LayerNorm

Versioned name: LayerNorm-1

Category: Normalization

Short description: Reference

Attributes:

• keep_stats

  • Description: keep_stats is used to indicate whether to output mean&&var. One typical usage is to pass mean&&var to backwords op.

  • Range of values: False or True

  • Type: bool

  • Default value: True

  • Required: no

• begin_norm_axis

  • Description: 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.

  • Range of values: [-r, r-1] where r = rank(input)

  • Type: s64

  • Default value: -1

  • Required: no

• use_affine

  • Description: when set to True, this module has learnable per-element affine parameters.

  • Range of values: False or True

  • Type: bool

  • Default value: True

  • Required: no

• epsilon

  • Description: epsilon is a constant to improve numerical stability

  • Range of values: arbitrary positive f32 value

  • Type: f32

  • Default value: 1e-5

  • Required: no

Inputs

• 1: input - input tensor with data for normalization. Required.

  • Type: T1

• 2: gamma - gamma scaling for normalized value. A 1D tensor with the same span as input’s channel axis. Required by attribute use_affine. Optional.

  • Type: T2

• 3: beta - bias added to the scaled normalized value. A 1D tensor with the same span as input’s channel axis.Required by attribute use_affine. Optional.

  • Type: T2

Outputs

• 1: output The result of normalization. A tensor of the same shape with 1st input tensor. Required.

  • Type: T1

• 2: mean Output the mean calculated along the given axis. Optional.

  • Type: T2

• 3: variance Output the std calculated along the given axis. Optional.

  • Type: T2

Types

• T1: f32, f16, bf16.

• T2: f32, bf16.

• Constraints: T2 can be bf16 only when T1 is bf16.