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[CMake 1/7] Fix test compilation with strict compiler warnings #3501

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Merged
SteveBronder merged 1 commit into from
Nov 5, 2026
Merged

[CMake 1/7] Fix test compilation with strict compiler warnings #3501

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6 changes: 0 additions & 6 deletions stan/math/fwd/fun/mdivide_left_tri_low.hpp
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Expand Up @@ -20,9 +20,6 @@ template <typename T1, typename T2,
inline Eigen::Matrix<value_type_t<T1>, std::decay_t<T1>::RowsAtCompileTime,
std::decay_t<T2>::ColsAtCompileTime>
mdivide_left_tri_low(T1&& A, T2&& b) {
constexpr int S1 = std::decay_t<T1>::RowsAtCompileTime;
constexpr int C2 = std::decay_t<T2>::ColsAtCompileTime;

check_square("mdivide_left_tri_low", "A", A);
check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
if (A.size() == 0) {
Expand All @@ -47,7 +44,6 @@ template <typename T1, typename T2, require_eigen_t<T1>* = nullptr,
inline Eigen::Matrix<value_type_t<T2>, std::decay_t<T1>::RowsAtCompileTime,
std::decay_t<T2>::ColsAtCompileTime>
mdivide_left_tri_low(T1&& A, T2&& b) {
constexpr int S1 = std::decay_t<T1>::RowsAtCompileTime;
check_square("mdivide_left_tri_low", "A", A);
check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
if (A.size() == 0) {
Expand All @@ -64,8 +60,6 @@ template <typename T1, typename T2, require_eigen_vt<is_fvar, T1>* = nullptr,
inline Eigen::Matrix<value_type_t<T1>, std::decay_t<T1>::RowsAtCompileTime,
std::decay_t<T2>::ColsAtCompileTime>
mdivide_left_tri_low(T1&& A, T2&& b) {
constexpr int S1 = std::decay_t<T1>::RowsAtCompileTime;
constexpr int C2 = std::decay_t<T2>::ColsAtCompileTime;
check_square("mdivide_left_tri_low", "A", A);
check_multiplicable("mdivide_left_tri_low", "A", A, "b", b);
if (A.size() == 0) {
Expand Down
3 changes: 0 additions & 3 deletions stan/math/opencl/kernel_generator/colwise_reduction.hpp
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Expand Up @@ -222,7 +222,6 @@ class colwise_sum_ : public colwise_reduction<colwise_sum_<T>, T, sum_op> {
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto colwise_sum(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return colwise_sum_<as_operation_cl_t<T>>(
as_operation_cl(std::forward<T>(a)));
}
Expand Down Expand Up @@ -266,7 +265,6 @@ class colwise_prod_ : public colwise_reduction<colwise_prod_<T>, T, prod_op> {
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto colwise_prod(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return colwise_prod_<as_operation_cl_t<T>>(
as_operation_cl(std::forward<T>(a)));
}
Expand Down Expand Up @@ -315,7 +313,6 @@ class colwise_max_ : public colwise_reduction<
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto colwise_max(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return colwise_max_<as_operation_cl_t<T>>(
as_operation_cl(std::forward<T>(a)));
}
Expand Down
3 changes: 0 additions & 3 deletions stan/math/opencl/kernel_generator/reduction_2d.hpp
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Expand Up @@ -231,7 +231,6 @@ class sum_2d_ : public reduction_2d<sum_2d_<T>, T, sum_op> {
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto sum_2d(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return sum_2d_<as_operation_cl_t<T>>(as_operation_cl(std::forward<T>(a)));
}

Expand Down Expand Up @@ -274,7 +273,6 @@ class prod_2d_ : public reduction_2d<prod_2d_<T>, T, prod_op> {
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto prod_2d(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return prod_2d_<as_operation_cl_t<T>>(as_operation_cl(std::forward<T>(a)));
}

Expand Down Expand Up @@ -321,7 +319,6 @@ class max_2d_
*/
template <typename T, require_all_kernel_expressions_t<T>* = nullptr>
inline auto max_2d(T&& a) {
auto&& arg_copy = as_operation_cl(std::forward<T>(a)).deep_copy();
return max_2d_<as_operation_cl_t<T>>(as_operation_cl(std::forward<T>(a)));
}

Expand Down
1 change: 0 additions & 1 deletion stan/math/opencl/prim/binomial_logit_glm_lpmf.hpp
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Expand Up @@ -34,7 +34,6 @@ inline return_type_t<T_x_cl, T_alpha_cl, T_beta_cl> binomial_logit_glm_lpmf(
const T_beta_cl& beta) {
static const char* function = "binomial_logit_glm_lpmf(OpenCL)";
using T_partials_return = partials_return_t<T_x_cl, T_alpha_cl, T_beta_cl>;
constexpr bool is_y_vector = !is_stan_scalar<T_n_cl>::value;
constexpr bool is_alpha_vector = !is_stan_scalar<T_alpha_cl>::value;

const size_t N_instances
Expand Down
1 change: 0 additions & 1 deletion stan/math/opencl/zeros_strict_tri.hpp
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Expand Up @@ -44,7 +44,6 @@ inline void matrix_cl<T>::zeros_strict_tri() try {
return;
}
this->view_ = both(this->view_, invert(matrix_view));
cl::CommandQueue& cmdQueue = opencl_context.queue();
opencl_kernels::fill_strict_tri(cl::NDRange(this->rows(), this->cols()),
*this, 0.0, this->rows(), this->cols(),
matrix_view);
Expand Down
2 changes: 1 addition & 1 deletion stan/math/prim/constraint/unit_vector_free.hpp
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Expand Up @@ -25,7 +25,7 @@ inline auto unit_vector_free(EigVec&& x) {
auto&& x_ref = to_ref(std::forward<EigVec>(x));
check_unit_vector("stan::math::unit_vector_free", "Unit vector variable",
x_ref);
return x_ref;
return std::forward<decltype(x_ref)>(x_ref);
}

/**
Expand Down
3 changes: 2 additions & 1 deletion stan/math/prim/fun/log_softmax.hpp
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Expand Up @@ -7,6 +7,7 @@
#include <stan/math/prim/fun/log_sum_exp.hpp>
#include <stan/math/prim/fun/to_ref.hpp>
#include <stan/math/prim/functor/apply_vector_unary.hpp>
#include <utility>

namespace stan {
namespace math {
Expand Down Expand Up @@ -53,7 +54,7 @@ inline auto log_softmax(Container&& x) {
std::forward<decltype(a)>(a),
[](auto&& v) -> plain_type_t<decltype(v)> {
if (v.size() == 0) {
return v;
return std::forward<decltype(v)>(v);
}
return (v.array() - log_sum_exp(v)).matrix();
});
Expand Down
3 changes: 2 additions & 1 deletion stan/math/prim/fun/softmax.hpp
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Expand Up @@ -6,6 +6,7 @@
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/math/prim/fun/to_ref.hpp>
#include <stan/math/prim/functor/apply_vector_unary.hpp>
#include <utility>

namespace stan {
namespace math {
Expand Down Expand Up @@ -56,7 +57,7 @@ inline auto softmax(Container&& x) {
std::forward<decltype(a)>(a),
[](auto&& v) -> plain_type_t<decltype(v)> {
if (v.size() == 0) {
return v;
return std::forward<decltype(v)>(v);
}
const auto theta = (v.array() - v.maxCoeff()).exp();
return (theta / theta.sum()).matrix();
Expand Down
2 changes: 0 additions & 2 deletions stan/math/prim/prob/normal_sufficient_lpdf.hpp
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Expand Up @@ -75,8 +75,6 @@ inline return_type_t<T_y, T_s, T_loc, T_scale> normal_sufficient_lpdf(
decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
decltype(auto) s_squared_val
= to_ref(as_value_column_array_or_scalar(s_squared_ref));
decltype(auto) n_obs_val_int
= to_ref(as_value_column_array_or_scalar(n_obs_ref));
decltype(auto) n_obs_val = to_ref(
promote_scalar<double>(as_value_column_array_or_scalar(n_obs_ref)));
decltype(auto) mu_val = to_ref(as_value_column_array_or_scalar(mu_ref));
Expand Down
1 change: 0 additions & 1 deletion stan/math/prim/prob/student_t_qf.hpp
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Expand Up @@ -72,7 +72,6 @@ template <typename T_p, typename T_nu, typename T_mu, typename T_sigma,
inline auto student_t_qf(const T_p& p, const T_nu& nu, const T_mu& mu,
const T_sigma& sigma) {
using ret_scalar = return_type_t<T_p, T_nu, T_mu, T_sigma>;
static constexpr const char* function = "student_t_qf";
const size_t max_size_all = max_size(p, nu, mu, sigma);
Eigen::Matrix<ret_scalar, -1, 1> result(max_size_all);

