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GPU: Raise CUDA/HIP minimum and remove legacy guards

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- Raise CUDA minimum from 9.0 to 11.4 (sm_70/Volta).
- Raise HIP minimum to GFX906 (Vega 20/MI50) / ROCm 5.6.
- Remove EIGEN_HAS_{CUDA,HIP,GPU}_FP16 guards — FP16 is always available
  on sm_70+ and GFX906+.
- Remove obsolete __HIP_ARCH_HAS_* preprocessor branches.
- C++14 cleanup: remove pre-C++14 workarounds in GPU code.
- Fix NVCC warnings (deprecated register keyword, unreachable code,
  tautological comparisons).
- Fix HIP test execution on gfx1151.
- Update CI configuration for new minimum versions.
This commit is contained in:
Rasmus Munk Larsen
2026-04-09 13:38:04 -07:00
parent e055e4e415
commit 6a9405bf7a
28 changed files with 336 additions and 700 deletions
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28
unsupported/test/CMakeLists.txt
View File

@@ -237,7 +237,7 @@ if("${CMAKE_SIZEOF_VOID_P}" EQUAL "8" AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "MS
ei_add_test(cxx11_tensor_uint128)
endif()
find_package(CUDA 9.0)
find_package(CUDA 11.4)
if(CUDA_FOUND AND EIGEN_TEST_CUDA)
# Make sure to compile without the -pedantic, -Wundef, -Wnon-virtual-dtor
# and -fno-check-new flags since they trigger thousands of compilation warnings
@@ -281,26 +281,11 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
ei_add_test(cxx11_tensor_argmax_gpu)
ei_add_test(cxx11_tensor_cast_float16_gpu)
ei_add_test(cxx11_tensor_scan_gpu)
set(EIGEN_CUDA_OLDEST_COMPUTE_ARCH 9999)
foreach(ARCH IN LISTS EIGEN_CUDA_COMPUTE_ARCH)
if(${ARCH} LESS ${EIGEN_CUDA_OLDEST_COMPUTE_ARCH})
set(EIGEN_CUDA_OLDEST_COMPUTE_ARCH ${ARCH})
endif()
endforeach()
# Contractions require arch 3.0 or higher
if (${EIGEN_CUDA_OLDEST_COMPUTE_ARCH} GREATER 29)
ei_add_test(cxx11_tensor_device)
ei_add_test(cxx11_tensor_gpu)
ei_add_test(cxx11_tensor_contract_gpu)
ei_add_test(cxx11_tensor_of_float16_gpu)
endif()
# The random number generation code requires arch 3.5 or greater.
if (${EIGEN_CUDA_OLDEST_COMPUTE_ARCH} GREATER 34)
ei_add_test(cxx11_tensor_random_gpu)
endif()
ei_add_test(cxx11_tensor_device)
ei_add_test(cxx11_tensor_gpu)
ei_add_test(cxx11_tensor_contract_gpu)
ei_add_test(cxx11_tensor_of_float16_gpu)
ei_add_test(cxx11_tensor_random_gpu)
unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
endif()
@@ -341,7 +326,6 @@ if (EIGEN_TEST_HIP)
ei_add_test(cxx11_tensor_cast_float16_gpu)
ei_add_test(cxx11_tensor_scan_gpu)
ei_add_test(cxx11_tensor_device)
ei_add_test(cxx11_tensor_gpu)
ei_add_test(cxx11_tensor_contract_gpu)
ei_add_test(cxx11_tensor_of_float16_gpu)

