Developer Guide and Reference

  • 2023.0
  • 12/16/2022
  • Public Content
Contents

Sum Primitive Example

This C++ API example demonstrates how to create and execute a Sum primitive.
Key optimizations included in this example:
  • Identical memory formats for source (src) and destination (dst) tensors.
/******************************************************************************* * Copyright 2020-2022 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 <algorithm> #include <cmath> #include <iostream> #include <string> #include <vector> #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void sum_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<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); }); // Number of src tensors. const int num_src = 10; // Scaling factors. std::vector<float> scales(num_src); std::generate(scales.begin(), scales.end(), [](int n = 0) { return sin(float(n)); }); // Create an array of memory descriptors and memory objects for src tensors. std::vector<memory::desc> src_md; std::vector<memory> src_mem; for (int n = 0; n < num_src; ++n) { auto md = memory::desc(src_dims, dt::f32, tag::nchw); auto mem = memory(md, engine); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), mem); src_md.push_back(md); src_mem.push_back(mem); } // Create primitive descriptor. auto sum_pd = sum::primitive_desc(engine, scales, src_md); // Create the primitive. auto sum_prim = sum(sum_pd); // Create memory object for dst. auto dst_mem = memory(sum_pd.dst_desc(), engine); // Primitive arguments. std::unordered_map<int, memory> sum_args; sum_args.insert({DNNL_ARG_DST, dst_mem}); for (int n = 0; n < num_src; ++n) { sum_args.insert({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)); }

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.