Description: Returns the remainder of
[object Object]divided by[object Object].Formula:
For the input parameter
[object Object]and the comparison tensor[object Object],[object Object]can be expressed by the following 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.[object Object][object Object][object Object][object Object]
Parameters:
[object Object](aclTensor*, compute input): aclTensor on the device. The data type must meet the data type deduction rules with[object Object](see ). The shape must meet the with[object Object]. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be DOUBLE, BFLOAT16, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be DOUBLE, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
[object Object](aclTensor*, compute input): aclTensor on the host. The data type must meet the data type deduction rules with[object Object](see ). The shape must meet the with[object Object]. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be DOUBLE, BFLOAT16, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be DOUBLE, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
[object Object](aclTensor*, compute output): aclTensor on the device. The shape must be the broadcasted shape of[object Object]and[object Object]. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be DOUBLE, BFLOAT16, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be DOUBLE, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
[object Object](uint64_t*, output): size of the workspace to be allocated on the device.[object Object](aclOpExecutor**, output): operator executor, containing the operator computation process.
Returns:
Parameters:
[object Object](void *, input): memory address of the workspace to be allocated on the device.[object Object](uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling the first-phase API[object Object].[object Object](aclOpExecutor *, input): operator executor, containing the operator computation process.[object Object](aclrtStream, input): stream for executing the task.
Returns:
Parameters:
[object Object](aclTensor*, compute input): input and output tensor, aclTensor on the device. The data type must meet the data type deduction rules with[object Object](see ). The shape must meet the with[object Object]. The shape after broadcasting must be the same as that of[object Object]. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be DOUBLE, BFLOAT16, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be DOUBLE, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
[object Object](aclTensor*, compute input): aclTensor on the host. The data type must meet the data type deduction rules with[object Object](see ). The shape must meet the with[object Object]. The shape after broadcasting must be the same as that of[object Object]. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be DOUBLE, BFLOAT16, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be DOUBLE, FLOAT16, FLOAT32, INT32, INT64, INT8, or UINT8.
[object Object](uint64_t*, output): size of the workspace to be allocated on the device.[object Object](aclOpExecutor**, output): operator executor, containing the operator computation process.
Returns:
Parameters:
[object Object](void *, input): memory address of the workspace to be allocated on the device.[object Object](uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling the first-phase API[object Object].[object Object](aclOpExecutor *, input): operator executor, containing the operator computation process.[object Object](aclrtStream, input): stream for executing the task.
Returns:
- Deterministic computation:
[object Object]and[object Object]default to a deterministic implementation.
The following example is for reference only. For details, see .
aclnnFmodTensor sample code:
aclnnInplaceFmodTensor sample code: