ceres-solver/internal/ceres/program_evaluator.h

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
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//
// Author: keir@google.com (Keir Mierle)
//
// The ProgramEvaluator runs the cost functions contained in each residual block
// and stores the result into a jacobian. The particular type of jacobian is
// abstracted out using two template parameters:
//
//   - An "EvaluatePreparer" that is responsible for creating the array with
//     pointers to the jacobian blocks where the cost function evaluates to.
//   - A "JacobianWriter" that is responsible for storing the resulting
//     jacobian blocks in the passed sparse matrix.
//
// This abstraction affords an efficient evaluator implementation while still
// supporting writing to multiple sparse matrix formats. For example, when the
// ProgramEvaluator is parameterized for writing to block sparse matrices, the
// residual jacobians are written directly into their final position in the
// block sparse matrix by the user's CostFunction; there is no copying.
//
// The evaluation is threaded with OpenMP or C++ threads.
//
// The EvaluatePreparer and JacobianWriter interfaces are as follows:
//
//   class EvaluatePreparer {
//     // Prepare the jacobians array for use as the destination of a call to
//     // a cost function's evaluate method.
//     void Prepare(const ResidualBlock* residual_block,
//                  int residual_block_index,
//                  SparseMatrix* jacobian,
//                  double** jacobians);
//   }
//
//   class JacobianWriter {
//     // Create a jacobian that this writer can write. Same as
//     // Evaluator::CreateJacobian.
//     std::unique_ptr<SparseMatrix> CreateJacobian() const;
//
//     // Create num_threads evaluate preparers.Resulting preparers are valid
//     // while *this is.
//
//     std::unique_ptr<EvaluatePreparer[]> CreateEvaluatePreparers(
//                                           int num_threads);
//
//     // Write the block jacobians from a residual block evaluation to the
//     // larger sparse jacobian.
//     void Write(int residual_id,
//                int residual_offset,
//                double** jacobians,
//                SparseMatrix* jacobian);
//   }
//
// Note: The ProgramEvaluator is not thread safe, since internally it maintains
// some per-thread scratch space.

#ifndef CERES_INTERNAL_PROGRAM_EVALUATOR_H_
#define CERES_INTERNAL_PROGRAM_EVALUATOR_H_

// This include must come before any #ifndef check on Ceres compile options.
// clang-format off
#include "ceres/internal/config.h"
// clang-format on

#include <atomic>
#include <map>
#include <memory>
#include <string>
#include <vector>

#include "ceres/evaluation_callback.h"
#include "ceres/execution_summary.h"
#include "ceres/internal/eigen.h"
#include "ceres/parallel_for.h"
#include "ceres/parameter_block.h"
#include "ceres/program.h"
#include "ceres/residual_block.h"
#include "ceres/small_blas.h"

namespace ceres {
namespace internal {

struct NullJacobianFinalizer {
  void operator()(SparseMatrix* jacobian, int num_parameters) {}
};

template <typename EvaluatePreparer,
          typename JacobianWriter,
          typename JacobianFinalizer = NullJacobianFinalizer>
class ProgramEvaluator final : public Evaluator {
 public:
  ProgramEvaluator(const Evaluator::Options& options, Program* program)
      : options_(options),
        program_(program),
        jacobian_writer_(options, program),
        evaluate_preparers_(std::move(
            jacobian_writer_.CreateEvaluatePreparers(options.num_threads))) {
#ifdef CERES_NO_THREADS
    if (options_.num_threads > 1) {
      LOG(WARNING) << "No threading support is compiled into this binary; "
                   << "only options.num_threads = 1 is supported. Switching "
                   << "to single threaded mode.";
      options_.num_threads = 1;
    }
#endif  // CERES_NO_THREADS

    BuildResidualLayout(*program, &residual_layout_);
    evaluate_scratch_ = std::move(CreateEvaluatorScratch(
        *program, static_cast<unsigned>(options.num_threads)));
  }

  // Implementation of Evaluator interface.
  std::unique_ptr<SparseMatrix> CreateJacobian() const final {
    return jacobian_writer_.CreateJacobian();
  }

  bool Evaluate(const Evaluator::EvaluateOptions& evaluate_options,
                const double* state,
                double* cost,
                double* residuals,
                double* gradient,
                SparseMatrix* jacobian) final {
    ScopedExecutionTimer total_timer("Evaluator::Total", &execution_summary_);
    ScopedExecutionTimer call_type_timer(
        gradient == nullptr && jacobian == nullptr ? "Evaluator::Residual"
                                                   : "Evaluator::Jacobian",
        &execution_summary_);

    // The parameters are stateful, so set the state before evaluating.
    if (!program_->StateVectorToParameterBlocks(state)) {
      return false;
    }

    // Notify the user about a new evaluation point if they are interested.
    if (options_.evaluation_callback != nullptr) {
      program_->CopyParameterBlockStateToUserState();
      options_.evaluation_callback->PrepareForEvaluation(
          /*jacobians=*/(gradient != nullptr || jacobian != nullptr),
          evaluate_options.new_evaluation_point);
    }

    if (residuals != nullptr) {
      VectorRef(residuals, program_->NumResiduals()).setZero();
    }

    if (jacobian != nullptr) {
      jacobian->SetZero();
    }

    // Each thread gets it's own cost and evaluate scratch space.
    for (int i = 0; i < options_.num_threads; ++i) {
      evaluate_scratch_[i].cost = 0.0;
      if (gradient != nullptr) {
        VectorRef(evaluate_scratch_[i].gradient.get(),
                  program_->NumEffectiveParameters())
            .setZero();
      }
    }

    const int num_residual_blocks = program_->NumResidualBlocks();
    // This bool is used to disable the loop if an error is encountered without
    // breaking out of it. The remaining loop iterations are still run, but with
    // an empty body, and so will finish quickly.
    std::atomic_bool abort(false);
    ParallelFor(
        options_.context,
        0,
        num_residual_blocks,
        options_.num_threads,
        [&](int thread_id, int i) {
          if (abort) {
            return;
          }

          EvaluatePreparer* preparer = &evaluate_preparers_[thread_id];
          EvaluateScratch* scratch = &evaluate_scratch_[thread_id];

          // Prepare block residuals if requested.
          const ResidualBlock* residual_block = program_->residual_blocks()[i];
          double* block_residuals = nullptr;
          if (residuals != nullptr) {
            block_residuals = residuals + residual_layout_[i];
          } else if (gradient != nullptr) {
            block_residuals = scratch->residual_block_residuals.get();
          }

          // Prepare block jacobians if requested.
          double** block_jacobians = nullptr;
          if (jacobian != nullptr || gradient != nullptr) {
            preparer->Prepare(residual_block,
                              i,
                              jacobian,
                              scratch->jacobian_block_ptrs.get());
            block_jacobians = scratch->jacobian_block_ptrs.get();
          }

          // Evaluate the cost, residuals, and jacobians.
          double block_cost;
          if (!residual_block->Evaluate(
                  evaluate_options.apply_loss_function,
                  &block_cost,
                  block_residuals,
                  block_jacobians,
                  scratch->residual_block_evaluate_scratch.get())) {
            abort = true;
            return;
          }

          scratch->cost += block_cost;

          // Store the jacobians, if they were requested.
          if (jacobian != nullptr) {
            jacobian_writer_.Write(
                i, residual_layout_[i], block_jacobians, jacobian);
          }

          // Compute and store the gradient, if it was requested.
          if (gradient != nullptr) {
            int num_residuals = residual_block->NumResiduals();
            int num_parameter_blocks = residual_block->NumParameterBlocks();
            for (int j = 0; j < num_parameter_blocks; ++j) {
              const ParameterBlock* parameter_block =
                  residual_block->parameter_blocks()[j];
              if (parameter_block->IsConstant()) {
                continue;
              }

              MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
                  block_jacobians[j],
                  num_residuals,
                  parameter_block->TangentSize(),
                  block_residuals,
                  scratch->gradient.get() + parameter_block->delta_offset());
            }
          }
        });

    if (!abort) {
      const int num_parameters = program_->NumEffectiveParameters();

