LMC.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
#ifdef MOOSE_LIBTORCH_ENABLED
 
#include "LMC.h"
#include "MooseRandom.h"
#include "MathUtils.h"
 
registerMooseObject("StochasticToolsApp", LMC);
 
InputParameters
LMC::validParams()
{
  InputParameters params = CovarianceFunctionBase::validParams();
  params.addClassDescription("Covariance function for multioutput Gaussian Processes based on the "
                             "Linear Model of Coregionalization (LMC).");
  params.addParam<unsigned int>(
      "num_latent_funcs", 1., "The number of latent functions for the expansion of the outputs.");
  params.makeParamRequired<unsigned int>("num_outputs");
  params.makeParamRequired<std::vector<UserObjectName>>("covariance_functions");
  return params;
}
 
LMC::LMC(const InputParameters & parameters)
  : CovarianceFunctionBase(parameters),
    _num_expansion_terms(getParam<unsigned int>("num_latent_funcs"))
{
  // We use a random number generator to obtain the initial guess for the
  // hyperparams
  MooseRandom generator_latent;
  generator_latent.seed(0, 1980);
 
  // First add and initialize the a A coefficients in the (aa^T+lambda*I) matrix
  for (const auto exp_i : make_range(_num_expansion_terms))
  {
    const std::string a_coeff_name = "acoeff_" + std::to_string(exp_i);
    std::vector<Real> acoeff_values(_num_outputs);
    for (const auto out_i : make_range(_num_outputs))
      acoeff_values[out_i] = 3.0 * generator_latent.rand(0) + 1.0;
    auto & acoeff_vector = addVectorRealHyperParameter(a_coeff_name, acoeff_values, true);
    _a_coeffs.push_back(&acoeff_vector);
  }
 
  // Then add and initialize the lambda coefficients in the (aa^T+lambda*I) matrix
  for (const auto exp_i : make_range(_num_expansion_terms))
  {
    const std::string lambda_name = "lambda_" + std::to_string(exp_i);
    std::vector<Real> lambda_values(_num_outputs);
    for (const auto out_i : make_range(_num_outputs))
      lambda_values[out_i] = 3.0 * generator_latent.rand(0) + 1.0;
    auto & lambda_vector = addVectorRealHyperParameter(lambda_name, lambda_values, true);
    _lambdas.push_back(&lambda_vector);
  }
}
 
void
LMC::computeCovarianceMatrix(torch::Tensor & K,
                             const torch::Tensor & x,
                             const torch::Tensor & xp,
                             const bool is_self_covariance) const
{
  const auto options = x.options().dtype(at::kDouble);
  // Create temporary vectors for constructing the covariance matrix
  torch::Tensor K_params = torch::zeros({x.sizes()[0], xp.sizes()[0]}, options);
  torch::Tensor B = torch::zeros({_num_outputs, _num_outputs}, options);
  K = torch::zeros({x.sizes()[0] * _num_outputs, xp.sizes()[0] * _num_outputs}, options);
  torch::Tensor K_working;
 
  // For every expansion term we add the contribution to the covariance matrix
  for (const auto exp_i : make_range(_num_expansion_terms))
  {
    _covariance_functions[exp_i]->computeCovarianceMatrix(K_params, x, xp, is_self_covariance);
    computeBMatrix(B, exp_i);
    K_working = torch::kron(B, K_params);
    K += K_working;
  }
}
 
bool
LMC::computedKdhyper(torch::Tensor & dKdhp,
                     const torch::Tensor & x,
                     const std::string & hyper_param_name,
                     unsigned int ind) const
{
  // Early return in the paramter name is longer than the expected [name] prefix.
  // We prefix the parameter names with the name of the covariance function.
  if (name().length() + 1 > hyper_param_name.length())
    return false;
 
  // Strip the prefix from the given parameter name
  const std::string name_without_prefix = hyper_param_name.substr(name().length() + 1);
 
  // Check if the parameter is tunable
  if (_tunable_hp.find(hyper_param_name) != _tunable_hp.end())
  {
    const std::string acoeff_prefix = "acoeff_";
    const std::string lambda_prefix = "lambda_";
 
    // Allocate storage for the factors of the total gradient matrix
    const auto options = x.options().dtype(at::kDouble);
    torch::Tensor dBdhp = torch::zeros({_num_outputs, _num_outputs}, options);
    torch::Tensor K_params = torch::zeros({x.sizes()[0], x.sizes()[0]}, options);
 
    if (name_without_prefix.find(acoeff_prefix) != std::string::npos)
    {
      // Automatically grab the expansion index
      const int number = std::stoi(name_without_prefix.substr(acoeff_prefix.length()));
      computeAGradient(dBdhp, number, ind);
      _covariance_functions[number]->computeCovarianceMatrix(K_params, x, x, true);
    }
    else if (name_without_prefix.find(lambda_prefix) != std::string::npos)
    {
      // Automatically grab the expansion index
      const int number = std::stoi(name_without_prefix.substr(lambda_prefix.length()));
      computeLambdaGradient(dBdhp, number, ind);
      _covariance_functions[number]->computeCovarianceMatrix(K_params, x, x, true);
    }
    dKdhp = torch::kron(dBdhp, K_params);
    return true;
  }
  else
  {
    // Allocate storage for the matrix factors
    const auto options = x.options().dtype(at::kDouble);
    torch::Tensor B_tmp = torch::zeros({_num_outputs, _num_outputs}, options);
    torch::Tensor B = torch::zeros({_num_outputs, _num_outputs}, options);
    torch::Tensor dKdhp_sub = torch::zeros({x.sizes()[0], x.sizes()[0]}, options);
 
    // First, check the dependent covariances
    bool found = false;
    for (const auto dependent_covar : _covariance_functions)
      if (!found)
        found = dependent_covar->computedKdhyper(dKdhp_sub, x, hyper_param_name, ind);
 
    if (!found)
      mooseError("Hyperparameter ", hyper_param_name, "not found!");
 
    // Then we compute the output covariance
    for (const auto exp_i : make_range(_num_expansion_terms))
    {
      computeBMatrix(B_tmp, exp_i);
      B += B_tmp;
    }
 
    dKdhp = torch::kron(B, dKdhp_sub);
 
    return true;
  }
 
  return false;
}
 
void
LMC::computeBMatrix(torch::Tensor & Bmat, const unsigned int exp_i) const
{
  const auto & a_coeffs = *_a_coeffs[exp_i];
  const auto & lambda_coeffs = *_lambdas[exp_i];
  Bmat = torch::outer(a_coeffs, a_coeffs) + torch::diag(lambda_coeffs);
}
 
void
LMC::computeAGradient(torch::Tensor & grad,
                      const unsigned int exp_i,
                      const unsigned int index) const
{
  const auto & a_coeffs = *_a_coeffs[exp_i];
  mooseAssert(cast_int<int64_t>(index) < a_coeffs.numel(), "Incorrect LMC coefficient index.");
  auto basis = torch::zeros_like(a_coeffs);
  const auto index_tensor =
      torch::tensor({cast_int<int64_t>(index)},
                    torch::TensorOptions().dtype(torch::kLong).device(a_coeffs.device()));
  basis.index_fill_(0, index_tensor, 1.0);
  grad = torch::outer(basis, a_coeffs) + torch::outer(a_coeffs, basis);
}
 
void
LMC::computeLambdaGradient(torch::Tensor & grad,
                           const unsigned int exp_i,
                           const unsigned int index) const
{
  mooseAssert(index < _num_outputs, "Incorrect LMC lambda index.");
  auto basis = torch::zeros_like(*_lambdas[exp_i]);
  const auto index_tensor =
      torch::tensor({cast_int<int64_t>(index)},
                    torch::TensorOptions().dtype(torch::kLong).device(_lambdas[exp_i]->device()));
  basis.index_fill_(0, index_tensor, 1.0);
  grad = torch::diag(basis);
}
 
#endif