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• Description: Performs backpropagation of .

• Formula:

  Gelu forward (x can be a scalar or tensor):

  $$Gelu(x)=x \cdot \Phi(x)=x/2 \cdot [1+erf(x/\sqrt{2})]$$

  The formula of erf is as follows:

  $$erf(x)=\frac{2}{\sqrt \pi}\sum^{\infty}_{n=0}{\frac{(-1)^n \cdot x^{2n+1}}{n! \cdot (2n+1)}}$$

  The relationship between gradInput and gradOutput can be expressed as follows:

  $$gradInput = gradOutput \cdot (\frac{1}{2}+\frac{1}{2} \cdot erf(\frac{x}{\sqrt2})+\frac{x}{\sqrt{2\pi}} \cdot e^{-\frac{x^2}{2}})$$

  The approximate formula of Gelu is as follows:

  $$Gelu(x)=0.5x(1+tanh(\sqrt{2/\pi}(x+0.044715x^3)))$$

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

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• Parameters:

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  • [object Object]Atlas inference series products[object Object] and [object Object]Atlas training series products[object Object]: The data type can be FLOAT or FLOAT16.

• Returns:

  aclnnStatus: status code. For details, see . The first-phase API implements input parameter verification. The following errors may be thrown.

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• Parameters:

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• Returns:

  aclnnStatus: status code. For details, see .

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

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