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• Description: Performs backpropagation of . It is used to compute the gradient of the input tensor, so that the model parameters can be updated during backpropagation.
• Formula:$$res\_for\_gamma = (input - mean) \times rstd$$ $$dy\_g = gradOut \times weightOptional$$ $$temp_1 = 1/N \times \sum_{reduce\_axis\_1} gradOut \times weightOptional$$ $$temp_2 = 1/N \times (input - mean) \times rstd \times \sum_{reduce\_axis\_1}(gradOut \times weightOptional \times (input - mean) \times rstd)$$ $$gradInputOut = (gradOut \times weightOptional - (temp_1 + temp_2)) \times rstd$$ $$gradWeightOut = \sum_{reduce\_axis\_0}gradOut \times (input - mean) \times rstd$$ $$gradBiasOut = \sum_{reduce\_axis\_0}gradOut$$ N indicates the dimension of the axis on which normalization computation is performed, that is, the size of the normalized axis dimension.

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

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• Parameters

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  • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]:

    The data type of gradOut, input, mean, rstd, weightOptional, biasOptional, gradInputOut, gradWeightOut, and gradBiasOut does not support BFLOAT16.

• Returns

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

  The first-phase API implements input parameter validation. The following error codes may be returned.

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• Parameters

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• Returns

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

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• Shape constraints:

  The shape of gradOut, input, mean, rstd, weightOptional (non-empty), biasOptional (non-empty), gradInputOut (non-empty), gradWeightOut (non-empty), or gradBiasOut (non-empty) supports one to eight dimensions.

• Deterministic computation:

  aclnnLayerNormBackward defaults to a deterministic implementation.

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