.. index:: pair: example; matmul.cpp
.. _doxid-matmul_8cpp-example:

matmul.cpp
==========

Annotated version: :ref:`Matmul Primitive Example <doxid-matmul_example_cpp>`

Annotated version: :ref:`Matmul Primitive Example <doxid-matmul_example_cpp>`



.. ref-code-block:: cpp

	/*******************************************************************************
	* 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
	*
	*     http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing, software
	* distributed under the License is distributed on an "AS IS" BASIS,
	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	* 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"
	#include "oneapi/dnnl/dnnl.hpp"
	
	using namespace :ref:`dnnl <doxid-namespacednnl>`;
	
	using :ref:`tag <doxid-structdnnl_1_1memory_1a8e71077ed6a5f7fb7b3e6e1a5a2ecf3f>` = :ref:`memory::format_tag <doxid-structdnnl_1_1memory_1a8e71077ed6a5f7fb7b3e6e1a5a2ecf3f>`;
	using :ref:`dt <doxid-structdnnl_1_1memory_1a8e83474ec3a50e08e37af76c8c075dce>` = :ref:`memory::data_type <doxid-structdnnl_1_1memory_1a8e83474ec3a50e08e37af76c8c075dce>`;
	
	void matmul_example(:ref:`dnnl::engine::kind <doxid-structdnnl_1_1engine_1a2635da16314dcbdb9bd9ea431316bb1a>` engine_kind) {
	
	    // Create execution dnnl::engine.
	    :ref:`dnnl::engine <doxid-structdnnl_1_1engine>` :ref:`engine <doxid-structdnnl_1_1engine>`(engine_kind, 0);
	
	    // Create dnnl::stream.
	    :ref:`dnnl::stream <doxid-structdnnl_1_1stream>` engine_stream(:ref:`engine <doxid-structdnnl_1_1engine>`);
	
	    // Tensor dimensions.
	    const :ref:`memory::dim <doxid-structdnnl_1_1memory_1a6ad818e4699872cc913474fa5f122cd5>` MB = 3, // batch size
	            M = 128, K = 256, N = 512;
	
	    // Source (src), weights, bias, and destination (dst) tensors dimensions.
	    :ref:`memory::dims <doxid-structdnnl_1_1memory_1afdd20764d58c0b517d5a31276672aeb8>` src_dims = {MB, M, K};
	    :ref:`memory::dims <doxid-structdnnl_1_1memory_1afdd20764d58c0b517d5a31276672aeb8>` weights_dims = {MB, K, N};
	    :ref:`memory::dims <doxid-structdnnl_1_1memory_1afdd20764d58c0b517d5a31276672aeb8>` bias_dims = {1, 1, N};
	    :ref:`memory::dims <doxid-structdnnl_1_1memory_1afdd20764d58c0b517d5a31276672aeb8>` 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 :ref:`src_md <doxid-group__dnnl__api__primitives__common_1gga94efdd650364f4d9776cfb9b711cbdc1a90a729e395453e1d9411ad416c796819>` = :ref:`memory::desc <doxid-structdnnl_1_1memory_1_1desc>`(src_dims, dt::f32, tag::abc);
	    auto :ref:`weights_md <doxid-group__dnnl__api__primitives__common_1gga94efdd650364f4d9776cfb9b711cbdc1a06ba7b00a8c95dcf3a90e16d00eeb0e9>` = :ref:`memory::desc <doxid-structdnnl_1_1memory_1_1desc>`(weights_dims, dt::f32, tag::abc);
	    auto bias_md = :ref:`memory::desc <doxid-structdnnl_1_1memory_1_1desc>`(bias_dims, dt::f32, tag::abc);
	    auto :ref:`dst_md <doxid-group__dnnl__api__primitives__common_1gga94efdd650364f4d9776cfb9b711cbdc1a701158248eed4e5fc84610f2f6026493>` = :ref:`memory::desc <doxid-structdnnl_1_1memory_1_1desc>`(dst_dims, dt::f32, tag::abc);
	
