mma_optimizer.f90

module mma_optimizer
  use optimizer, only: optimizer_t
  use problem, only : problem_t
  use mma, only: mma_t
  use problem, only: problem_t
  use num_types, only: rp
  use utils, only: neko_error
  use json_module, only: json_file
  use json_utils, only: json_get, json_get_or_default
  use simulation_m, only: simulation_t
  use design, only: design_t
  use brinkman_design, only: brinkman_design_t
  use field, only: field_t
  use field_registry, only: neko_field_registry
  use profiler, only: profiler_start_region, profiler_end_region
 
  use vector, only: vector_t
  use matrix, only: matrix_t
 
  !only to print nglobal when running in parallel
  use comm, only: neko_comm, pe_rank
  use mpi_f08, only: mpi_integer, mpi_sum, mpi_allreduce
 
  use neko_config, only: neko_bcknd_device
  ! Inclusions from external dependencies and standard libraries
  use, intrinsic :: iso_fortran_env, only: stderr => error_unit
 
  use math, only: copy, cmult
  use device_math, only: device_copy, device_cmult
  use field_math, only: field_rzero
  use vector_math, only: vector_cmult
  use neko_ext, only: reset
  use mask_ops, only: mask_exterior_const
  use device, only: device_memcpy, host_to_device, device_to_host
 
  use constraint, only: constraint_t
  use dummy_constraint, only: dummy_constraint_t
  implicit none
  private
  public :: mma_optimizer_t
 
  ! Concrete type for MMA optimizer
  type, extends(optimizer_t) :: mma_optimizer_t
 
     type(mma_t) :: mma
 
     real(kind=rp) :: scale
     logical :: auto_scale
 
     ! Set to flase to remove logging for optimal performance
     logical :: enable_output
 
   contains
 
     ! Override the deferred methods
     generic :: init => init_from_json, init_from_components
     procedure, pass(this) :: init_from_json => mma_optimizer_init_from_json
     procedure, pass(this) :: init_from_components => &
          mma_optimizer_init_from_components
 
     procedure, pass(this) :: run => mma_optimizer_run
     procedure, pass(this) :: validate => mma_optimizer_validate
     procedure, pass(this) :: free => mma_optimizer_free
 
  end type mma_optimizer_t
 
contains
 
  subroutine mma_optimizer_init_from_json(this, parameters, problem, design, &
       simulation)
    class(mma_optimizer_t), intent(inout) :: this
    type(json_file), intent(inout) :: parameters
    class(problem_t), intent(in) :: problem
    class(design_t), intent(in) :: design
    type(simulation_t), optional, intent(in) :: simulation
    logical :: enable_output
    integer :: max_iterations
    real(kind=rp) :: tolerance
 
    character(len=1024) :: optimization_header
    character(len=1024) :: problem_header
 
    type(vector_t) :: x
    type(json_file) :: solver_parameters
    logical :: unconstrained_problem
 
    ! Initialize the logger
    call this%logger%init('optimization_data.csv')
 
    ! Write the header
    problem_header = problem%get_log_header()
    optimization_header = 'iter, ' // trim(problem_header) // &
         ', KKTmax, KKTnorm2, scaling factor'
 
    call design%get_values(x)
 
    if (pe_rank .eq. 0) then
       print *, "Initializing mma_optimizer with steady_state_problem_t."
    end if
 
    call json_get(parameters, "optimization.solver", &
         solver_parameters)
 
    ! Initialize mma_t, handling the dummy_constraint added for unconstrained
    ! problems in mma_optimizer_run()
    unconstrained_problem = (problem%get_n_constraints() == 0)
    if (unconstrained_problem) then
       call this%mma%init(x, design%size(), 1, &
            solver_parameters, this%scale, this%auto_scale)
    else
       call this%mma%init(x, design%size(), problem%get_n_constraints(), &
            solver_parameters, this%scale, this%auto_scale)
    end if
 
    call json_get_or_default(parameters, "optimization.solver.max_iterations", &
         max_iterations, 100)
    call json_get_or_default(parameters, "optimization.solver.tolerance", &
         tolerance, 1.0e-3_rp)
    call json_get_or_default(parameters, "optimization.solver.enable_output", &
         enable_output, .true.)
 
    call this%init_from_components(problem, design, &
         max_iterations, tolerance, enable_output, simulation)
 
    call this%logger%set_header(trim(optimization_header) // &
         this%mma%get_backend_and_subsolver())
    call x%free()
  end subroutine mma_optimizer_init_from_json
 
  subroutine mma_optimizer_init_from_components(this, problem, design, &
       max_iterations, tolerance, enable_output, simulation)
    class(mma_optimizer_t), intent(inout) :: this
    class(problem_t), intent(in) :: problem
    class(design_t), intent(in) :: design
    integer, intent(in) :: max_iterations
    real(kind=rp), intent(in) :: tolerance
    type(simulation_t), intent(in), optional :: simulation
    logical, intent(in) :: enable_output
 
