//===-- Loader Implementation for NVPTX devices --------------------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file impelements a simple loader to run images supporting the NVPTX
// architecture. The file launches the '_start' kernel which should be provided
// by the device application start code and call ultimately call the 'main'
// function.
//
//===----------------------------------------------------------------------===//
#include "Loader.h"
#include "cuda.h"
#include "llvm/Object/ELF.h"
#include "llvm/Object/ELFObjectFile.h"
#include <cstddef>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <vector>
using namespace llvm;
using namespace object;
static void handle_error_impl(const char *file, int32_t line, CUresult err) {
if (err == CUDA_SUCCESS)
return;
const char *err_str = nullptr;
CUresult result = cuGetErrorString(err, &err_str);
if (result != CUDA_SUCCESS)
fprintf(stderr, "%s:%d:0: Unknown Error\n", file, line);
else
fprintf(stderr, "%s:%d:0: Error: %s\n", file, line, err_str);
exit(1);
}
// Gets the names of all the globals that contain functions to initialize or
// deinitialize. We need to do this manually because the NVPTX toolchain does
// not contain the necessary binary manipulation tools.
template <typename Alloc>
Expected<void *> get_ctor_dtor_array(const void *image, const size_t size,
Alloc allocator, CUmodule binary) {
auto mem_buffer = MemoryBuffer::getMemBuffer(
StringRef(reinterpret_cast<const char *>(image), size), "image",
/*RequiresNullTerminator=*/false);
Expected<ELF64LEObjectFile> elf_or_err =
ELF64LEObjectFile::create(*mem_buffer);
if (!elf_or_err)
handle_error(toString(elf_or_err.takeError()).c_str());
std::vector<std::pair<const char *, uint16_t>> ctors;
std::vector<std::pair<const char *, uint16_t>> dtors;
// CUDA has no way to iterate over all the symbols so we need to inspect the
// ELF directly using the LLVM libraries.
for (const auto &symbol : elf_or_err->symbols()) {
auto name_or_err = symbol.getName();
if (!name_or_err)
handle_error(toString(name_or_err.takeError()).c_str());
// Search for all symbols that contain a constructor or destructor.
if (!name_or_err->starts_with("__init_array_object_") &&
!name_or_err->starts_with("__fini_array_object_"))
continue;
uint16_t priority;
if (name_or_err->rsplit('_').second.getAsInteger(10, priority))
handle_error("Invalid priority for constructor or destructor");
if (name_or_err->starts_with("__init"))
ctors.emplace_back(std::make_pair(name_or_err->data(), priority));
else
dtors.emplace_back(std::make_pair(name_or_err->data(), priority));
}
// Lower priority constructors are run before higher ones. The reverse is true
// for destructors.
llvm::sort(ctors, [](auto x, auto y) { return x.second < y.second; });
llvm::sort(dtors, [](auto x, auto y) { return x.second < y.second; });
// Allocate host pinned memory to make these arrays visible to the GPU.
CUdeviceptr *dev_memory = reinterpret_cast<CUdeviceptr *>(allocator(
ctors.size() * sizeof(CUdeviceptr) + dtors.size() * sizeof(CUdeviceptr)));
uint64_t global_size = 0;
// Get the address of the global and then store the address of the constructor
// function to call in the constructor array.
CUdeviceptr *dev_ctors_start = dev_memory;
CUdeviceptr *dev_ctors_end = dev_ctors_start + ctors.size();
for (uint64_t i = 0; i < ctors.size(); ++i) {
CUdeviceptr dev_ptr;
if (CUresult err =
cuModuleGetGlobal(&dev_ptr, &global_size, binary, ctors[i].first))
handle_error(err);
if (CUresult err =
cuMemcpyDtoH(&dev_ctors_start[i], dev_ptr, sizeof(uintptr_t)))
handle_error(err);
}
// Get the address of the global and then store the address of the destructor
// function to call in the destructor array.
CUdeviceptr *dev_dtors_start = dev_ctors_end;
CUdeviceptr *dev_dtors_end = dev_dtors_start + dtors.size();
for (uint64_t i = 0; i < dtors.size(); ++i) {
CUdeviceptr dev_ptr;
if (CUresult err =
cuModuleGetGlobal(&dev_ptr, &global_size, binary, dtors[i].first))
handle_error(err);
if (CUresult err =
cuMemcpyDtoH(&dev_dtors_start[i], dev_ptr, sizeof(uintptr_t)))
handle_error(err);
}
// Obtain the address of the pointers the startup implementation uses to
// iterate the constructors and destructors.
CUdeviceptr init_start;
if (CUresult err = cuModuleGetGlobal(&init_start, &global_size, binary,
"__init_array_start"))
handle_error(err);
CUdeviceptr init_end;
if (CUresult err = cuModuleGetGlobal(&init_end, &global_size, binary,
"__init_array_end"))
handle_error(err);
CUdeviceptr fini_start;
if (CUresult err = cuModuleGetGlobal(&fini_start, &global_size, binary,
"__fini_array_start"))
handle_error(err);
CUdeviceptr fini_end;
if (CUresult err = cuModuleGetGlobal(&fini_end, &global_size, binary,
"__fini_array_end"))
handle_error(err);
// Copy the pointers to the newly written array to the symbols so the startup
// implementation can iterate them.
if (CUresult err =
cuMemcpyHtoD(init_start, &dev_ctors_start, sizeof(uintptr_t)))
handle_error(err);
if (CUresult err = cuMemcpyHtoD(init_end, &dev_ctors_end, sizeof(uintptr_t)))
handle_error(err);
if (CUresult err =
cuMemcpyHtoD(fini_start, &dev_dtors_start, sizeof(uintptr_t)))
handle_error(err);
if (CUresult err = cuMemcpyHtoD(fini_end, &dev_dtors_end, sizeof(uintptr_t)))
handle_error(err);
return dev_memory;
}
void print_kernel_resources(CUmodule binary, const char *kernel_name) {
CUfunction function;
if (CUresult err = cuModuleGetFunction(&function, binary, kernel_name))
handle_error(err);
int num_regs;
if (CUresult err =
cuFuncGetAttribute(&num_regs, CU_FUNC_ATTRIBUTE_NUM_REGS, function))
handle_error(err);
printf("Executing kernel %s:\n", kernel_name);
printf("%6s registers: %d\n", kernel_name, num_regs);
}
template <typename args_t>
CUresult launch_kernel(CUmodule binary, CUstream stream,
rpc_device_t rpc_device, const LaunchParameters ¶ms,
const char *kernel_name, args_t kernel_args,
bool print_resource_usage) {
// look up the '_start' kernel in the loaded module.
CUfunction function;
if (CUresult err = cuModuleGetFunction(&function, binary, kernel_name))
handle_error(err);
// Set up the arguments to the '_start' kernel on the GPU.
uint64_t args_size = sizeof(args_t);
void *args_config[] = {CU_LAUNCH_PARAM_BUFFER_POINTER, &kernel_args,
CU_LAUNCH_PARAM_BUFFER_SIZE, &args_size,
CU_LAUNCH_PARAM_END};
// Initialize a non-blocking CUDA stream to allocate memory if needed. This
// needs to be done on a separate stream or else it will deadlock with the
// executing kernel.
CUstream memory_stream;
if (CUresult err = cuStreamCreate(&memory_stream, CU_STREAM_NON_BLOCKING))
handle_error(err);
// Register RPC callbacks for the malloc and free functions on HSA.
register_rpc_callbacks<32>(rpc_device);
rpc_register_callback(
rpc_device, RPC_MALLOC,
[](rpc_port_t port, void *data) {
auto malloc_handler = [](rpc_buffer_t *buffer, void *data) -> void {
CUstream memory_stream = *static_cast<CUstream *>(data);
uint64_t size = buffer->data[0];
CUdeviceptr dev_ptr;
if (CUresult err = cuMemAllocAsync(&dev_ptr, size, memory_stream))
dev_ptr = 0UL;
// Wait until the memory allocation is complete.
while (cuStreamQuery(memory_stream) == CUDA_ERROR_NOT_READY)
;
buffer->data[0] = static_cast<uintptr_t>(dev_ptr);
};
rpc_recv_and_send(port, malloc_handler, data);
},
&memory_stream);
rpc_register_callback(
rpc_device, RPC_FREE,
[](rpc_port_t port, void *data) {
auto free_handler = [](rpc_buffer_t *buffer, void *data) {
CUstream memory_stream = *static_cast<CUstream *>(data);
if (CUresult err = cuMemFreeAsync(
static_cast<CUdeviceptr>(buffer->data[0]), memory_stream))
handle_error(err);
};
rpc_recv_and_send(port, free_handler, data);
},
&memory_stream);
if (print_resource_usage)
print_kernel_resources(binary, kernel_name);
// Call the kernel with the given arguments.
if (CUresult err = cuLaunchKernel(
function, params.num_blocks_x, params.num_blocks_y,
params.num_blocks_z, params.num_threads_x, params.num_threads_y,
params.num_threads_z, 0, stream, nullptr, args_config))
handle_error(err);
// Wait until the kernel has completed execution on the device. Periodically
// check the RPC client for work to be performed on the server.
while (cuStreamQuery(stream) == CUDA_ERROR_NOT_READY)
if (rpc_status_t err = rpc_handle_server(rpc_device))
handle_error(err);
// Handle the server one more time in case the kernel exited with a pending
// send still in flight.
if (rpc_status_t err = rpc_handle_server(rpc_device))
handle_error(err);
return CUDA_SUCCESS;
}
int load(int argc, const char **argv, const char **envp, void *image,
size_t size, const LaunchParameters ¶ms,
bool print_resource_usage) {
if (CUresult err = cuInit(0))
handle_error(err);
// Obtain the first device found on the system.
uint32_t device_id = 0;
CUdevice device;
if (CUresult err = cuDeviceGet(&device, device_id))
handle_error(err);
// Initialize the CUDA context and claim it for this execution.
CUcontext context;
if (CUresult err = cuDevicePrimaryCtxRetain(&context, device))
handle_error(err);
if (CUresult err = cuCtxSetCurrent(context))
handle_error(err);
// Increase the stack size per thread.
// TODO: We should allow this to be passed in so only the tests that require a
// larger stack can specify it to save on memory usage.
if (CUresult err = cuCtxSetLimit(CU_LIMIT_STACK_SIZE, 3 * 1024))
handle_error(err);
// Initialize a non-blocking CUDA stream to execute the kernel.
CUstream stream;
if (CUresult err = cuStreamCreate(&stream, CU_STREAM_NON_BLOCKING))
handle_error(err);
// Load the image into a CUDA module.
CUmodule binary;
if (CUresult err = cuModuleLoadDataEx(&binary, image, 0, nullptr, nullptr))
handle_error(err);
// Allocate pinned memory on the host to hold the pointer array for the
// copied argv and allow the GPU device to access it.
auto allocator = [&](uint64_t size) -> void * {
void *dev_ptr;
if (CUresult err = cuMemAllocHost(&dev_ptr, size))
handle_error(err);
return dev_ptr;
};
auto memory_or_err = get_ctor_dtor_array(image, size, allocator, binary);
if (!memory_or_err)
handle_error(toString(memory_or_err.takeError()).c_str());
void *dev_argv = copy_argument_vector(argc, argv, allocator);
if (!dev_argv)
handle_error("Failed to allocate device argv");
// Allocate pinned memory on the host to hold the pointer array for the
// copied environment array and allow the GPU device to access it.
void *dev_envp = copy_environment(envp, allocator);
if (!dev_envp)
handle_error("Failed to allocate device environment");
// Allocate space for the return pointer and initialize it to zero.
CUdeviceptr dev_ret;
if (CUresult err = cuMemAlloc(&dev_ret, sizeof(int)))
handle_error(err);
if (CUresult err = cuMemsetD32(dev_ret, 0, 1))
handle_error(err);
uint32_t warp_size = 32;
auto rpc_alloc = [](uint64_t size, void *) -> void * {
void *dev_ptr;
if (CUresult err = cuMemAllocHost(&dev_ptr, size))
handle_error(err);
return dev_ptr;
};
rpc_device_t rpc_device;
if (rpc_status_t err = rpc_server_init(&rpc_device, RPC_MAXIMUM_PORT_COUNT,
warp_size, rpc_alloc, nullptr))
handle_error(err);
// Initialize the RPC client on the device by copying the local data to the
// device's internal pointer.
CUdeviceptr rpc_client_dev = 0;
uint64_t client_ptr_size = sizeof(void *);
if (CUresult err = cuModuleGetGlobal(&rpc_client_dev, &client_ptr_size,
binary, rpc_client_symbol_name))
handle_error(err);
CUdeviceptr rpc_client_host = 0;
if (CUresult err =
cuMemcpyDtoH(&rpc_client_host, rpc_client_dev, sizeof(void *)))
handle_error(err);
if (CUresult err =
cuMemcpyHtoD(rpc_client_host, rpc_get_client_buffer(rpc_device),
rpc_get_client_size()))
handle_error(err);
LaunchParameters single_threaded_params = {1, 1, 1, 1, 1, 1};
begin_args_t init_args = {argc, dev_argv, dev_envp};
if (CUresult err =
launch_kernel(binary, stream, rpc_device, single_threaded_params,
"_begin", init_args, print_resource_usage))
handle_error(err);
start_args_t args = {argc, dev_argv, dev_envp,
reinterpret_cast<void *>(dev_ret)};
if (CUresult err = launch_kernel(binary, stream, rpc_device, params, "_start",
args, print_resource_usage))
handle_error(err);
// Copy the return value back from the kernel and wait.
int host_ret = 0;
if (CUresult err = cuMemcpyDtoH(&host_ret, dev_ret, sizeof(int)))
handle_error(err);
if (CUresult err = cuStreamSynchronize(stream))
handle_error(err);
end_args_t fini_args = {host_ret};
if (CUresult err =
launch_kernel(binary, stream, rpc_device, single_threaded_params,
"_end", fini_args, print_resource_usage))
handle_error(err);
// Free the memory allocated for the device.
if (CUresult err = cuMemFreeHost(*memory_or_err))
handle_error(err);
if (CUresult err = cuMemFree(dev_ret))
handle_error(err);
if (CUresult err = cuMemFreeHost(dev_argv))
handle_error(err);
if (rpc_status_t err = rpc_server_shutdown(
rpc_device, [](void *ptr, void *) { cuMemFreeHost(ptr); }, nullptr))
handle_error(err);
// Destroy the context and the loaded binary.
if (CUresult err = cuModuleUnload(binary))
handle_error(err);
if (CUresult err = cuDevicePrimaryCtxRelease(device))
handle_error(err);
return host_ret;
}
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