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/*******************************************************************************
* Copyright 2023-2025 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_weights_compression.cpp
/// > Annotated version: @ref cpu_matmul_weights_compression_cpp
///
/// This C++ API example demonstrates how to create and execute a
/// [MatMul](@ref dev_guide_matmul) primitive that uses a weights tensor
/// encoded with the packed sparse encoding.
///
/// @page cpu_matmul_weights_compression_cpp MatMul Primitive Example
///
/// @include cpu_matmul_weights_compression.cpp
#include <algorithm>
#include <cmath>
#include <iostream>
#include <random>
#include <string>
#include <vector>
#include "example_utils.hpp"
#include "oneapi/dnnl/dnnl.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 M = 512, K = 512, N = 512;
// Source (src), weights, and destination (dst) tensors dimensions.
memory::dims src_dims = {M, K};
memory::dims weights_dims = {K, N};
memory::dims dst_dims = {M, N};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> dst_data(product(dst_dims));
// Initialize src, weights.
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 const float density = 0.1f;
static std::default_random_engine def_gen;
static std::bernoulli_distribution b_dist(density);
const auto is_one = b_dist(def_gen);
static int i = 1;
return std::sin(i++ * 2.f) * is_one;
});
const memory::dim nnz = std::count_if(weights_data.begin(),
weights_data.end(), [](float v) { return v != 0.0f; });
auto src_md = memory::desc(
src_dims, memory::data_type::f32, memory::format_tag::ab);
auto dst_md = memory::desc(
dst_dims, memory::data_type::f32, memory::format_tag::ab);
auto src_mem = memory(src_md, engine);
auto dst_mem = memory(dst_md, engine);
auto user_src_mem = memory(
{src_dims, memory::data_type::f32, memory::format_tag::ab}, engine);
auto user_weights_mem = memory(
{weights_dims, memory::data_type::f32, memory::format_tag::ab},
engine);
auto user_dst_mem = memory(
{dst_dims, memory::data_type::f32, memory::format_tag::ab}, engine);
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(weights_data.data(), user_weights_mem);
auto matmul_src_md = memory::desc(
src_dims, memory::data_type::u8, memory::format_tag::any);
auto matmul_weights_md
= memory::desc::packed(weights_dims, memory::data_type::s8, nnz);
auto matmul_dst_md = memory::desc(
dst_dims, memory::data_type::u8, memory::format_tag::any);
matmul::primitive_desc matmul_pd;
try {
matmul_pd = matmul::primitive_desc(
engine, matmul_src_md, matmul_weights_md, matmul_dst_md);
} catch (error &e) {
if (e.status == dnnl_unimplemented)
throw example_allows_unimplemented {
"No matmul implementation with packed encoding support is "
"available for this platform.\nPlease refer to the "
"developer guide for details."};
// on any other error just re-throw
throw;
}
auto matmul_src_mem = user_src_mem;
auto matmul_weights_mem = user_weights_mem;
auto matmul_dst_mem = user_dst_mem;
auto matmul_prim = matmul(matmul_pd);
if (matmul_pd.src_desc() != user_src_mem.get_desc()) {
matmul_src_mem = memory(matmul_pd.src_desc(), engine);
reorder(user_src_mem, matmul_src_mem)
.execute(engine_stream, user_src_mem, matmul_src_mem);
}
// Use reorder to pack the weights.
auto wei_packed_md = matmul_pd.weights_desc();
const int nhandles = wei_packed_md.get_num_handles();
std::vector<void *> wei_handles(nhandles);
std::vector<std::vector<char>> wei_buffers(nhandles);
for (int h = 0; h < nhandles; h++) {
const size_t buf_sz = wei_packed_md.get_size(h);
wei_buffers[h].resize(buf_sz);
wei_handles[h] = wei_buffers[h].data();
}
if (wei_packed_md != user_weights_mem.get_desc()) {
matmul_weights_mem
= memory(wei_packed_md, engine, std::move(wei_handles));
reorder(user_weights_mem, matmul_weights_mem)
.execute(engine_stream, user_weights_mem, matmul_weights_mem);
}
if (matmul_pd.dst_desc() != user_dst_mem.get_desc()) {
matmul_dst_mem = memory(matmul_pd.dst_desc(), engine);
reorder(user_dst_mem, matmul_dst_mem)
.execute(engine_stream, user_dst_mem, matmul_dst_mem);
}
// Primitive arguments.
std::unordered_map<int, memory> matmul_args;
matmul_args.insert({DNNL_ARG_SRC, matmul_src_mem});
matmul_args.insert({DNNL_ARG_WEIGHTS, matmul_weights_mem});
matmul_args.insert({DNNL_ARG_DST, matmul_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));
}
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