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

Annotated version: Matmul 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 matmul_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 MB = 3, // batch size
M = 128, K = 256, N = 512;
// Source (src), weights, bias, and destination (dst) tensors dimensions.
memory::dims src_dims = {MB, M, K};
memory::dims weights_dims = {MB, K, N};
memory::dims bias_dims = {1, 1, N};
memory::dims dst_dims = {MB, M, N};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> bias_data(product(bias_dims));
std::vector<float> dst_data(product(dst_dims));
// Initialize src, weights, bias.
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
std::generate(weights_data.begin(), weights_data.end(), []() {
static int i = 0;
return std::sin(i++ * 2.f);
std::generate(bias_data.begin(), bias_data.end(), []() {
static int i = 0;
return std::tanh(i++);
// Create memory descriptors and memory objects for src, weights, bias, and
// dst.
auto src_md = memory::desc(src_dims, dt::f32, tag::abc);
auto weights_md = memory::desc(weights_dims, dt::f32, tag::abc);
auto bias_md = memory::desc(bias_dims, dt::f32, tag::abc);
auto dst_md = memory::desc(dst_dims, dt::f32, tag::abc);
auto src_mem = memory(src_md, engine);
auto weights_mem = memory(weights_md, engine);
auto bias_mem = memory(bias_md, engine);
auto dst_mem = memory(dst_md, engine);
// Write data to memory object's handles.
write_to_dnnl_memory(, src_mem);
write_to_dnnl_memory(, weights_mem);
write_to_dnnl_memory(, bias_mem);
// Create operation descriptor
// Create primitive post-ops (ReLU).
const float scale = 1.0f;
const float alpha = 0.f;
const float beta = 0.f;
post_ops matmul_ops;
matmul_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta);
primitive_attr matmul_attr;
// Create primitive descriptor.
auto matmul_pd = matmul::primitive_desc(matmul_d, matmul_attr, engine);
// Create the primitive.
auto matmul_prim = matmul(matmul_pd);
// Primitive arguments.
std::unordered_map<int, memory> matmul_args;
matmul_args.insert({DNNL_ARG_SRC, src_mem});
matmul_args.insert({DNNL_ARG_WEIGHTS, weights_mem});
matmul_args.insert({DNNL_ARG_BIAS, bias_mem});
matmul_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution: matrix multiplication with ReLU.
matmul_prim.execute(engine_stream, matmul_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(matmul_example, parse_engine_kind(argc, argv));