[object Object]

[object Object][object Object]undefined[object Object]

Description: Returns the sum of each row in a specified dimension of the input tensor.

[object Object]

Each operator has calls. First, [object Object] is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, [object Object] is called to perform computation.

• [object Object]
• [object Object]

[object Object]

• Parameters:

  • [object Object] (aclTensor*, computation input): aclTensor on the device. The shape supports zero to eight dimensions. are supported. The can be ND.

    • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, COMPLEX64, or COMPLEX128. If the input is an empty tensor, the output type cannot be 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 FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, COMPLEX64, COMPLEX128, or BFLOAT16. If the input is an empty tensor, the output type cannot be COMPLEX64 or COMPLEX128.

  • [object Object] (aclIntArray*, computation input): aclIntArray on the host, which specifies the reduced dimensions. The data type can be INT64. The value range is [–self.dim(), self.dim() – 1].

  • [object Object] (bool, computation input): BOOL value on the host, which specifies whether to retain the dimensions of the input tensor in the output tensor.

  • [object Object] (aclDataType, computation input): aclDataType on the device, which specifies the data type of the output tensor.

    • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, 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 FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, COMPLEX64, COMPLEX128, or BFLOAT16.

  • [object Object] (aclTensor*, computation output): aclTensor on the device. are supported. The can be ND.

    • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, 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 FLOAT16, FLOAT32, INT8, INT16, INT32, INT64, UINT8, BOOL, DOUBLE, 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:

  [object Object]: status code. For details, see .

[object Object]

• 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:

  [object Object]: status code. For details, see .

[object Object]

• Deterministic computation:
  • [object Object] defaults to a deterministic implementation.

[object Object]

The following example is for reference only. For details, see .