devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Clang-format tests, examples, libraries, benchmarks, etc.

Browse Source
This commit is contained in:
Antonio Sánchez
2023-12-05 21:22:55 +00:00
committed by Rasmus Munk Larsen
parent 3252ecc7a4
commit 46e9cdb7fe
876 changed files with 33453 additions and 37795 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
test/gpu_example.cu
View File

@@ -18,11 +18,10 @@
struct AddKernel {
// Parameters must be POD or serializable Eigen types (e.g. Matrix,
// Array). The return value must be a POD or serializable value type.
template<typename Type1, typename Type2, typename Type3>
EIGEN_DEVICE_FUNC
Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
C = A + B; // Populate output parameter.
Type3 D = A + B; // Populate return value.
template <typename Type1, typename Type2, typename Type3>
EIGEN_DEVICE_FUNC Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
C = A + B; // Populate output parameter.
Type3 D = A + B; // Populate return value.
return D;
}
};
@@ -36,7 +35,7 @@ void test_add(const T& type) {
// Create random inputs.
const T A = T::Random(rows, cols);
const T B = T::Random(rows, cols);
T C; // Output parameter.
T C; // Output parameter.
// Create kernel.
AddKernel add_kernel;
@@ -54,16 +53,15 @@ void test_add(const T& type) {
// In a GPU-only test, we can verify that the CPU and GPU produce the
// same results.
T C_cpu, C_gpu;
T D_cpu = run_on_cpu(add_kernel, A, B, C_cpu); // Runs on CPU.
T D_gpu = run_on_gpu(add_kernel, A, B, C_gpu); // Runs on GPU.
T D_cpu = run_on_cpu(add_kernel, A, B, C_cpu); // Runs on CPU.
T D_gpu = run_on_gpu(add_kernel, A, B, C_gpu); // Runs on GPU.
VERIFY_IS_CWISE_EQUAL(C_cpu, C_gpu);
VERIFY_IS_CWISE_EQUAL(D_cpu, D_gpu);
};
struct MultiplyKernel {
template<typename Type1, typename Type2, typename Type3>
EIGEN_DEVICE_FUNC
Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
template <typename Type1, typename Type2, typename Type3>
EIGEN_DEVICE_FUNC Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
C = A * B;
return A * B;
}
@@ -85,9 +83,9 @@ void test_multiply(const T1& type1, const T2& type2, const T3& type3) {
// 2 outputs of size (A * B). For each matrix output, the buffer will store
// the number of rows, columns, and the data.
size_t buffer_capacity_hint = 2 * ( // 2 output parameters
2 * sizeof(typename T3::Index) // # Rows, # Cols
+ A.rows() * B.cols() * sizeof(typename T3::Scalar)); // Output data
size_t buffer_capacity_hint = 2 * ( // 2 output parameters
2 * sizeof(typename T3::Index) // # Rows, # Cols
+ A.rows() * B.cols() * sizeof(typename T3::Scalar)); // Output data
T3 D = run_with_hint(buffer_capacity_hint, multiply_kernel, A, B, C);
@@ -97,34 +95,26 @@ void test_multiply(const T1& type1, const T2& type2, const T3& type3) {
T3 C_cpu, C_gpu;
T3 D_cpu = run_on_cpu(multiply_kernel, A, B, C_cpu);
T3 D_gpu = run_on_gpu_with_hint(buffer_capacity_hint,
multiply_kernel, A, B, C_gpu);
T3 D_gpu = run_on_gpu_with_hint(buffer_capacity_hint, multiply_kernel, A, B, C_gpu);
VERIFY_IS_CWISE_APPROX(C_cpu, C_gpu);
VERIFY_IS_CWISE_APPROX(D_cpu, D_gpu);
}
// Declare the test fixture.
EIGEN_DECLARE_TEST(gpu_example)
{
EIGEN_DECLARE_TEST(gpu_example) {
// For the number of repeats, call the desired subtests.
for(int i = 0; i < g_repeat; i++) {
for (int i = 0; i < g_repeat; i++) {
// Call subtests with different sized/typed inputs.
CALL_SUBTEST( test_add(Eigen::Vector3f()) );
CALL_SUBTEST( test_add(Eigen::Matrix3d()) );
CALL_SUBTEST( test_add(Eigen::MatrixX<int>(10, 10)) );
CALL_SUBTEST(test_add(Eigen::Vector3f()));
CALL_SUBTEST(test_add(Eigen::Matrix3d()));
CALL_SUBTEST(test_add(Eigen::MatrixX<int>(10, 10)));
CALL_SUBTEST( test_add(Eigen::Array44f()) );
CALL_SUBTEST( test_add(Eigen::ArrayXd(20)) );
CALL_SUBTEST( test_add(Eigen::ArrayXXi(13, 17)) );
CALL_SUBTEST(test_add(Eigen::Array44f()));
CALL_SUBTEST(test_add(Eigen::ArrayXd(20)));
CALL_SUBTEST(test_add(Eigen::ArrayXXi(13, 17)));
CALL_SUBTEST( test_multiply(Eigen::Matrix3d(),
Eigen::Matrix3d(),
Eigen::Matrix3d()) );
CALL_SUBTEST( test_multiply(Eigen::MatrixX<int>(10, 10),
Eigen::MatrixX<int>(10, 10),
Eigen::MatrixX<int>()) );
CALL_SUBTEST( test_multiply(Eigen::MatrixXf(12, 1),
Eigen::MatrixXf(1, 32),
Eigen::MatrixXf()) );
CALL_SUBTEST(test_multiply(Eigen::Matrix3d(), Eigen::Matrix3d(), Eigen::Matrix3d()));
CALL_SUBTEST(test_multiply(Eigen::MatrixX<int>(10, 10), Eigen::MatrixX<int>(10, 10), Eigen::MatrixX<int>()));
CALL_SUBTEST(test_multiply(Eigen::MatrixXf(12, 1), Eigen::MatrixXf(1, 32), Eigen::MatrixXf()));
}
}