[object Object]

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

• Operator function: Computes the tangent of each element in the input tensor [object Object] and stores the result in the output tensor [object Object].

• Formula:

  $$out[i] = tan(self[i])$$

• Example:

  [object Object]

[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]

[object Object]

• Parameter Description:

  • [object Object] (aclTensor*, computation input): input [object Object] in the formula. are supported. The can be ND. The shape dimension must be less than or equal to 8, and must be the same as that of [object Object]. The data type must meet the type deduction rules with [object Object].
    • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, 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, INT8, INT16, INT32, INT64, UINT8, BOOL, COMPLEX64, COMPLEX128, or BFLOAT16.
  • [object Object] (aclTensor*, computation output): [object Object] in the formula. are supported. The can be ND. The shape must be the same as that of [object Object]. The data type must meet the type deduction rules with [object Object].
    • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, 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 FLOAT, FLOAT16, 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]

• Parameter Description:

  • [object Object] (void*, input): address of the workspace to be allocated on the device.
  • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling [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]

• Parameter Description:

  • [object Object] (aclTensor*, computation input | computation output): [object Object] in the formula, which is an 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 FLOAT, FLOAT16, 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 FLOAT, FLOAT16, 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]

• Parameter Description:

  • [object Object] (void*, input): 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 [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] and [object Object] default to deterministic implementation.

  • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The input values of the FLOAT, FLOAT16, INT32, and INT64 data types must be within the range of [-65504, 65504] to meet precision requirements. If this range is exceeded, precision is not guaranteed and you are advised to use the CPU for computation.

  • [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 input values of the FLOAT, BFLOAT16, FLOAT16, INT32, and INT64 data types must be within the range of [-65504, 65504] to meet precision requirements. If this range is exceeded, precision is not guaranteed and you are advised to use the CPU for computation. Excessive computation load may cause the operator to time out (AI Core error, errorStr: "timeout or trap error"). This typically occurs when the product of the last two dimensions is less than 16 while the product of the leading dimensions is excessively large.

[object Object]

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

aclnnTan API call example:

aclnnInplaceTan API call example: