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fused_kernel.cpp
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fused_kernel.cpp
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#include <torch/csrc/jit/codegen/fuser/cuda/fused_kernel.h>
#include <torch/csrc/jit/codegen/fuser/compiler.h>
#include <ATen/ATen.h>
#include <ATen/CUDAGeneratorImpl.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
#include <THC/THC.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/csrc/jit/resource_guard.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cmath>
#include <sstream>
#include <stdexcept>
#include <tuple>
#include <vector>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// See NOTE [ USE OF NVRTC AND DRIVER API ]
const at::cuda::NVRTC& nvrtc() {
return at::globalContext().getNVRTC();
}
static void getMajorMinor(
const cudaDeviceProp* const prop,
int& major,
int& minor) {
int nvrtc_major, nvrtc_minor;
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcVersion(&nvrtc_major, &nvrtc_minor));
// Short-circuits if NVRTC version too low
AT_ASSERT(nvrtc_major >= 6);
// Major and minor is determined by device properties and
// possibly "downcompiled" to a lower (compatible) compute architecture
// based on the NVRTC version
major = prop->major;
minor = prop->minor;
if (nvrtc_major <= 7 && prop->major > 5) { // 7 supports 2-5.x
major = 5;
minor = 0;
} else if (nvrtc_major <= 8 && prop->major > 6) { // 8 supports 2-6.x
major = 6;
minor = 0;
} else if (nvrtc_major <= 9 && prop->major >= 7) { // 9 supports 3-7.2
major = 7;
if (prop->major == 7 && prop->minor <= 2)
minor = prop->minor;
else
minor = 0;
} else if (nvrtc_major <= 10 && prop->major >= 7) { // 10 supports 3-7.5
major = 7;
if (prop->major == 7 && prop->minor <= 5)
minor = prop->minor;
else
minor = 0;
}
}
// Compiles the specified kernel and stores the metadata required to run it
FusedKernelCUDA::FusedKernelCUDA(
at::DeviceIndex device,
std::string name,
std::string code,
std::vector<TensorDesc> input_desc,
std::vector<TensorDesc> output_desc,
std::vector<PartitionDesc> chunk_desc,
std::vector<PartitionDesc> concat_desc,
bool has_random)
: FusedKernel(
std::move(name),
std::move(code),
std::move(input_desc),
std::move(output_desc),
std::move(chunk_desc),
std::move(concat_desc),
has_random),
device_(device) {
// Initializes driver's API context (if necessary)
CUcontext pctx = 0;
AT_CUDA_DRIVER_CHECK(nvrtc().cuCtxGetCurrent(&pctx));
if (!pctx) {
std::unique_lock<std::mutex> cudaFreeMutexLock(
*(c10::cuda::CUDACachingAllocator::getFreeMutex()));
cudaFree(0);
}
// Note: hacked at::DeviceGuard since at::DeviceGuard was failing to work
// properly in some scenarios
const auto prior_device = at::cuda::current_device();
at::cuda::set_device(device_);
// Acquires device and NVRTC properties (for compile arch and occupancy
// calculations)
prop_ = at::cuda::getCurrentDeviceProperties();
int major, minor;
getMajorMinor(prop_, major, minor);
// Creates the NVRTC program
nvrtcProgram program;
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcCreateProgram(
&program, code_.c_str(), nullptr, 0, nullptr, nullptr));
#ifdef __HIP_PLATFORM_HCC__
std::vector<const char*> args = {};
#else
const std::string compute = "--gpu-architecture=compute_" +
std::to_string(major) + std::to_string(minor);
const std::vector<const char*> args = {
"--std=c++14", compute.c_str(), "-default-device"};
#endif
const auto result =
nvrtc().nvrtcCompileProgram(program, args.size(), args.data());
if (result != NVRTC_SUCCESS) {
size_t logsize;
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcGetProgramLogSize(program, &logsize));
std::vector<char> log(logsize);
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcGetProgramLog(program, log.data()));
std::stringstream cu;
cu << log.data();
throw std::runtime_error(cu.str());
}
ResourceGuard holdProgram(
[&] { AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcDestroyProgram(&program)); });
AT_CUDA_NVRTC_CHECK(result);
size_t ptx_size;
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcGetPTXSize(program, &ptx_size));
ptx_.resize(ptx_size);
AT_CUDA_NVRTC_CHECK(nvrtc().nvrtcGetPTX(program, ptx_.data()));
AT_CUDA_DRIVER_CHECK(nvrtc().cuModuleLoadData(&module_, ptx_.data()));
AT_CUDA_DRIVER_CHECK(
nvrtc().cuModuleGetFunction(&function_, module_, name_.c_str()));
// Computes max blocks
#if defined(__HIP_PLATFORM_HCC__) && HIP_VERSION < 305
// HIP function signature is not compatible yet
uint32_t max_blocks;
AT_CUDA_DRIVER_CHECK(nvrtc().hipOccupancyMaxActiveBlocksPerMultiprocessor(
&max_blocks, function_, 128, 0));
maxBlocks_ = max_blocks;
#else
AT_CUDA_DRIVER_CHECK(nvrtc().cuOccupancyMaxActiveBlocksPerMultiprocessor(
&maxBlocks_, function_, 128, 0));
#endif
maxBlocks_ *= prop_->multiProcessorCount;
// Resets device (end of hacked at::DeviceGuard)
at::cuda::set_device(prior_device);
}
static int ceilDiv(const int a, const int b) {
return (a + b - 1) / b;
}
void FusedKernelCUDA::launch_raw(
const uint32_t numel,
std::vector<void*>& arguments) const {
at::cuda::CUDAGuard{device_};
// Hacked at::DeviceGuard (see note above)
const auto prior_device = at::cuda::current_device();
at::cuda::set_device(device_);
const auto nBlocks = std::min(maxBlocks_, ceilDiv(numel, kBlockSize));
// Adds random state to arguments if necessary
// Note: philox_engine_inputs defined here so its lifetime extends to the
// launch
std::pair<uint64_t, uint64_t> philox_engine_inputs;
if (has_random_) {
const auto rand_offset =
4 * (std::ceil(numel / (4.0 * kBlockSize * nBlocks)) + 1);
auto gen = at::cuda::detail::getDefaultCUDAGenerator();
{
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(gen.mutex());
philox_engine_inputs =
at::check_generator<at::CUDAGeneratorImpl>(gen)->philox_engine_inputs(
rand_offset);
}
arguments.push_back(&philox_engine_inputs.first);
arguments.push_back(&philox_engine_inputs.second);
}
// Launches kernel on current stream (device was set by executor)
auto stream = at::cuda::getCurrentCUDAStream();
AT_CUDA_DRIVER_CHECK(nvrtc().cuLaunchKernel(
function_,
nBlocks,
1,
1,
kBlockSize,
1,
1,
0,
stream,
arguments.data(),
nullptr));
// Resets device (see at::DeviceGuard notes above)
at::cuda::set_device(prior_device);
}
FusedKernelCUDA::~FusedKernelCUDA() {
nvrtc().cuModuleUnload(module_);
}
static std::shared_ptr<FusedKernel> createFusionKernel(
int16_t device,
std::string name,
std::string code,
std::vector<TensorDesc> input_desc,
std::vector<TensorDesc> output_desc,
std::vector<PartitionDesc> chunk_desc,
std::vector<PartitionDesc> concat_desc,
bool has_random) {
return std::make_shared<FusedKernelCUDA>(
static_cast<at::DeviceIndex>(device),
std::move(name),
std::move(code),
std::move(input_desc),
std::move(output_desc),
std::move(chunk_desc),
std::move(concat_desc),
has_random);
}
RegisterFusionBackend reg(DeviceType::CUDA, createFusionKernel);
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch