[object Object]

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

• Description: Performs backpropagation of . It is used to compute the element-level gradient of the input tensor, so that the model parameters can be updated during backpropagation.
• Formula:$$gradInput = ({gradOut} - \frac{sumDy}{ {counter}}) - ((input - mean) * (invstd^{2} * (\frac{sumDyXmu}{ {counter}}))) * invstd * weight$$

[object Object]

Each operator has calls. First, aclnnBatchNormElemtBackwardGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnBatchNormElemtBackward is called to perform computation.

[object Object]

• Parameters

  [object Object]
  • [object Object]Atlas training products[object Object]: The data types of [object Object], [object Object], [object Object], [object Object], [object Object], [object Object], [object Object], and [object Object] cannot be BFLOAT16.

• Returns:

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

  The first-phase API implements input parameter verification. The following errors may be thrown.

  [object Object]

[object Object]

• Parameters

  [object Object]

• Returns:

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

[object Object]

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

[object Object]

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