In a nutshell, this moves our libomptarget code to populate the offload subproject. With this commit, users need to enable the new LLVM/Offload subproject as a runtime in their cmake configuration. No further changes are expected for downstream code. Tests and other components still depend on OpenMP and have also not been renamed. The results below are for a build in which OpenMP and Offload are enabled runtimes. In addition to the pure `git mv`, we needed to adjust some CMake files. Nothing is intended to change semantics. ``` ninja check-offload ``` Works with the X86 and AMDGPU offload tests ``` ninja check-openmp ``` Still works but doesn't build offload tests anymore. ``` ls install/lib ``` Shows all expected libraries, incl. - `libomptarget.devicertl.a` - `libomptarget-nvptx-sm_90.bc` - `libomptarget.rtl.amdgpu.so` -> `libomptarget.rtl.amdgpu.so.18git` - `libomptarget.so` -> `libomptarget.so.18git` Fixes: https://github.com/llvm/llvm-project/issues/75124 --------- Co-authored-by: Saiyedul Islam <Saiyedul.Islam@amd.com>
158 lines
4.8 KiB
C++
158 lines
4.8 KiB
C++
// RUN: %libomptarget-compilexx-generic -O3 && %libomptarget-run-generic
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// RUN: %libomptarget-compilexx-generic -O3 -ffast-math && \
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// RUN: %libomptarget-run-generic
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// RUN: %libomptarget-compileoptxx-generic -O3 && %libomptarget-run-generic
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// RUN: %libomptarget-compileoptxx-generic -O3 -ffast-math && \
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// RUN: %libomptarget-run-generic
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// UNSUPPORTED: x86_64-pc-linux-gnu
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// UNSUPPORTED: x86_64-pc-linux-gnu-LTO
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// UNSUPPORTED: aarch64-unknown-linux-gnu
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// UNSUPPORTED: aarch64-unknown-linux-gnu-LTO
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// UNSUPPORTED: s390x-ibm-linux-gnu
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// UNSUPPORTED: s390x-ibm-linux-gnu-LTO
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// UNSUPPORTED: amdgcn-amd-amdhsa
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// UNSUPPORTED: nvptx64-nvidia-cuda
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// UNSUPPORTED: nvptx64-nvidia-cuda-LTO
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#include <cassert>
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#include <cmath>
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#include <iostream>
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#include <limits>
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#include <memory>
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#include <vector>
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class BlockMatrix {
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private:
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const int rowsPerBlock;
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const int colsPerBlock;
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const long nRows;
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const long nCols;
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const int nBlocksPerRow;
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const int nBlocksPerCol;
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std::vector<std::vector<std::unique_ptr<float[]>>> Blocks;
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public:
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BlockMatrix(const int _rowsPerBlock, const int _colsPerBlock,
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const long _nRows, const long _nCols)
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: rowsPerBlock(_rowsPerBlock), colsPerBlock(_colsPerBlock), nRows(_nRows),
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nCols(_nCols), nBlocksPerRow(_nRows / _rowsPerBlock),
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nBlocksPerCol(_nCols / _colsPerBlock), Blocks(nBlocksPerCol) {
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for (int i = 0; i < nBlocksPerCol; i++) {
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for (int j = 0; j < nBlocksPerRow; j++) {
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Blocks[i].emplace_back(new float[_rowsPerBlock * _colsPerBlock]);
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}
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}
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};
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// Initialize the BlockMatrix from 2D arrays
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void Initialize(const std::vector<float> &matrix) {
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for (int i = 0; i < nBlocksPerCol; i++)
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for (int j = 0; j < nBlocksPerRow; j++) {
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float *CurrBlock = GetBlock(i, j);
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for (int ii = 0; ii < colsPerBlock; ++ii)
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for (int jj = 0; jj < rowsPerBlock; ++jj) {
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int curri = i * colsPerBlock + ii;
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int currj = j * rowsPerBlock + jj;
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CurrBlock[ii + jj * colsPerBlock] = matrix[curri + currj * nCols];
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}
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}
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}
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void Compare(const std::vector<float> &matrix) const {
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for (int i = 0; i < nBlocksPerCol; i++)
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for (int j = 0; j < nBlocksPerRow; j++) {
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float *CurrBlock = GetBlock(i, j);
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for (int ii = 0; ii < colsPerBlock; ++ii)
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for (int jj = 0; jj < rowsPerBlock; ++jj) {
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int curri = i * colsPerBlock + ii;
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int currj = j * rowsPerBlock + jj;
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float m_value = matrix[curri + currj * nCols];
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float bm_value = CurrBlock[ii + jj * colsPerBlock];
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assert(std::fabs(bm_value - m_value) <
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std::numeric_limits<float>::epsilon());
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}
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}
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}
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float *GetBlock(int i, int j) const {
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assert(i < nBlocksPerCol && j < nBlocksPerRow && "Accessing outside block");
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return Blocks[i][j].get();
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}
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};
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constexpr const int BS = 16;
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constexpr const int N = 256;
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int BlockMatMul_TargetNowait(BlockMatrix &A, BlockMatrix &B, BlockMatrix &C) {
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#pragma omp parallel
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#pragma omp master
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for (int i = 0; i < N / BS; ++i)
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for (int j = 0; j < N / BS; ++j) {
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float *BlockC = C.GetBlock(i, j);
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for (int k = 0; k < N / BS; ++k) {
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float *BlockA = A.GetBlock(i, k);
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float *BlockB = B.GetBlock(k, j);
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// clang-format off
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#pragma omp target depend(in: BlockA[0], BlockB[0]) depend(inout: BlockC[0]) \
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map(to: BlockA[:BS * BS], BlockB[:BS * BS]) \
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map(tofrom: BlockC[:BS * BS]) nowait
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// clang-format on
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#pragma omp parallel for
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for (int ii = 0; ii < BS; ii++)
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for (int jj = 0; jj < BS; jj++) {
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for (int kk = 0; kk < BS; ++kk)
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BlockC[ii + jj * BS] +=
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BlockA[ii + kk * BS] * BlockB[kk + jj * BS];
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}
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}
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}
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return 0;
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}
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void Matmul(const std::vector<float> &a, const std::vector<float> &b,
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std::vector<float> &c) {
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for (int i = 0; i < N; ++i) {
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for (int j = 0; j < N; ++j) {
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float sum = 0.0;
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for (int k = 0; k < N; ++k) {
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sum = sum + a[i * N + k] * b[k * N + j];
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}
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c[i * N + j] = sum;
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}
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}
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}
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int main(int argc, char *argv[]) {
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std::vector<float> a(N * N);
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std::vector<float> b(N * N);
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std::vector<float> c(N * N, 0.0);
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for (int i = 0; i < N; ++i) {
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for (int j = 0; j < N; ++j) {
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a[i * N + j] = b[i * N + j] = i + j % 100;
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}
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}
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auto BlockedA = BlockMatrix(BS, BS, N, N);
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auto BlockedB = BlockMatrix(BS, BS, N, N);
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auto BlockedC = BlockMatrix(BS, BS, N, N);
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BlockedA.Initialize(a);
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BlockedB.Initialize(b);
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BlockedC.Initialize(c);
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BlockedA.Compare(a);
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BlockedB.Compare(b);
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BlockedC.Compare(c);
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Matmul(a, b, c);
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BlockMatMul_TargetNowait(BlockedA, BlockedB, BlockedC);
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BlockedC.Compare(c);
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std::cout << "PASS\n";
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return 0;
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}
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// CHECK: PASS
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