log_softmax.hpp

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#ifndef STAN_MATH_FWD_FUN_LOG_SOFTMAX_HPP
#define STAN_MATH_FWD_FUN_LOG_SOFTMAX_HPP
 
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/math/fwd/core.hpp>
#include <stan/math/fwd/meta.hpp>
#include <stan/math/fwd/fun/softmax.hpp>
#include <stan/math/prim/err.hpp>
#include <stan/math/prim/fun/log_softmax.hpp>
#include <stan/math/prim/fun/to_ref.hpp>
#include <stan/math/prim/functor/apply_vector_unary.hpp>
 
namespace stan {
namespace math {
 
template <typename T, require_std_vector_st<is_fvar, T>* = nullptr>
inline auto log_softmax(T&& x) {
  return apply_vector_unary<T>::apply(std::forward<T>(x), [](auto&& v) {
    return log_softmax(std::forward<decltype(v)>(v));
  });
}
 
template <typename Vec, require_eigen_vector_vt<is_fvar, Vec>* = nullptr>
inline auto log_softmax(Vec&& x) {
  using vec = std::decay_t<Vec>;
  constexpr int Rows = vec::RowsAtCompileTime;
  constexpr int Cols = vec::ColsAtCompileTime;
  using T = typename value_type_t<Vec>::Scalar;
  check_nonzero_size("log_softmax", "x", x);
  decltype(auto) x_ref = to_ref(std::forward<Vec>(x));
  const auto s = softmax(value_of(x_ref));
  const auto d_in = x_ref.d();
  const auto dot_sd = s.dot(d_in);
  Eigen::Matrix<fvar<T>, Rows, Cols> result(x_ref.size());
  result.val() = s.array().log().matrix();
  result.d() = (d_in.array() - dot_sd).matrix();
  return result;
}
 
}  // namespace math
}  // namespace stan
#endif