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/*******************************************************************************
* Copyright 2024-2025 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.
*******************************************************************************/
/// @example cpu_matmul_coo.cpp
/// > Annotated version: @ref cpu_matmul_coo_cpp
///
/// This C++ API example demonstrates how to create and execute a
/// [MatMul](@ref dev_guide_matmul) primitive that uses a source tensor
/// encoded with the COO sparse encoding.
///
/// @page cpu_matmul_coo_cpp MatMul Primitive Example
///
/// @include cpu_matmul_coo.cpp
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "dnnl.hpp"
#include "example_utils.hpp"
using namespace dnnl;
bool check_result(dnnl::memory dst_mem) {
// clang-format off
const std::vector<float> expected_result = {8.750000, 11.250000, 2.500000,
6.000000, 2.250000, 3.750000,
19.000000, 15.500000, 5.250000,
4.000000, 7.000000, 3.000000};
// clang-format on
std::vector<float> dst_data(expected_result.size());
read_from_dnnl_memory(dst_data.data(), dst_mem);
return expected_result == dst_data;
}
void sparse_matmul() {
dnnl::engine engine(engine::kind::cpu, 0);
const memory::dim M = 4;
const memory::dim N = 3;
const memory::dim K = 6;
// A sparse matrix represented in the COO format.
std::vector<float> src_coo_values = {2.5f, 1.5f, 1.5f, 2.5f, 2.0f};
std::vector<int32_t> src_coo_row_indices = {0, 1, 2, 2, 3};
std::vector<int32_t> src_coo_col_indices = {0, 2, 0, 5, 1};
// clang-format off
std::vector<float> weights_data = {3.5f, 4.5f, 1.0f,
2.0f, 3.5f, 1.5f,
4.0f, 1.5f, 2.5f,
3.5f, 5.5f, 4.5f,
1.5f, 2.5f, 5.5f,
5.5f, 3.5f, 1.5f};
// clang-format on
const int nnz = static_cast<int>(src_coo_values.size());
// Create a memory descriptor for COO format by providing information
// about number of non-zero entries and data types of metadata.
const auto src_coo_md = memory::desc::coo(
{M, K}, memory::data_type::f32, nnz, memory::data_type::s32);
const auto wei_md = memory::desc(
{K, N}, memory::data_type::f32, memory::format_tag::oi);
const auto dst_md = memory::desc(
{M, N}, memory::data_type::f32, memory::format_tag::nc);
// This memory is created for the given values and metadata of COO format.
memory src_coo_mem(src_coo_md, engine,
{src_coo_values.data(), src_coo_row_indices.data(),
src_coo_col_indices.data()});
memory wei_mem(wei_md, engine, weights_data.data());
memory dst_mem(dst_md, engine);
dnnl::stream stream(engine);
auto sparse_matmul_pd
= matmul::primitive_desc(engine, src_coo_md, wei_md, dst_md);
auto sparse_matmul_prim = matmul(sparse_matmul_pd);
std::unordered_map<int, memory> sparse_matmul_args;
sparse_matmul_args.insert({DNNL_ARG_SRC, src_coo_mem});
sparse_matmul_args.insert({DNNL_ARG_WEIGHTS, wei_mem});
sparse_matmul_args.insert({DNNL_ARG_DST, dst_mem});
sparse_matmul_prim.execute(stream, sparse_matmul_args);
stream.wait();
if (!check_result(dst_mem)) throw std::runtime_error("Unexpected output.");
}
int main(int argc, char **argv) {
return handle_example_errors({engine::kind::cpu}, sparse_matmul);
}
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