style: cleanup oft

extensions-builtin/Lora/network_oft.py

@@ -1,6 +1,5 @@
 import torch
 import network
-from modules import devices
 
 
 class ModuleTypeOFT(network.ModuleType):
@@ -31,33 +30,24 @@ class NetworkModuleOFT(network.NetworkModule):
 
         self.org_module: list[torch.Module] = [self.sd_module]
         self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)
-        #self.org_weight = self.org_module[0].weight.to(devices.cpu, copy=True)
+
         init_multiplier = self.multiplier() * self.calc_scale()
         self.last_multiplier = init_multiplier
+
         self.R = self.get_weight(self.oft_blocks, init_multiplier)
 
         self.merged_weight = self.merge_weight()
         self.apply_to()
         self.merged = False
 
-        # weights_backup = getattr(self.org_module[0], 'network_weights_backup', None)
-        # if weights_backup is None:
-        #     self.org_module[0].network_weights_backup = self.org_weight
-
-
     def merge_weight(self):
-        #org_sd = self.org_module[0].state_dict()
         R = self.R.to(self.org_weight.device, dtype=self.org_weight.dtype)
         if self.org_weight.dim() == 4:
             weight = torch.einsum("oihw, op -> pihw", self.org_weight, R)
         else:
             weight = torch.einsum("oi, op -> pi", self.org_weight, R)
-        #org_sd['weight'] = weight
-        # replace weight
-        #self.org_module[0].load_state_dict(org_sd)
         return weight
-        pass
+
-    
     def replace_weight(self, new_weight):
         org_sd = self.org_module[0].state_dict()
         org_sd['weight'] = new_weight
@@ -70,9 +60,7 @@ class NetworkModuleOFT(network.NetworkModule):
         self.org_module[0].load_state_dict(org_sd)
         self.merged = False
 
-
+    # FIXME: hook forward method of original linear, but how do we undo the hook when we are done?
-    # replace forward method of original linear rather than replacing the module
-    # 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
@@ -90,82 +78,26 @@ class NetworkModuleOFT(network.NetworkModule):
         block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
         block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
         R = torch.block_diag(*block_R_weighted)
-        #R = torch.block_diag(*block_R)
 
         return R
 
     def calc_updown(self, orig_weight):
-        #oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-
-        #R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
-        ##self.R = R
-
-        #R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
-        ##self.R = 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 = weight
         updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)
-        #updown = orig_weight
         output_shape = orig_weight.shape
         orig_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)
         #output_shape = self.oft_blocks.shape
 
         return self.finalize_updown(updown, orig_weight, output_shape)
-    
+
     def pre_forward_hook(self, module, input):
         multiplier = self.multiplier() * self.calc_scale()
-        if not multiplier==self.last_multiplier or not self.merged:
 
-        #if multiplier != self.last_multiplier or not self.merged:
+        if not multiplier==self.last_multiplier or not self.merged:
             self.R = self.get_weight(self.oft_blocks, multiplier)
             self.last_multiplier = multiplier
             self.merged_weight = self.merge_weight()
             self.replace_weight(self.merged_weight)
-        #elif not self.merged:
-        #    self.replace_weight(self.merged_weight)
 
-    
+
     def forward_hook(self, module, args, output):
         pass
-        #output = output * self.multiplier() * self.calc_scale()
-        #if len(args) > 0:
-        #    y = args[0]
-        #    output = output + y
-        #return output
-        #if self.merged:
-        #    pass
-            #self.restore_weight()
-        #print(f'Forward hook in {self.network_key} called')
-
-        #x = output
-        #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
-
-    # 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