Expand Down
2 changes: 0 additions & 2 deletions stan/math/rev/prob/student_t_qf.hpp
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Expand Up @@ -39,7 +39,6 @@ inline var student_t_qf(const T_p& p, const T_nu& nu, const T_mu& mu,
const double p_val_flip = p_val < 0.5 ? p_val : 1.0 - p_val;
const double p_sign = p_val < 0.5 ? -1.0 : 1.0;
const double ibeta_arg = inv_inc_beta(0.5 * nu_val, 0.5, 2 * p_val_flip);
const double sqrt_inv_ibeta_m1 = sqrt(inv(ibeta_arg) - 1.0);
double ret_val
= mu_val
+ p_sign * sigma_val * sqrt(nu_val) * sqrt(-1.0 + 1.0 / ibeta_arg);
Expand All @@ -49,7 +48,6 @@ inline var student_t_qf(const T_p& p, const T_nu& nu, const T_mu& mu,
const double mu_val = value_of(mu);
const double sigma_val = value_of(sigma);
const double nu_val = value_of(nu);
const double p_val_flip = p_val < 0.5 ? p_val : 1.0 - p_val;
const double p_sign = p_val < 0.5 ? -1.0 : 1.0;
const double sqrt_inv_ibeta_m1 = sqrt(inv(ibeta_arg) - 1.0);
if constexpr (is_autodiff_v<T_p>) {
Expand Down
1 change: 0 additions & 1 deletion test/unit/math/opencl/kernel_generator/calc_if_test.cpp
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Expand Up @@ -50,7 +50,6 @@ TEST(KernelGenerator, calc_if_multi_result_pass_test) {
}

TEST(KernelGenerator, calc_if_multi_result_no_output_test) {
double eps = 0;
MatrixXd m(2, 2);
m << 1, 2, 3, 4;
matrix_cl<double> m_cl(m);
Expand Down
20 changes: 20 additions & 0 deletions test/unit/math/opencl/kernel_generator/colwise_reduction_test.cpp
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Expand Up @@ -59,6 +59,26 @@ TEST(KernelGenerator, colwise_prod_test) {
EXPECT_MATRIX_NEAR(correct, res, 1e-9);
}

TEST(KernelGenerator, colwise_reductions_rvalue_input) {
MatrixXd m(3, 2);
m << 1.1, 1.2, 1.3, 1.4, 1.5, 1.6;

matrix_cl<double> res_cl = stan::math::colwise_sum(matrix_cl<double>(m));
MatrixXd res = stan::math::from_matrix_cl(res_cl).colwise().sum();
MatrixXd correct = m.colwise().sum();
EXPECT_MATRIX_NEAR(correct, res, 1e-9);

matrix_cl<double> prod_cl = stan::math::colwise_prod(matrix_cl<double>(m));
MatrixXd prod = stan::math::from_matrix_cl(prod_cl).colwise().prod();
MatrixXd correct_prod = m.colwise().prod();
EXPECT_MATRIX_NEAR(correct_prod, prod, 1e-9);

matrix_cl<double> max_cl = stan::math::colwise_max(matrix_cl<double>(m));
MatrixXd max = stan::math::from_matrix_cl(max_cl).colwise().maxCoeff();
MatrixXd correct_max = m.colwise().maxCoeff();
EXPECT_MATRIX_NEAR(correct_max, max, 1e-9);
}

TEST(KernelGenerator, colwise_min_test) {
MatrixXd m(3, 2);
m << 1.1, 1.2, 1.3, 1.4, 1.5, 1.6;
Expand Down
17 changes: 17 additions & 0 deletions test/unit/math/opencl/kernel_generator/reduction_2d_test.cpp
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Expand Up @@ -57,6 +57,23 @@ TEST(KernelGenerator, prod_2d_test) {
EXPECT_NEAR(correct, res, 1e-9);
}

TEST(KernelGenerator, reductions_2d_rvalue_input) {
MatrixXd m(3, 2);
m << 1.1, 1.2, 1.3, 1.4, 1.5, 1.6;

matrix_cl<double> res_cl = stan::math::sum_2d(matrix_cl<double>(m));
double res = stan::math::from_matrix_cl(res_cl).sum();
EXPECT_NEAR(m.sum(), res, 1e-9);

matrix_cl<double> prod_cl = stan::math::prod_2d(matrix_cl<double>(m));
double prod = stan::math::from_matrix_cl(prod_cl).prod();
EXPECT_NEAR(m.prod(), prod, 1e-9);

matrix_cl<double> max_cl = stan::math::max_2d(matrix_cl<double>(m));
double max = stan::math::from_matrix_cl(max_cl).maxCoeff();
EXPECT_NEAR(m.maxCoeff(), max, 1e-9);
}

TEST(KernelGenerator, min_2d_test) {
MatrixXd m(3, 2);
m << 1.1, 1.2, 1.3, 1.4, 1.5, 1.6;
Expand Down
1 change: 0 additions & 1 deletion test/unit/math/opencl/matrix_cl_test.cpp
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Expand Up @@ -50,7 +50,6 @@ TEST(MathMatrixCL, matrix_cl_value) {
TEST(MathMatrixCL, assignment) {
using stan::math::matrix_cl;
Eigen::Matrix<double, 2, 2> mat_1;
Eigen::Matrix<double, 2, 2> mat_2;
mat_1 << 1, 2, 3, 4;
matrix_cl<double> mat1_cl(mat_1);
matrix_cl<double> mat2_cl(2, 2);
Expand Down
2 changes: 0 additions & 2 deletions test/unit/math/opencl/prim/mdivide_left_tri_low_test.cpp
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Expand Up @@ -49,7 +49,6 @@ inline void mdivide_left_tri_low_Ab_test(int size) {
stan::math::matrix_cl<double> m0_cl(m1, stan::math::matrix_cl_view::Lower);
stan::math::matrix_d m1_cl = stan::math::from_matrix_cl(
stan::math::mdivide_left_tri_low(m0_cl, m0_cl));
double max_error = 0;
for (int i = 0; i < m1_cpu.size(); i++) {
stan::test::expect_near_rel("mdivide_left_tri_low (OpenCL)", m1_cpu(i),
m1_cl(i));
Expand Down Expand Up @@ -77,7 +76,6 @@ inline void mdivide_left_tri_low_A_test(int size) {

stan::math::matrix_cl<double> m1_A_cl(m1, stan::math::matrix_cl_view::Lower);
m1_cl = stan::math::from_matrix_cl(stan::math::mdivide_left_tri_low(m1_A_cl));
double max_error = 0;
for (int i = 0; i < size; i++) {
for (int j = 0; j <= i; j++) {
stan::test::expect_near_rel("mdivide_left_tri_low (OpenCL)", m1_cpu(i, j),
Expand Down
1 change: 0 additions & 1 deletion test/unit/math/opencl/prim/mdivide_right_tri_low_test.cpp
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Expand Up @@ -48,7 +48,6 @@ inline void mdivide_right_tri_low_Ab_test(int size) {
stan::math::matrix_cl<double> m1_A_cl(m1, stan::math::matrix_cl_view::Lower);
stan::math::matrix_d m1_cl = stan::math::from_matrix_cl(
stan::math::mdivide_right_tri_low(m1_A_cl, m1_A_cl));
double max_error = 0;
for (int i = 0; i < m1_cpu.size(); i++) {
stan::test::expect_near_rel("mdivide_right_tri_low (OpenCL)", m1_cpu(i),
m1_cl(i));
Expand Down
2 changes: 0 additions & 2 deletions test/unit/math/opencl/rev/append_array_test.cpp
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Expand Up @@ -15,8 +15,6 @@ TEST(OpenCLAppendArray, prim_rev_values_small) {
}

TEST(OpenCLAppendArray, prim_rev_size_0) {
int N = 0;

std::vector<double> a{};
std::vector<double> b{};
stan::math::test::compare_cpu_opencl_prim_rev(append_array_functor, a, b);
Expand Down
4 changes: 0 additions & 4 deletions test/unit/math/opencl/rev/bernoulli_cdf_test.cpp
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Expand Up @@ -45,7 +45,6 @@ auto bernoulli_cdf_functor = [](const auto& n, const auto& theta) {

TEST(ProbDistributionsBernoulliCdf, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

std::vector<int> n{0, 1, 3};
Eigen::VectorXd theta(N);
Expand All @@ -59,7 +58,6 @@ TEST(ProbDistributionsBernoulliCdf, opencl_matches_cpu_small) {

TEST(ProbDistributionsBernoulliCdf, opencl_matches_cpu_small_n_negative) {
int N = 3;
int M = 2;

std::vector<int> n{0, 1, -5};
Eigen::VectorXd theta(N);
Expand All @@ -83,8 +81,6 @@ TEST(ProbDistributionsBernoulliCdf, opencl_broadcast_n) {
}

TEST(ProbDistributionsBernoulliCdf, opencl_broadcast_theta) {
int N = 3;

std::vector<int> n{0, 1, 0};
double theta_scal = 0.4;

Expand Down
5 changes: 0 additions & 5 deletions test/unit/math/opencl/rev/bernoulli_lccdf_test.cpp
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Expand Up @@ -45,7 +45,6 @@ auto bernoulli_lccdf_functor = [](const auto& n, const auto& theta) {

TEST(ProbDistributionsBernoulliLccdf, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

std::vector<int> n{0, 0, 0};
Eigen::VectorXd theta(N);
Expand All @@ -59,7 +58,6 @@ TEST(ProbDistributionsBernoulliLccdf, opencl_matches_cpu_small) {

TEST(ProbDistributionsBernoulliLccdf, opencl_matches_cpu_small_n_negative) {
int N = 3;
int M = 2;

std::vector<int> n{0, 0, -5};
Eigen::VectorXd theta(N);
Expand All @@ -73,7 +71,6 @@ TEST(ProbDistributionsBernoulliLccdf, opencl_matches_cpu_small_n_negative) {

TEST(ProbDistributionsBernoulliLccdf, opencl_matches_cpu_small_n_over_one) {
int N = 3;
int M = 2;

std::vector<int> n{0, 1, 5};
Eigen::VectorXd theta(N);
Expand All @@ -97,8 +94,6 @@ TEST(ProbDistributionsBernoulliLccdf, opencl_broadcast_n) {
}

TEST(ProbDistributionsBernoulliLccdf, opencl_broadcast_theta) {
int N = 3;

std::vector<int> n{0, 0, 0};
double theta_scal = 0.4;

Expand Down
3 changes: 0 additions & 3 deletions test/unit/math/opencl/rev/bernoulli_logit_lpmf_test.cpp
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Expand Up @@ -48,7 +48,6 @@ auto bernoulli_logit_lpmf_functor_propto

TEST(ProbDistributionsBernoulliLogit, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

std::vector<int> n{0, 1, 1};
Eigen::VectorXd theta(N);
Expand Down Expand Up @@ -78,8 +77,6 @@ TEST(ProbDistributionsBernoulliLogit, opencl_broadcast_n) {
}

TEST(ProbDistributionsBernoulliLogit, opencl_broadcast_theta) {
int N = 3;

std::vector<int> n{0, 1, 0};
double theta = 0.4;

Expand Down
3 changes: 0 additions & 3 deletions test/unit/math/opencl/rev/bernoulli_lpmf_test.cpp
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Expand Up @@ -47,7 +47,6 @@ auto bernoulli_lpmf_functor_propto = [](const auto& n, const auto& theta) {

TEST(ProbDistributionsBernoulli, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

std::vector<int> n{0, 1, 0};
Eigen::VectorXd theta(N);
Expand Down Expand Up @@ -77,8 +76,6 @@ TEST(ProbDistributionsBernoulli, opencl_broadcast_n) {
}

TEST(ProbDistributionsBernoulli, opencl_broadcast_theta) {
int N = 3;

std::vector<int> n{0, 1, 0};
double theta_scal = 0.4;

Expand Down
1 change: 0 additions & 1 deletion test/unit/math/opencl/rev/beta_lpdf_test.cpp
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Expand Up @@ -85,7 +85,6 @@ auto beta_lpdf_functor_propto

TEST(ProbDistributionsBeta, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

Eigen::VectorXd y(N);
y << 0, 0.5, 1;
Expand Down
1 change: 0 additions & 1 deletion test/unit/math/opencl/rev/beta_proportion_lpdf_test.cpp
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Expand Up @@ -65,7 +65,6 @@ auto beta_proportion_lpdf_functor_propto

TEST(ProbDistributionsBetaProportion, opencl_matches_cpu_small) {
int N = 3;
int M = 2;

Eigen::VectorXd y(N);
y << 0.3, 0.8, 1.0;
Expand Down
2 changes: 0 additions & 2 deletions test/unit/math/opencl/rev/binomial_logit_lpmf_test.cpp
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Expand Up @@ -114,8 +114,6 @@ TEST(ProbDistributionsBinomialLogit, opencl_broadcast_N) {
}

TEST(ProbDistributionsBinomialLogit, opencl_broadcast_alpha) {
int N = 3;

std::vector<int> n{0, 1, 12};
std::vector<int> m{0, 1, 123};
double alpha_scal = 0.4;
Expand Down
2 changes: 0 additions & 2 deletions test/unit/math/opencl/rev/binomial_lpmf_test.cpp
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Expand Up @@ -119,8 +119,6 @@ TEST(ProbDistributionsBinomial, opencl_broadcast_N) {
}

TEST(ProbDistributionsBinomial, opencl_broadcast_theta) {
int N = 3;

std::vector<int> n{0, 1, 12};
std::vector<int> m{0, 1, 123};
double theta_scal = 0.4;
Expand Down
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