LayerNormBackward

LayerNormBackward performs the backward of LayerNorm operation.

The backward propagation computes \(\diffsrc(t, n, c)\), \(\diffgamma(c)^*\), and \(\diffbeta(c)^*\) based on \(\diffdst(t, n, c)\), \(src(t, n, c)\), \(\mu(t, n)\), \(\sigma^2(t, n)\), \(\gamma(c) ^*\), and \(\beta(c) ^*\).

The tensors marked with an asterisk are used only when the operation is configured to use \(\gamma(c)\), and \(\beta(c)\)

Operation Attributes

Attribute Name	Description	Value Type	Supported Values	Required or Optional
begin_norm_axis	begin_norm_axis is used to indicate which axis to start layer normalization. The normalization is from begin_norm_axis to last dimension. Negative values means indexing from right to left. This op normalizes over the last dimension by default, e.g. C in TNC for 3D and LDNC for 4D.	s64	[-r,r-1],where r=rank(src). -1 is default	Optional
use_affine	When set to True, this module has learnable per-element affine parameters.	bool	false, true (default)	Optional
epsilon	The constant to improve numerical stability	f32	Arbitrary positive f32 value, 1e-5 (default)	Optional

Execution Arguments

The inputs and outputs must be provided according to the below index order when constructing an operation.

Inputs

Index	Argument Name	Required or Optional
0	src	Required
1	diff_dst	Required
2	mean	Required
3	variance	Required
4	gamma	Optional
5	beta	Optional

@note gamma is scaling for normalized value. beta is the bias added to the scaled normalized value. They are both 1D tensor with the same span as src’s channel axis and required if attribute use_affine is set to True.

Outputs

Index	Argument Name	Required or Optional
0	diff_src	Required
1	diff_gamma	Optional
2	diff_beta	Optional

Supported Data Types

LayerNormBackward operation supports the following data type combinations.

Src / Diff_dst / Diff_src	Gamma / Beta / Mean / Variance / Diff_gamma / Diff_beta
f32	f32
bf16	f32, bf16
f16	f32