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mindspore
提交 bbafa9db 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!5832 Use the unified Execute function to run Graph or Single Graph. 

Merge pull request !5832 from 张清华/master
上级 4535faa2 c0070d3d
隐藏空白更改
内联 并排
Showing with 1291 addition and 1298 deletion
mindspore/ccsrc/backend/session/ascend_session.cc
浏览文件 @ bbafa9db
......@@ -318,7 +318,7 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
#endif
{
// run task on device
Execute(kernel_graph);
Execute(kernel_graph, true);
}
// summary
Summary(kernel_graph.get());
......@@ -348,17 +348,6 @@ void AscendSession::RunOpHardwareOptimize(const std::shared_ptr<session::KernelG
MS_LOG(INFO) << "Finish";
}
void AscendSession::RunOpExecTask(const std::shared_ptr<KernelGraph> &kernel_graph) const {
MS_LOG(INFO) << "Start!";
auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
MS_EXCEPTION_IF_NULL(runtime_instance);
bool ret_ok = runtime_instance->LaunchKernel(kernel_graph.get());
if (!ret_ok) {
MS_LOG(EXCEPTION) << "Run task error!";
}
MS_LOG(INFO) << "Finish!";
}
bool AscendSession::GraphCacheExist(const GraphInfo &graph_info) const {
return run_op_graphs_.find(graph_info) != run_op_graphs_.end();
}
......@@ -398,7 +387,7 @@ void AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_i
// load input data to device
LoadInputData(graph, input_tensors);
// run op
RunOpExecTask(graph);
Execute(graph, false);
// get output
if (op_run_info.value != nullptr) {
std::vector<tensor::TensorPtr> pre_output_tensors;
......@@ -552,21 +541,30 @@ void AscendSession::RunOpMemoryClear(const KernelGraph *kernel_graph) const {
void AscendSession::Load(const std::shared_ptr<KernelGraph> &kernel_graph) const {
MS_LOG(INFO) << "Start!";
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
bool is_task_sink = context_ptr->get_param<bool>(MS_CTX_ENABLE_TASK_SINK);
(void)device::KernelAdjust::GetInstance().StepLoadCtrlInputs(kernel_graph);
auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
MS_EXCEPTION_IF_NULL(runtime_instance);
bool ret_ok = runtime_instance->Load(kernel_graph.get());
bool ret_ok = runtime_instance->Load(kernel_graph.get(), is_task_sink);
if (!ret_ok) {
MS_LOG(EXCEPTION) << "Load task error!";
}
MS_LOG(INFO) << "Finish!";
}
void AscendSession::Execute(const std::shared_ptr<KernelGraph> &kernel_graph) const {
void AscendSession::Execute(const std::shared_ptr<KernelGraph> &kernel_graph, bool is_task) const {
MS_LOG(INFO) << "Start!";
bool is_task_sink = false;
if (is_task) {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
is_task_sink = context_ptr->get_param<bool>(MS_CTX_ENABLE_TASK_SINK);
}
auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
MS_EXCEPTION_IF_NULL(runtime_instance);
bool ret_ok = runtime_instance->Run(kernel_graph.get());
bool ret_ok = runtime_instance->Run(kernel_graph.get(), is_task_sink);
if (!ret_ok) {
MS_LOG(EXCEPTION) << "run task error!";
}
......
mindspore/ccsrc/backend/session/ascend_session.h
浏览文件 @ bbafa9db
......@@ -13,8 +13,10 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_SESSION_ASCEND_SESSION_H
#define MINDSPORE_CCSRC_BACKEND_SESSION_ASCEND_SESSION_H
#include <unordered_map>
#include <string>
#include <memory>
......@@ -82,13 +84,12 @@ class AscendSession : public SessionBasic {
KernelGraph *kernel_graph) const;
void RunOpMemoryClear(const KernelGraph *kernel_graph) const;
void Load(const std::shared_ptr<KernelGraph> &kernel_graph) const;
void Execute(const std::shared_ptr<KernelGraph> &kernel_graph) const;
void Execute(const std::shared_ptr<KernelGraph> &kernel_graph, bool is_task) const;
void Dump(const std::shared_ptr<KernelGraph> &kernel_graph) const;
void DumpAllGraphs(const std::vector<KernelGraphPtr> &all_graphs);
void LoadTensor(const std::shared_ptr<KernelGraph> &kernel_graph) const;
// below functions are used for run op
void RunOpHardwareOptimize(const std::shared_ptr<session::KernelGraph> &kernel_graph) const;
void RunOpExecTask(const std::shared_ptr<KernelGraph> &kernel_graph) const;
static void BackendOptimization(const std::vector<KernelGraphPtr> &all_graphs);
static void LinkChildGraphs(NotNull<KernelGraphPtr> graph);
......
mindspore/ccsrc/backend/session/cpu_session.cc
浏览文件 @ bbafa9db
......@@ -118,7 +118,7 @@ void CPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::Ten
debugger_->PreExecute(kernel_graph);
}
#endif
bool ret = runtime_.Run(kernel_graph.get());
bool ret = runtime_.Run(kernel_graph.get(), false);
if (!ret) {
MS_LOG(EXCEPTION) << "Run graph failed";
}
......
mindspore/ccsrc/backend/session/gpu_session.cc
浏览文件 @ bbafa9db
......@@ -191,9 +191,9 @@ void GPUSession::Execute(const std::shared_ptr<KernelGraph> &kernel_graph) const
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
MS_EXCEPTION_IF_NULL(runtime_instance);
#ifdef ENABLE_DEBUGGER
if (!runtime_instance->Run(kernel_graph.get(), debugger_.get())) {
if (!runtime_instance->Run(kernel_graph.get(), false, debugger_.get())) {
#else
if (!runtime_instance->Run(kernel_graph.get())) {
if (!runtime_instance->Run(kernel_graph.get(), false)) {
#endif
MS_LOG(EXCEPTION) << "GPU execute graph failed!";
}
......
mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.cc
浏览文件 @ bbafa9db
......@@ -454,10 +454,7 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size
return std::make_shared<AscendDeviceAddress>(device_ptr, device_size, format, type_id);
}
bool AscendKernelRuntime::Load(session::KernelGraph *graph) {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
bool is_task_sink = context_ptr->get_param<bool>(MS_CTX_ENABLE_TASK_SINK);
bool AscendKernelRuntime::Load(session::KernelGraph *graph, bool is_task_sink) {
if (!is_task_sink) {
return true;
}
......@@ -609,17 +606,14 @@ void AscendKernelRuntime::DebugTaskIdName(GraphId graph_id) {
}
}
bool AscendKernelRuntime::Run(session::KernelGraph *graph, Debugger *debugger) {
bool AscendKernelRuntime::Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger) {
bool ret = false;
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
#if defined(_WIN32) || defined(_WIN64)
auto start_time = std::chrono::steady_clock::now();
#else
struct timeval start_time, end_time;
(void)gettimeofday(&start_time, nullptr);
#endif
bool is_task_sink = context_ptr->get_param<bool>(MS_CTX_ENABLE_TASK_SINK);
if (is_task_sink) {
ret = RunTask(graph);
} else {
......
mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.h
浏览文件 @ bbafa9db
......@@ -44,8 +44,8 @@ class AscendKernelRuntime : public KernelRuntime {
bool GenTask(const session::KernelGraph *graph);
bool LoadTask(const session::KernelGraph *graph);
bool RunTask(const session::KernelGraph *graph);
bool Load(session::KernelGraph *graph) override;
bool Run(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
bool Load(session::KernelGraph *graph, bool is_task_sink) override;
bool Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger = nullptr) override;
void ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
const std::unordered_set<ValueNodePtr> &value_nodes,
const std::vector<CNodePtr> &execution_order) override;
......
mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.cc
浏览文件 @ bbafa9db
......@@ -287,7 +287,7 @@ void CPUKernelRuntime::DecreaseSummaryRefCount(const session::NamedSummaryOutput
resource_manager_.DecreaseSummaryRefCount(summary_outputs);
}
bool CPUKernelRuntime::Run(session::KernelGraph *kernel_graph, Debugger *debugger) {
bool CPUKernelRuntime::Run(session::KernelGraph *kernel_graph, bool is_task_sink, Debugger *debugger) {
MS_EXCEPTION_IF_NULL(kernel_graph);
resource_manager_.IncreaseAddressRefCount(kernel_graph);
......
mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.h
浏览文件 @ bbafa9db
......@@ -36,7 +36,7 @@ class CPUKernelRuntime : public KernelRuntime {
~CPUKernelRuntime() override = default;
bool Init() override { return true; }
bool Run(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
bool Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger = nullptr) override;
void AssignKernelAddress(session::KernelGraph *kernel_graph);
void BindInputOutput(session::KernelGraph *kernel_graph, const std::vector<tensor::TensorPtr> &inputs,
VectorRef *outputs);
......
