculating R here is excruciatingly slow
+    #     #R = self.get_weight().to(x.device, dtype=x.dtype)
+    #     R = self.R.to(x.device, dtype=x.dtype)
+
+    #     if x.dim() == 4:
+    #         x = x.permute(0, 2, 3, 1)
+    #         x = torch.matmul(x, R)
+    #         x = x.permute(0, 3, 1, 2)
+    #     else:
+    #         x = torch.matmul(x, R)
+    #     return x