78ff5b55fd
Since we can now build the DeviceRTL with SPIR-V, redo the `XFAIL/UNSUPPORTED` specifications for the tests we see passing/failing on the Level Zero backend with the DeviceRTL being used. The tests marked `UNSUPPORTED` hang or sporadically fail and those are tracked in https://github.com/llvm/llvm-project/issues/182119. This change will allow us to enable CI testing with the DeviceRTL. Here are the full test results with this change applied, running only the `spirv64-intel` `check-offload` tests: ``` Total Discovered Tests: 453 Unsupported : 206 (45.47%) Passed : 141 (31.13%) Expectedly Failed: 106 (23.40%) ``` 31% is not a bad start. --------- Signed-off-by: Nick Sarnie <nick.sarnie@intel.com>
73 lines
2.2 KiB
C++
73 lines
2.2 KiB
C++
// RUN: %libomptarget-compilexx-and-run-generic
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// RUN: %libomptarget-compileoptxx-and-run-generic
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// FIXME: This is a bug in host offload, this should run fine.
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// REQUIRES: gpu
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// https://github.com/llvm/llvm-project/issues/182119
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// UNSUPPORTED: intelgpu
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// This test validates that the OpenMP target reductions to find a minimum work
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// as intended for a few common data types.
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#include <algorithm>
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#include <cassert>
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#include <limits>
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#include <vector>
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template <class Tp> void test_min_idx_reduction() {
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const Tp length = 1000;
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const Tp nminimas = 8;
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std::vector<float> a(length, 3.0f);
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const Tp step = length / nminimas;
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for (Tp i = 0; i < nminimas; i++) {
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a[i * step] -= 1.0f;
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}
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for (Tp i = 0; i < nminimas; i++) {
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Tp idx = a.size();
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float *b = a.data();
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#pragma omp target teams distribute parallel for reduction(min : idx) \
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map(always, to : b[0 : length])
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for (Tp j = 0; j < length - 1; j++) {
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if (b[j] < b[j + 1]) {
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idx = std::min(idx, j);
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}
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}
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assert(idx == i * step &&
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"#pragma omp target teams distribute parallel for "
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"reduction(min:<identifier list>) does not work as intended.");
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a[idx] += 1.0f;
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}
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}
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template <class Tp> void test_min_val_reduction() {
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const int length = 1000;
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const int half = length / 2;
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std::vector<Tp> a(length, (Tp)3);
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a[half] -= (Tp)1;
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Tp min_val = std::numeric_limits<Tp>::max();
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Tp *b = a.data();
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#pragma omp target teams distribute parallel for reduction(min : min_val) \
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map(always, to : b[0 : length])
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for (int i = 0; i < length; i++) {
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min_val = std::min(min_val, b[i]);
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}
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assert(std::abs(((double)a[half + 1]) - ((double)min_val) - 1.0) < 1e-6 &&
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"#pragma omp target teams distribute parallel for "
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"reduction(min:<identifier list>) does not work as intended.");
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}
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int main() {
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// Reducing over indices
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test_min_idx_reduction<int>();
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test_min_idx_reduction<unsigned int>();
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test_min_idx_reduction<long>();
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// Reducing over values
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test_min_val_reduction<int>();
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test_min_val_reduction<unsigned int>();
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test_min_val_reduction<long>();
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test_min_val_reduction<float>();
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test_min_val_reduction<double>();
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return 0;
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}
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