mindspore/ccsrc/runtime/device/gpu/gpu_kernel_runtime.cc
浏览文件 @ bbafa9db
/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "runtime/device/gpu/gpu_kernel_runtime.h"
#include <algorithm>
#include "runtime/device/gpu/gpu_device_address.h"
#include "runtime/device/gpu/cuda_driver.h"
#include "runtime/device/gpu/gpu_buffer_mgr.h"
#include "runtime/device/gpu/gpu_device_manager.h"
#include "runtime/device/gpu/gpu_memory_allocator.h"
#include "runtime/device/gpu/distribution/collective_init.h"
#include "utils/convert_utils.h"
#include "utils/ms_context.h"
#include "runtime/device/kernel_runtime_manager.h"
#include "runtime/device/gpu/gpu_common.h"
#include "utils/ms_utils.h"
#include "runtime/device/gpu/gpu_memory_manager.h"
#include "backend/kernel_compiler/common_utils.h"
#include "runtime/device/gpu/gpu_memory_copy_manager.h"
#include "common/trans.h"
#include "ir/dtype.h"
#include "profiler/device/gpu/gpu_profiling.h"
#include "utils/shape_utils.h"
#ifdef ENABLE_DEBUGGER
#include "debug/debug_services.h"
#endif
namespace mindspore {
namespace device {
namespace gpu {
using mindspore::device::memswap::MemSwapInfoSet;
using mindspore::device::memswap::MemSwapManager;
using mindspore::device::memswap::SwapKind;
static const size_t PARAMETER_OUTPUT_INDEX = 0;
bool GPUKernelRuntime::SyncStream() { return GPUDeviceManager::GetInstance().SyncStream(stream_); }
bool GPUKernelRuntime::Init() {
if (device_init_ == true) {
GPUMemoryAllocator::GetInstance().CheckMaxDeviceMemory();
return true;
}
bool ret = false;
#ifdef ENABLE_DUMP_E2E
ret = SetDumpConf();
if (!ret) {
MS_LOG(INFO) << "No dump conf to set!";
}
#endif
ret = InitDevice();
if (!ret) {
MS_LOG(ERROR) << "InitDevice error.";
return ret;
}
mem_manager_ = std::make_shared<GPUMemoryManager>();
MS_EXCEPTION_IF_NULL(mem_manager_);
mem_manager_->MallocDeviceMemory();
const void *collective_handle_ = CollectiveInitializer::instance().collective_handle();
bool collective_inited = CollectiveInitializer::instance().collective_inited();
if (collective_inited && collective_handle_ != nullptr) {
auto init_nccl_comm_funcptr =
reinterpret_cast<InitNCCLComm>(dlsym(const_cast<void *>(collective_handle_), "InitNCCLComm"));
MS_EXCEPTION_IF_NULL(init_nccl_comm_funcptr);
(*init_nccl_comm_funcptr)();
}
device_init_ = true;
return ret;
}
#ifdef ENABLE_DUMP_E2E
namespace {
void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path, DumpConfPtr dump_conf,
Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(dump_conf);
bool trans_flag = dump_conf->trans_flag();
const auto &apply_kernels = graph->execution_order();
for (const auto &node : apply_kernels) {
MS_EXCEPTION_IF_NULL(node);
auto node_name = AnfAlgo::GetCNodeName(node);
std::string kernel_name = node->fullname_with_scope();
if (!dump_conf->IsKernelNeedDump(kernel_name)) {
continue;
}
const std::string strsrc = "/";
const std::string strdst = "--";
std::string::size_type pos = 0;
std::string::size_type srclen = strsrc.size();
std::string::size_type dstlen = strdst.size();
while ((pos = kernel_name.find(strsrc, pos)) != std::string::npos) {
kernel_name.replace(pos, srclen, strdst);
pos += dstlen;
}
auto output_size = AnfAlgo::GetOutputTensorNum(node);
for (size_t j = 0; j < output_size; ++j) {
auto addr = AnfAlgo::GetOutputAddr(node, j);
TypeId addr_type_id = addr->type_id();
std::string addr_format = addr->format();
ShapeVector int_shapes;
if (trans_flag) {
int_shapes = trans::GetRuntimePaddingShape(node, j);
} else {
auto shape = AnfAlgo::GetOutputDeviceShape(node, j);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
}
auto type = AnfAlgo::GetOutputInferDataType(node, j);
auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
std::string original_kernel_name = node->fullname_with_scope();
size_t slot = j;
auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, filepath, format, int_shapes, type,
addr_type_id, addr_format, slot);
if (!ret) {
std::string error = "DumpTensorToFile Failed: flag:" + std::to_string(trans_flag) + ", path:" + filepath +
", host_format:" + format + ".!";
}
}
}
}
void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_path, DumpConfPtr dump_conf,
Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(dump_conf);
bool trans_flag = dump_conf->trans_flag();
const auto &parameters = graph->inputs();
for (auto &item : parameters) {
if (!item->isa<Parameter>()) {
continue;
}
std::string parameter_name = item->fullname_with_scope();
if (!dump_conf->IsKernelNeedDump(parameter_name)) {
continue;
}
auto addr = AnfAlgo::GetOutputAddr(item, PARAMETER_OUTPUT_INDEX);
TypeId addr_type_id = addr->type_id();
std::string addr_format = addr->format();
ShapeVector int_shapes;
if (trans_flag) {
int_shapes = trans::GetRuntimePaddingShape(item, PARAMETER_OUTPUT_INDEX);
} else {
auto shape = AnfAlgo::GetOutputDeviceShape(item, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
}
auto type = AnfAlgo::GetOutputInferDataType(item, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
std::string original_kernel_name = parameter_name;
size_t slot = 0;
auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, filepath, format, int_shapes, type,
addr_type_id, addr_format, slot);
if (!ret) {
std::string error = "DumpTensorToFile Failed: flag:" + std::to_string(trans_flag) + ", path:" + filepath +
", host_format:" + format + ".!";
}
}
}
} // namespace
bool GPUKernelRuntime::DumpData(mindspore::session::KernelGraph *graph, Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_LOG(INFO) << "Start dump step";
DumpConfPtr dump_conf = GetDumpConf();
MS_EXCEPTION_IF_NULL(dump_conf);
dump_conf->UpdataCurIter();
bool dump_flag = dump_conf->dump_enable();
if (!dump_flag) {
MS_LOG(INFO) << "Dump flag is disable, pass dump step";
return true;
}
uint32_t cur_iter = dump_conf->cur_iter();
if (dump_conf->dump_iter() != 0) {
if (cur_iter != dump_conf->dump_iter()) {
return true;
}
}
MS_LOG(INFO) << "Cur iter is " << cur_iter;
std::string net_name = dump_conf->dump_net_name();
std::string iterator = std::to_string(cur_iter);
std::string dump_path = dump_conf->dump_path();
if (dump_path.back() == '/') {
dump_path = dump_path + net_name + '/' + iterator;
} else {
dump_path = dump_path + '/' + net_name + '/' + iterator;
}
// dump output
DumpOutput(graph, dump_path, dump_conf, debugger);
// dump parameters
DumpParameters(graph, dump_path, dump_conf, debugger);
return true;
}
#endif
#ifdef ENABLE_DEBUGGER
namespace {
void LoadKernelData(Debugger *debugger, const CNodePtr &kernel,
const std::vector<mindspore::kernel::AddressPtr> &kernel_inputs,
const std::vector<mindspore::kernel::AddressPtr> &kernel_workspaces,
const std::vector<mindspore::kernel::AddressPtr> &kernel_outputs, int exec_order, void *stream_ptr,
bool dump_enabled) {
// check if we should read the kernel data
bool read_data = false;
std::string kernel_name = kernel->fullname_with_scope();
if (debugger) {
debugger->SetCurNode(kernel_name);
if (dump_enabled) {
read_data = true;
} else if (debugger->debugger_enabled()) {
read_data = debugger->ReadNodeDataRequired();
}
}
if (!read_data) {
return;
}
// get inputs
auto input_size = AnfAlgo::GetInputTensorNum(kernel);
for (size_t j = 0; j < input_size; ++j) {
auto input_kernel = kernel->input(j + 1);
std::string input_kernel_name = input_kernel->fullname_with_scope();
auto addr = kernel_inputs[j];
auto type = AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
auto gpu_addr = std::make_unique<GPUDeviceAddress>(addr->addr, addr->size, format, type);
string input_tensor_name = input_kernel_name + ':' + "0";
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(input_kernel, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(input_tensor_name, exec_order, format, int_shapes, type, 0, debugger, false);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << input_tensor_name << ", host_format:" << format << ".!";
}
}
// get outputs
auto output_size = AnfAlgo::GetOutputTensorNum(kernel);
for (size_t j = 0; j < output_size; ++j) {
auto addr = kernel_outputs[j];
auto type = AnfAlgo::GetOutputInferDataType(kernel, j);
auto format = kOpFormat_DEFAULT;
auto gpu_addr = std::make_unique<GPUDeviceAddress>(addr->addr, addr->size, format, type);
string tensor_name = kernel_name + ':' + std::to_string(j);
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(kernel, j);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(tensor_name, exec_order, format, int_shapes, type, j, debugger, false);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << tensor_name << ", host_format:" << format << ".!";
}
}
debugger->PostExecuteNode();
}
void UpdateStepNum(Debugger *debugger, bool dump_enabled) {
if (debugger && (debugger->debugger_enabled() || dump_enabled)) {
auto cur_step_num = debugger->step_num();
cur_step_num = cur_step_num + 1;
debugger->SetStepNum(cur_step_num);
}
}
void LoadParameters(const session::KernelGraph *graph, Debugger *debugger, bool dump_enabled) {
MS_EXCEPTION_IF_NULL(graph);
if (!(debugger && dump_enabled)) {
return;
}
const auto &parameters = graph->inputs();
// for parameters, set its execution order to be 0;
int exec_order = 0;
for (auto &item : parameters) {
if (!item->isa<Parameter>()) {
continue;
}
std::string parameter_name = item->fullname_with_scope();
auto addr = AnfAlgo::GetOutputAddr(item, PARAMETER_OUTPUT_INDEX);
auto type = AnfAlgo::GetOutputInferDataType(item, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
string tensor_name = parameter_name + ':' + "0";
auto gpu_addr = dynamic_cast<const mindspore::device::gpu::GPUDeviceAddress *>(addr);
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(item, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(tensor_name, exec_order, format, int_shapes, type, 0, debugger, true);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << tensor_name << ", host_format:" << format << ".!";
}
}
}
void ClearCurrentData(Debugger *debugger, bool dump_enabled) {
if (debugger && (debugger->debugger_enabled() || dump_enabled)) {
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
tensor_loader->EmptyCurrentTensor();
}
}
} // namespace
#endif
DeviceAddressPtr GPUKernelRuntime::CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
TypeId type_id) {
return std::make_shared<GPUDeviceAddress>(device_ptr, device_size, format, type_id);
}
bool GPUKernelRuntime::InitDevice() {
if (GPUDeviceManager::GetInstance().device_count() <= 0) {
MS_LOG(ERROR) << "No GPU device found.";
return false;
}
const void *collective_handle_ = CollectiveInitializer::instance().collective_handle();
bool collective_inited = CollectiveInitializer::instance().collective_inited();
if (collective_inited && collective_handle_ != nullptr) {
auto get_local_rank_funcptr =
reinterpret_cast<GetLocalRankId>(dlsym(const_cast<void *>(collective_handle_), "local_rank_id"));
MS_EXCEPTION_IF_NULL(get_local_rank_funcptr);
device_id_ = IntToUint((*get_local_rank_funcptr)());
}
if (!GPUDeviceManager::GetInstance().is_device_id_init()) {
if (!GPUDeviceManager::GetInstance().set_cur_device_id(device_id_)) {
MS_LOG(ERROR) << "Failed to set current device to " << SizeToInt(device_id_);
return false;
}
}
GPUDeviceManager::GetInstance().InitDevice();
stream_ = GPUDeviceManager::GetInstance().default_stream();
if (stream_ == nullptr) {
MS_LOG(ERROR) << "No default CUDA stream found.";
return false;
}
return true;
}
void GPUKernelRuntime::ReleaseDeviceRes() {
// For dataset mode.
if (GpuBufferMgr::GetInstance().IsInit()) {
if (!GpuBufferMgr::GetInstance().IsClosed()) {
if (!GpuBufferMgr::GetInstance().CloseNotify()) {
MS_LOG(EXCEPTION) << "Could not close gpu data queue.";
}
}
CHECK_OP_RET_WITH_EXCEPT(GpuBufferMgr::GetInstance().Destroy(), "Could not destroy gpu data queue.");
}
// Destroy remaining memory swap events and free host memory.
for (auto &item : mem_swap_map_) {
auto &mem_swap_manager = item.second;
MS_EXCEPTION_IF_NULL(mem_swap_manager);
if (mem_swap_manager->trigger_swap()) {
mem_swap_manager->ClearSwapQueue(false);
mem_swap_manager->ReleaseHostPinnedMem();
}
}
GPUDeviceManager::GetInstance().ReleaseDevice();
if (mem_manager_ != nullptr) {
mem_manager_->FreeDeviceMemory();
}
kernel::KernelMeta *bin_map = kernel::KernelMeta::GetInstance();
MS_EXCEPTION_IF_NULL(bin_map);
bin_map->RemoveKernelCache();
}
void GPUKernelRuntime::ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
const std::unordered_set<ValueNodePtr> &value_nodes,
const std::vector<CNodePtr> &execution_order) {
MS_LOG(INFO) << "Clear graph:" << graph_id << " GPU runtime resource";
// Release the kernel resource.
for (const auto &kernel : execution_order) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
if (kernel_mod == nullptr) {
continue;
}
kernel_mod->ReleaseResource();
}
// Clear the output address of graph.