    !set the enable_output flag
    this%enable_output = enable_output
 
    call this%init_base(max_iterations, tolerance)
 
  end subroutine mma_optimizer_init_from_components
 
  subroutine mma_optimizer_run(this, problem, design, simulation)
    class(mma_optimizer_t), intent(inout) :: this
    class(problem_t), intent(inout) :: problem
    class(design_t), intent(inout) :: design
    type(simulation_t), optional, intent(inout) :: simulation
 
    type(vector_t) :: x
 
    integer :: iter, ierr, nglobal, n
    real(kind=rp) :: scaling_factor
 
    real(kind=rp) :: objective_value
    type(vector_t) :: all_objectives
    type(vector_t) :: constraint_value
    type(vector_t) :: objective_sensitivities
    type(matrix_t) :: constraint_sensitivities
 
    type(vector_t) :: log_data
    logical :: unconstrained_problem = .false.
    class(constraint_t), allocatable :: dummy_con
    type(json_file) :: parameters
 
    n = design%size()
    call mpi_allreduce(n, nglobal, 1, mpi_integer, mpi_sum, neko_comm, ierr)
 
    unconstrained_problem = (problem%get_n_constraints() == 0)
    if (unconstrained_problem) then
       allocate(dummy_constraint_t::dummy_con)
       call dummy_con%init(parameters, design)
       call problem%add_constraint(dummy_con)
    end if
 
    ! Initialize the vectors
    call x%init(n)
    call all_objectives%init(problem%get_n_objectives())
    call constraint_value%init(problem%get_n_constraints())
    call objective_sensitivities%init(n)
    call constraint_sensitivities%init(problem%get_n_constraints(), n)
 
    scaling_factor = 1.0_rp
    if (pe_rank .eq. 0) then
       print *, "max_iterations for the optimization loop = ", &
            this%max_iterations
    end if
    call profiler_start_region("Optimizer iteration")
 
    call problem%compute(design, simulation)
    call problem%compute_sensitivity(design, simulation)
 
    call problem%get_objective_value(objective_value)
    call problem%get_constraint_values(constraint_value)
    select type (des => design)
    type is (brinkman_design_t)
       call des%get_sensitivity(objective_sensitivities)
    class default
       call problem%get_objective_sensitivities(objective_sensitivities)
    end select
    call problem%get_constraint_sensitivities(constraint_sensitivities)
 
    call profiler_end_region("Optimizer iteration")
 
    if (this%enable_output) then
       call profiler_start_region("Optimizer logging")
       ! Stamp the initial condition
       call problem%get_all_objective_values(all_objectives)
       call mma_logger_assemble_data(log_data, 0, objective_value, &
            all_objectives, constraint_value, 0.0_rp, 0.0_rp, scaling_factor, &
            problem%get_n_objectives(), problem%get_n_constraints(), &
            unconstrained_problem)
       call this%logger%write(log_data)
 
       if (present(simulation)) then
          call simulation%write(0)
       end if
 
       call profiler_end_region("Optimizer logging")
    end if
    call design%write(0)
 
    do iter = 1, this%max_iterations
       if (this%mma%get_residumax() .lt. this%tolerance) exit
 
       call design%get_values(x)
 
       call profiler_start_region("Optimizer iteration")
 
       ! Scaling
       if (this%auto_scale .eqv. .true.) then
          scaling_factor = abs(this%scale/constraint_value%x(1))
       else
          scaling_factor = abs(this%scale)
       end if
 
       call vector_cmult(constraint_value, scaling_factor)
 
       if (neko_bcknd_device .eq. 1) then
          call device_cmult(constraint_sensitivities%x_d, scaling_factor, &
               constraint_sensitivities%size())
       else
          call cmult(constraint_sensitivities%x, scaling_factor, &
               constraint_sensitivities%size())
       end if
 
