Files
llvm-project/offload/test/offloading/bug49334.cpp
Nick Sarnie 78ff5b55fd [offload][lit] Enable/disable tests on Level Zero when using DeviceRTL (#182128)
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>
2026-02-18 21:53:19 +00:00

154 lines
4.6 KiB
C++

// RUN: %libomptarget-compilexx-generic -O3 && %libomptarget-run-generic
// RUN: %libomptarget-compilexx-generic -O3 -ffast-math && \
// RUN: %libomptarget-run-generic
// RUN: %libomptarget-compileoptxx-generic -O3 && %libomptarget-run-generic
// RUN: %libomptarget-compileoptxx-generic -O3 -ffast-math && \
// RUN: %libomptarget-run-generic
// REQUIRES: gpu
// UNSUPPORTED: nvidiagpu
// UNSUPPORTED: amdgpu
// https://github.com/llvm/llvm-project/issues/182119
// UNSUPPORTED: intelgpu
#include <cassert>
#include <cmath>
#include <iostream>
#include <limits>
#include <memory>
#include <vector>
class BlockMatrix {
private:
const int rowsPerBlock;
const int colsPerBlock;
const long nRows;
const long nCols;
const int nBlocksPerRow;
const int nBlocksPerCol;
std::vector<std::vector<std::unique_ptr<float[]>>> Blocks;
public:
BlockMatrix(const int _rowsPerBlock, const int _colsPerBlock,
const long _nRows, const long _nCols)
: rowsPerBlock(_rowsPerBlock), colsPerBlock(_colsPerBlock), nRows(_nRows),
nCols(_nCols), nBlocksPerRow(_nRows / _rowsPerBlock),
nBlocksPerCol(_nCols / _colsPerBlock), Blocks(nBlocksPerCol) {
for (int i = 0; i < nBlocksPerCol; i++) {
for (int j = 0; j < nBlocksPerRow; j++) {
Blocks[i].emplace_back(new float[_rowsPerBlock * _colsPerBlock]);
}
}
};
// Initialize the BlockMatrix from 2D arrays
void Initialize(const std::vector<float> &matrix) {
for (int i = 0; i < nBlocksPerCol; i++)
for (int j = 0; j < nBlocksPerRow; j++) {
float *CurrBlock = GetBlock(i, j);
for (int ii = 0; ii < colsPerBlock; ++ii)
for (int jj = 0; jj < rowsPerBlock; ++jj) {
int curri = i * colsPerBlock + ii;
int currj = j * rowsPerBlock + jj;
CurrBlock[ii + jj * colsPerBlock] = matrix[curri + currj * nCols];
}
}
}
void Compare(const std::vector<float> &matrix) const {
for (int i = 0; i < nBlocksPerCol; i++)
for (int j = 0; j < nBlocksPerRow; j++) {
float *CurrBlock = GetBlock(i, j);
for (int ii = 0; ii < colsPerBlock; ++ii)
for (int jj = 0; jj < rowsPerBlock; ++jj) {
int curri = i * colsPerBlock + ii;
int currj = j * rowsPerBlock + jj;
float m_value = matrix[curri + currj * nCols];
float bm_value = CurrBlock[ii + jj * colsPerBlock];
assert(std::fabs(bm_value - m_value) <
std::numeric_limits<float>::epsilon());
}
}
}
float *GetBlock(int i, int j) const {
assert(i < nBlocksPerCol && j < nBlocksPerRow && "Accessing outside block");
return Blocks[i][j].get();
}
};
constexpr const int BS = 16;
constexpr const int N = 256;
int BlockMatMul_TargetNowait(BlockMatrix &A, BlockMatrix &B, BlockMatrix &C) {
#pragma omp parallel
#pragma omp master
for (int i = 0; i < N / BS; ++i)
for (int j = 0; j < N / BS; ++j) {
float *BlockC = C.GetBlock(i, j);
for (int k = 0; k < N / BS; ++k) {
float *BlockA = A.GetBlock(i, k);
float *BlockB = B.GetBlock(k, j);
// clang-format off
#pragma omp target depend(in: BlockA[0], BlockB[0]) depend(inout: BlockC[0]) \
map(to: BlockA[:BS * BS], BlockB[:BS * BS]) \
map(tofrom: BlockC[:BS * BS]) nowait
// clang-format on
#pragma omp parallel for
for (int ii = 0; ii < BS; ii++)
for (int jj = 0; jj < BS; jj++) {
for (int kk = 0; kk < BS; ++kk)
BlockC[ii + jj * BS] +=
BlockA[ii + kk * BS] * BlockB[kk + jj * BS];
}
}
}
return 0;
}
void Matmul(const std::vector<float> &a, const std::vector<float> &b,
std::vector<float> &c) {
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
float sum = 0.0;
for (int k = 0; k < N; ++k) {
sum = sum + a[i * N + k] * b[k * N + j];
}
c[i * N + j] = sum;
}
}
}
int main(int argc, char *argv[]) {
std::vector<float> a(N * N);
std::vector<float> b(N * N);
std::vector<float> c(N * N, 0.0);
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
a[i * N + j] = b[i * N + j] = i + j % 100;
}
}
auto BlockedA = BlockMatrix(BS, BS, N, N);
auto BlockedB = BlockMatrix(BS, BS, N, N);
auto BlockedC = BlockMatrix(BS, BS, N, N);
BlockedA.Initialize(a);
BlockedB.Initialize(b);
BlockedC.Initialize(c);
BlockedA.Compare(a);
BlockedB.Compare(b);
BlockedC.Compare(c);
Matmul(a, b, c);
BlockMatMul_TargetNowait(BlockedA, BlockedB, BlockedC);
BlockedC.Compare(c);
std::cout << "PASS\n";
return 0;
}
// CHECK: PASS