ClearOutputAddress(inputs, value_nodes, execution_order);
}
void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
MS_EXCEPTION_IF_NULL(mem_manager_);
mem_manager_->ResetDynamicMemory();
AssignStaticMemoryInput(graph);
AssignStaticMemoryValueNode(graph);
bool is_enable_dynamic_mem = context_ptr->get_param<bool>(MS_CTX_ENABLE_DYNAMIC_MEM_POOL);
if (is_enable_dynamic_mem) {
// Use the dynamic memory pool.
InitKernelRefCount(graph);
InitMemorySwapInfo(graph);
InitKernelOutputAddress(graph);
InitKernelWorkspaceAddress(graph);
SaveGraphOutputNode(graph);
} else {
AssignDynamicMemory(graph);
}
}
bool GPUKernelRuntime::Run(session::KernelGraph *graph, Debugger *debugger) {
struct timeval start_time, end_time;
(void)gettimeofday(&start_time, nullptr);
bool ret = true;
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
bool is_enable_dynamic_mem = context_ptr->get_param<bool>(MS_CTX_ENABLE_DYNAMIC_MEM_POOL);
bool is_enable_pynative_infer = context_ptr->get_param<bool>(MS_CTX_ENABLE_PYNATIVE_INFER);
if (is_enable_dynamic_mem && !is_enable_pynative_infer) {
auto graph_id = graph->graph_id();
auto iter = mem_swap_map_.find(graph_id);
if (iter == mem_swap_map_.end()) {
MS_LOG(EXCEPTION) << "Find memory swap map failed.";
}
mem_swap_manager_ = iter->second;
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
auto mem_reuse_iter = mem_reuse_util_map_.find(graph_id);
if (mem_reuse_iter == mem_reuse_util_map_.end()) {
MS_LOG(EXCEPTION) << "Find memory reuse map failed.";
}
mem_reuse_util_ = mem_reuse_iter->second;
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
ret = RunOneStep(graph, debugger);
} else {
ret = LaunchKernel(graph);
}
(void)gettimeofday(&end_time, nullptr);
const uint64_t kUSecondInSecond = 1000000;
uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
MS_LOG(DEBUG) << "GPU kernel runtime run graph in " << cost << " us";
return ret;
}
bool GPUKernelRuntime::RunOneStep(const session::KernelGraph *graph, Debugger *debugger) {
bool ret = true;
auto graph_id = graph->graph_id();
if (!is_first_step_map_[graph_id]) {
// Normally run graph
ret = LaunchKernelDynamic(graph, debugger);
} else {
// Mock run first step
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (ret) {
// Normally run graph
ret = LaunchKernelDynamic(graph, debugger);
} else {
// Trigger memory swap
ret = SearchMemSwapScheme(graph, debugger);
}
is_first_step_map_[graph_id] = false;
}
return ret;
}
bool GPUKernelRuntime::SearchMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger) {
MS_LOG(WARNING) << "Run out of memory and try memory swapping, it may take some time, please wait a moment.";
bool ret = false;
ClearKernelOldOutputAndWorkspace(graph);
if (!mem_swap_manager_->mem_swap_init()) {
if (!mem_swap_manager_->Init(graph)) {
return false;
}
}
while (!ret) {
if (!mem_swap_manager_->RetreatSwapInfo()) {
return false;
}
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (!ret) {
ClearKernelOldOutputAndWorkspace(graph);
}
}
mem_swap_manager_->AssignHostMemory();
// Time profiling
ret = LaunchKernelDynamic(graph, debugger, false, true);
if (!ret) {
return ret;
}
return RefineMemSwapScheme(graph, debugger);
}
bool GPUKernelRuntime::RefineMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger) {
MS_LOG(WARNING) << "Refine memory swap scheme, it may take some time, please wait a moment.";
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
if (!mem_swap_manager_->QueryKernelTriggerSwapIn(kernel)) {
continue;
}
size_t swap_in_task_num = mem_swap_manager_->QueryKernelTriggerSwapInTaskNum(kernel);
for (size_t swap_in_task_idx = 0; swap_in_task_idx < swap_in_task_num; swap_in_task_idx++) {
bool ret = false;
while (!ret) {
mem_swap_manager_->AdjustSwapInPos(kernel, swap_in_task_idx);
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (!ret) {
ClearKernelOldOutputAndWorkspace(graph);
ClearSwapInfo(true);
}
}
}
}
return true;
}
void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
// Init the kernel reference count.
if (!mem_reuse_util_ptr->InitDynamicKernelRef(graph)) {
MS_LOG(EXCEPTION) << "Init kernel reference count failed";
}
mem_reuse_util_ptr->SetKernelDefMap();
mem_reuse_util_ptr->SetReuseRefCount();
// Can't free the device address of graph output, so set the reference count of graph output specially.
mem_reuse_util_ptr->SetGraphOutputRefCount();
// Can't free the device address of summary nodes, so set the reference count of summary nodes specially.
mem_reuse_util_ptr->SetSummaryNodesRefCount();
auto graph_id = graph->graph_id();
mem_reuse_util_map_[graph_id] = mem_reuse_util_ptr;
}
void GPUKernelRuntime::InitMemorySwapInfo(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
GPUMemCopyManagerPtr gpu_mem_copy_manager = std::make_shared<GPUMemCopyManager>();
MS_EXCEPTION_IF_NULL(gpu_mem_copy_manager);
MemSwapManagerPtr mem_swap_manager = std::make_shared<MemSwapManager>(gpu_mem_copy_manager);
MS_EXCEPTION_IF_NULL(mem_swap_manager);
auto graph_id = graph->graph_id();
mem_swap_map_[graph_id] = mem_swap_manager;
is_first_step_map_[graph_id] = true;
}
void GPUKernelRuntime::InitKernelOutputAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
if (AnfAlgo::OutputAddrExist(kernel, i)) {
continue;
}
std::string output_format = AnfAlgo::GetOutputFormat(kernel, i);
auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i);
auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type);
AnfAlgo::SetOutputAddr(device_address, i, kernel.get());
}
}
}
void GPUKernelRuntime::InitKernelWorkspaceAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto workspace_sizes = kernel_mod->GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
auto device_address = CreateDeviceAddress(nullptr, workspace_sizes[i], "", kTypeUnknown);
AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get());
}
}
}
void GPUKernelRuntime::SaveGraphOutputNode(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto graph_id = graph->graph_id();
const auto &output_nodes = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem});
for (const auto &node : output_nodes) {
graph_output_map_[graph_id].insert(node);
}
}
bool GPUKernelRuntime::IsGraphOutput(const session::KernelGraph *graph, const mindspore::AnfNodePtr &kernel) const {
MS_EXCEPTION_IF_NULL(graph);
auto graph_id = graph->graph_id();
auto iter = graph_output_map_.find(graph_id);
if (iter == graph_output_map_.end()) {
MS_LOG(EXCEPTION) << "Find graph output info failed.";
}
auto &graph_output_set = iter->second;
return (graph_output_set.find(kernel) != graph_output_set.end());
}
void GPUKernelRuntime::ClearKernelOldOutputAndWorkspace(const session::KernelGraph *graph) {
ClearKernelOutputAddress(graph);
ClearKernelWorkspaceAddress(graph);
}
void GPUKernelRuntime::ClearKernelOutputAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
if (IsGraphOutput(graph, kernel)) {
continue;
}
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
if (!AnfAlgo::OutputAddrExist(kernel, i)) {
continue;
}
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
}
void GPUKernelRuntime::ClearKernelWorkspaceAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto workspace_sizes = kernel_mod->GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
}
}
}
bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, Debugger *debugger, bool mock,
bool profiling) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
// Reset the reference count.
mem_reuse_util_->ResetDynamicUsedRefCount();
// The inputs and outputs memory of communication kernel need be continuous, so separate processing.
