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Description: Performs backpropagation on the result of the API.

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

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• Parameters:

  • gradOutput (aclTensor*, compute input): aclTensor on the device. The supports ND. The shapes of gradOutput and self must meet the . are supported.
    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT or FLOAT16.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be FLOAT, FLOAT16, or BFLOAT16.
  • self (aclTensor*, compute input): aclTensor on the device. The can be ND. are supported.
    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT or FLOAT16.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be FLOAT, FLOAT16, or BFLOAT16.
  • target (aclTensor*, compute input): aclTensor on the device. The supports ND. The shapes of target and self must meet the . are supported.
    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT or FLOAT16.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be FLOAT, FLOAT16, or BFLOAT16.
  • reduction (int64_t, compute input): int64_t on the host. The supports ND. It specifies the reduction to be applied to the output. The value can be 0 ('none') | 1 ('mean') | 2 ('sum') | 3 ('batchmean'). none indicates that no reduction is applied; mean indicates that the sum of the output will be divided by the number of elements in the output; sum indicates that the output will be summed; batchmean indicates that the sum of the output will be divided by the number of batches.
  • logTarget (bool, compute input): BOOL type on the host, indicating whether to perform log space conversion on target.
  • out (aclTensor*, compute output): aclTensor on the device. are supported.
    • [object Object]Atlas training series products[object Object] and [object Object]Atlas inference series products[object Object]: The data type can be FLOAT or FLOAT16.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]: The data type can be FLOAT, FLOAT16, or BFLOAT16.
  • workspaceSize (uint64_t*, output): size of the workspace to be allocated on the device.
  • executor (aclOpExecutor**, output): operator executor, containing the operator computation process.

• Returns:

  aclnnStatus: status code. For details, see .

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• Parameters:

  • workspace (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 the first-phase API aclnnKlDivBackwardGetWorkspaceSize.
  • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.
  • stream (aclrtStream, input): stream for executing the task.

• Returns:

  aclnnStatus: status code. For details, see .

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• Deterministic compute:
  • aclnnKlDivBackward defaults to a deterministic implementation.

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