Address comments

Author: Andrew Johnson

stan/math/fwd/fun/log_softmax.hpp

@@ -22,7 +22,7 @@ namespace math {
  */
 template <typename T, require_t<is_fvar<scalar_type_t<T>>>...>
 inline auto log_softmax(const T& x) {
-  return apply_vector_unary<T>::apply(x, [&](auto& alpha) {
+  return apply_vector_unary<T>::apply(x, [&](const auto& alpha) {
     using T_fvar = value_type_t<decltype(alpha)>;
     using T_fvar_inner = typename T_fvar::Scalar;
 

stan/math/fwd/fun/log_sum_exp.hpp

@@ -52,7 +52,7 @@ inline fvar<T> log_sum_exp(const fvar<T>& x1, double x2) {
  */
 template <typename T, require_t<is_fvar<scalar_type_t<T>>>...>
 inline auto log_sum_exp(const T& x) {
-  return apply_vector_unary<T>::reduce(x, [&](auto& v) {
+  return apply_vector_unary<T>::reduce(x, [&](const auto& v) {
     using T_fvar_inner = typename value_type_t<decltype(v)>::Scalar;
     using mat_type = Eigen::Matrix<T_fvar_inner, -1, -1>;
     mat_type vals = v.val();

stan/math/prim/fun/log_softmax.hpp

@@ -39,7 +39,7 @@ namespace math {
  */
 template <typename T, require_t<std::is_arithmetic<scalar_type_t<T>>>...>
 inline auto log_softmax(const T& x) {
-  return apply_vector_unary<T>::apply(x, [&](auto& v) {
+  return apply_vector_unary<T>::apply(x, [&](const auto& v) {
     check_nonzero_size("log_softmax", "v", v);
     return (v.array() - log_sum_exp(v)).matrix();
   });

stan/math/prim/fun/log_sum_exp.hpp

@@ -78,7 +78,7 @@ inline return_type_t<T1, T2> log_sum_exp(const T2& a, const T1& b) {
  */
 template <typename T, require_t<std::is_arithmetic<scalar_type_t<T>>>...>
 inline auto log_sum_exp(const T& x) {
-  return apply_vector_unary<T>::reduce(x, [&](auto& v) {
+  return apply_vector_unary<T>::reduce(x, [&](const auto& v) {
     if (v.size() == 0) {
       return NEGATIVE_INFTY;
     }

stan/math/prim/vectorize/apply_vector_unary.hpp

@@ -1,7 +1,7 @@
 #ifndef STAN_MATH_PRIM_VECTORIZE_APPLY_VECTOR_UNARY_HPP
 #define STAN_MATH_PRIM_VECTORIZE_APPLY_VECTOR_UNARY_HPP
 
-#include <stan/math/prim/mat/fun/Eigen.hpp>
+#include <stan/math/prim/fun/Eigen.hpp>
 #include <stan/math/prim/meta.hpp>
 #include <vector>
 

stan/math/rev/core/matrix_vari.hpp

@@ -17,7 +17,7 @@ class op_matrix_vari : public vari {
 
  public:
   template <typename T, require_eigen_vt<is_var, T>...>
-  op_matrix_vari(double f, T&& vs) : vari(f), size_(vs.size()) {
+  op_matrix_vari(double f, const T& vs) : vari(f), size_(vs.size()) {
     vis_ = ChainableStack::instance_->memalloc_.alloc_array<vari*>(size_);
     Eigen::Map<Eigen::Matrix<vari*, -1, -1>>(vis_, vs.rows(), vs.cols())
         = vs.vi();

stan/math/rev/fun/log_softmax.hpp

@@ -57,7 +57,7 @@ class log_softmax_elt_vari : public vari {
  */
 template <typename T, require_t<is_var<scalar_type_t<T>>>...>
 inline auto log_softmax(const T& x) {
-  return apply_vector_unary<T>::apply(x, [&](auto& alpha) {
+  return apply_vector_unary<T>::apply(x, [&](const auto& alpha) {
     const int a_size = alpha.size();
 
     check_nonzero_size("log_softmax", "alpha", alpha);

stan/math/rev/fun/log_sum_exp.hpp

@@ -65,8 +65,8 @@ namespace internal {
 class log_sum_exp_matrix_vari : public op_matrix_vari {
  public:
   template <typename T>
-  explicit log_sum_exp_matrix_vari(T&& x)
-      : op_matrix_vari(log_sum_exp(x.val()), std::forward<T>(x)) {}
+  explicit log_sum_exp_matrix_vari(const T& x)
+      : op_matrix_vari(log_sum_exp(x.val()), x) {}
   void chain() {
     Eigen::Map<vector_vi> vis_map(vis_, size_);
     vis_map.adj().array() += adj_ * (vis_map.val().array() - val_).exp();
@@ -82,7 +82,7 @@ class log_sum_exp_matrix_vari : public op_matrix_vari {
  */
 template <typename T, require_t<is_var<scalar_type_t<T>>>...>
 inline auto log_sum_exp(const T& x) {
-  return apply_vector_unary<T>::reduce(x, [&](auto& v) {
+  return apply_vector_unary<T>::reduce(x, [&](const auto& v) {
     return var(new internal::log_sum_exp_matrix_vari(v));
   });
 }