Fix MHA updown err and support ex-bias for no-bias layer

extensions-builtin/Lora/networks.py

@@ -277,7 +277,15 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
             self.weight.copy_(weights_backup)
 
     if bias_backup is not None:
-        self.bias.copy_(bias_backup)
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias.copy_(bias_backup)
+        else:
+            self.bias.copy_(bias_backup)
+    else:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias = None
+        else:
+            self.bias = None
 
 
 def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
@@ -305,7 +313,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
     bias_backup = getattr(self, "network_bias_backup", None)
     if bias_backup is None and getattr(self, 'bias', None) is not None:
-        bias_backup = self.bias.to(devices.cpu, copy=True)
+        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
+            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
+        elif getattr(self, 'bias', None) is not None:
+            bias_backup = self.bias.to(devices.cpu, copy=True)
+        else:
+            bias_backup = None
         self.network_bias_backup = bias_backup
 
     if current_names != wanted_names:
@@ -322,8 +335,11 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                         updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
                     self.weight += updown
-                    if ex_bias is not None and getattr(self, 'bias', None) is not None:
-                        self.bias += ex_bias
+                    if ex_bias is not None and hasattr(self, 'bias'):
+                        if self.bias is None:
+                            self.bias = torch.nn.Parameter(ex_bias)
+                        else:
+                            self.bias += ex_bias
                     continue
 
             module_q = net.modules.get(network_layer_name + "_q_proj", None)
@@ -333,14 +349,19 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
             if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
                 with torch.no_grad():
-                    updown_q = module_q.calc_updown(self.in_proj_weight)
-                    updown_k = module_k.calc_updown(self.in_proj_weight)
-                    updown_v = module_v.calc_updown(self.in_proj_weight)
+                    updown_q, _ = module_q.calc_updown(self.in_proj_weight)
+                    updown_k, _ = module_k.calc_updown(self.in_proj_weight)
+                    updown_v, _ = module_v.calc_updown(self.in_proj_weight)
                     updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-                    updown_out = module_out.calc_updown(self.out_proj.weight)
+                    updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
 
                     self.in_proj_weight += updown_qkv
                     self.out_proj.weight += updown_out
+                    if ex_bias is not None:
+                        if self.out_proj.bias is None:
+                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
+                        else:
+                            self.out_proj.bias += ex_bias
                     continue
 
             if module is None: