.. index:: pair: page; Sum Primitive Example .. _doxid-sum_example_cpp: Sum Primitive Example ===================== This C++ API example demonstrates how to create and execute a :ref:`Sum ` primitive. Key optimizations included in this example: * Identical memory formats for source (src) and destination (dst) tensors. .. 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 sum_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. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Number of src tensors. const int num_src = 10; // Scaling factors. std::vector scales(num_src); std::generate( scales.begin(), scales.end(), [](int n = 0) { return sin(n); }); // Create an array of memory descriptors and memory objects for src tensors. std::vector :ref:`src_md `; std::vector src_mem; for (int n = 0; n < num_src; ++n) { auto md = :ref:`memory::desc `(src_dims, dt::f32, tag::nchw); auto mem = :ref:`memory `(md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), mem); :ref:`src_md `.push_back(md); src_mem.push_back(mem); } // Create primitive descriptor. auto sum_pd = :ref:`sum::primitive_desc `(scales, src_md, :ref:`engine `); // Create the primitive. auto sum_prim = :ref:`sum `(sum_pd); // Create memory object for dst. auto dst_mem = :ref:`memory `(sum_pd.dst_desc(), :ref:`engine `); // Primitive arguments. std::unordered_map sum_args; sum_args.insert({:ref:`DNNL_ARG_DST `, dst_mem}); for (int n = 0; n < num_src; ++n) { sum_args.insert({:ref:`DNNL_ARG_MULTIPLE_SRC ` + n, src_mem[n]}); } // Primitive execution: sum. sum_prim.execute(engine_stream, sum_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(sum_example, parse_engine_kind(argc, argv)); }