Contents

Local Response Normalization (LRN)

API Reference

General

The LRN primitive performs a forward or backward local response normalization.

Forward

The LRN operation is defined by the following formulas (the variable names follow the standard Naming Conventions):

LRN across channels :

\[\dst(n, c, h, w) = \left\{k + \frac{\alpha}{n_{l}} \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2-1} (\src(n, c+i, h, w))^2 \right\}^{-\beta} \cdot \src(n, c, h, w),\]

LRN within channel :

\[\dst(n, c, h, w) = \left\{k + \frac{\alpha}{n_{l}} \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2-1} \sum\limits_{j=-(n_{l}-1)/2}^{(n_{l}+1)/2-1} (\src(n, c, h+i, w+j))^2 \right\}^{-\beta} \cdot \src(n, c, h, w),\]

where \(n_{l}\) is the local_size. Formulas are provided for 2D spatial data case.

Backward

The backward propagation computes \(\diffsrc(n, c, h, w)\), based on \(\diffdst(n, c, h, w)\) and \(\src(n, c, h, w)\).

Execution Arguments

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

\(\dst\)

DNNL_ARG_DST

workspace

DNNL_ARG_WORKSPACE

\(\diffsrc\)

DNNL_ARG_DIFF_SRC

\(\diffdst\)

DNNL_ARG_DIFF_DST

Implementation Details

General Notes

  1. During training, LRN might or might not require a workspace on forward and backward passes. The behavior is implementation specific. Optimized implementations typically require a workspace and use it to save some intermediate results from the forward pass that accelerate computations on the backward pass. To check whether a workspace is required, query the LRN primitive descriptor for the workspace. Success indicates that the workspace is required and its description will be returned.

Data Type Support

The LRN primitive supports the following combinations of data types:

Propagation

Source / Destination

forward / backward

f32, bf16, f16

Warning

There might be hardware and/or implementation specific restrictions. Check the Implementation Limitations section below.

Data Representation

Source, Destination, and Their Gradients

Like most other primitives, the LRN primitive expects the following tensors:

Spatial

Source / Destination

0D

\(N \times C\)

1D

\(N \times C \times W\)

2D

\(N \times C \times H \times W\)

3D

\(N \times C \times D \times H \times W\)

The LRN primitive is optimized for the following memory formats:

Spatial

Logical tensor

Implementations optimized for memory formats

2D

NCHW

dnnl_nchw ( dnnl_abcd ), dnnl_nhwc ( dnnl_acdb ), optimized^

Here, optimized^ means the format that comes out of any preceding compute-intensive primitive.

Post-ops and Attributes

The LRN primitive does not support any post-ops or attributes.

Implementation Limitations

  1. Refer to Data Types for limitations related to data types support.

  2. GPU

    • Supports only 2D spatial case.

Performance Tips

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

Example

LRN Primitive Example

This C++ API demonstrates how to create and execute a Local response normalization primitive in forward training propagation mode.

Layer Normalization Pooling