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

Annotated version: Batch 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 batch_normalization_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};
// Scale/shift tensor dimensions.
memory::dims scale_shift_dims = {2, IC};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> scale_shift_data(product(scale_shift_dims));
// Initialize src.
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
auto mid = scale_shift_data.begin() + IC;
// Initialize scale.
std::generate(scale_shift_data.begin(), mid, []() {
static int i = 0;
return std::sin(i++ * 2.f);
// Initialize shift.
std::generate(mid, scale_shift_data.end(), []() {
static int i = 0;
return std::tan(i++);
// Create src and scale/shift memory descriptors and memory objects.
auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
auto scale_shift_md = memory::desc(scale_shift_dims, dt::f32, tag::nc);
auto src_mem = memory(src_md, engine);
auto scale_shift_mem = memory(scale_shift_md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(, src_mem);
write_to_dnnl_memory(, scale_shift_mem);
// Create operation descriptor.
// Create primitive descriptor.
auto bnorm_pd
// Create memory objects using memory descriptors created by the primitive
// descriptor: mean, variance, workspace.
// NOTE: Here, the ReLU post-ops require a workspace for later usage in
// backward propagation mode.
auto mean_mem = memory(bnorm_pd.mean_desc(), engine);
auto variance_mem = memory(bnorm_pd.variance_desc(), engine);
auto workspace_mem = memory(bnorm_pd.workspace_desc(), engine);
// Create the primitive.
auto bnorm_prim = batch_normalization_forward(bnorm_pd);
// Primitive arguments. Set up in-place execution by assigning src as DST.
std::unordered_map<int, memory> bnorm_args;
bnorm_args.insert({DNNL_ARG_SRC, src_mem});
bnorm_args.insert({DNNL_ARG_MEAN, mean_mem});
bnorm_args.insert({DNNL_ARG_VARIANCE, variance_mem});
bnorm_args.insert({DNNL_ARG_SCALE_SHIFT, scale_shift_mem});
bnorm_args.insert({DNNL_ARG_WORKSPACE, workspace_mem});
bnorm_args.insert({DNNL_ARG_DST, src_mem});
// Primitive execution: batch normalization with ReLU.
bnorm_prim.execute(engine_stream, bnorm_args);
// Wait for the computation to finalize.
// Read data from memory object's handle.
read_from_dnnl_memory(, src_mem);
int main(int argc, char **argv) {
return handle_example_errors(
batch_normalization_example, parse_engine_kind(argc, argv));