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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, aclnnGeluBackwardV2GetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnGeluBackwardV2 is called to perform computation.

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• Parameters:

  [object Object]
  • [object Object]Atlas inference products[object Object] and [object Object]Atlas training products[object Object]: The data type can be FLOAT16 or FLOAT32.

• Returns:

  [object Object]: 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:

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

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• Deterministic computation:
  • aclnnGeluBackwardV2 defaults to a deterministic implementation.

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