.. index:: pair: example; reorder.cpp .. _doxid-reorder_8cpp-example: reorder.cpp =========== Annotated version: :ref:`Reorder Primitive Example ` Annotated version: :ref:`Reorder Primitive Example ` .. 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 #include #include #include #include #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace :ref:`dnnl `; using :ref:`tag ` = :ref:`memory::format_tag `; using :ref:`dt ` = :ref:`memory::data_type `; void reorder_example(:ref:`dnnl::engine::kind ` engine_kind) { // Create execution dnnl::engine. :ref:`dnnl::engine ` :ref:`engine `(engine_kind, 0); // Create dnnl::stream. :ref:`dnnl::stream ` engine_stream(:ref:`engine `); // Tensor dimensions. const :ref:`memory::dim ` N = 3, // batch size IC = 3, // channels IH = 227, // tensor height IW = 227; // tensor width // Source (src) and destination (dst) tensors dimensions. :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector 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 :ref:`src_md ` = :ref:`memory::desc `(src_dims, dt::f32, tag::nchw); auto :ref:`dst_md ` = :ref:`memory::desc `(src_dims, dt::s8, tag::nhwc); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto dst_mem = :ref:`memory `(dst_md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); // Per-channel scales. std::vector scales(IC); std::generate(scales.begin(), scales.end(), []() { static int i = 0; return 64 + 5 * 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_output_scales(0 | (1 << ic_dim), scales); // Create primitive descriptor. auto reorder_pd = :ref:`reorder::primitive_desc `( :ref:`engine `, src_md, :ref:`engine `, dst_md, reorder_attr); // Create the primitive. auto reorder_prim = :ref:`reorder `(reorder_pd); // Primitive arguments. std::unordered_map reorder_args; reorder_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); reorder_args.insert({:ref:`DNNL_ARG_DST `, dst_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)); }