AllocCommunicationOpDynamicRes(graph);
#ifdef ENABLE_DEBUGGER
bool dump_enabled = GPUKernelRuntime::DumpDataEnabledIteration();
if (!mock) {
UpdateStepNum(debugger, dump_enabled);
}
#endif
auto &kernels = graph->execution_order();
int exec_order = 1;
auto profiler_inst = profiler::gpu::GPUProfiler::GetInstance();
MS_EXCEPTION_IF_NULL(profiler_inst);
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
AddressPtrList kernel_inputs;
AddressPtrList kernel_workspaces;
AddressPtrList kernel_outputs;
auto ret = AllocKernelDynamicRes(*kernel_mod, kernel, &kernel_inputs, &kernel_workspaces, &kernel_outputs, mock);
if (!ret) {
#ifdef ENABLE_DEBUGGER
if (!mock) {
// invalidate current data collected by the debugger
ClearCurrentData(debugger, dump_enabled);
}
#endif
return false;
}
if (!mock) {
if (!profiling) {
if (profiler_inst->GetEnableFlag()) {
profiler_inst->OpDataProducerBegin(kernel->fullname_with_scope(), stream_);
}
CHECK_OP_RET_WITH_EXCEPT(kernel_mod->Launch(kernel_inputs, kernel_workspaces, kernel_outputs, stream_),
"Launch kernel failed.");
if (profiler_inst->GetEnableFlag()) {
profiler_inst->OpDataProducerEnd();
if (profiler_inst->GetSyncEnableFlag()) {
CHECK_OP_RET_WITH_ERROR(SyncStream(), "Profiler SyncStream failed.");
}
}
} else {
LaunchKernelWithTimeProfiling(kernel, kernel_inputs, kernel_workspaces, kernel_outputs);
}
#ifdef ENABLE_DEBUGGER
// called once per kernel to collect the outputs to the kernel (does a SyncDeviceToHost)
LoadKernelData(debugger, kernel, kernel_inputs, kernel_workspaces, kernel_outputs, exec_order, stream_,
dump_enabled);
#endif
}
exec_order = exec_order + 1;
FreeKernelDynamicRes(kernel);
if (!UpdateMemorySwapTask(kernel, mock, profiling)) {
#ifdef ENABLE_DEBUGGER
if (!mock) {
// invalidate current data collected by the debugger
ClearCurrentData(debugger, dump_enabled);
}
#endif
return false;
}
}
if (!mock) {
#ifdef ENABLE_DEBUGGER
// collect weights and bias for dump mode
LoadParameters(graph, debugger, dump_enabled);
#endif
CHECK_OP_RET_WITH_EXCEPT(SyncStream(), "SyncStream failed.");
}
ClearSwapInfo(mock);
return true;
}
void GPUKernelRuntime::LaunchKernelWithTimeProfiling(const AnfNodePtr &kernel, const AddressPtrList &inputs,
const AddressPtrList &workspace, const AddressPtrList &outputs) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
float cost_time = 0;
DeviceEvent start = nullptr;
DeviceEvent end = nullptr;
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateEvent(&start), "Failed to create event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateEvent(&end), "Failed to create event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::RecordEvent(start, stream_), "Failed to record event to stream.");
CHECK_OP_RET_WITH_EXCEPT(kernel_mod->Launch(inputs, workspace, outputs, stream_), "Launch kernel failed.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::RecordEvent(end, stream_), "Failed to record event to stream.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::SyncEvent(start), "Failed to sync event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::SyncEvent(end), "Failed to sync event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::ElapsedTime(&cost_time, start, end), "Failed to record elapsed time.");
mem_swap_manager_->AddKernelExecutionPerform(kernel, cost_time);
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::DestroyEvent(start), "Failed to destroy event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::DestroyEvent(end), "Failed to destroy event.");
}
bool GPUKernelRuntime::AddMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
const MemSwapInfoSet &mem_swap_info_set = mem_swap_manager_->QueryKernelMemSwapInfo(kernel);
for (auto &mem_swap_info : mem_swap_info_set) {
auto need_swap_kernel = mem_swap_manager_->QueryKernelByTopoOrder(mem_swap_info.topo_order_);
MS_EXCEPTION_IF_NULL(need_swap_kernel);
const HostAddress &host_address =
mem_swap_manager_->QueryKernelHostAddr(need_swap_kernel, mem_swap_info.output_idx_);
auto device_address = AnfAlgo::GetMutableOutputAddr(need_swap_kernel, mem_swap_info.output_idx_, false);
if (mem_swap_info.swap_kind_ == SwapKind::kDeviceToHost) {
if (mem_swap_manager_->QueryKernelHostAddrIsDirty(need_swap_kernel, mem_swap_info.output_idx_)) {
mem_swap_manager_->AddMemSwapTask(SwapKind::kDeviceToHost, device_address, host_address, mock);
mem_swap_manager_->AddKernelHostAddrIsDirty(need_swap_kernel, mem_swap_info.output_idx_, false);
} else {
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInHost);
}
} else if (mem_swap_info.swap_kind_ == SwapKind::kHostToDevice) {
auto status = device_address->status();
if (status == DeviceAddressStatus::kInDeviceToHost) {
device_address->set_status(DeviceAddressStatus::kInDevice);
} else if (status == DeviceAddressStatus::kInHost) {
if (!device_address->ptr_ && !AttemptMallocMem(device_address, device_address->size_, mock)) {
return false;
}
float cost_time = 0;
mem_swap_manager_->AddMemSwapTask(SwapKind::kHostToDevice, device_address, host_address, mock, profiling,
&cost_time);
if (profiling) {
mem_swap_manager_->AddKernelSwapPerform(need_swap_kernel, mem_swap_info.output_idx_,
std::make_pair(0, cost_time));
}
}
}
}
return true;
}
bool GPUKernelRuntime::UpdateMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return true;
}
if (mem_swap_manager_->QueryKernelTriggerSwap(kernel)) {
if (!mock) {
CHECK_OP_RET_WITH_EXCEPT(SyncStream(), "SyncStream failed.");
}
if (!AddMemorySwapTask(kernel, mock, profiling)) {
return false;
}
if (!mock) {
CHECK_OP_RET_WITH_EXCEPT(mem_swap_manager_->SyncMemCopyStream(SwapKind::kDeviceToHost), "SyncCopyStream failed.");
}
}
return true;
}
void GPUKernelRuntime::UpdateHostSwapInQueue(const DeviceAddressPtr device_address, bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
while (auto device_address_swap_in = mem_swap_manager_->UpdateSwapQueue(SwapKind::kHostToDevice, mock)) {
device_address_swap_in->set_status(DeviceAddressStatus::kInDevice);
}
auto status = device_address->status();
switch (status) {
case DeviceAddressStatus::kInDevice:
break;
case DeviceAddressStatus::kInDeviceToHost: {
device_address->set_status(DeviceAddressStatus::kInDevice);
break;
}
case DeviceAddressStatus::kInHostToDevice: {
while (device_address->status() != DeviceAddressStatus::kInDevice) {
while (auto device_address_swap_in = mem_swap_manager_->UpdateSwapQueue(SwapKind::kHostToDevice, mock)) {
device_address_swap_in->set_status(DeviceAddressStatus::kInDevice);
}
}
break;
}
case DeviceAddressStatus::kInHost:
MS_LOG(WARNING) << "Unexpected device address status: " << status;
break;
default:
MS_LOG(EXCEPTION) << "Invaild device address status: " << status;
}
}
void GPUKernelRuntime::UpdateHostSwapOutQueue(bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
while (auto device_address_swap_out = mem_swap_manager_->UpdateSwapQueue(SwapKind::kDeviceToHost, mock)) {
if (device_address_swap_out->status() == DeviceAddressStatus::kInDeviceToHost && device_address_swap_out->ptr_) {
device_address_swap_out->set_status(DeviceAddressStatus::kInHost);
mem_manager_->FreeMemFromMemPool(device_address_swap_out);
}
}
}
void GPUKernelRuntime::ClearSwapInfo(bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
mem_swap_manager_->ClearSwapQueue(mock);
mem_swap_manager_->ResetHostAddrIsDirty();
}
bool GPUKernelRuntime::AttemptMallocMem(const DeviceAddressPtr &device_address, size_t size, bool mock) {
MS_EXCEPTION_IF_NULL(mem_manager_);
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
auto ret = mem_manager_->MallocMemFromMemPool(device_address, size);
if (!ret) {
if (!mem_swap_manager_->trigger_swap()) {
return false;
}
if (!mock) {
mem_swap_manager_->SyncMemCopyStream(SwapKind::kDeviceToHost);
}
UpdateHostSwapOutQueue(mock);
ret = mem_manager_->MallocMemFromMemPool(device_address, size);
if (!ret) {
return false;
}
}
return true;
}
bool GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs,
AddressPtrList *kernel_workspaces, AddressPtrList *kernel_outputs,
bool mock) {
if (!AllocKernelInputDynamicRes(kernel, kernel_inputs, mock)) {
return false;
}
if (!AllocKernelOutputDynamicRes(kernel_mod, kernel, kernel_outputs, mock)) {
return false;
}
if (!AllocKernelWorkspaceDynamicRes(kernel_mod, kernel, kernel_workspaces, mock)) {
return false;
}
return true;
}
bool GPUKernelRuntime::AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs,
bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_inputs);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
MS_EXCEPTION_IF_NULL(device_address);
UpdateHostSwapInQueue(device_address, mock);
MS_EXCEPTION_IF_NULL(device_address->ptr_);
kernel::AddressPtr input = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(input);
input->addr = device_address->ptr_;
input->size = device_address->size_;
kernel_inputs->emplace_back(input);
}
return true;
}
bool GPUKernelRuntime::AllocKernelOutputDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_outputs,
bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_outputs);
UpdateHostSwapOutQueue(mock);
auto output_sizes = kernel_mod.GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr && !AttemptMallocMem(device_address, output_sizes[i], mock)) {
return false;
}
kernel::AddressPtr output = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(output);
output->addr = device_address->ptr_;
output->size = output_sizes[i];
kernel_outputs->emplace_back(output);
}
return true;
}
bool GPUKernelRuntime::AllocKernelWorkspaceDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_workspaces, bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_workspaces);
auto workspace_sizes = kernel_mod.GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
if (workspace_sizes[i] == 0) {
kernel_workspaces->emplace_back(nullptr);
continue;
}
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
if (device_address->ptr_ == nullptr && !AttemptMallocMem(device_address, workspace_sizes[i], mock)) {
return false;
}
kernel::AddressPtr workspace = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(workspace);
workspace->addr = device_address->ptr_;
workspace->size = workspace_sizes[i];
kernel_workspaces->emplace_back(workspace);
}
return true;
}
void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (auto &kernel : kernels) {
MS_EXCEPTION_IF_NULL(kernel);
if (AnfAlgo::IsCommunicationOp(kernel)) {
AllocCommunicationOpInputDynamicRes(kernel);
AllocCommunicationOpOutputDynamicRes(kernel);
}
}
}
void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
bool is_need_alloc_memory = false;
bool is_need_free_memory = false;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto intput_sizes = kernel_mod->GetInputSizeList();
for (size_t i = 0; i < intput_sizes.size(); ++i) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr) {
is_need_alloc_memory = true;
} else {
is_need_free_memory = true;
}
total_size += intput_sizes[i];
size_list.emplace_back(intput_sizes[i]);
addr_list.emplace_back(device_address);
}
AllocCommunicationOpMemory(is_need_alloc_memory, is_need_free_memory, addr_list, total_size, size_list);
}
void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
bool is_need_alloc_memory = false;
bool is_need_free_memory = false;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr) {
is_need_alloc_memory = true;
} else {
is_need_free_memory = true;
}
total_size += output_sizes[i];
size_list.emplace_back(output_sizes[i]);
addr_list.emplace_back(device_address);
}
AllocCommunicationOpMemory(is_need_alloc_memory, is_need_free_memory, addr_list, total_size, size_list);
}
void GPUKernelRuntime::AllocCommunicationOpMemory(bool is_need_alloc_memory, bool is_need_free_memory,
const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list) {
MS_EXCEPTION_IF_NULL(mem_manager_);
if (!is_need_alloc_memory) {
return;
}
if (is_need_free_memory) {
for (const auto &iter : addr_list) {
MS_EXCEPTION_IF_NULL(iter);
// Free the inputs/outputs of communication kernel which are not released.
if (iter->ptr_ != nullptr) {
mem_manager_->FreeMemFromMemPool(iter);
}
}
}
auto ret = mem_manager_->MallocContinuousMemFromMemPool(addr_list, total_size, size_list);
if (!ret) {
MS_LOG(EXCEPTION) << "Malloc device memory failed.";
}
}
void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_manager_);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
auto cnode = kernel->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (AnfAlgo::IsCommunicationOp(kernel)) {
return;
}
// Free the input of kernel by reference count.
for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
auto kernel_ref_count_ptr = mem_reuse_util_->GetKernelInputRef(cnode, i);
if (kernel_ref_count_ptr == nullptr) {
continue;
}
kernel_ref_count_ptr->ref_count_dynamic_use_--;
if (kernel_ref_count_ptr->ref_count_dynamic_use_ < 0) {
MS_LOG(EXCEPTION) << "Check dynamic reference count failed.";
}
if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
// Free the output of kernel, if output has no reference.
for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(kernel); ++i) {
auto kernel_ref_count_ptr = mem_reuse_util_->GetRef(cnode, i);
if (kernel_ref_count_ptr == nullptr) {
continue;
}
if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
// Free the workspace of kernel.
