Performance building option.

Introduction

In this chapter we will present network build option that improves performance. Note this option can change memory layouts. This chapter also shows how to get primitives profiling info.

/*
// Copyright (c) 2017 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 <../api/CPP/cldnn_defs.h>
#include <../api/CPP/engine.hpp>
#include <../api/CPP/input_layout.hpp>
#include <../api/CPP/memory.hpp>
#include <../api/CPP/data.hpp>
#include <../api/CPP/topology.hpp>
#include <../api/CPP/network.hpp>
#include <iostream>
#include <chrono>

#include "helper_functions.h"

using namespace cldnn;


void chapter_5(engine& engine, topology& topology)
{
    std::cout << std::endl << "-- Chapter 5 --" << std::endl;

    build_options build_opt;
    // Optimize_data flag can change weights and outputs layouts. Let take a look at 
    // final result and fc weights.
    build_opt.set_option(build_option::outputs(topology.get_primitive_ids()));
    // Set option to optimize data.
    build_opt.set_option(build_option::optimize_data(true));
    network network(engine, topology, build_opt);
    memory input_prim = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 1 } });
    set_values(input_prim, { -3.0f, -2.0f, 2.5f });
    // Set input.
    network.set_input_data("input", input_prim);
    // Ready to go.
    auto outputs = network.execute();

    for (auto& it : outputs)
    {
        // Print id and output values.
        std::cout << "optimized " << it.first << std::endl;
        auto mem_pointer = it.second.get_memory().pointer<float>();
        for (auto i : mem_pointer)
        {
            std::cout << i << " ";
        }
        std::cout << std::endl;
    }

    // Now, we want to check what is the time of execution of each primitive:
    std::vector<cldnn::instrumentation::profiling_info> profiling_table;
    for (auto& p : outputs)
    {
        profiling_table.push_back({ p.first, p.second.get_event().get_profiling_info() });
    }

    // We have table of profiling metrics.
    for (auto& p : profiling_table)
    {
        std::cout << p.name << ":" << std::endl;
        for (auto& q : p.intervals)
        {
            std::cout << "\t" << q.name << ": " << std::chrono::duration_cast<std::chrono::duration<double, std::chrono::nanoseconds::period>>(q.value->value()).count()
                << " nanoseconds" << std::endl;
        }
    }
}