Description: Computes the element-wise logical OR operation of a given input tensor. When the two input tensors are of the non-Boolean type, [object Object] is considered as [object Object] and a non-zero value is considered as [object Object].
[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 shape supports 0 to 8 dimensions and must meet the with[object Object]. are supported. The supports ND.- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
- [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 FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
[object Object](aclTensor*, compute input): aclTensor on the device. The shape supports 0 to 8 dimensions and must meet the with[object Object]. are supported. The supports ND.- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
- [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 FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
[object Object](aclTensor *, compute output): aclTensor on the device. The shape supports 0 to 8 dimensions and must be the shape after[object Object]and[object Object]are broadcast. are supported. The supports ND.- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
- [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 FLOAT, FLOAT16, BFLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
[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 the first-phase API[object Object].[object Object](aclOpExecutor *, input): operator executor, covering the operator computation process.[object Object](aclrtStream, input): stream for executing the task.
Returns:
Parameters:
[object Object](aclTensor *, compute input | compute output): aclTensor on the device. The shape supports 0 to 8 dimensions and must meet the with[object Object], and the shape after broadcasting must be the same as that of[object Object]. are supported. The supports ND.- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
- [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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, COMPLEX128, or BFLOAT16.
[object Object](aclTensor*, compute input): aclTensor on the device. The shape supports 0 to 8 dimensions and must meet the with[object Object]. are supported. The supports ND.- [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, or COMPLEX128.
- [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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, BOOL, COMPLEX64, COMPLEX128, or BFLOAT16.
[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 the first-phase API[object Object].[object Object](aclOpExecutor *, input): operator executor, covering the operator computation process.[object Object](aclrtStream, input): stream for executing the task.
Returns:
- Deterministic computation
[object Object]and[object Object]default to deterministic implementation.
The following example is for reference only. For details, see .
aclnnLogicalOr sample code:
aclnnInplaceLogicalOr sample code: