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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 reorder.cpp
/// > Annotated version: @ref reorder_example_cpp
///
/// @page reorder_example_cpp_short
///
/// This C++ API demonstrates how to create and execute a
/// [Reorder](@ref dev_guide_reorder) primitive.
///
/// Key optimizations included in this example:
/// - Primitive attributes for output scaling.
///
/// @page reorder_example_cpp Reorder Primitive Example
/// @copydetails reorder_example_cpp_short
///
/// @include reorder.cpp
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
#include "oneapi/dnnl/dnnl.hpp"
using namespace dnnl;
void reorder_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 = 3, // 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<int8_t> dst_data(product(src_dims));
// Initialize src tensor.
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
// Create memory descriptors 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::s8, memory::format_tag::nhwc);
auto src_mem = memory(src_md, engine);
auto dst_mem = memory(dst_md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(src_data.data(), src_mem);
// Per-channel scales.
std::vector<float> scales(IC);
std::generate(scales.begin(), scales.end(), []() {
static int i = 0;
return 64.f + 5.f * i++;
});
// Dimension of the dst tensor where the output scales will be applied
const int ic_dim = 1;
// Create primitive post-ops (per-channel output scales)
primitive_attr reorder_attr;
reorder_attr.set_scales_mask(DNNL_ARG_DST, 1 << ic_dim);
auto dst_scales_mem = memory(
{{IC}, memory::data_type::f32, memory::format_tag::x}, engine);
write_to_dnnl_memory(scales.data(), dst_scales_mem);
// Create primitive descriptor.
auto reorder_pd = reorder::primitive_desc(
engine, src_md, engine, dst_md, reorder_attr);
// Create the primitive.
auto reorder_prim = reorder(reorder_pd);
// Primitive arguments.
std::unordered_map<int, memory> reorder_args;
reorder_args.insert({DNNL_ARG_SRC, src_mem});
reorder_args.insert({DNNL_ARG_DST, dst_mem});
reorder_args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, dst_scales_mem});
// Primitive execution: reorder with scaled sum.
reorder_prim.execute(engine_stream, reorder_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(
reorder_example, parse_engine_kind(argc, argv));
}
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