       ! Use scaled sensitivities to update the design variable
       call profiler_start_region("MMA update")
       call this%mma%update(iter, x, objective_sensitivities, &
            constraint_value, constraint_sensitivities)
       call profiler_end_region("MMA update")
 
       call design%update_design(x)
 
       call problem%compute(design, simulation)
       call problem%compute_sensitivity(design, simulation)
 
       call problem%get_objective_value(objective_value)
       call problem%get_constraint_values(constraint_value)
       select type (des => design)
       type is (brinkman_design_t)
          call des%get_sensitivity(objective_sensitivities)
       class default
          call problem%get_objective_sensitivities(objective_sensitivities)
       end select
       call problem%get_constraint_sensitivities(constraint_sensitivities)
 
       call profiler_start_region("MMA KKT computation")
       call this%mma%KKT(x, objective_sensitivities, &
            constraint_value, constraint_sensitivities)
       call profiler_end_region("MMA KKT computation")
 
       call profiler_end_region("Optimizer iteration")
 
       if (this%enable_output) then
          call profiler_start_region("Optimizer logging")
          ! Stamp the i^th iteration
          call problem%get_all_objective_values(all_objectives)
          call mma_logger_assemble_data(log_data, iter, objective_value, &
               all_objectives, constraint_value, this%mma%get_residumax(), &
               this%mma%get_residunorm(), scaling_factor, &
               problem%get_n_objectives(), problem%get_n_constraints(), &
               unconstrained_problem)
          call this%logger%write(log_data)
 
          if (present(simulation)) then
             call simulation%write(iter)
          end if
          call design%write(iter)
          call profiler_end_region("Optimizer logging")
       end if
 
    end do
 
    call this%validate(problem, design)
 
    ! Final state after optimization
    if (pe_rank .eq. 0) then
       print *, "MMA Optimization completed after", iter-1, "iterations."
    end if
 
    ! Free local resources
    call x%free()
    call log_data%free()
    call all_objectives%free()
    call constraint_value%free()
    call objective_sensitivities%free()
    call constraint_sensitivities%free()
 
  end subroutine mma_optimizer_run
 
  subroutine mma_optimizer_validate(this, problem, design)
    class(mma_optimizer_t), intent(inout) :: this
    class(problem_t), intent(in) :: problem
    class(design_t), intent(in) :: design
 
    type(vector_t) :: constraint_values
 
    call constraint_values%init(problem%get_n_constraints())
    call problem%get_constraint_values(constraint_values)
    if (neko_bcknd_device .eq. 1) then
       call device_memcpy(constraint_values%x, constraint_values%x_d, &
            constraint_values%size(), host_to_device, .true.)
    end if
 
    if (any(constraint_values%x .gt. 0.0_rp)) then
       call neko_error("MMA optimizer validation failed: " // &
            "Constraints are not satisfied.")
    end if
 
    ! Free local resources
    call constraint_values%free()
 
  end subroutine mma_optimizer_validate
 
  ! Free resources associated with the MMA optimizer
  subroutine mma_optimizer_free(this)
    class(mma_optimizer_t), intent(inout) :: this
 
    ! Free MMA-specific data
    call this%mma%free()
  end subroutine mma_optimizer_free
 
  ! package up the log data
  subroutine mma_logger_assemble_data(log_data, iter, objective_value, &
       all_objectives, constraint_value, residumax, residunorm, &
       scaling_factor, n, m, unconstrained_problem)
    type(vector_t), intent(out) :: log_data
    integer, intent(in) :: iter
    real(kind=rp), intent(in) ::objective_value
    type(vector_t), intent(in) :: all_objectives
    type(vector_t), intent(in) :: constraint_value
    real(kind=rp), intent(in) :: residumax, residunorm, scaling_factor
    logical, intent(in) :: unconstrained_problem
    integer, intent(in) :: n, m
    integer :: i_tmp1, i_tmp2
 
 
 
    ! initialize the logger data
    ! iter | tot F | F_1 | .. |F_n | C_1 | ... | C_n | KKT | KKT2 | scale |
    if (unconstrained_problem) then
       call log_data%init(5 + n)
    else
       call log_data%init(5 + n + m)
    endif
 
    ! iteration
    log_data%x(1) = real(iter, kind=rp)
 
    ! total objective
    log_data%x(2) = objective_value
 
    ! individual objectives
    i_tmp1 = 3
    i_tmp2 = i_tmp1 + n - 1
    log_data%x(i_tmp1 : i_tmp2) = all_objectives%x
 
    ! constraints
    if (.not. unconstrained_problem) then
       i_tmp1 = i_tmp2 + 1
       i_tmp2 = i_tmp1 + m - 1
       log_data%x(i_tmp1 : i_tmp2) = constraint_value%x
    end if
 
 
    ! convergence stuff
    log_data%x(i_tmp2 + 1) = residumax
    log_data%x(i_tmp2 + 2) = residunorm
    log_data%x(i_tmp2 + 3) = scaling_factor
 
  end subroutine mma_logger_assemble_data
end module mma_optimizer