Joseph Huber 47166968db [OpenMP] Deprecate the old driver for OpenMP offloading
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
2022-08-26 13:47:09 -05:00

149 lines
4.3 KiB
C++

// RUN: %libomptarget-compilexx-run-and-check-generic
// Currently hangs on amdgpu
// UNSUPPORTED: amdgcn-amd-amdhsa
// UNSUPPORTED: amdgcn-amd-amdhsa-LTO
// UNSUPPORTED: x86_64-pc-linux-gnu
// UNSUPPORTED: x86_64-pc-linux-gnu-LTO
#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