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
for (size_t i = 0; i < kernel_mod->GetWorkspaceSizeList().size(); ++i) {
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
}
}
} // namespace gpu
} // namespace device
} // namespace mindspore
/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "runtime/device/gpu/gpu_kernel_runtime.h"
#include <algorithm>
#include "runtime/device/gpu/gpu_device_address.h"
#include "runtime/device/gpu/cuda_driver.h"
#include "runtime/device/gpu/gpu_buffer_mgr.h"
#include "runtime/device/gpu/gpu_device_manager.h"
#include "runtime/device/gpu/gpu_memory_allocator.h"
#include "runtime/device/gpu/distribution/collective_init.h"
#include "utils/convert_utils.h"
#include "utils/ms_context.h"
#include "runtime/device/kernel_runtime_manager.h"
#include "runtime/device/gpu/gpu_common.h"
#include "utils/ms_utils.h"
#include "runtime/device/gpu/gpu_memory_manager.h"
#include "backend/kernel_compiler/common_utils.h"
#include "runtime/device/gpu/gpu_memory_copy_manager.h"
#include "common/trans.h"
#include "ir/dtype.h"
#include "profiler/device/gpu/gpu_profiling.h"
#include "utils/shape_utils.h"
#ifdef ENABLE_DEBUGGER
#include "debug/debug_services.h"
#endif
namespace mindspore {
namespace device {
namespace gpu {
using mindspore::device::memswap::MemSwapInfoSet;
using mindspore::device::memswap::MemSwapManager;
using mindspore::device::memswap::SwapKind;
static const size_t PARAMETER_OUTPUT_INDEX = 0;
bool GPUKernelRuntime::SyncStream() { return GPUDeviceManager::GetInstance().SyncStream(stream_); }
bool GPUKernelRuntime::Init() {
if (device_init_ == true) {
GPUMemoryAllocator::GetInstance().CheckMaxDeviceMemory();
return true;
}
bool ret = false;
#ifdef ENABLE_DUMP_E2E
ret = SetDumpConf();
if (!ret) {
MS_LOG(INFO) << "No dump conf to set!";
}
#endif
ret = InitDevice();
if (!ret) {
MS_LOG(ERROR) << "InitDevice error.";
return ret;
}
mem_manager_ = std::make_shared<GPUMemoryManager>();
MS_EXCEPTION_IF_NULL(mem_manager_);
mem_manager_->MallocDeviceMemory();
const void *collective_handle_ = CollectiveInitializer::instance().collective_handle();
bool collective_inited = CollectiveInitializer::instance().collective_inited();
if (collective_inited && collective_handle_ != nullptr) {
auto init_nccl_comm_funcptr =
reinterpret_cast<InitNCCLComm>(dlsym(const_cast<void *>(collective_handle_), "InitNCCLComm"));
MS_EXCEPTION_IF_NULL(init_nccl_comm_funcptr);
(*init_nccl_comm_funcptr)();
}
device_init_ = true;
return ret;
}
#ifdef ENABLE_DUMP_E2E
namespace {
void DumpOutput(mindspore::session::KernelGraph *graph, const string &dump_path, DumpConfPtr dump_conf,
Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(dump_conf);
bool trans_flag = dump_conf->trans_flag();
const auto &apply_kernels = graph->execution_order();
for (const auto &node : apply_kernels) {
MS_EXCEPTION_IF_NULL(node);
auto node_name = AnfAlgo::GetCNodeName(node);
std::string kernel_name = node->fullname_with_scope();
if (!dump_conf->IsKernelNeedDump(kernel_name)) {
continue;
}
const std::string strsrc = "/";
const std::string strdst = "--";
std::string::size_type pos = 0;
std::string::size_type srclen = strsrc.size();
std::string::size_type dstlen = strdst.size();
while ((pos = kernel_name.find(strsrc, pos)) != std::string::npos) {
kernel_name.replace(pos, srclen, strdst);
pos += dstlen;
}
auto output_size = AnfAlgo::GetOutputTensorNum(node);
for (size_t j = 0; j < output_size; ++j) {
auto addr = AnfAlgo::GetOutputAddr(node, j);
TypeId addr_type_id = addr->type_id();
std::string addr_format = addr->format();
ShapeVector int_shapes;
if (trans_flag) {
int_shapes = trans::GetRuntimePaddingShape(node, j);
} else {
auto shape = AnfAlgo::GetOutputDeviceShape(node, j);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
}
auto type = AnfAlgo::GetOutputInferDataType(node, j);
auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + kernel_name + '_' + "output_" + std::to_string(j);
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
std::string original_kernel_name = node->fullname_with_scope();
size_t slot = j;
auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, filepath, format, int_shapes, type,
addr_type_id, addr_format, slot);
if (!ret) {
std::string error = "DumpTensorToFile Failed: flag:" + std::to_string(trans_flag) + ", path:" + filepath +
", host_format:" + format + ".!";
}
}
}
}
void DumpParameters(mindspore::session::KernelGraph *graph, const string &dump_path, DumpConfPtr dump_conf,
Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(dump_conf);
bool trans_flag = dump_conf->trans_flag();
const auto &parameters = graph->inputs();
for (auto &item : parameters) {
if (!item->isa<Parameter>()) {
continue;
}
std::string parameter_name = item->fullname_with_scope();
if (!dump_conf->IsKernelNeedDump(parameter_name)) {
continue;
}
auto addr = AnfAlgo::GetOutputAddr(item, PARAMETER_OUTPUT_INDEX);
TypeId addr_type_id = addr->type_id();
std::string addr_format = addr->format();
ShapeVector int_shapes;
if (trans_flag) {
int_shapes = trans::GetRuntimePaddingShape(item, PARAMETER_OUTPUT_INDEX);
} else {
auto shape = AnfAlgo::GetOutputDeviceShape(item, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
}
auto type = AnfAlgo::GetOutputInferDataType(item, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
string filepath = dump_path + '/' + parameter_name + '_' + "output_0";
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
std::string original_kernel_name = parameter_name;
size_t slot = 0;
auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, filepath, format, int_shapes, type,
addr_type_id, addr_format, slot);
if (!ret) {
std::string error = "DumpTensorToFile Failed: flag:" + std::to_string(trans_flag) + ", path:" + filepath +
", host_format:" + format + ".!";
}
}
}
} // namespace
bool GPUKernelRuntime::DumpData(mindspore::session::KernelGraph *graph, Debugger *debugger) {
MS_EXCEPTION_IF_NULL(graph);
MS_LOG(INFO) << "Start dump step";
DumpConfPtr dump_conf = GetDumpConf();
MS_EXCEPTION_IF_NULL(dump_conf);
dump_conf->UpdataCurIter();
bool dump_flag = dump_conf->dump_enable();
if (!dump_flag) {
MS_LOG(INFO) << "Dump flag is disable, pass dump step";
return true;
}
uint32_t cur_iter = dump_conf->cur_iter();
if (dump_conf->dump_iter() != 0) {
if (cur_iter != dump_conf->dump_iter()) {
return true;
}
}
MS_LOG(INFO) << "Cur iter is " << cur_iter;
std::string net_name = dump_conf->dump_net_name();
std::string iterator = std::to_string(cur_iter);
std::string dump_path = dump_conf->dump_path();
if (dump_path.back() == '/') {
dump_path = dump_path + net_name + '/' + iterator;
} else {
dump_path = dump_path + '/' + net_name + '/' + iterator;
}
// dump output
DumpOutput(graph, dump_path, dump_conf, debugger);
// dump parameters
DumpParameters(graph, dump_path, dump_conf, debugger);
return true;
}
#endif
#ifdef ENABLE_DEBUGGER
namespace {
void LoadKernelData(Debugger *debugger, const CNodePtr &kernel,
const std::vector<mindspore::kernel::AddressPtr> &kernel_inputs,
const std::vector<mindspore::kernel::AddressPtr> &kernel_workspaces,
const std::vector<mindspore::kernel::AddressPtr> &kernel_outputs, int exec_order, void *stream_ptr,
bool dump_enabled) {
// check if we should read the kernel data
bool read_data = false;
std::string kernel_name = kernel->fullname_with_scope();
if (debugger) {
debugger->SetCurNode(kernel_name);
if (dump_enabled) {
read_data = true;
} else if (debugger->debugger_enabled()) {
read_data = debugger->ReadNodeDataRequired();
}
}
if (!read_data) {
return;
}
// get inputs
auto input_size = AnfAlgo::GetInputTensorNum(kernel);
for (size_t j = 0; j < input_size; ++j) {
auto input_kernel = kernel->input(j + 1);
std::string input_kernel_name = input_kernel->fullname_with_scope();
auto addr = kernel_inputs[j];
auto type = AnfAlgo::GetOutputInferDataType(input_kernel, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
auto gpu_addr = std::make_unique<GPUDeviceAddress>(addr->addr, addr->size, format, type);
string input_tensor_name = input_kernel_name + ':' + "0";
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(input_kernel, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(input_tensor_name, exec_order, format, int_shapes, type, 0, debugger, false);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << input_tensor_name << ", host_format:" << format << ".!";
}
}
// get outputs
auto output_size = AnfAlgo::GetOutputTensorNum(kernel);
for (size_t j = 0; j < output_size; ++j) {
auto addr = kernel_outputs[j];
auto type = AnfAlgo::GetOutputInferDataType(kernel, j);
auto format = kOpFormat_DEFAULT;
auto gpu_addr = std::make_unique<GPUDeviceAddress>(addr->addr, addr->size, format, type);
string tensor_name = kernel_name + ':' + std::to_string(j);
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(kernel, j);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(tensor_name, exec_order, format, int_shapes, type, j, debugger, false);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << tensor_name << ", host_format:" << format << ".!";
}
}
debugger->PostExecuteNode();
}
void UpdateStepNum(Debugger *debugger, bool dump_enabled) {
if (debugger && (debugger->debugger_enabled() || dump_enabled)) {
auto cur_step_num = debugger->step_num();
cur_step_num = cur_step_num + 1;
debugger->SetStepNum(cur_step_num);
}
}
void LoadParameters(const session::KernelGraph *graph, Debugger *debugger, bool dump_enabled) {
MS_EXCEPTION_IF_NULL(graph);
if (!(debugger && dump_enabled)) {
return;
}
const auto &parameters = graph->inputs();
// for parameters, set its execution order to be 0;
int exec_order = 0;
for (auto &item : parameters) {
if (!item->isa<Parameter>()) {
continue;
}
std::string parameter_name = item->fullname_with_scope();
auto addr = AnfAlgo::GetOutputAddr(item, PARAMETER_OUTPUT_INDEX);
auto type = AnfAlgo::GetOutputInferDataType(item, PARAMETER_OUTPUT_INDEX);
auto format = kOpFormat_DEFAULT;
string tensor_name = parameter_name + ':' + "0";
auto gpu_addr = dynamic_cast<const mindspore::device::gpu::GPUDeviceAddress *>(addr);
ShapeVector int_shapes;
auto shape = AnfAlgo::GetOutputDeviceShape(item, PARAMETER_OUTPUT_INDEX);
(void)std::transform(shape.begin(), shape.end(), std::back_inserter(int_shapes),
[](size_t inner_item) { return SizeToInt(inner_item); });
auto ret = gpu_addr->LoadMemToHost(tensor_name, exec_order, format, int_shapes, type, 0, debugger, true);
if (!ret) {
MS_LOG(ERROR) << "LoadMemToHost:"
<< ", tensor_name:" << tensor_name << ", host_format:" << format << ".!";
}
}
}
void ClearCurrentData(Debugger *debugger, bool dump_enabled) {
if (debugger && (debugger->debugger_enabled() || dump_enabled)) {
DebugServices *debug_services = debugger->debug_services();
TensorLoader *tensor_loader = debug_services->tensor_loader();
tensor_loader->EmptyCurrentTensor();
}
}
} // namespace
#endif
DeviceAddressPtr GPUKernelRuntime::CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
TypeId type_id) {
return std::make_shared<GPUDeviceAddress>(device_ptr, device_size, format, type_id);
}
bool GPUKernelRuntime::InitDevice() {
if (GPUDeviceManager::GetInstance().device_count() <= 0) {
MS_LOG(ERROR) << "No GPU device found.";
return false;
}
const void *collective_handle_ = CollectiveInitializer::instance().collective_handle();
bool collective_inited = CollectiveInitializer::instance().collective_inited();
if (collective_inited && collective_handle_ != nullptr) {
auto get_local_rank_funcptr =
reinterpret_cast<GetLocalRankId>(dlsym(const_cast<void *>(collective_handle_), "local_rank_id"));
MS_EXCEPTION_IF_NULL(get_local_rank_funcptr);
device_id_ = IntToUint((*get_local_rank_funcptr)());
}
if (!GPUDeviceManager::GetInstance().is_device_id_init()) {
if (!GPUDeviceManager::GetInstance().set_cur_device_id(device_id_)) {
MS_LOG(ERROR) << "Failed to set current device to " << SizeToInt(device_id_);
return false;
}
}
GPUDeviceManager::GetInstance().InitDevice();
stream_ = GPUDeviceManager::GetInstance().default_stream();
if (stream_ == nullptr) {
MS_LOG(ERROR) << "No default CUDA stream found.";
return false;
}
return true;
}
void GPUKernelRuntime::ReleaseDeviceRes() {
// For dataset mode.
if (GpuBufferMgr::GetInstance().IsInit()) {
if (!GpuBufferMgr::GetInstance().IsClosed()) {
if (!GpuBufferMgr::GetInstance().CloseNotify()) {
MS_LOG(EXCEPTION) << "Could not close gpu data queue.";
}
}
CHECK_OP_RET_WITH_EXCEPT(GpuBufferMgr::GetInstance().Destroy(), "Could not destroy gpu data queue.");
}
// Destroy remaining memory swap events and free host memory.
for (auto &item : mem_swap_map_) {
auto &mem_swap_manager = item.second;
MS_EXCEPTION_IF_NULL(mem_swap_manager);
if (mem_swap_manager->trigger_swap()) {
mem_swap_manager->ClearSwapQueue(false);
mem_swap_manager->ReleaseHostPinnedMem();
}
}
GPUDeviceManager::GetInstance().ReleaseDevice();
if (mem_manager_ != nullptr) {
mem_manager_->FreeDeviceMemory();
}
kernel::KernelMeta *bin_map = kernel::KernelMeta::GetInstance();
MS_EXCEPTION_IF_NULL(bin_map);
bin_map->RemoveKernelCache();
}
void GPUKernelRuntime::ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
const std::unordered_set<ValueNodePtr> &value_nodes,
const std::vector<CNodePtr> &execution_order) {
MS_LOG(INFO) << "Clear graph:" << graph_id << " GPU runtime resource";
// Release the kernel resource.
for (const auto &kernel : execution_order) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
if (kernel_mod == nullptr) {
continue;
}
kernel_mod->ReleaseResource();
}
// Clear the output address of graph.
ClearOutputAddress(inputs, value_nodes, execution_order);
}
void GPUKernelRuntime::AssignMemory(session::KernelGraph *graph) {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
MS_EXCEPTION_IF_NULL(mem_manager_);
mem_manager_->ResetDynamicMemory();
AssignStaticMemoryInput(graph);
AssignStaticMemoryValueNode(graph);
bool is_enable_dynamic_mem = context_ptr->get_param<bool>(MS_CTX_ENABLE_DYNAMIC_MEM_POOL);
if (is_enable_dynamic_mem) {
// Use the dynamic memory pool.
InitKernelRefCount(graph);
InitMemorySwapInfo(graph);
InitKernelOutputAddress(graph);
InitKernelWorkspaceAddress(graph);
SaveGraphOutputNode(graph);
} else {
AssignDynamicMemory(graph);
}
}
bool GPUKernelRuntime::Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger) {
struct timeval start_time, end_time;
(void)gettimeofday(&start_time, nullptr);
bool ret = true;
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
bool is_enable_dynamic_mem = context_ptr->get_param<bool>(MS_CTX_ENABLE_DYNAMIC_MEM_POOL);
bool is_enable_pynative_infer = context_ptr->get_param<bool>(MS_CTX_ENABLE_PYNATIVE_INFER);
if (is_enable_dynamic_mem && !is_enable_pynative_infer) {
auto graph_id = graph->graph_id();
auto iter = mem_swap_map_.find(graph_id);
if (iter == mem_swap_map_.end()) {
MS_LOG(EXCEPTION) << "Find memory swap map failed.";
}
mem_swap_manager_ = iter->second;
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
auto mem_reuse_iter = mem_reuse_util_map_.find(graph_id);
if (mem_reuse_iter == mem_reuse_util_map_.end()) {
MS_LOG(EXCEPTION) << "Find memory reuse map failed.";
}
mem_reuse_util_ = mem_reuse_iter->second;
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
ret = RunOneStep(graph, debugger);
} else {
ret = LaunchKernel(graph);
}
(void)gettimeofday(&end_time, nullptr);
const uint64_t kUSecondInSecond = 1000000;
uint64_t cost = kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec - start_time.tv_sec);
cost += static_cast<uint64_t>(end_time.tv_usec - start_time.tv_usec);
MS_LOG(DEBUG) << "GPU kernel runtime run graph in " << cost << " us";
return ret;
}
bool GPUKernelRuntime::RunOneStep(const session::KernelGraph *graph, Debugger *debugger) {
bool ret = true;
auto graph_id = graph->graph_id();
if (!is_first_step_map_[graph_id]) {
// Normally run graph
ret = LaunchKernelDynamic(graph, debugger);
} else {
// Mock run first step
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (ret) {
// Normally run graph
ret = LaunchKernelDynamic(graph, debugger);
} else {
// Trigger memory swap
ret = SearchMemSwapScheme(graph, debugger);
}
is_first_step_map_[graph_id] = false;
}
return ret;
}
bool GPUKernelRuntime::SearchMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger) {
MS_LOG(WARNING) << "Run out of memory and try memory swapping, it may take some time, please wait a moment.";
bool ret = false;
ClearKernelOldOutputAndWorkspace(graph);
if (!mem_swap_manager_->mem_swap_init()) {
if (!mem_swap_manager_->Init(graph)) {
return false;
}
}
while (!ret) {
if (!mem_swap_manager_->RetreatSwapInfo()) {
return false;
}
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (!ret) {
ClearKernelOldOutputAndWorkspace(graph);
}
}
mem_swap_manager_->AssignHostMemory();
// Time profiling
ret = LaunchKernelDynamic(graph, debugger, false, true);
if (!ret) {
return ret;
}
return RefineMemSwapScheme(graph, debugger);
}
bool GPUKernelRuntime::RefineMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger) {
MS_LOG(WARNING) << "Refine memory swap scheme, it may take some time, please wait a moment.";
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
if (!mem_swap_manager_->QueryKernelTriggerSwapIn(kernel)) {
continue;
}
size_t swap_in_task_num = mem_swap_manager_->QueryKernelTriggerSwapInTaskNum(kernel);
for (size_t swap_in_task_idx = 0; swap_in_task_idx < swap_in_task_num; swap_in_task_idx++) {
bool ret = false;
while (!ret) {
mem_swap_manager_->AdjustSwapInPos(kernel, swap_in_task_idx);
ret = LaunchKernelDynamic(graph, debugger, true, false);
if (!ret) {
ClearKernelOldOutputAndWorkspace(graph);
ClearSwapInfo(true);
}
}
}
}
return true;
}
void GPUKernelRuntime::InitKernelRefCount(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
MemReuseUtilPtr mem_reuse_util_ptr = std::make_shared<memreuse::MemReuseUtil>();
MS_EXCEPTION_IF_NULL(mem_reuse_util_ptr);
// Init the kernel reference count.
if (!mem_reuse_util_ptr->InitDynamicKernelRef(graph)) {
MS_LOG(EXCEPTION) << "Init kernel reference count failed";
}
mem_reuse_util_ptr->SetKernelDefMap();
mem_reuse_util_ptr->SetReuseRefCount();
// Can't free the device address of graph output, so set the reference count of graph output specially.
mem_reuse_util_ptr->SetGraphOutputRefCount();
// Can't free the device address of summary nodes, so set the reference count of summary nodes specially.
