[object Object]

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

• Description: After the scalar [object Object] is broadcast into a tensor with the same shape as the tensor [object Object], converts each element in the tensor to the remainder obtained by dividing the corresponding element of the tensor [object Object]. The sign of the result is the same as that of the divisor [object Object], and the absolute value of the result is less than that of [object Object]. The actual computation of [object Object] is equivalent to the following formula:

  $$out_i = self - floor(self / other_i) * other_i$$

• Example:

[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] (aclScalar*, computation input): input [object Object] in the formula, which is an aclScalar on the host.
    • [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 INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. The data type and the data type of [object Object] must meet the data type deduction rules (see ), and the deduced data type must be convertible to the data type of [object Object].
    • [object Object]Atlas training products[object Object]: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. The data type and the data type of [object Object] must meet the data type deduction rules (see ), and the deduced data type must be convertible to the data type of [object Object].

  [object Object] (aclTensor*, computation input): [object Object] in the formula, aclTensor on the device. It supports . Its can be ND, and the data dimensions cannot exceed 8.

  • [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 INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16. Its data type and the data type of [object Object] must meet the type deduction rules (see ). The deduced data type must be convertible to that of [object Object].
  • [object Object]Atlas training products[object Object]: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE. Its data type and the data type of [object Object] must meet the type deduction rules (see ). The deduced data type must be convertible to that of [object Object].

  • out(aclTensor *, compute output): output [object Object] in the formula, aclTensor on the device. The shape must be the same as that of [object Object]. are supported. The can be ND.

    • [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 INT32, INT64, FLOAT16, FLOAT, DOUBLE, or BFLOAT16.
    • [object Object]Atlas training products[object Object]: The data type can be INT32, INT64, FLOAT16, FLOAT, or DOUBLE.

  • [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): address of the workspace to be allocated on the device.

  • [object Object] (uint64_t, input): workspace size to be allocated on the device, which is obtained by 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 deterministic implementation.

• When the data type of [object Object] is INT32, the functionality and precision within the range of [–2^24, 2^24] are preferentially ensured.

• When [object Object] is 0 and the data type of [object Object] is an integer, the result of [object Object] is [object Object].

[object Object]

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