Sampler.C

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
 
// STL includes
#include <iterator>
 
// MOOSE includes
#include "Sampler.h"
 
InputParameters
Sampler::validParams()
{
  InputParameters params = MooseObject::validParams();
  params += SetupInterface::validParams();
  params += DistributionInterface::validParams();
  params.addClassDescription("A base class for distribution sampling.");
 
  ExecFlagEnum & exec_enum = params.set<ExecFlagEnum>("execute_on", true);
  exec_enum.addAvailableFlags(EXEC_PRE_MULTIAPP_SETUP);
  exec_enum = {EXEC_INITIAL};
 
  params.addParam<unsigned int>("seed", 0, "Random number generator initial seed");
  params.registerBase("Sampler");
  params.registerSystemAttributeName("Sampler");
 
  // Define the allowable limits for data returned by getSamples/getLocalSamples/getNextLocalRow
  // to prevent system for going over allowable limits. The DenseMatrix object uses unsigned int
  // for size definition, so as start the limits will be based the max of unsigned int. Note,
  // the values here are the limits of the number of items in the complete container. dof_id_type
  // is used just in case in the future we need more.
  params.addParam<dof_id_type>("limit_get_global_samples",
                               0.1 * std::numeric_limits<unsigned int>::max(),
                               "The maximum allowed number of items in the DenseMatrix returned by "
                               "getGlobalSamples method.");
  params.addParam<dof_id_type>(
      "limit_get_local_samples",
      0.1 * std::numeric_limits<unsigned int>::max(),
      "The maximum allowed number of items in the DenseMatrix returned by getLocalSamples method.");
  params.addParam<dof_id_type>(
      "limit_get_next_local_row",
      0.1 * std::numeric_limits<unsigned int>::max(),
      "The maximum allowed number of items in the std::vector returned by getNextLocalRow method.");
 
  params.addParam<unsigned int>(
      "min_procs_per_row",
      1,
      "This will ensure that the sampler is partitioned properly when "
      "'MultiApp/*/min_procs_per_app' is specified. It is not recommended to use otherwise.");
  params.addParam<unsigned int>(
      "max_procs_per_row",
      std::numeric_limits<unsigned int>::max(),
      "This will ensure that the sampler is partitioned properly when "
      "'MultiApp/*/max_procs_per_app' is specified. It is not recommended to use otherwise.");
  return params;
}
 
Sampler::Sampler(const InputParameters & parameters)
  : MooseObject(parameters),
    SetupInterface(this),
    DistributionInterface(this),
    PerfGraphInterface(this),
    SamplerInterface(this),
    VectorPostprocessorInterface(this),
    ReporterInterface(this),
    _min_procs_per_row(getParam<unsigned int>("min_procs_per_row") > n_processors()
                           ? n_processors()
                           : getParam<unsigned int>("min_procs_per_row")),
    _max_procs_per_row(getParam<unsigned int>("max_procs_per_row")),
    _n_rows(0),
    _n_cols(0),
    _n_seeds(1),
    _next_local_row_requires_state_restore(true),
    _initialized(false),
    _needs_reinit(true),
    _has_executed(false),
    _limit_get_global_samples(getParam<dof_id_type>("limit_get_global_samples")),
    _limit_get_local_samples(getParam<dof_id_type>("limit_get_local_samples")),
    _limit_get_next_local_row(getParam<dof_id_type>("limit_get_next_local_row")),
    _auto_advance_generators(true)
{
}
 
void
Sampler::init()
{
  // The init() method is private so it is un-likely to be called, but just in case the following
  // was added to help avoid future mistakes.
  if (_initialized)
    mooseError("The Sampler::init() method is called automatically and should not be called.");
 
  // Initialize the parallel partition of sample to return
  reinit();
 
  // Seed the "master" seed generator
  const unsigned int seed = getParam<unsigned int>("seed");
  MooseRandom seed_generator;
  seed_generator.seed(0, seed);
 
  // See the "secondary" generator that will be used for the random number generation
  _generators.resize(_n_seeds);
  for (std::size_t i = 0; i < _n_seeds; ++i)
  {
    const auto gseed = seed_generator.randl(0);
    _generators[i] = std::make_unique<MooseRandomStateless>(gseed);
  }
 
  // Mark class as initialized, which locks out certain methods
  _initialized = true;
}
 
void
Sampler::reinit()
{
  _rank_config.first = constructRankConfig(false);
  _rank_config.second = constructRankConfig(true);
  if (_rank_config.first.num_local_sims != _rank_config.second.num_local_sims ||
      _rank_config.first.first_local_sim_index != _rank_config.second.first_local_sim_index ||
      _rank_config.first.is_first_local_rank != _rank_config.second.is_first_local_rank)
    mooseError("Sampler has inconsistent partitionings for normal and batch mode.");
 
  _n_local_rows = _rank_config.first.is_first_local_rank ? _rank_config.first.num_local_sims : 0;
  _local_row_begin = _rank_config.first.first_local_sim_index;
  _local_row_end = _local_row_begin + _n_local_rows;
 
  // Set the next row iterator index
  _next_local_row = _local_row_begin;
 
  // Create communicator that only has processors with rows
  _communicator.split(_n_local_rows > 0 ? 1 : MPI_UNDEFINED, processor_id(), _local_comm);
 
  // Update reinit() flag (see execute method)
  _needs_reinit = false;
}
 
LocalRankConfig
Sampler::constructRankConfig(bool batch_mode) const
{
  return rankConfig(
      processor_id(), n_processors(), _n_rows, _min_procs_per_row, _max_procs_per_row, batch_mode);
}
 
void
Sampler::setNumberOfRows(dof_id_type n_rows)
{
  if (n_rows == 0)
    mooseError("The number of rows cannot be zero.");
 
  _needs_reinit = true;
  _n_rows = n_rows;
}
 
void
Sampler::setNumberOfCols(dof_id_type n_cols)
{
  if (n_cols == 0)
    mooseError("The number of columns cannot be zero.");
 
  _needs_reinit = true;
  _n_cols = n_cols;
}
 
void
Sampler::setNumberOfRandomSeeds(std::size_t n_seeds)
{
  if (_initialized)
    mooseError("The 'setNumberOfRandomSeeds()' method can not be called after the Sampler has been "
               "initialized; "
               "this method should be called in the constructor of the Sampler object.");
 
  if (n_seeds == 0)
    mooseError("The number of seeds must be larger than zero.");
 
  _n_seeds = n_seeds;
}
 
void
Sampler::execute()
{
  executeSetUp();
  if (_needs_reinit)
    reinit();
 
  if (_has_executed)
    advanceGeneratorsInternal(_n_rows * _n_cols);
 
  executeTearDown();
  _has_executed = true;
}
 
DenseMatrix<Real>
Sampler::getGlobalSamples()
{
  TIME_SECTION("getGlobalSamples", 1, "Retrieving Global Samples");
 
  checkReinitStatus();
 
  if (_n_rows * _n_cols > _limit_get_global_samples)
    paramError("limit_get_global_samples",
               "The number of entries in the DenseMatrix (",
               _n_rows * _n_cols,
               ") exceeds the allowed limit of ",
               _limit_get_global_samples,
               ".");
 
  _next_local_row_requires_state_restore = true;
  DenseMatrix<Real> output(_n_rows, _n_cols);
  computeSampleMatrix(output);
  return output;
}
 
DenseMatrix<Real>
Sampler::getLocalSamples()
{
  TIME_SECTION("getLocalSamples", 1, "Retrieving Local Samples");
 
  checkReinitStatus();
 
  if (_n_local_rows * _n_cols > _limit_get_local_samples)
    paramError("limit_get_local_samples",
               "The number of entries in the DenseMatrix (",
               _n_local_rows * _n_cols,
               ") exceeds the allowed limit of ",
               _limit_get_local_samples,
               ".");
 
  DenseMatrix<Real> output(_n_local_rows, _n_cols);
  if (_n_local_rows == 0)
    return output;
 
  _next_local_row_requires_state_restore = true;
  computeLocalSampleMatrix(output);
  return output;
}
 
std::vector<Real>
Sampler::getNextLocalRow()
{
  checkReinitStatus();
 
  if (_next_local_row_requires_state_restore)
  {
    _next_local_row_requires_state_restore = false;
 
    if (_n_cols > _limit_get_next_local_row)
      paramError("limit_get_next_local_row",
                 "The number of entries in the std::vector (",
                 _n_cols,
                 ") exceeds the allowed limit of ",
                 _limit_get_next_local_row,
                 ".");
  }
 
  std::vector<Real> output(_n_cols);
  computeSampleRow(_next_local_row, output);
  mooseAssert(output.size() == _n_cols, "The row of sample data is not sized correctly.");
  _next_local_row++;
 
  if (_next_local_row == _local_row_end)
  {
    _next_local_row = _local_row_begin;
    _next_local_row_requires_state_restore = true;
  }
 
  return output;
}
 
void
Sampler::computeSampleMatrix(DenseMatrix<Real> & matrix)
{
  TIME_SECTION("computeSampleMatrix", 2, "Computing Sample Matrix");
 
  for (dof_id_type i = 0; i < _n_rows; ++i)
  {
    std::vector<Real> row(_n_cols, 0);
    computeSampleRow(i, row);
    mooseAssert(row.size() == _n_cols, "The row of sample data is not sized correctly.");
    mooseAssert(!_needs_reinit,
                "Changing the size of the sample must not occur during matrix access.");
    std::copy(row.begin(), row.end(), matrix.get_values().begin() + i * _n_cols);
  }
}
 
void
Sampler::computeLocalSampleMatrix(DenseMatrix<Real> & matrix)
{
  TIME_SECTION("computeLocalSampleMatrix", 2, "Computing Local Sample Matrix");
 
  for (dof_id_type i = _local_row_begin; i < _local_row_end; ++i)
  {
    std::vector<Real> row(_n_cols, 0);
    computeSampleRow(i, row);
    mooseAssert(row.size() == _n_cols, "The row of sample data is not sized correctly.");
    mooseAssert(!_needs_reinit,
                "Changing the size of the sample must not occur during matrix access.");
    std::copy(
        row.begin(), row.end(), matrix.get_values().begin() + ((i - _local_row_begin) * _n_cols));
  }
}
 
void
Sampler::computeSampleRow(dof_id_type i, std::vector<Real> & data)
{
  for (dof_id_type j = 0; j < _n_cols; ++j)
  {
    data[j] = computeSample(i, j);
    mooseAssert(!_needs_reinit,
                "Changing the size of the sample must not occur during matrix access.");
  }
}
 
void
Sampler::advanceGenerators(const dof_id_type count)
{
  TIME_SECTION("advanceGenerators", 2, "Advancing Generators");
 
  for (std::size_t j = 0; j < _generators.size(); ++j)
    advanceGenerator(j, count);
}
void
Sampler::advanceGenerator(const unsigned int seed_index, const dof_id_type count)
{
  mooseAssert(seed_index < _generators.size(), "The seed number index does not exists.");
  _generators[seed_index]->advance(count);
}
 
void
Sampler::advanceGeneratorsInternal(const dof_id_type count)
{
  if (_auto_advance_generators)
    advanceGenerators(count);
}
 
void
Sampler::setAutoAdvanceGenerators(const bool state)
{
  _auto_advance_generators = state;
}
 
Real
Sampler::getRand(std::size_t n, unsigned int index) const
{
  mooseAssert(index < _generators.size(), "The seed number index does not exists.");
  return _generators[index]->rand(n);
}
 
unsigned int
Sampler::getRandl(std::size_t n, unsigned int lower, unsigned int upper, unsigned int index) const
{
  mooseAssert(index < _generators.size(), "The seed number index does not exists.");
  return _generators[index]->randl(n, lower, upper);
}
 
dof_id_type
Sampler::getNumberOfRows() const
{
  checkReinitStatus();
  return _n_rows;
}
 
dof_id_type
Sampler::getNumberOfCols() const
{
  checkReinitStatus();
  return _n_cols;
}
 
dof_id_type
Sampler::getNumberOfLocalRows() const
{
  checkReinitStatus();
  return _n_local_rows;
}
 
dof_id_type
Sampler::getLocalRowBegin() const
{
  checkReinitStatus();
  return _local_row_begin;
}
 
dof_id_type
Sampler::getLocalRowEnd() const
{
  checkReinitStatus();
  return _local_row_end;
}
 
void
Sampler::checkReinitStatus() const
{
  if (_needs_reinit)
    mooseError("A call to 'setNumberOfRows()/Columns()' was made after initialization, as such the "
               "expected Sampler output has changed and a new sample must be created. However, a "
               "call to Sampler::reinit() was not performed. The renit() method is automatically "
               "called during Sampler execution, which occurs according to the 'execute_on' "
               "settings of the Sampler object. An adjustment to this parameter may be required. "
               "It is recommended that calls to 'setNumberOfRows()/Columns() occur within the "
               "Sampler::executeSetUp() method; this will ensure that the reinitialize is handled "
               "correctly. Have a nice day.");
}