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

Annotated version: Binary 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 binary_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 = 150, // tensor height
IW = 150; // tensor width
// Source (src_0 and src_1) and destination (dst) tensors dimensions.
memory::dims src_0_dims = {N, IC, IH, IW};
memory::dims src_1_dims = {N, IC, IH, 1};
// Allocate buffers.
std::vector<float> src_0_data(product(src_0_dims));
std::vector<float> src_1_data(product(src_1_dims));
// Initialize src_0 and src_1 (src).
std::generate(src_0_data.begin(), src_0_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
std::generate(src_1_data.begin(), src_1_data.end(), []() {
static int i = 0;
return std::sin(i++ * 2.f);
// Create src and dst memory descriptors.
auto src_0_md = memory::desc(src_0_dims, dt::f32, tag::nchw);
auto src_1_md = memory::desc(src_1_dims, dt::f32, tag::nchw);
auto dst_md = memory::desc(src_0_dims, dt::f32, tag::nchw);
// Create src memory objects.
auto src_0_mem = memory(src_0_md, engine);
auto src_1_mem = memory(src_1_md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(, src_0_mem);
write_to_dnnl_memory(, src_1_mem);
// Create operation descriptor.
auto binary_d
= binary::desc(algorithm::binary_mul, src_0_md, src_1_md, dst_md);
// Create primitive post-ops (ReLU).
const float scale = 1.0f;
const float alpha = 0.f;
const float beta = 0.f;
post_ops binary_ops;
binary_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta);
primitive_attr binary_attr;
// Create primitive descriptor.
auto binary_pd = binary::primitive_desc(binary_d, binary_attr, engine);
// Create the primitive.
auto binary_prim = binary(binary_pd);
// Primitive arguments. Set up in-place execution by assigning src_0 as DST.
std::unordered_map<int, memory> binary_args;
binary_args.insert({DNNL_ARG_SRC_0, src_0_mem});
binary_args.insert({DNNL_ARG_SRC_1, src_1_mem});
binary_args.insert({DNNL_ARG_DST, src_0_mem});
// Primitive execution: binary with ReLU.
binary_prim.execute(engine_stream, binary_args);
// Wait for the computation to finalize.
// Read data from memory object's handle.
read_from_dnnl_memory(, src_0_mem);
int main(int argc, char **argv) {
return handle_example_errors(binary_example, parse_engine_kind(argc, argv));