mem_reuse_util_ptr->SetSummaryNodesRefCount();
auto graph_id = graph->graph_id();
mem_reuse_util_map_[graph_id] = mem_reuse_util_ptr;
}
void GPUKernelRuntime::InitMemorySwapInfo(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
GPUMemCopyManagerPtr gpu_mem_copy_manager = std::make_shared<GPUMemCopyManager>();
MS_EXCEPTION_IF_NULL(gpu_mem_copy_manager);
MemSwapManagerPtr mem_swap_manager = std::make_shared<MemSwapManager>(gpu_mem_copy_manager);
MS_EXCEPTION_IF_NULL(mem_swap_manager);
auto graph_id = graph->graph_id();
mem_swap_map_[graph_id] = mem_swap_manager;
is_first_step_map_[graph_id] = true;
}
void GPUKernelRuntime::InitKernelOutputAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
if (AnfAlgo::OutputAddrExist(kernel, i)) {
continue;
}
std::string output_format = AnfAlgo::GetOutputFormat(kernel, i);
auto output_type = AnfAlgo::GetOutputDeviceDataType(kernel, i);
auto device_address = CreateDeviceAddress(nullptr, output_sizes[i], output_format, output_type);
AnfAlgo::SetOutputAddr(device_address, i, kernel.get());
}
}
}
void GPUKernelRuntime::InitKernelWorkspaceAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto workspace_sizes = kernel_mod->GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
auto device_address = CreateDeviceAddress(nullptr, workspace_sizes[i], "", kTypeUnknown);
AnfAlgo::SetWorkspaceAddr(device_address, i, kernel.get());
}
}
}
void GPUKernelRuntime::SaveGraphOutputNode(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto graph_id = graph->graph_id();
const auto &output_nodes = AnfAlgo::GetAllOutput(graph->output(), {prim::kPrimTupleGetItem});
for (const auto &node : output_nodes) {
graph_output_map_[graph_id].insert(node);
}
}
bool GPUKernelRuntime::IsGraphOutput(const session::KernelGraph *graph, const mindspore::AnfNodePtr &kernel) const {
MS_EXCEPTION_IF_NULL(graph);
auto graph_id = graph->graph_id();
auto iter = graph_output_map_.find(graph_id);
if (iter == graph_output_map_.end()) {
MS_LOG(EXCEPTION) << "Find graph output info failed.";
}
auto &graph_output_set = iter->second;
return (graph_output_set.find(kernel) != graph_output_set.end());
}
void GPUKernelRuntime::ClearKernelOldOutputAndWorkspace(const session::KernelGraph *graph) {
ClearKernelOutputAddress(graph);
ClearKernelWorkspaceAddress(graph);
}
void GPUKernelRuntime::ClearKernelOutputAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
if (IsGraphOutput(graph, kernel)) {
continue;
}
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
if (!AnfAlgo::OutputAddrExist(kernel, i)) {
continue;
}
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
}
void GPUKernelRuntime::ClearKernelWorkspaceAddress(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto workspace_sizes = kernel_mod->GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
}
}
}
bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, Debugger *debugger, bool mock,
bool profiling) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
// Reset the reference count.
mem_reuse_util_->ResetDynamicUsedRefCount();
// The inputs and outputs memory of communication kernel need be continuous, so separate processing.
AllocCommunicationOpDynamicRes(graph);
#ifdef ENABLE_DEBUGGER
bool dump_enabled = GPUKernelRuntime::DumpDataEnabledIteration();
if (!mock) {
UpdateStepNum(debugger, dump_enabled);
}
#endif
auto &kernels = graph->execution_order();
int exec_order = 1;
auto profiler_inst = profiler::gpu::GPUProfiler::GetInstance();
MS_EXCEPTION_IF_NULL(profiler_inst);
for (const auto &kernel : kernels) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
AddressPtrList kernel_inputs;
AddressPtrList kernel_workspaces;
AddressPtrList kernel_outputs;
auto ret = AllocKernelDynamicRes(*kernel_mod, kernel, &kernel_inputs, &kernel_workspaces, &kernel_outputs, mock);
if (!ret) {
#ifdef ENABLE_DEBUGGER
if (!mock) {
// invalidate current data collected by the debugger
ClearCurrentData(debugger, dump_enabled);
}
#endif
return false;
}
if (!mock) {
if (!profiling) {
if (profiler_inst->GetEnableFlag()) {
profiler_inst->OpDataProducerBegin(kernel->fullname_with_scope(), stream_);
}
CHECK_OP_RET_WITH_EXCEPT(kernel_mod->Launch(kernel_inputs, kernel_workspaces, kernel_outputs, stream_),
"Launch kernel failed.");
if (profiler_inst->GetEnableFlag()) {
profiler_inst->OpDataProducerEnd();
if (profiler_inst->GetSyncEnableFlag()) {
CHECK_OP_RET_WITH_ERROR(SyncStream(), "Profiler SyncStream failed.");
}
}
} else {
LaunchKernelWithTimeProfiling(kernel, kernel_inputs, kernel_workspaces, kernel_outputs);
}
#ifdef ENABLE_DEBUGGER
// called once per kernel to collect the outputs to the kernel (does a SyncDeviceToHost)
LoadKernelData(debugger, kernel, kernel_inputs, kernel_workspaces, kernel_outputs, exec_order, stream_,
dump_enabled);
#endif
}
exec_order = exec_order + 1;
FreeKernelDynamicRes(kernel);
if (!UpdateMemorySwapTask(kernel, mock, profiling)) {
#ifdef ENABLE_DEBUGGER
if (!mock) {
// invalidate current data collected by the debugger
ClearCurrentData(debugger, dump_enabled);
}
#endif
return false;
}
}
if (!mock) {
#ifdef ENABLE_DEBUGGER
// collect weights and bias for dump mode
LoadParameters(graph, debugger, dump_enabled);
#endif
CHECK_OP_RET_WITH_EXCEPT(SyncStream(), "SyncStream failed.");
}
ClearSwapInfo(mock);
return true;
}
void GPUKernelRuntime::LaunchKernelWithTimeProfiling(const AnfNodePtr &kernel, const AddressPtrList &inputs,
const AddressPtrList &workspace, const AddressPtrList &outputs) {
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
float cost_time = 0;
DeviceEvent start = nullptr;
DeviceEvent end = nullptr;
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateEvent(&start), "Failed to create event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::CreateEvent(&end), "Failed to create event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::RecordEvent(start, stream_), "Failed to record event to stream.");
CHECK_OP_RET_WITH_EXCEPT(kernel_mod->Launch(inputs, workspace, outputs, stream_), "Launch kernel failed.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::RecordEvent(end, stream_), "Failed to record event to stream.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::SyncEvent(start), "Failed to sync event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::SyncEvent(end), "Failed to sync event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::ElapsedTime(&cost_time, start, end), "Failed to record elapsed time.");
mem_swap_manager_->AddKernelExecutionPerform(kernel, cost_time);
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::DestroyEvent(start), "Failed to destroy event.");
CHECK_OP_RET_WITH_EXCEPT(CudaDriver::DestroyEvent(end), "Failed to destroy event.");
}
bool GPUKernelRuntime::AddMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
const MemSwapInfoSet &mem_swap_info_set = mem_swap_manager_->QueryKernelMemSwapInfo(kernel);
for (auto &mem_swap_info : mem_swap_info_set) {
auto need_swap_kernel = mem_swap_manager_->QueryKernelByTopoOrder(mem_swap_info.topo_order_);
MS_EXCEPTION_IF_NULL(need_swap_kernel);
const HostAddress &host_address =
mem_swap_manager_->QueryKernelHostAddr(need_swap_kernel, mem_swap_info.output_idx_);
auto device_address = AnfAlgo::GetMutableOutputAddr(need_swap_kernel, mem_swap_info.output_idx_, false);
if (mem_swap_info.swap_kind_ == SwapKind::kDeviceToHost) {
if (mem_swap_manager_->QueryKernelHostAddrIsDirty(need_swap_kernel, mem_swap_info.output_idx_)) {
mem_swap_manager_->AddMemSwapTask(SwapKind::kDeviceToHost, device_address, host_address, mock);
mem_swap_manager_->AddKernelHostAddrIsDirty(need_swap_kernel, mem_swap_info.output_idx_, false);
} else {
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInHost);
}
} else if (mem_swap_info.swap_kind_ == SwapKind::kHostToDevice) {
auto status = device_address->status();
if (status == DeviceAddressStatus::kInDeviceToHost) {
device_address->set_status(DeviceAddressStatus::kInDevice);
} else if (status == DeviceAddressStatus::kInHost) {
if (!device_address->ptr_ && !AttemptMallocMem(device_address, device_address->size_, mock)) {
return false;
}
float cost_time = 0;
mem_swap_manager_->AddMemSwapTask(SwapKind::kHostToDevice, device_address, host_address, mock, profiling,
&cost_time);
if (profiling) {
mem_swap_manager_->AddKernelSwapPerform(need_swap_kernel, mem_swap_info.output_idx_,
std::make_pair(0, cost_time));
}
}
}
}
return true;
}
bool GPUKernelRuntime::UpdateMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return true;
}
if (mem_swap_manager_->QueryKernelTriggerSwap(kernel)) {
if (!mock) {
CHECK_OP_RET_WITH_EXCEPT(SyncStream(), "SyncStream failed.");
}
if (!AddMemorySwapTask(kernel, mock, profiling)) {
return false;
}
if (!mock) {
CHECK_OP_RET_WITH_EXCEPT(mem_swap_manager_->SyncMemCopyStream(SwapKind::kDeviceToHost), "SyncCopyStream failed.");
}
}
return true;
}
void GPUKernelRuntime::UpdateHostSwapInQueue(const DeviceAddressPtr device_address, bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
while (auto device_address_swap_in = mem_swap_manager_->UpdateSwapQueue(SwapKind::kHostToDevice, mock)) {
device_address_swap_in->set_status(DeviceAddressStatus::kInDevice);
}
auto status = device_address->status();
switch (status) {
case DeviceAddressStatus::kInDevice:
break;
case DeviceAddressStatus::kInDeviceToHost: {
device_address->set_status(DeviceAddressStatus::kInDevice);
break;
}
case DeviceAddressStatus::kInHostToDevice: {
while (device_address->status() != DeviceAddressStatus::kInDevice) {
while (auto device_address_swap_in = mem_swap_manager_->UpdateSwapQueue(SwapKind::kHostToDevice, mock)) {
device_address_swap_in->set_status(DeviceAddressStatus::kInDevice);
}
}
break;
}
case DeviceAddressStatus::kInHost:
MS_LOG(WARNING) << "Unexpected device address status: " << status;
break;
default:
MS_LOG(EXCEPTION) << "Invaild device address status: " << status;
}
}
void GPUKernelRuntime::UpdateHostSwapOutQueue(bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
while (auto device_address_swap_out = mem_swap_manager_->UpdateSwapQueue(SwapKind::kDeviceToHost, mock)) {
if (device_address_swap_out->status() == DeviceAddressStatus::kInDeviceToHost && device_address_swap_out->ptr_) {
device_address_swap_out->set_status(DeviceAddressStatus::kInHost);
mem_manager_->FreeMemFromMemPool(device_address_swap_out);
}
}
}
void GPUKernelRuntime::ClearSwapInfo(bool mock) {
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
if (!mem_swap_manager_->trigger_swap()) {
