A primitive to perform local response normalization (LRN) across or within channels. More...

Functions
mkldnn_status_t MKLDNN_API	mkldnn_lrn_forward_desc_init (mkldnn_lrn_desc_t lrn_desc, mkldnn_prop_kind_t prop_kind, mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t data_desc, int local_size, float alpha, float beta, float k)
	Initializes an `lrn_desc` for forward propagation using `prop_kind` (possible values are mkldnn_forward_training and mkldnn_forward_inference), `alg_kind`, memory descriptor `data_desc`, and regularization parameters `local_size`, `alpha`, `beta`, and `k`. More...

mkldnn_status_t MKLDNN_API	mkldnn_lrn_backward_desc_init (mkldnn_lrn_desc_t lrn_desc, mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t diff_data_desc, const mkldnn_memory_desc_t *data_desc, int local_size, float alpha, float beta, float k)
	Initializes an `lrn_desc` for backward propagation using `alg_kind`, memory descriptors `data_desc` and `diff_data_desc`, and regularization parameters `local_size`, `alpha`, `beta`, and `k`. More...

Detailed Description

A primitive to perform local response normalization (LRN) across or within channels.

LRN accross channels:

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

LRN within channels:

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

where $n_{l}$ is the local_size.

During training, LRN might or might not require a workspace on forward (mkldnn_forward_training) and backward (mkldnn_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 (mkldnn_query_workspace_pd). Success indicates that the workspace is required and its description will be returned.

See also: mkldnn_primitive_desc_query and mkldnn_primitive_desc_query_pd

Function Documentation

◆ mkldnn_lrn_forward_desc_init()

mkldnn_status_t MKLDNN_API mkldnn_lrn_forward_desc_init	(	mkldnn_lrn_desc_t *	lrn_desc,
		mkldnn_prop_kind_t	prop_kind,
		mkldnn_alg_kind_t	alg_kind,
		const mkldnn_memory_desc_t *	data_desc,
		int	local_size,
		float	alpha,
		float	beta,
		float	k
	)

Initializes an lrn_desc for forward propagation using prop_kind (possible values are mkldnn_forward_training and mkldnn_forward_inference), alg_kind, memory descriptor data_desc, and regularization parameters local_size, alpha, beta, and k.

Order of inputs:

src (mkldnn_query_src_pd, 0)

Order of outputs:

dst (mkldnn_query_dst_pd, 0)
workspace (mkldnn_query_workspace_pd, 0), if the underlying implementation requires

◆ mkldnn_lrn_backward_desc_init()

mkldnn_status_t MKLDNN_API mkldnn_lrn_backward_desc_init	(	mkldnn_lrn_desc_t *	lrn_desc,
		mkldnn_alg_kind_t	alg_kind,
		const mkldnn_memory_desc_t *	diff_data_desc,
		const mkldnn_memory_desc_t *	data_desc,
		int	local_size,
		float	alpha,
		float	beta,
		float	k
	)

Initializes an lrn_desc for backward propagation using alg_kind, memory descriptors data_desc and diff_data_desc, and regularization parameters local_size, alpha, beta, and k.