[object Object]

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

• Description: Performs division and rounds down the remainder.
• Formula:$$out_i = floor(\frac{input_i}{other})$$

[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 input parameters and calculate the required workspace size based on the computation process. Then, [object Object] or [object Object] is called to perform computation.

• [object Object]

• [object Object]

• [object Object]

• [object Object]

[object Object]

• Parameters:

  • [object Object] (aclTensor*, compute input): aclTensor on the device. The shape must meet the with [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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT8, UINT8, INT16, BOOL, or BFLOAT16, and must meet the data type deduction rules (see ) with [object Object].
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, or BOOL, and must meet the data type deduction rules (see ) with [object Object].
  • [object Object] (aclScalar*, compute input): 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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT8, UINT8, INT16, BOOL, or BFLOAT16, and must meet the data type deduction rules (see ) with [object Object].
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, or BOOL, and must meet the data type deduction rules (see ) with [object Object].
  • [object Object] (aclTensor*, compute output): aclTensor on the device. The data type must be convertible from the deduced data type of [object Object] and [object Object]. The shape must be the broadcasted shape of [object Object] and [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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT8, UINT8, INT16, BOOL, or BFLOAT16.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, or BOOL.
  • [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]

• Parameters:

  • [object Object] (aclTensor*, compute input): aclTensor on the device. The shape must meet the with [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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT8, UINT8, INT16, BOOL, or BFLOAT16, and must meet the data type deduction rules (see ) with [object Object].
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, or BOOL, and must meet the data type deduction rules (see ) with [object Object].
  • [object Object] (aclScalar*, compute input): 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 FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT8, UINT8, INT16, BOOL, or BFLOAT16, and must meet the data type deduction rules (see ) with [object Object].
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, DOUBLE, INT32, INT64, INT16, INT8, UINT8, or BOOL, and must meet the data type deduction rules (see ) with [object Object].
  • [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] and [object Object] default to a deterministic implementation.

• Due to the limited precision of the FLOAT16 and BFLOAT16, not all decimals can be represented, which may lead to errors in floor computations. It is recommended to use higher-precision data types, such as FLOAT32.

[object Object]

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