gradient_expression_graph.hpp

// Copyright (c) Sleipnir contributors
 
#pragma once
 
#include <ranges>
#include <utility>
 
#include <Eigen/SparseCore>
#include <gch/small_vector.hpp>
 
#include "sleipnir/autodiff/expression_graph.hpp"
#include "sleipnir/autodiff/variable.hpp"
#include "sleipnir/autodiff/variable_matrix.hpp"
#include "sleipnir/util/assert.hpp"
#include "sleipnir/util/empty.hpp"
 
namespace slp::detail {
 
template <typename Scalar>
class GradientExpressionGraph {
 public:
  explicit GradientExpressionGraph(const Variable<Scalar>& root)
      : m_top_list{topological_sort(root.expr)} {
    for (const auto& node : m_top_list) {
      m_col_list.emplace_back(node->col);
    }
  }
 
  void update_values() { detail::update_values(m_top_list); }
 
  VariableMatrix<Scalar> generate_tree(
      const VariableMatrix<Scalar>& wrt) const {
    slp_assert(wrt.cols() == 1);
 
    // Read docs/algorithms.md#Reverse_accumulation_automatic_differentiation
    // for background on reverse accumulation automatic differentiation.
 
    if (m_top_list.empty()) {
      return VariableMatrix<Scalar>{detail::empty, wrt.rows(), 1};
    }
 
    // Set root node's adjoint to 1 since df/df is 1
    m_top_list[0]->adjoint_expr = constant_ptr(Scalar(1));
 
    // df/dx = (df/dy)(dy/dx). The adjoint of x is equal to the adjoint of y
    // multiplied by dy/dx. If there are multiple "paths" from the root node to
    // variable; the variable's adjoint is the sum of each path's adjoint
    // contribution.
    for (auto& node : m_top_list) {
      auto& lhs = node->args[0];
      auto& rhs = node->args[1];
 
      if (lhs != nullptr) {
        if (rhs != nullptr) {
          // Binary operator
          lhs->adjoint_expr += node->grad_expr_l(lhs, rhs, node->adjoint_expr);
          rhs->adjoint_expr += node->grad_expr_r(lhs, rhs, node->adjoint_expr);
        } else {
          // Unary operator
          lhs->adjoint_expr += node->grad_expr_l(lhs, rhs, node->adjoint_expr);
        }
      }
    }
 
    // Move gradient tree to return value
    VariableMatrix<Scalar> grad{detail::empty, wrt.rows(), 1};
    for (int row = 0; row < grad.rows(); ++row) {
      grad[row] = Variable{std::move(wrt[row].expr->adjoint_expr)};
    }
 
    // Unlink adjoints to avoid circular references between them and their
    // parent expressions. This ensures all expressions are returned to the free
    // list.
    for (auto& node : m_top_list) {
      node->adjoint_expr = nullptr;
    }
 
    return grad;
  }
 
  void append_triplets(gch::small_vector<Eigen::Triplet<Scalar>>& triplets,
                       int row) const {
    // Read docs/algorithms.md#Reverse_accumulation_automatic_differentiation
    // for background on reverse accumulation automatic differentiation.
 
    if (m_top_list.empty()) {
      return;
    }
 
    // Set root node's adjoint to 1 since df/df is 1
    m_top_list[0]->adjoint = Scalar(1);
 
    // Zero the rest of the adjoints
    for (auto& node : m_top_list | std::views::drop(1)) {
      node->adjoint = Scalar(0);
    }
 
    // df/dx = (df/dy)(dy/dx). The adjoint of x is equal to the adjoint of y
    // multiplied by dy/dx. If there are multiple "paths" from the root node to
    // variable; the variable's adjoint is the sum of each path's adjoint
    // contribution.
    for (const auto& node : m_top_list) {
      auto& lhs = node->args[0];
      auto& rhs = node->args[1];
 
      if (lhs != nullptr) {
        if (rhs != nullptr) {
          // Binary operator
          lhs->adjoint += node->grad_l(lhs->val, rhs->val, node->adjoint);
          rhs->adjoint += node->grad_r(lhs->val, rhs->val, node->adjoint);
        } else {
          // Unary operator
          lhs->adjoint += node->grad_l(lhs->val, Scalar(0), node->adjoint);
        }
      }
    }
 
    for (const auto& [col, node] : std::views::zip(m_col_list, m_top_list)) {
      // Append adjoints of wrt to sparse matrix triplets
      if (col != -1) {
        triplets.emplace_back(row, col, node->adjoint);
      }
    }
  }
 
 private:
  gch::small_vector<Expression<Scalar>*> m_top_list;
 
  gch::small_vector<int> m_col_list;
};
 
}  // namespace slp::detail