73
unsupported/test/cxx11_tensor_gpu.cu
View File

@@ -850,6 +850,7 @@ void test_gpu_igamma() {
Tensor<Scalar, 2> a(6, 6);
Tensor<Scalar, 2> x(6, 6);
Tensor<Scalar, 2> out(6, 6);
Tensor<Scalar, 2> expected_out(6, 6);
out.setZero();
Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
@@ -862,14 +863,11 @@ void test_gpu_igamma() {
}
}
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
Scalar igamma_s[][6] = {
{0.0, nan, nan, nan, nan, nan},
{0.0, 0.6321205588285578, 0.7768698398515702, 0.9816843611112658, 9.999500016666262e-05, 1.0},
{0.0, 0.4275932955291202, 0.608374823728911, 0.9539882943107686, 7.522076445089201e-07, 1.0},
{0.0, 0.01898815687615381, 0.06564245437845008, 0.5665298796332909, 4.166333347221828e-18, 1.0},
{0.0, 0.9999780593618628, 0.9999899967080838, 0.9999996219837988, 0.9991370418689945, 1.0},
{0.0, 0.0, 0.0, 0.0, 0.0, 0.5042041932513908}};
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
expected_out(i, j) = numext::igamma(a(i, j), x(i, j));
}
}
std::size_t bytes = a.size() * sizeof(Scalar);
@@ -897,10 +895,10 @@ void test_gpu_igamma() {
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
if ((std::isnan)(igamma_s[i][j])) {
if ((std::isnan)(expected_out(i, j))) {
VERIFY((std::isnan)(out(i, j)));
} else {
VERIFY_IS_APPROX(out(i, j), igamma_s[i][j]);
VERIFY_IS_APPROX(out(i, j), expected_out(i, j));
}
}
}
@@ -915,6 +913,7 @@ void test_gpu_igammac() {
Tensor<Scalar, 2> a(6, 6);
Tensor<Scalar, 2> x(6, 6);
Tensor<Scalar, 2> out(6, 6);
Tensor<Scalar, 2> expected_out(6, 6);
out.setZero();
Scalar a_s[] = {Scalar(0), Scalar(1), Scalar(1.5), Scalar(4), Scalar(0.0001), Scalar(1000.5)};
@@ -927,14 +926,11 @@ void test_gpu_igammac() {
}
}
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
Scalar igammac_s[][6] = {
{nan, nan, nan, nan, nan, nan},
{1.0, 0.36787944117144233, 0.22313016014842982, 0.018315638888734182, 0.9999000049998333, 0.0},
{1.0, 0.5724067044708798, 0.3916251762710878, 0.04601170568923136, 0.9999992477923555, 0.0},
{1.0, 0.9810118431238462, 0.9343575456215499, 0.4334701203667089, 1.0, 0.0},
{1.0, 2.1940638138146658e-05, 1.0003291916285e-05, 3.7801620118431334e-07, 0.0008629581310054535, 0.0},
{1.0, 1.0, 1.0, 1.0, 1.0, 0.49579580674813944}};
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
expected_out(i, j) = numext::igammac(a(i, j), x(i, j));
}
}
std::size_t bytes = a.size() * sizeof(Scalar);
@@ -962,10 +958,10 @@ void test_gpu_igammac() {
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
if ((std::isnan)(igammac_s[i][j])) {
if ((std::isnan)(expected_out(i, j))) {
VERIFY((std::isnan)(out(i, j)));
} else {
VERIFY_IS_APPROX(out(i, j), igammac_s[i][j]);
VERIFY_IS_APPROX(out(i, j), expected_out(i, j));
}
}
}
@@ -1068,15 +1064,9 @@ void test_gpu_ndtri() {
in_x(7) = Scalar(0.99);
in_x(8) = Scalar(0.01);
expected_out(0) = std::numeric_limits<Scalar>::infinity();
expected_out(1) = -std::numeric_limits<Scalar>::infinity();
expected_out(2) = Scalar(0.0);
expected_out(3) = Scalar(-0.8416212335729142);
expected_out(4) = Scalar(0.8416212335729142);
expected_out(5) = Scalar(1.2815515655446004);
expected_out(6) = Scalar(-1.2815515655446004);
expected_out(7) = Scalar(2.3263478740408408);
expected_out(8) = Scalar(-2.3263478740408408);
for (int i = 0; i < 9; ++i) {
expected_out(i) = numext::ndtri(in_x(i));
}
std::size_t bytes = in_x.size() * sizeof(Scalar);
@@ -1090,15 +1080,15 @@ void test_gpu_ndtri() {
Eigen::GpuStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 6);
Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 6);
Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 9);
Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 9);
gpu_out.device(gpu_device) = gpu_in_x.ndtri();
assert(gpuMemcpyAsync(out.data(), d_out, bytes, gpuMemcpyDeviceToHost, gpu_device.stream()) == gpuSuccess);
assert(gpuStreamSynchronize(gpu_device.stream()) == gpuSuccess);
for (int i = 0; i < 6; ++i) {
for (int i = 0; i < 9; ++i) {
VERIFY_IS_CWISE_APPROX(out(i), expected_out(i));
}
@@ -1115,12 +1105,9 @@ void test_gpu_betainc() {
Tensor<Scalar, 1> expected_out(125);
out.setZero();
Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
Array<Scalar, 1, Dynamic> x(125);
Array<Scalar, 1, Dynamic> a(125);
Array<Scalar, 1, Dynamic> b(125);
Array<Scalar, 1, Dynamic> v(125);
a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
@@ -1160,25 +1147,11 @@ void test_gpu_betainc() {
0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1;
v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan,
0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan, 0.999995949033062, 0.9999999999993698,
0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan,
nan, nan, 0.006827081192655869, 0.0210336989586256, 0.04813160422599567, nan, nan, 0.20014344256217678,
0.5000000000000001, 0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403, 0.9999999999999999, nan,
nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan, nan, nan,
1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, nan, nan, 7.864342668429763e-23,
3.015969667594166e-10, 0.0008598571564165444, nan, nan, 6.031987710123844e-08, 0.5000000000000007,
0.9999999396801229, nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan, nan, nan,
nan, nan, 0.0, 7.029920380986636e-306, 2.2450728208591345e-101, nan, nan, 0.0, 9.275871147869727e-302,
1.2232913026152827e-97, nan, nan, 0.0, 3.0891393081932924e-252, 2.9303043666183996e-60, nan, nan,
2.248913486879199e-196, 0.5000000000004947, 0.9999999999999999, nan;
for (int i = 0; i < 125; ++i) {
in_x(i) = x(i);
in_a(i) = a(i);
in_b(i) = b(i);
expected_out(i) = v(i);
expected_out(i) = numext::betainc(a(i), b(i), x(i));
}
std::size_t bytes = in_x.size() * sizeof(Scalar);

7
unsupported/test/cxx11_tensor_of_float16_gpu.cu
View File

@@ -53,8 +53,6 @@ void test_gpu_numext() {
gpu_device.deallocate(d_res_float);
}
#ifdef EIGEN_HAS_GPU_FP16
template <typename>
void test_gpu_conversion() {
Eigen::GpuStreamDevice stream;
@@ -442,12 +440,10 @@ void test_gpu_forced_evals() {
gpu_device.deallocate(d_res_half2);
gpu_device.deallocate(d_res_float);
}
#endif
EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_gpu) {
CALL_SUBTEST_1(test_gpu_numext<void>());
#ifdef EIGEN_HAS_GPU_FP16
CALL_SUBTEST_1(test_gpu_conversion<void>());
CALL_SUBTEST_1(test_gpu_unary<void>());
CALL_SUBTEST_1(test_gpu_elementwise<void>());
@@ -456,7 +452,4 @@ EIGEN_DECLARE_TEST(cxx11_tensor_of_float16_gpu) {
CALL_SUBTEST_3(test_gpu_reductions<void>());
CALL_SUBTEST_4(test_gpu_full_reductions<void>());
CALL_SUBTEST_5(test_gpu_forced_evals<void>());
#else
std::cout << "Half floats are not supported by this version of gpu: skipping the test" << std::endl;
#endif
}