- Description: Each element of
[object Object]is used as the power of base 2 for computation. - Formula:
[object Object]and[object Object]implement the same function in different ways. Select a proper operator based on your requirements.[object Object]: An output tensor object needs to be created to store the computation result.[object Object]: No output tensor object needs to be created, and the computation result is stored in the memory of the input tensor.
Each operator has calls. First,
[object Object]or[object Object]is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then,[object Object]or[object Object]is called to perform computation.
Parameters:
[object Object]- [object Object]Atlas training products[object Object] and [object Object]Atlas 200I/500 A2 inference products[object Object]: The
[object Object]data type is not supported.
- [object Object]Atlas training products[object Object] and [object Object]Atlas 200I/500 A2 inference products[object Object]: The
Returns:
[object Object]: status code. For details, see .The first-phase API performs input parameter validation. The following errors may be returned:
[object Object]
Parameters:
[object Object]- [object Object]Atlas training products[object Object] and [object Object]Atlas 200I/500 A2 inference products[object Object]: The
[object Object]data type is not supported.
- [object Object]Atlas training products[object Object] and [object Object]Atlas 200I/500 A2 inference products[object Object]: The
Returns:
[object Object]: status code. For details, see .The first-phase API performs input parameter validation. The following errors may be returned:
[object Object]
- Deterministic computation:
[object Object]and[object Object]default to a deterministic implementation.
The following example is for reference only. For details, see .
aclnnExp2 sample code:
aclnnInplaceExp2 sample code: