Project: 026_SD-LoRA_Scalable_Decoupled_Low-Rank_Adaptation_for | Function: compute_ortho_loss (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Computes orthogonality regularization loss between current and previous LoRA directions to ensure the learned directions are diverse and non-redundant, supporting the theoretical foundation of SD-LoRA.

**KEY TERMS:** {
  "w_As": "LoRA A matrices from previous tasks",
  "self.w_As": "current task LoRA A matrices",
  "torch.matmul": "matrix multiplication for orthogonality computation",
  "torch.square": "squared residual computation"
}

**CONTEXT CODE:**                 # nn.init.kaiming_uniform_(w_A.linear_b_q.weight, a=math.sqrt(5) )
            for w_B in self.w_Bs:
                nn.init.zeros_(w_B.weight)


    def save_wrap_param(self, filename):
        if self.task_id ==1:   
            scaling_param = torch.zeros(20,20)
        else:
            scaling_param = torch.load(filename + 'scaling_factor'+str(self.task_id-2)+'.pt')
        i = self.task_id-1
        # print('save i', i)
        for j in range(i+1):
            if j == i:
                scaling_param[i][j] = self.wrapped_param[0].param.clone()
            else:
                scaling_param[i][j] = self.wrapped_param_prev[j].param.clone()  
        torch.save(scaling_param, filename + 'scaling_factor'+str(self.task_id-1)+'.pt')
        
    def save_lora_parameters(self, filename: str, task_id) -> None:
        self.task_id += 1
        if not os.path.exists(filename):
           os.makedirs(filename)
        torch.save(self.w_As, filename + 'lora_w_a_'+str(task_id)+'.pt')
        torch.save(self.w_Bs, filename + 'lora_w_b_'+str(task_id)+'.pt')

    def generate_fc(self, in_dim, out_dim):
        fc = SimpleLinear(in_dim, out_dim)
        return fc

    def load_eval_vit(self):
        self.lora_vit = copy.deepcopy(self.base_vit)
        saved_lora_A, saved_lora_B = {}, {}
        for i in range(self.task_id):
            file_path = self.save_file+'lora_w_a_'+str(i)+'.pt'
            saved_lora_A['saved_A_'+str(i)] = torch.load(file_path)
            file_path = self.save_file+'lora_w_b_'+str(i)+'.pt'
            saved_lora_B['saved_B_'+str(i)] = torch.load(file_path)

        # for param in self.eval_vit.parameters():
        for param in self.lora_vit.parameters():
            param.requires_grad = False
        
        # for t_layer_i, blk in enumerate(self.eval_vit.blocks):
        for t_layer_i, blk in enumerate(self.lora_vit.blocks):
            w_qkv_linear = blk.attn.qkv
            self.dim = w_qkv_linear.in_features
            blk.attn.qkv = _LoRA_qkv_timm_eval(self.task_id, w_qkv_linear, saved_lora_A, saved_lora_B, t_layer_i, self.rank)    
        self.reset_lora_vit_head()

    def compute_ortho_loss(self):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    def forward(self, x: Tensor, loss= False, eval=False) -> Tensor:
        if eval:
            self.reset(eval=True)
            return self.lora_vit(x)
        elif loss:
            loss = self.compute_ortho_loss()
            return self.lora_vit(x), loss
        else:
            return self.lora_vit(x)


Complete the function implementation replacing "raise NotImplementedError".