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

• Description: Computes the backward pass of (high-performance version) to obtain the gradient of the input tensor for model parameter updates.

• Formula:

  • When training is true:

    $$gradInput = \frac{weight}{ n{\sqrt{saveVar + eps}} }(n * gradOut - \sum^m_{i=0}{gradOut} - \frac{x-saveMean}{ {\sqrt{saveVar + eps}} }\sum^m_{i=0}({gradOut} *\frac{x-saveMean}{ {\sqrt{saveVar + eps}} } ))$$ $$gradWeight = \sum^m_{i=0}[{gradOut} * (x - saveMean)] * \frac{1}{ {\sqrt{saveVar + eps}} }$$ $$gradBias = \sum^m_{i=0}{gradOut}$$

  • When training is false:

    $$gradInput = gradOut * \frac{1}{ {\sqrt{runningVar + eps}} } * weight$$ $$gradWeight = \sum^m_{i=0}[{gradOut} * (x - runningMean)] * \frac{1}{ {\sqrt{runningVar + eps}} }$$ $$gradBias = \sum^m_{i=0}{gradOut}$$

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

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• Parameters

  [object Object]

  • [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 types of [object Object], [object Object], [object Object], [object Object], [object Object], [object Object], and [object Object] must be the same as that of [object Object].

• Return Value

  [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]

• Return Value

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

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• Deterministic computation:
  • aclnnFastBatchNormBackward defaults to a deterministic implementation.

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