      // Sum the cost and gradient (if requested) from each thread.
      (*cost) = 0.0;
      if (gradient != nullptr) {
        VectorRef(gradient, num_parameters).setZero();
      }
      for (int i = 0; i < options_.num_threads; ++i) {
        (*cost) += evaluate_scratch_[i].cost;
        if (gradient != nullptr) {
          VectorRef(gradient, num_parameters) +=
              VectorRef(evaluate_scratch_[i].gradient.get(), num_parameters);
        }
      }

      // Finalize the Jacobian if it is available.
      // `num_parameters` is passed to the finalizer so that additional
      // storage can be reserved for additional diagonal elements if
      // necessary.
      if (jacobian != nullptr) {
        JacobianFinalizer f;
        f(jacobian, num_parameters);
      }
    }
    return !abort;
  }

  bool Plus(const double* state,
            const double* delta,
            double* state_plus_delta) const final {
    return program_->Plus(state, delta, state_plus_delta);
  }

  int NumParameters() const final { return program_->NumParameters(); }
  int NumEffectiveParameters() const final {
    return program_->NumEffectiveParameters();
  }

  int NumResiduals() const final { return program_->NumResiduals(); }

  std::map<std::string, CallStatistics> Statistics() const final {
    return execution_summary_.statistics();
  }

 private:
  // Per-thread scratch space needed to evaluate and store each residual block.
  struct EvaluateScratch {
    void Init(int max_parameters_per_residual_block,
              int max_scratch_doubles_needed_for_evaluate,
              int max_residuals_per_residual_block,
              int num_parameters) {
      residual_block_evaluate_scratch =
          std::make_unique<double[]>(max_scratch_doubles_needed_for_evaluate);
      gradient = std::make_unique<double[]>(num_parameters);
      VectorRef(gradient.get(), num_parameters).setZero();
      residual_block_residuals =
          std::make_unique<double[]>(max_residuals_per_residual_block);
      jacobian_block_ptrs =
          std::make_unique<double*[]>(max_parameters_per_residual_block);
    }

    double cost;
    std::unique_ptr<double[]> residual_block_evaluate_scratch;
    // The gradient on the manifold.
    std::unique_ptr<double[]> gradient;
    // Enough space to store the residual for the largest residual block.
    std::unique_ptr<double[]> residual_block_residuals;
    std::unique_ptr<double*[]> jacobian_block_ptrs;
  };

  static void BuildResidualLayout(const Program& program,
                                  std::vector<int>* residual_layout) {
    const std::vector<ResidualBlock*>& residual_blocks =
        program.residual_blocks();
    residual_layout->resize(program.NumResidualBlocks());
    int residual_pos = 0;
    for (int i = 0; i < residual_blocks.size(); ++i) {
      const int num_residuals = residual_blocks[i]->NumResiduals();
      (*residual_layout)[i] = residual_pos;
      residual_pos += num_residuals;
    }
  }

  // Create scratch space for each thread evaluating the program.
  static std::unique_ptr<EvaluateScratch[]> CreateEvaluatorScratch(
      const Program& program, unsigned num_threads) {
    int max_parameters_per_residual_block =
        program.MaxParametersPerResidualBlock();
    int max_scratch_doubles_needed_for_evaluate =
        program.MaxScratchDoublesNeededForEvaluate();
    int max_residuals_per_residual_block =
        program.MaxResidualsPerResidualBlock();
    int num_parameters = program.NumEffectiveParameters();

    auto evaluate_scratch = std::make_unique<EvaluateScratch[]>(num_threads);
    for (int i = 0; i < num_threads; i++) {
      evaluate_scratch[i].Init(max_parameters_per_residual_block,
                               max_scratch_doubles_needed_for_evaluate,
                               max_residuals_per_residual_block,
                               num_parameters);
    }
    return evaluate_scratch;
  }

  Evaluator::Options options_;
  Program* program_;
  JacobianWriter jacobian_writer_;
  std::unique_ptr<EvaluatePreparer[]> evaluate_preparers_;
  std::unique_ptr<EvaluateScratch[]> evaluate_scratch_;
  std::vector<int> residual_layout_;
  ::ceres::internal::ExecutionSummary execution_summary_;
};

}  // namespace internal
}  // namespace ceres

#endif  // CERES_INTERNAL_PROGRAM_EVALUATOR_H_