如果未使用带有初始化检查的方法,直接使用了`Tensor->type()`,可能会导致报出`holder_ should not be null. Tensor not initialized yet when Tensor::type()`的错误,例如[Paddle issue #19522](https://github.com/PaddlePaddle/Paddle/issues/19522) ,用户仅凭该错误信息将无法得知具体出错的Op,不利于调试。
如果未使用带有初始化检查的方法,直接使用了`Tensor->type()`,可能会导致报出`holder_ should not be null. Tensor not initialized yet when Tensor::type()`的错误,例如[Paddle issue #19522](https://github.com/PaddlePaddle/Paddle/issues/19522) ,用户仅凭该错误信息将无法得知具体出错的Op,不利于调试。
@@ -110,7 +110,7 @@ Never make any modification of the input data inside Op, as there may be other O
...
@@ -110,7 +110,7 @@ Never make any modification of the input data inside Op, as there may be other O
Currently all OpKernel are required to register double and float data types.
Currently all OpKernel are required to register double and float data types.
### 4.Op compatibility issue
### 4.Op compatibility issue
The modification of Op needs to consider the compatibility problem. Please ensure that the previous model can be loaded and run normally after the modification of Op. <fontcolor="#FF0000">**So it is not allowed to add input or output to the existing Ops. It is not allowed to remove the existing properties of Op and modify the default value**</font>.
The modification of Op needs to consider the compatibility problem. Please ensure that the previous model can be loaded and run normally after the modification of Op which means that the model trained by the old version can be loaded and run with Paddle inference library of new version. <fontcolor="#FF0000">**So developers should ensure that the Input, Output and Attribute of OPs cannot be modified (except for documents) or deleted. And developers can add Input, Output and Attribute, but the added Input and Output must be set to be dispensable, and the default value of added Attribute must be set. For more details, please refer to [OP Input/Output/Attribute Compatibility Modification](https://github.com/PaddlePaddle/Paddle/wiki/OP-Input-Output-Attribute-Compatibility-Modification(English-Version))**</font>.
### 5.Call ShareDataWith
### 5.Call ShareDataWith
The function of ShareDataWith is to make the two Tensors share the underlying buffer. When calling this operation, special attention should be paid. In the Op, the ShareDataWith cannot be applied to the output of Op. In other words, the Tensor of the Op output must be from Malloc.
The function of ShareDataWith is to make the two Tensors share the underlying buffer. When calling this operation, special attention should be paid. In the Op, the ShareDataWith cannot be applied to the output of Op. In other words, the Tensor of the Op output must be from Malloc.