这个是生成cambricon的代码，然后这个model，我在测试的时候也有输出模型内传没传过去参数，是传过去的
下面的是一个测试案例

  def forward(
        self,
        inputs_ids: torch.LongTensor = None,
        causal_mask_shape: torch.LongTensor = None,
        position_ids: Optional[torch.LongTensor] = None,
        cache_position: torch.LongTensor = None,
       
    ) -> Union[Tuple,  ModelOutputWithPast]:

 
        dtype, device = inputs_ids.dtype, inputs_ids.device

       
        inputs_ids = inputs_ids.to(device)
        causal_mask_shape = causal_mask_shape.to(device)
        position_ids = position_ids.to(device) if position_ids is not None else None
        cache_position = cache_position.to(device)

        print(f"inputs_ids device: {inputs_ids.device}, dtype: {inputs_ids.dtype}")
        print(f"causal_mask_shape device: {causal_mask_shape.device}, dtype: {causal_mask_shape.dtype}")
        print(f"position_ids device: {position_ids.device if position_ids is not None else 'None'}")
        print(f"cache_position device: {cache_position.device}")


        if 'mlu' in str(device):

            print(f"causal_mask device: {causal_mask.device}")
            print(f"causal_mask: {causal_mask.to('cpu')}")
        else:

            causal_mask = torch.tensor([[1.0, 2.0], [3.0, 4.0]], dtype=dtype, device=device)
       
        # 返回结果
        return causal_mask

但是总是报错