Layer Normalization

API Reference

General

The layer normalization primitive performs a forward or backward layer normalization operation on a 2-5D data tensor.

Forward

The layer normalization operation performs normalization over the last logical axis of the data tensor and is defined by the following formulas. We show formulas only for 3D data, which are straightforward to generalize to cases of higher dimensions. Variable names follow the standard Naming Conventions.

\[\dst(t, n, c) = \gamma(c) \cdot \frac{\src(t, n, c) - \mu(t, n)} {\sqrt{\sigma^2(t, n) + \varepsilon}} + \beta(c),\]

where

Mean and variance are computed at runtime or provided by a user. When mean and variance are computed at runtime, the following formulas are used:

  • \(\mu(t, n) = \frac{1}{C} \sum\limits_{c} \src(t, n, c)_{}\),

  • \(\sigma^2(t, n) = \frac{1}{C} \sum\limits_{c} {}_{} (\src(t, n, c) - \mu(t, n))^2\).

The \(\gamma(c)\) and \(\beta(c)\) tensors are considered learnable.

Difference Between Forward Training and Forward Inference

Backward

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 primitive is configured to use \(\gamma(c)\), and \(\beta(c)\) (i.e., dnnl_use_scaleshift, dnnl_use_scale or dnnl_use_shift are set).

Execution Arguments

Depending on the flags and propagation kind, the layer normalization primitive requires different inputs and outputs. For clarity, a summary is shown below.

dnnl_normalization_flags_none

Inputs : \(\src\) Outputs : \(\dst\)

Inputs : \(\src\) Outputs : \(\dst\) , \(\mu\) , \(\sigma^2\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) Outputs : \(\diffsrc\)

Same as for dnnl_backward

dnnl_use_global_stats

Inputs : \(\src\) , \(\mu\) , \(\sigma^2\) Outputs : \(\dst\)

Inputs : \(\src\) , \(\mu\) , \(\sigma^2\) Outputs : \(\dst\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) Outputs : \(\diffsrc\)

Same as for dnnl_backward

dnnl_use_scaleshift

Inputs : \(\src\) , \(\gamma\) , \(\beta\) Outputs : \(\dst\)

Inputs : \(\src\) , \(\gamma\) , \(\beta\) Outputs : \(\dst\) , \(\mu\) , \(\sigma^2\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) , \(\gamma\) , \(\beta\) Outputs : \(\diffsrc\) , \(\diffgamma\) , \(\diffbeta\)

Not supported

dnnl_use_scale

Inputs : \(\src\) , \(\gamma\) Outputs : \(\dst\)

Inputs : \(\src\) , \(\gamma\) Outputs : \(\dst\) , \(\mu\) , \(\sigma^2\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) , \(\gamma\) Outputs : \(\diffsrc\) , \(\diffgamma\)

Not supported

dnnl_use_shift

Inputs : \(\src\) , \(\beta\) Outputs : \(\dst\)

Inputs : \(\src\) , \(\beta\) Outputs : \(\dst\) , \(\mu\) , \(\sigma^2\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) , \(\beta\) Outputs : \(\diffsrc\) , \(\diffbeta\)

Not supported

dnnl_use_global_stats | dnnl_use_scaleshift

Inputs : \(\src\) , \(\mu\) , \(\sigma^2\) , \(\gamma\) , \(\beta\) Outputs : \(\dst\)

Inputs : \(\src\) , \(\mu\) , \(\sigma^2\) , \(\gamma\) , \(\beta\) Outputs : \(\dst\)

Inputs : \(\diffdst\) , \(\src\) , \(\mu\) , \(\sigma^2\) , \(\gamma\) , \(\beta\) Outputs : \(\diffsrc\) , \(\diffgamma\) , \(\diffbeta\)

Not supported

When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table.

Primitive input/output

Execution argument index

\(\src\)

DNNL_ARG_SRC

\(\gamma, \beta\)

DNNL_ARG_SCALE_SHIFT

\(\gamma\)

DNNL_ARG_SCALE

\(\beta\)

DNNL_ARG_SHIFT

mean ( \(\mu\) )

DNNL_ARG_MEAN

variance ( \(\sigma\) )

DNNL_ARG_VARIANCE

\(\dst\)

DNNL_ARG_DST

\(\diffdst\)

DNNL_ARG_DIFF_DST

\(\diffsrc\)

DNNL_ARG_DIFF_SRC

\(\diffgamma\) , \(\diffbeta\)

DNNL_ARG_DIFF_SCALE_SHIFT

\(\diffgamma\)

DNNL_ARG_DIFF_SCALE

\(\diffbeta\)

DNNL_ARG_DIFF_SHIFT

Implementation Details

General Notes

  1. The different flavors of the primitive are partially controlled by the flags parameter that is passed to the operation descriptor initialization function (e.g., dnnl::layer_normalization_forward::desc::desc()). Multiple flags can be set using the bitwise OR operator (|). Flag dnnl_use_scaleshift cannot be mixed with dnnl_use_scale or dnnl_use_shift.

  2. For forward propagation, the mean and variance might be either computed at runtime (in which case they are outputs of the primitive) or provided by a user (in which case they are inputs). In the latter case, a user must set the dnnl_use_global_stats flag. For the backward propagation, the mean and variance are always input parameters.

  3. The memory format and data type for src and dst are assumed to be the same, and in the API they are typically referred to as data (e.g., see data_desc in dnnl::layer_normalization_forward::desc::desc()). The same is true for diff_src and diff_dst. The corresponding memory descriptors are referred to as diff_data_desc.

  4. Both forward and backward propagation support in-place operations, meaning that \(\src\) can be used as input and output for forward propagation, and \(\diffdst\) can be used as input and output for backward propagation. In case of an in-place operation, the original data will be overwritten. Note, however, that backward propagation requires original \(\src\), hence the corresponding forward propagation should not be performed in-place.

Data Type Support

The operation supports the following combinations of data types:

Propagation

Source / Destination

Mean / Variance / ScaleShift

forward / backward

f32, bf16

f32

forward

f16

f32

Data Representation

Mean and Variance

The mean (\(\mu\)) and variance (\(\sigma^2\)) are separate tensors with number of dimensions equal to (\(data\_ndims - 1\)) and size \((data\_dim[0], data\_dim[1], ..., data\_dim[ndims - 2])\).

The corresponding memory object can have an arbitrary memory format. Unless mean and variance are computed at runtime and not exposed (i.e., propagation kind is dnnl_forward_inference and dnnl_use_global_stats is not set), the user should provide a memory descriptor for statistics when initializing the layer normalization descriptor. For best performance, it is advised to use the memory format that follows the data memory format; i.e., if the data format is dnnl_tnc, the best performance can be expected for statistics with the dnnl_tn format and suboptimal for statistics with the dnnl_nt format.

Scale and Shift

If dnnl_use_scaleshift is used, the scale (\(\gamma\)) and shift (\(\beta\)) are combined in a single 2D tensor of shape \(2 \times C\).

If dnnl_use_scale or dnnl_use_shift are used, the scale (\(\gamma\)) and shift (\(\beta\)) are separate 1D tensors of shape \(C\).

The format of the corresponding memory object must be dnnl_nc (dnnl_ab).

Source, Destination, and Their Gradients

The layer normalization primitive works with an arbitrary data tensor; however, it was designed for RNN data tensors (i.e., dnnl_nc, dnnl_tnc, dnnl_ldnc). Unlike CNN data tensors, RNN data tensors have a single feature dimension. Layer normalization performs normalization over the last logical dimension (feature dimension for RNN tensors) across non-feature dimensions.

The layer normalization primitive is optimized for the following memory formats:

Logical tensor

Implementations optimized for memory formats

NC

dnnl_nc ( dnnl_ab )

TNC

dnnl_tnc ( dnnl_abc ), dnnl_ntc ( dnnl_bac )

LDNC

dnnl_ldnc ( dnnl_abcd )

Performance Tips

  1. For data tensors (src, dst, diff_src, diff_dst), use memory formats for which the last logical axis is the last in the physical memory layout.

  2. For mean / variance, use the memory format that follows the data memory format; i.e., if the data format is dnnl_tnc, the best performance can be expected for statistics with dnnl_tn and suboptimal for statistics with the dnnl_nt format.

  3. For backward propagation, use the same memory format for src, diff_dst, and diff_src (the format of diff_dst and diff_src are always the same because of the API). Different formats are functionally supported but lead to highly suboptimal performance.

  4. Use in-place operations whenever possible (see caveats in General Notes).

Examples

layer_normalization_example_cpp - CPU/GPU

This C++ API example demonstrates how to create and execute a Layer normalization primitive in forward propagation mode.

Key optimizations included in this example:

  • In-place primitive execution;

  • Creation of memory objects using the primitive descriptor.