	    auto src_mem = :ref:`memory <doxid-structdnnl_1_1memory>`(src_md, :ref:`engine <doxid-structdnnl_1_1engine>`);
	    auto weights_mem = :ref:`memory <doxid-structdnnl_1_1memory>`(weights_md, :ref:`engine <doxid-structdnnl_1_1engine>`);
	    auto bias_mem = :ref:`memory <doxid-structdnnl_1_1memory>`(bias_md, :ref:`engine <doxid-structdnnl_1_1engine>`);
	    auto dst_mem = :ref:`memory <doxid-structdnnl_1_1memory>`(dst_md, :ref:`engine <doxid-structdnnl_1_1engine>`);
	
	    // Write data to memory object's handles.
	    write_to_dnnl_memory(src_data.data(), src_mem);
	    write_to_dnnl_memory(weights_data.data(), weights_mem);
	    write_to_dnnl_memory(bias_data.data(), bias_mem);
	
	    // Create operation descriptor
	    auto :ref:`matmul_d <doxid-group__dnnl__api__primitives__common_1gga94efdd650364f4d9776cfb9b711cbdc1a4a7fa4b07a2f5ef2534d7159225f480d>` = :ref:`matmul::desc <doxid-structdnnl_1_1matmul_1_1desc>`(src_md, weights_md, bias_md, dst_md);
	
	    // Create primitive post-ops (ReLU).
	    const float scale = 1.0f;
	    const float alpha = 0.f;
	    const float beta = 0.f;
	    :ref:`post_ops <doxid-structdnnl_1_1post__ops>` matmul_ops;
	    matmul_ops.:ref:`append_eltwise <doxid-structdnnl_1_1post__ops_1a28460923ad579c780e4027a6256fe207>`(scale, :ref:`algorithm::eltwise_relu <doxid-group__dnnl__api__attributes_1gga00377dd4982333e42e8ae1d09a309640aba09bebb742494255b90b43871c01c69>`, alpha, beta);
	    primitive_attr matmul_attr;
	    matmul_attr.set_post_ops(matmul_ops);
	
	    // Create primitive descriptor.
	    auto matmul_pd = :ref:`matmul::primitive_desc <doxid-structdnnl_1_1matmul_1_1primitive__desc>`(matmul_d, matmul_attr, :ref:`engine <doxid-structdnnl_1_1engine>`);
	
	    // Create the primitive.
	    auto matmul_prim = :ref:`matmul <doxid-structdnnl_1_1matmul>`(matmul_pd);
	
	    // Primitive arguments.
	    std::unordered_map<int, memory> matmul_args;
	    matmul_args.insert({:ref:`DNNL_ARG_SRC <doxid-group__dnnl__api__primitives__common_1gac37ad67b48edeb9e742af0e50b70fe09>`, src_mem});
	    matmul_args.insert({:ref:`DNNL_ARG_WEIGHTS <doxid-group__dnnl__api__primitives__common_1gaf279f28c59a807e71a70c719db56c5b3>`, weights_mem});
	    matmul_args.insert({:ref:`DNNL_ARG_BIAS <doxid-group__dnnl__api__primitives__common_1gad0cbc09942aba93fbe3c0c2e09166f0d>`, bias_mem});
	    matmul_args.insert({:ref:`DNNL_ARG_DST <doxid-group__dnnl__api__primitives__common_1ga3ca217e4a06d42a0ede3c018383c388f>`, dst_mem});
	
	    // Primitive execution: matrix multiplication with ReLU.
	    matmul_prim.execute(engine_stream, matmul_args);
	
	    // Wait for the computation to finalize.
	    engine_stream.wait();
	
	    // Read data from memory object's handle.
	    read_from_dnnl_memory(dst_data.data(), dst_mem);
	}
	
	int main(int argc, char **argv) {
	    return handle_example_errors(matmul_example, parse_engine_kind(argc, argv));
	}