oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning

Annotated version: Local Response Normalization Primitive Example

* Copyright 2020 Intel Corporation
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* See the License for the specific language governing permissions and
* limitations under the License.
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using namespace dnnl;
void lrn_example(dnnl::engine::kind engine_kind) {
// Create execution dnnl::engine.
dnnl::engine engine(engine_kind, 0);
// Create dnnl::stream.
dnnl::stream engine_stream(engine);
// Tensor dimensions.
const memory::dim N = 3, // batch size
IC = 3, // channels
IH = 227, // tensor height
IW = 227; // tensor width
// Source (src) and destination (dst) tensors dimensions.
memory::dims src_dims = {N, IC, IH, IW};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> dst_data(product(src_dims));
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
// Create src and dst memory descriptors and memory objects.
auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
auto src_mem = memory(src_md, engine);
auto dst_mem = memory(src_md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(, src_mem);
// Create operation descriptor.
const memory::dim local_size = 5;
const float alpha = 1.e-4f;
const float beta = 0.75f;
const float k = 1.f;
algorithm::lrn_across_channels, src_md, local_size, alpha, beta, k);
// Create primitive descriptor.
// Create workspace memory object using memory descriptors created by the
// primitive descriptor.
// NOTE: Here, workspace may or may not be required in forward training
// mode, and is used to speed-up the backward propagation.
auto workspace_mem = memory(lrn_pd.workspace_desc(), engine);
// Create the primitive.
auto lrn_prim = lrn_forward(lrn_pd);
// Primitive arguments.
std::unordered_map<int, memory> lrn_args;
lrn_args.insert({DNNL_ARG_SRC, src_mem});
lrn_args.insert({DNNL_ARG_WORKSPACE, workspace_mem});
lrn_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution.
lrn_prim.execute(engine_stream, lrn_args);
// Wait for the computation to finalize.
// Read data from memory object's handle.
read_from_dnnl_memory(, dst_mem);
int main(int argc, char **argv) {
return handle_example_errors(lrn_example, parse_engine_kind(argc, argv));