diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index ba621139..1645b822 100644 --- a/extensions-builtin/Lora/networks.py +++ b/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: