Pilot.h

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//
// Class Pilot
//   The Optimization Pilot (Master): Coordinates requests by optimizer
//   to workers and reports results back on a given communicator.
//
//   Every worker thread notifies the master here if idle or not. When
//   available the master dispatches one of the pending simulations to the
//   worker who will run the specified simulation and report results back to
//   the master. The Optimizer class will poll the scheduler to check if some
//   (or all) results are available and continue to optimize and request new
//   simulation results.
//
//   @see Worker
//   @see Optimizer
//
//   @tparam Opt_t type of the optimizer
//   @tparam Sim_t type of the simulation
//   @tparam SolPropagationGraph_t strategy to distribute solution between
//           master islands
//   @tparam Comm_t comm splitter strategy
//
// Copyright (c) 2010 - 2013, Yves Ineichen, ETH Zürich
// All rights reserved
//
// Implemented as part of the PhD thesis
// "Toward massively parallel multi-objective optimization with application to
// particle accelerators" (https://doi.org/10.3929/ethz-a-009792359)
//
// This file is part of OPAL.
//
// OPAL is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// You should have received a copy of the GNU General Public License
// along with OPAL. If not, see <https://www.gnu.org/licenses/>.
//
#ifndef __PILOT_H__
#define __PILOT_H__
 
#include <mpi.h>
#include <iostream>
#include <string>
#include <unistd.h>
 
#include "Comm/MasterNode.h"
#include "Comm/CommSplitter.h"
 
#include "Util/AnsiColors.h"
#include "Util/Types.h"
#include "Util/CmdArguments.h"
#include "Util/OptPilotException.h"
 
#include "Pilot/Poller.h"
#include "Pilot/Worker.h"
#include "Optimizer/Optimizer.h"
 
#include "Util/Trace/Trace.h"
#include "Util/Trace/FileSink.h"
#include "Util/Trace/TraceComponent.h"
 
#include "Expression/Parser/function.hpp"
 
 
template <
          class Opt_t
        , class Sim_t
        , class SolPropagationGraph_t
        , class Comm_t
>
class Pilot : protected Poller {
 
public:
 
    // constructor only for Pilot classes inherited from this class
    // they have their own setup function
    Pilot(CmdArguments_t args, std::shared_ptr<Comm_t> comm,
          const DVarContainer_t &dvar)
        : Poller(comm->mpiComm())
        , comm_(comm)
        , cmd_args_(args)
        , dvars_(dvar)
    {
        // do nothing
    }
 
    Pilot(CmdArguments_t args, std::shared_ptr<Comm_t> comm,
          functionDictionary_t known_expr_funcs)
        : Poller(comm->mpiComm())
        , comm_(comm)
        , cmd_args_(args)
    {
        setup(known_expr_funcs);
    }
 
    Pilot(CmdArguments_t args, std::shared_ptr<Comm_t> comm,
          functionDictionary_t known_expr_funcs,
          const DVarContainer_t &dvar,
          const Expressions::Named_t &obj,
          const Expressions::Named_t &cons,
          std::vector<double> hypervolRef = {},
          bool isOptimizerRun = true,
          const std::map<std::string, std::string> &userVariables = {})
        : Poller(comm->mpiComm())
        , comm_(comm)
        , cmd_args_(args)
        , objectives_(obj)
        , constraints_(cons)
        , dvars_(dvar)
        , hypervolRef_(hypervolRef)
    {
        if (isOptimizerRun)
            setup(known_expr_funcs, userVariables);
    }
 
    virtual ~Pilot()
    {
        for (auto itr = objectives_.begin(); itr != objectives_.end(); ++ itr)
            delete itr->second;
 
        for (auto itr = constraints_.begin(); itr != constraints_.end(); ++ itr)
            delete itr->second;
    }
 
 
protected:
 
    MPI_Comm worker_comm_;
    MPI_Comm opt_comm_;
    MPI_Comm coworker_comm_;
 
    std::shared_ptr<Comm_t> comm_;
    CmdArguments_t cmd_args_;
 
    int global_rank_;
    int my_rank_in_worker_comm_;
    int my_rank_in_opt_comm_;
 
    int num_coworkers_;
 
    typedef MasterNode< typename Opt_t::SolutionState_t,
                        SolPropagationGraph_t > MasterNode_t;
    std::unique_ptr< MasterNode_t > master_node_;
 
    std::string input_file_;
 
    int  total_available_workers_;
    bool has_opt_converged_;
    bool continue_polling_;
 
    Expressions::Named_t objectives_;  
    Expressions::Named_t constraints_; 
    DVarContainer_t      dvars_;       
    std::vector<double> hypervolRef_;  
 
 
    // keep track of state of all workers
    std::vector<bool> is_worker_idle_;
 
    typedef std::map<size_t, std::pair<Param_t, reqVarContainer_t> > Jobs_t;
    typedef Jobs_t::iterator JobIter_t;
    Jobs_t  running_job_list_;
    Jobs_t  request_queue_;
 
    //DEBUG
    std::unique_ptr<Trace> job_trace_;
 
private:
    void setup(functionDictionary_t known_expr_funcs,
               const std::map<std::string, std::string> &userVariables) {
        global_rank_ = comm_->globalRank();
 
        if(global_rank_ == 0) {
            std::cout << AnsiColors::BoldMagenta;
            std::cout << "             _                _ _       _   " << std::endl;
            std::cout << "            | |              (_) |     | |  " << std::endl;
            std::cout << "  ___  _ __ | |_ ______ _ __  _| | ___ | |_ " << std::endl;
            std::cout << " / _ \\| '_ \\| __|______| '_ \\| | |/ _ \\| __|" << std::endl;
            std::cout << "| (_) | |_) | |_       | |_) | | | (_) | |_ " << std::endl;
            std::cout << " \\___/| .__/ \\__|      | .__/|_|_|\\___/ \\__|" << std::endl;
            std::cout << "      | |              | |                  " << std::endl;
            std::cout << "      |_|              |_|                  " << std::endl;
            // ADA            std::cout << "☷ Version: \t"    << PACKAGE_VERSION << std::endl;
            //std::cout << "☷ Git: \t\t"      << GIT_VERSION     << std::endl;
            //std::cout << "☷ Build Date: \t" << BUILD_DATE      << std::endl;
            std::cout << AnsiColors::Reset;
            std::cout << std::endl;
        }
 
        MPI_Barrier(MPI_COMM_WORLD);
        parseInputFile(known_expr_funcs, true);
 
        // here the control flow starts to diverge
        if      ( comm_->isOptimizer() ) { startOptimizer(); }
        else if ( comm_->isWorker()    ) { startWorker(userVariables); }
        else if ( comm_->isPilot()     ) { startPilot();     }
    }
 
protected:
 
    void parseInputFile(functionDictionary_t /*known_expr_funcs*/, bool isOptimizationRun) {
 
        try {
            input_file_ = cmd_args_->getArg<std::string>("inputfile", true);
        } catch (OptPilotException &e) {
            std::cout << "Could not find 'inputfile' in arguments.. Aborting."
                << std::endl;
            MPI_Abort(comm_m, -101);
        }
 
        if((isOptimizationRun && objectives_.size() == 0) || dvars_.size() == 0) {
            throw OptPilotException("Pilot::Pilot()",
                    "No objectives or dvars specified");
        }
 
        if(global_rank_ == 0) {
            std::ostringstream os;
            os << AnsiColors::BoldMagenta;
            os << "  ✔ " << objectives_.size()
               << " objectives" << std::endl;
            if (isOptimizationRun) {
                os << "  ✔ " << constraints_.size()
                   << " constraints" << std::endl;
            }
            os << "  ✔ " << dvars_.size()
               << " dvars" << std::endl;
            os << AnsiColors::Reset;
            os << std::endl;
            std::cout << os.str() << std::flush;
        }
 
        MPI_Barrier(MPI_COMM_WORLD);
    }
 
    virtual
    void startOptimizer() {
 
        std::ostringstream os;
          os << AnsiColors::BoldMagenta << "  " << global_rank_ << " (PID: " << getpid() << ") ▶ Opt"
              << AnsiColors::Reset << std::endl;
        std::cout << os.str() << std::flush;
 
        const std::unique_ptr<Opt_t> opt(
                new Opt_t(objectives_, constraints_, dvars_, objectives_.size(),
                          comm_->getBundle(), cmd_args_, hypervolRef_, comm_->getNrWorkerGroups()));
        opt->initialize();
 
        std::cout << "Stop Opt.." << std::endl;
    }
 
    virtual
    void startWorker(const std::map<std::string, std::string> &userVariables) {
 
        std::ostringstream os;
          os << AnsiColors::BoldMagenta << "  " << global_rank_ << " (PID: " << getpid() << ") ▶ Worker"
              << AnsiColors::Reset << std::endl;
        std::cout << os.str() << std::flush;
 
        size_t pos = input_file_.find_last_of("/");
        std::string tmplfile = input_file_;
        if(pos != std::string::npos)
            tmplfile = input_file_.substr(pos+1);
        pos = tmplfile.find(".");
        std::string simName = tmplfile.substr(0,pos);
 
        const std::unique_ptr< Worker<Sim_t> > w(
                new Worker<Sim_t>(objectives_, constraints_, simName,
                    comm_->getBundle(), cmd_args_, userVariables));
 
        std::cout << "Stop Worker.." << std::endl;
    }
 
    virtual
    void startPilot() {
 
        std::ostringstream os;
          os << AnsiColors::BoldMagenta << "  " << global_rank_ << " (PID: " << getpid() << ") ▶ Pilot"
              << AnsiColors::Reset << std::endl;
        std::cout << os.str() << std::flush;
 
        // Traces
        std::ostringstream trace_filename;
        trace_filename << "pilot.trace." << comm_->getBundle().island_id;
        job_trace_.reset(new Trace("Optimizer Job Trace"));
        job_trace_->registerComponent( "sink",
            std::shared_ptr<TraceComponent>(new FileSink(trace_filename.str())));
 
        worker_comm_ = comm_->getBundle().worker;
        opt_comm_ = comm_->getBundle().opt;
        coworker_comm_ = comm_->getBundle().world;
 
        my_rank_in_worker_comm_ = 0;
        MPI_Comm_rank(worker_comm_, &my_rank_in_worker_comm_);
        my_rank_in_opt_comm_ = 0;
        MPI_Comm_rank(opt_comm_, &my_rank_in_opt_comm_);
 
        total_available_workers_ = 0;
        MPI_Comm_size(worker_comm_, &total_available_workers_);
        is_worker_idle_.resize(total_available_workers_, true);
        is_worker_idle_[my_rank_in_worker_comm_] = false;
 
        // setup master network
        num_coworkers_ = 0;
        MPI_Comm_size(coworker_comm_, &num_coworkers_);
        if(num_coworkers_ > 1) {
            //FIXME: proper upper bound for window size
            int alpha = cmd_args_->getArg<int>("initialPopulation", false);
            int opt_size = objectives_.size() + constraints_.size();
            int overhead = 10;
            size_t upperbound_buffer_size =
                sizeof(double) * alpha * (1 + opt_size) * 1000
                + overhead;
            master_node_.reset(
                new MasterNode< typename Opt_t::SolutionState_t,
                                SolPropagationGraph_t >(
                    coworker_comm_, upperbound_buffer_size, objectives_.size(),
                    comm_->getBundle().island_id));
        }
 
        has_opt_converged_ = false;
        continue_polling_  = true;
        run();
 
        std::cout << "Stop Pilot.." << std::endl;
    }
 
    virtual
    void setupPoll()
    {}
 
    virtual
    void prePoll()
    {}
 
    virtual
    void onStop()
    {}
 
    virtual
    void postPoll() {
        // terminating all workers is tricky since we do not know their state.
        // All workers are notified (to terminate) when opt has converged and
        // all workers are idle.
        bool all_worker_idle = true;
 
