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/*******************************************************************************
* Copyright 2020-2025 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 shuffle.cpp
/// > Annotated version: @ref shuffle_example_cpp
///
/// @page shuffle_example_cpp_short
///
/// This C++ API example demonstrates how to create and execute a
/// [Shuffle](@ref dev_guide_shuffle) primitive.
///
/// Key optimizations included in this example:
/// - Shuffle along axis 1 (channels).
///
/// @page shuffle_example_cpp Shuffle Primitive Example
/// @copydetails shuffle_example_cpp_short
///
/// @include shuffle.cpp
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
#include "oneapi/dnnl/dnnl.hpp"
using namespace dnnl;
void shuffle_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 = 72, // channels
IH = 227, // tensor height
IW = 227; // tensor width
// Source (src) and destination (dst) tensors dimensions.
memory::dims src_dims = {N, IC, IH, IW};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> dst_data(product(src_dims));
// Initialize src.
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
// Shuffle axis and group size.
const int shuffle_axis = 1;
const int group_size = 4;
// Create memory descriptor and memory objects for src and dst.
auto src_md = memory::desc(
src_dims, memory::data_type::f32, memory::format_tag::nchw);
auto dst_md = memory::desc(
src_dims, memory::data_type::f32, memory::format_tag::nchw);
auto src_mem = memory(src_md, engine);
auto dst_mem = memory(
{src_dims, memory::data_type::f32, memory::format_tag::abcd},
engine);
// Write data to memory object's handle.
write_to_dnnl_memory(src_data.data(), src_mem);
// Create primitive descriptor.
auto shuffle_pd = shuffle_forward::primitive_desc(engine,
prop_kind::forward_training, src_md, dst_md, shuffle_axis,
group_size);
// Create the primitive.
auto shuffle_prim = shuffle_forward(shuffle_pd);
// Primitive arguments.
std::unordered_map<int, memory> shuffle_args;
shuffle_args.insert({DNNL_ARG_SRC, src_mem});
shuffle_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution: shuffle.
shuffle_prim.execute(engine_stream, shuffle_args);
// Wait for the computation to finalize.
engine_stream.wait();
// Read data from memory object.
read_from_dnnl_memory(dst_data.data(), dst_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
shuffle_example, parse_engine_kind(argc, argv));
}
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