cuda-runtime-wrappers.cpp
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//===- cuda-runtime-wrappers.cpp - MLIR CUDA runner wrapper library -------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// Implements C wrappers around the CUDA library for easy linking in ORC jit.
// Also adds some debugging helpers that are helpful when writing MLIR code to
// run on GPUs.
//
//===----------------------------------------------------------------------===//
#include <cassert>
#include <numeric>
#include "mlir/ExecutionEngine/CRunnerUtils.h"
#include "llvm/ADT/ArrayRef.h"
#include "llvm/Support/raw_ostream.h"
#include "cuda.h"
namespace {
int32_t reportErrorIfAny(CUresult result, const char *where) {
if (result != CUDA_SUCCESS) {
llvm::errs() << "CUDA failed with " << result << " in " << where << "\n";
}
return result;
}
} // anonymous namespace
extern "C" int32_t mgpuModuleLoad(void **module, void *data) {
int32_t err = reportErrorIfAny(
cuModuleLoadData(reinterpret_cast<CUmodule *>(module), data),
"ModuleLoad");
return err;
}
extern "C" int32_t mgpuModuleGetFunction(void **function, void *module,
const char *name) {
return reportErrorIfAny(
cuModuleGetFunction(reinterpret_cast<CUfunction *>(function),
reinterpret_cast<CUmodule>(module), name),
"GetFunction");
}
// The wrapper uses intptr_t instead of CUDA's unsigned int to match
// the type of MLIR's index type. This avoids the need for casts in the
// generated MLIR code.
extern "C" int32_t mgpuLaunchKernel(void *function, intptr_t gridX,
intptr_t gridY, intptr_t gridZ,
intptr_t blockX, intptr_t blockY,
intptr_t blockZ, int32_t smem, void *stream,
void **params, void **extra) {
return reportErrorIfAny(
cuLaunchKernel(reinterpret_cast<CUfunction>(function), gridX, gridY,
gridZ, blockX, blockY, blockZ, smem,
reinterpret_cast<CUstream>(stream), params, extra),
"LaunchKernel");
}
extern "C" void *mgpuGetStreamHelper() {
CUstream stream;
reportErrorIfAny(cuStreamCreate(&stream, CU_STREAM_DEFAULT), "StreamCreate");
return stream;
}
extern "C" int32_t mgpuStreamSynchronize(void *stream) {
return reportErrorIfAny(
cuStreamSynchronize(reinterpret_cast<CUstream>(stream)), "StreamSync");
}
/// Helper functions for writing mlir example code
// Allows to register byte array with the CUDA runtime. Helpful until we have
// transfer functions implemented.
extern "C" void mgpuMemHostRegister(void *ptr, uint64_t sizeBytes) {
reportErrorIfAny(cuMemHostRegister(ptr, sizeBytes, /*flags=*/0),
"MemHostRegister");
}
// Allows to register a MemRef with the CUDA runtime. Initializes array with
// value. Helpful until we have transfer functions implemented.
template <typename T>
void mcuMemHostRegisterMemRef(const DynamicMemRefType<T> &mem_ref, T value) {
llvm::SmallVector<int64_t, 4> denseStrides(mem_ref.rank);
llvm::ArrayRef<int64_t> sizes(mem_ref.sizes, mem_ref.rank);
llvm::ArrayRef<int64_t> strides(mem_ref.strides, mem_ref.rank);
std::partial_sum(sizes.rbegin(), sizes.rend(), denseStrides.rbegin(),
std::multiplies<int64_t>());
auto count = denseStrides.front();
// Only densely packed tensors are currently supported.
std::rotate(denseStrides.begin(), denseStrides.begin() + 1,
denseStrides.end());
denseStrides.back() = 1;
assert(strides == llvm::makeArrayRef(denseStrides));
auto *pointer = mem_ref.data + mem_ref.offset;
std::fill_n(pointer, count, value);
mgpuMemHostRegister(pointer, count * sizeof(T));
}
extern "C" void mcuMemHostRegisterFloat(int64_t rank, void *ptr) {
UnrankedMemRefType<float> mem_ref = {rank, ptr};
mcuMemHostRegisterMemRef(DynamicMemRefType<float>(mem_ref), 1.23f);
}
extern "C" void mcuMemHostRegisterInt32(int64_t rank, void *ptr) {
UnrankedMemRefType<int32_t> mem_ref = {rank, ptr};
mcuMemHostRegisterMemRef(DynamicMemRefType<int32_t>(mem_ref), 123);
}