        // in the case where new requests became available after worker
        // delivered last results (and switched to idle state).
        for(int i = 0; i < total_available_workers_; i++) {
 
            if(i == my_rank_in_worker_comm_) continue;
 
            all_worker_idle = all_worker_idle && is_worker_idle_[i];
 
            if(is_worker_idle_[i] && !request_queue_.empty())
                sendNewJobToWorker(i);
        }
 
        // when all workers have been notified we can stop polling
        if(all_worker_idle && has_opt_converged_) {
            continue_polling_ = false;
            int dummy = 0;
            for(int worker = 0; worker < total_available_workers_; worker++) {
                MPI_Request req;
                MPI_Isend(&dummy, 1, MPI_INT, worker,
                          MPI_STOP_TAG, worker_comm_, &req);
            }
        }
    }
 
 
    virtual
    void sendNewJobToWorker(int worker) {
 
        // no new jobs once our opt has converged
        if(has_opt_converged_) return;
 
        JobIter_t job = request_queue_.begin();
        size_t jid = job->first;
 
        Param_t job_params = job->second.first;
        MPI_Send(&jid, 1, MPI_UNSIGNED_LONG, worker, MPI_WORK_JOBID_TAG, worker_comm_);
        MPI_Send_params(job_params, worker, worker_comm_);
 
        //reqVarContainer_t job_reqvars = job->second.second;
        //MPI_Send_reqvars(job_reqvars, worker, worker_comm_);
 
        running_job_list_.insert(std::pair<size_t,
                std::pair<Param_t, reqVarContainer_t> >(job->first, job->second));
        request_queue_.erase(jid);
        is_worker_idle_[worker] = false;
 
        std::ostringstream dump;
        dump << "sent job with ID " << jid << " to worker " << worker
             << std::endl;
        job_trace_->log(dump);
 
    }
 
 
    virtual
    bool onMessage(MPI_Status status, size_t recv_value){
 
        MPITag_t tag = MPITag_t(status.MPI_TAG);
        switch(tag) {
 
        case WORKER_FINISHED_TAG: {
 
            size_t job_id = recv_value;
 
            size_t dummy = 1;
            MPI_Send(&dummy, 1, MPI_UNSIGNED_LONG, status.MPI_SOURCE,
                     MPI_WORKER_FINISHED_ACK_TAG, worker_comm_);
 
            reqVarContainer_t res;
            MPI_Recv_reqvars(res, status.MPI_SOURCE, worker_comm_);
 
            running_job_list_.erase(job_id);
            is_worker_idle_[status.MPI_SOURCE] = true;
 
            std::ostringstream dump;
            dump << "worker finished job with ID " << job_id << std::endl;
            job_trace_->log(dump);
 
 
            // optimizer already terminated, cannot accept new messages
            if(has_opt_converged_) return true;
 
            int opt_master_rank = comm_->getLeader();
            MPI_Send(&job_id, 1, MPI_UNSIGNED_LONG, opt_master_rank,
                     MPI_OPT_JOB_FINISHED_TAG, opt_comm_);
 
            MPI_Send_reqvars(res, opt_master_rank, opt_comm_);
 
            // we keep worker busy _after_ results have been sent to optimizer
            if(!request_queue_.empty())
                sendNewJobToWorker(status.MPI_SOURCE);
 
            return true;
        }
 
        case OPT_NEW_JOB_TAG: {
 
            size_t job_id = recv_value;
            int opt_master_rank = comm_->getLeader();
 
            Param_t job_params;
            MPI_Recv_params(job_params, (size_t)opt_master_rank, opt_comm_);
 
            reqVarContainer_t reqVars;
            //MPI_Recv_reqvars(reqVars, (size_t)opt_master_rank, job_size, opt_comm_);
 
            std::pair<Param_t, reqVarContainer_t> job =
                std::pair<Param_t, reqVarContainer_t>(job_params, reqVars);
            request_queue_.insert(
                                  std::pair<size_t, std::pair<Param_t, reqVarContainer_t> >(
                                                                                            job_id, job));
 
            std::ostringstream dump;
            dump << "new opt job with ID " << job_id << std::endl;
            job_trace_->log(dump);
 
            return true;
        }
 
        case EXCHANGE_SOL_STATE_TAG: {
 
            if(num_coworkers_ <= 1) return true;
 
            std::ostringstream dump;
            dump << "starting solution exchange.. " << status.MPI_SOURCE << std::endl;
            job_trace_->log(dump);
 
            // we start by storing or local solution state
            size_t buffer_size = recv_value;
            int opt_master_rank = status.MPI_SOURCE; //comm_->getLeader();
 
            char *buffer = new char[buffer_size];
            MPI_Recv(buffer, buffer_size, MPI_CHAR, opt_master_rank,
                     MPI_EXCHANGE_SOL_STATE_DATA_TAG, opt_comm_, &status);
            master_node_->store(buffer, buffer_size);
            delete[] buffer;
 
            dump.clear();
            dump.str(std::string());
            dump << "getting " << buffer_size << " bytes from OPT "
                 << opt_master_rank << std::endl;
            job_trace_->log(dump);
 
            // and then continue collecting all other solution states
            std::ostringstream states;
            master_node_->collect(states);
            buffer_size = states.str().length();
 
            dump.clear();
            dump.str(std::string());
            dump << "collected solution states of other PILOTS: "
                 << buffer_size << " bytes" << std::endl;
            job_trace_->log(dump);
 
            // send collected solution states to optimizer;
            MPI_Send(&buffer_size, 1, MPI_UNSIGNED_LONG, opt_master_rank,
                     MPI_EXCHANGE_SOL_STATE_RES_SIZE_TAG, opt_comm_);
 
            buffer = new char[buffer_size];
            std::memcpy(buffer, states.str().c_str(), buffer_size);
            MPI_Send(buffer, buffer_size, MPI_CHAR, opt_master_rank,
                     MPI_EXCHANGE_SOL_STATE_RES_TAG, opt_comm_);
 
            dump.clear();
            dump.str(std::string());
            dump << "sent set of new solutions to OPT" << std::endl;
            job_trace_->log(dump);
 
            delete[] buffer;
 
            return true;
        }
 
        case OPT_CONVERGED_TAG: {
            return stop();
        }
 
        case WORKER_STATUSUPDATE_TAG: {
            is_worker_idle_[status.MPI_SOURCE] = true;
            return true;
        }
 
        default: {
            std::string msg = "(Pilot) Error: unexpected MPI_TAG: ";
            msg += status.MPI_TAG;
            throw OptPilotException("Pilot::onMessage", msg);
        }
        }
    }
 
    bool stop(bool isOpt = true) {
 
        if(has_opt_converged_) return true;
 
        has_opt_converged_ = true;
        request_queue_.clear();
        size_t dummy = 0;
        MPI_Request req;
        MPI_Isend(&dummy, 1, MPI_UNSIGNED_LONG, comm_->getLeader(), MPI_STOP_TAG, opt_comm_, &req);
 
        if(! isOpt) return true;
        if(num_coworkers_ <= 1) return true;
 
        if(! cmd_args_->getArg<bool>("one-pilot-converge", false, false))
            return true;
 
        // propagate converged message to other pilots
        // FIXME what happens if two island converge at the same time?
        int my_rank = 0;
        MPI_Comm_rank(coworker_comm_, &my_rank);
        for(int i=0; i < num_coworkers_; i++) {
            if(i == my_rank) continue;
            MPI_Request req;
            MPI_Isend(&dummy, 1, MPI_UNSIGNED_LONG, i, OPT_CONVERGED_TAG, coworker_comm_, &req);
        }
 
        return true;
    }
 
 
    // we overwrite run here to handle polling on two different communicators
    //XXX: would be nice to give the poller interface an array of comms and
    //     listeners to be called..
    void run() {
 
        MPI_Request opt_request;
        MPI_Request worker_request;
        MPI_Status status;
        int flag = 0;
        size_t recv_value_worker = 0;
        size_t recv_value_opt = 0;
 
        setupPoll();
 
        MPI_Irecv(&recv_value_opt, 1, MPI_UNSIGNED_LONG, MPI_ANY_SOURCE,
                  MPI_ANY_TAG, opt_comm_, &opt_request);
        MPI_Irecv(&recv_value_worker, 1, MPI_UNSIGNED_LONG, MPI_ANY_SOURCE,
                  MPI_ANY_TAG, worker_comm_, &worker_request);
 
        bool pending_opt_request    = true;
        bool pending_worker_request = true;
        bool pending_pilot_request  = false;
 
        MPI_Request pilot_request;
        size_t recv_value_pilot = 0;
        if(cmd_args_->getArg<bool>("one-pilot-converge", false, false)) {
            MPI_Irecv(&recv_value_pilot, 1, MPI_UNSIGNED_LONG, MPI_ANY_SOURCE,
                      MPI_ANY_TAG, coworker_comm_, &pilot_request);
            pending_pilot_request = true;
        }
 
        while(continue_polling_) {
 
            prePoll();
 
            if(opt_request != MPI_REQUEST_NULL) {
                MPI_Test(&opt_request, &flag, &status);
                if(flag) {
                    pending_opt_request = false;
                    if(status.MPI_TAG == MPI_STOP_TAG) {
                        return;
                    } else {
                        if(onMessage(status, recv_value_opt)) {
                            MPI_Irecv(&recv_value_opt, 1, MPI_UNSIGNED_LONG,
                                      MPI_ANY_SOURCE, MPI_ANY_TAG, opt_comm_,
                                      &opt_request);
                            pending_opt_request = true;
                        } else
                            return;
                    }
                }
            }
 
            if(worker_request != MPI_REQUEST_NULL) {
                MPI_Test(&worker_request, &flag, &status);
                if(flag) {
                    pending_worker_request = false;
                    if(status.MPI_TAG == MPI_STOP_TAG) {
                        return;
                    } else {
                        if(onMessage(status, recv_value_worker)) {
                            MPI_Irecv(&recv_value_worker, 1,
                                      MPI_UNSIGNED_LONG, MPI_ANY_SOURCE, MPI_ANY_TAG,
                                      worker_comm_, &worker_request);
                            pending_worker_request = true;
                        } else
                            return;
                    }
                }
            }
 
            if(cmd_args_->getArg<bool>("one-pilot-converge", false, false)) {
                if(pilot_request != MPI_REQUEST_NULL) {
                    MPI_Test(&pilot_request, &flag, &status);
                    if(flag) {
                        pending_pilot_request = false;
                        if(status.MPI_TAG == OPT_CONVERGED_TAG) {
                            stop(false);
                        } else {
                            MPI_Irecv(&recv_value_pilot, 1,
                                      MPI_UNSIGNED_LONG, MPI_ANY_SOURCE, MPI_ANY_TAG,
                                      coworker_comm_, &pilot_request);
                            pending_pilot_request = true;
                        }
                    }
                }
            }
 
            postPoll();
        }
 
        if(pending_opt_request)     MPI_Cancel( &opt_request );
        if(pending_worker_request)  MPI_Cancel( &worker_request );
        if(pending_pilot_request)   MPI_Cancel( &pilot_request );
    }
 
};
 
#endif