Recently OpenMP has transitioned to using the "new" driver which primarily merges the device and host linking phases into a single wrapper that handles both at the same time. This replaced a few tools that were only used for OpenMP offloading, such as the `clang-offload-wrapper` and `clang-nvlink-wrapper`. The new driver carries some marked benefits compared to the old driver that is now being deprecated. Things like device-side LTO, static library support, and more compatible tooling. As such, we should be able to completely deprecate the old driver, at least for OpenMP. The old driver support will still exist for CUDA and HIP, although both of these can currently be compiled on Linux with `--offload-new-driver` to use the new method. Note that this does not deprecate the `clang-offload-bundler`, although it is unused by OpenMP now, it is still used by the HIP toolchain both as their device binary format and object format. When I proposed deprecating this code I heard some vendors voice concernes about needing to update their code in their fork. They should be able to just revert this commit if it lands. Reviewed By: jdoerfert, MaskRay, ye-luo Differential Revision: https://reviews.llvm.org/D130020
149 lines
4.3 KiB
C++
149 lines
4.3 KiB
C++
// RUN: %libomptarget-compilexx-run-and-check-generic
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// Currently hangs on amdgpu
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// UNSUPPORTED: amdgcn-amd-amdhsa
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// UNSUPPORTED: amdgcn-amd-amdhsa-LTO
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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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#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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