寄存自定义运算 - 3.5 简体中文

要将量化模型转换为 XMODEL、vai_q_pytorch 会提供修饰器来允许您将单一运算或一组运算寄存为 XIR 无法识别的自定义运算：

# Decorator API
def register_custom_op(op_type: str, attrs_list: Optional[List[str]] = None):
  """The decorator is used to register the function as a custom operation.
  Args:
  op_type(str) - the operator type registered into quantizer. 
  The type should not conflict with pytorch_nndct

  attrs_list(Optional[List[str]], optional) - 
  the name list of attributes that define operation flavor. 
  For example, Convolution operation has such attributes as padding, dilation, stride and groups. 
  The order of names in attrs_list should be consistent with that of the arguments list. 
  Default: None

  """

from pytorch_nndct.utils import register_custom_op

@register_custom_op(op_type="MyOp", attrs_list=["scale_1", "scale_2"])
def custom_op(ctx, x: torch.Tensor, y:torch.Tensor, scale_1:float, scale_2:float) -> torch.Tensor:
  return scale_1 * x + scale_2 * y 

class MyModule(torch.nn.Module):
  def __init__(self):
  ...

  def forward(self, x, y):
          return custom_op(x, y, scale_1=2.0, scale_2=1.0)

限制：