return;
}
mem_swap_manager_->ClearSwapQueue(mock);
mem_swap_manager_->ResetHostAddrIsDirty();
}
bool GPUKernelRuntime::AttemptMallocMem(const DeviceAddressPtr &device_address, size_t size, bool mock) {
MS_EXCEPTION_IF_NULL(mem_manager_);
MS_EXCEPTION_IF_NULL(mem_swap_manager_);
auto ret = mem_manager_->MallocMemFromMemPool(device_address, size);
if (!ret) {
if (!mem_swap_manager_->trigger_swap()) {
return false;
}
if (!mock) {
mem_swap_manager_->SyncMemCopyStream(SwapKind::kDeviceToHost);
}
UpdateHostSwapOutQueue(mock);
ret = mem_manager_->MallocMemFromMemPool(device_address, size);
if (!ret) {
return false;
}
}
return true;
}
bool GPUKernelRuntime::AllocKernelDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs,
AddressPtrList *kernel_workspaces, AddressPtrList *kernel_outputs,
bool mock) {
if (!AllocKernelInputDynamicRes(kernel, kernel_inputs, mock)) {
return false;
}
if (!AllocKernelOutputDynamicRes(kernel_mod, kernel, kernel_outputs, mock)) {
return false;
}
if (!AllocKernelWorkspaceDynamicRes(kernel_mod, kernel, kernel_workspaces, mock)) {
return false;
}
return true;
}
bool GPUKernelRuntime::AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs,
bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_inputs);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
MS_EXCEPTION_IF_NULL(device_address);
UpdateHostSwapInQueue(device_address, mock);
MS_EXCEPTION_IF_NULL(device_address->ptr_);
kernel::AddressPtr input = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(input);
input->addr = device_address->ptr_;
input->size = device_address->size_;
kernel_inputs->emplace_back(input);
}
return true;
}
bool GPUKernelRuntime::AllocKernelOutputDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_outputs,
bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_outputs);
UpdateHostSwapOutQueue(mock);
auto output_sizes = kernel_mod.GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr && !AttemptMallocMem(device_address, output_sizes[i], mock)) {
return false;
}
kernel::AddressPtr output = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(output);
output->addr = device_address->ptr_;
output->size = output_sizes[i];
kernel_outputs->emplace_back(output);
}
return true;
}
bool GPUKernelRuntime::AllocKernelWorkspaceDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_workspaces, bool mock) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(kernel_workspaces);
auto workspace_sizes = kernel_mod.GetWorkspaceSizeList();
for (size_t i = 0; i < workspace_sizes.size(); ++i) {
if (workspace_sizes[i] == 0) {
kernel_workspaces->emplace_back(nullptr);
continue;
}
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
if (device_address->ptr_ == nullptr && !AttemptMallocMem(device_address, workspace_sizes[i], mock)) {
return false;
}
kernel::AddressPtr workspace = std::make_shared<kernel::Address>();
MS_EXCEPTION_IF_NULL(workspace);
workspace->addr = device_address->ptr_;
workspace->size = workspace_sizes[i];
kernel_workspaces->emplace_back(workspace);
}
return true;
}
void GPUKernelRuntime::AllocCommunicationOpDynamicRes(const session::KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto &kernels = graph->execution_order();
for (auto &kernel : kernels) {
MS_EXCEPTION_IF_NULL(kernel);
if (AnfAlgo::IsCommunicationOp(kernel)) {
AllocCommunicationOpInputDynamicRes(kernel);
AllocCommunicationOpOutputDynamicRes(kernel);
}
}
}
void GPUKernelRuntime::AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
bool is_need_alloc_memory = false;
bool is_need_free_memory = false;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto intput_sizes = kernel_mod->GetInputSizeList();
for (size_t i = 0; i < intput_sizes.size(); ++i) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr) {
is_need_alloc_memory = true;
} else {
is_need_free_memory = true;
}
total_size += intput_sizes[i];
size_list.emplace_back(intput_sizes[i]);
addr_list.emplace_back(device_address);
}
AllocCommunicationOpMemory(is_need_alloc_memory, is_need_free_memory, addr_list, total_size, size_list);
}
void GPUKernelRuntime::AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
bool is_need_alloc_memory = false;
bool is_need_free_memory = false;
size_t total_size = 0;
std::vector<size_t> size_list;
DeviceAddressPtrList addr_list;
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto output_sizes = kernel_mod->GetOutputSizeList();
for (size_t i = 0; i < output_sizes.size(); ++i) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_ == nullptr) {
is_need_alloc_memory = true;
} else {
is_need_free_memory = true;
}
total_size += output_sizes[i];
size_list.emplace_back(output_sizes[i]);
addr_list.emplace_back(device_address);
}
AllocCommunicationOpMemory(is_need_alloc_memory, is_need_free_memory, addr_list, total_size, size_list);
}
void GPUKernelRuntime::AllocCommunicationOpMemory(bool is_need_alloc_memory, bool is_need_free_memory,
const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list) {
MS_EXCEPTION_IF_NULL(mem_manager_);
if (!is_need_alloc_memory) {
return;
}
if (is_need_free_memory) {
for (const auto &iter : addr_list) {
MS_EXCEPTION_IF_NULL(iter);
// Free the inputs/outputs of communication kernel which are not released.
if (iter->ptr_ != nullptr) {
mem_manager_->FreeMemFromMemPool(iter);
}
}
}
auto ret = mem_manager_->MallocContinuousMemFromMemPool(addr_list, total_size, size_list);
if (!ret) {
MS_LOG(EXCEPTION) << "Malloc device memory failed.";
}
}
void GPUKernelRuntime::FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
MS_EXCEPTION_IF_NULL(mem_manager_);
MS_EXCEPTION_IF_NULL(mem_reuse_util_);
auto cnode = kernel->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (AnfAlgo::IsCommunicationOp(kernel)) {
return;
}
// Free the input of kernel by reference count.
for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(kernel); ++i) {
auto kernel_ref_count_ptr = mem_reuse_util_->GetKernelInputRef(cnode, i);
if (kernel_ref_count_ptr == nullptr) {
continue;
}
kernel_ref_count_ptr->ref_count_dynamic_use_--;
if (kernel_ref_count_ptr->ref_count_dynamic_use_ < 0) {
MS_LOG(EXCEPTION) << "Check dynamic reference count failed.";
}
if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
DeviceAddressPtr device_address;
if (mem_reuse_util_->is_all_nop_node()) {
// Graph may be all nop nodes and not remove nop node, so this can not skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, false);
} else {
// Graph may be "nop node + depend + node", the input of node is the depend, so this case need skip nop node.
device_address = AnfAlgo::GetPrevNodeMutableOutputAddr(kernel, i, true);
}
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
// Free the output of kernel, if output has no reference.
for (size_t i = 0; i < AnfAlgo::GetOutputTensorNum(kernel); ++i) {
auto kernel_ref_count_ptr = mem_reuse_util_->GetRef(cnode, i);
if (kernel_ref_count_ptr == nullptr) {
continue;
}
if (kernel_ref_count_ptr->ref_count_dynamic_use_ == 0) {
auto device_address = AnfAlgo::GetMutableOutputAddr(kernel, i, false);
mem_manager_->FreeMemFromMemPool(device_address);
device_address->set_status(DeviceAddressStatus::kInDevice);
}
}
// Free the workspace of kernel.
auto kernel_mod = AnfAlgo::GetKernelMod(kernel);
MS_EXCEPTION_IF_NULL(kernel_mod);
for (size_t i = 0; i < kernel_mod->GetWorkspaceSizeList().size(); ++i) {
auto device_address = AnfAlgo::GetMutableWorkspaceAddr(kernel, i);
MS_EXCEPTION_IF_NULL(device_address);
if (device_address->ptr_) {
mem_manager_->FreeMemFromMemPool(device_address);
}
}
}
} // namespace gpu
} // namespace device
} // namespace mindspore
mindspore/ccsrc/runtime/device/gpu/gpu_kernel_runtime.h
浏览文件 @ bbafa9db
/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
#define MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
#include <string>
#include <memory>
#include <vector>
#include <set>
#include <utility>
#include <unordered_map>
#include <unordered_set>
#include "runtime/device/kernel_runtime.h"
#include "runtime/device/kernel_runtime_manager.h"
#include "backend/optimizer/mem_reuse/mem_swap_manager.h"
namespace mindspore {
namespace device {
namespace gpu {
using mindspore::device::memswap::MemSwapManagerPtr;
class GPUKernelRuntime : public KernelRuntime {
public:
GPUKernelRuntime() = default;
~GPUKernelRuntime() override = default;
bool Init() override;
void ReleaseDeviceRes() override;
void ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
const std::unordered_set<ValueNodePtr> &value_nodes,
const std::vector<CNodePtr> &execution_order) override;
void AssignMemory(session::KernelGraph *graph) override;
bool Run(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
#ifdef ENABLE_DUMP_E2E
bool DumpData(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
#endif
protected:
DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
TypeId type_id) override;
bool SyncStream() override;
private:
GPUKernelRuntime(const GPUKernelRuntime &);
GPUKernelRuntime &operator=(const GPUKernelRuntime &);
bool InitDevice();
bool device_init_{false};
// The related functions and members for using dynamic memory pool.
void InitKernelRefCount(const session::KernelGraph *graph);
void InitKernelOutputAddress(const session::KernelGraph *graph);
void InitKernelWorkspaceAddress(const session::KernelGraph *graph);
void InitMemorySwapInfo(const session::KernelGraph *graph);
void SaveGraphOutputNode(const session::KernelGraph *graph);
bool IsGraphOutput(const session::KernelGraph *graph, const mindspore::AnfNodePtr &kernel) const;
void ClearKernelOutputAddress(const session::KernelGraph *graph);
void ClearKernelWorkspaceAddress(const session::KernelGraph *graph);
void ClearKernelOldOutputAndWorkspace(const session::KernelGraph *graph);
bool RunOneStep(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool SearchMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool RefineMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool LaunchKernelDynamic(const session::KernelGraph *graph, Debugger *debugger = nullptr, bool mock = false,
bool profiling = false);
void LaunchKernelWithTimeProfiling(const AnfNodePtr &kernel, const AddressPtrList &inputs,
const AddressPtrList &workspace, const AddressPtrList &outputs);
bool AttemptMallocMem(const DeviceAddressPtr &device_address, size_t size, bool mock);
bool AllocKernelDynamicRes(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_inputs, AddressPtrList *kernel_workspaces,
AddressPtrList *kernel_outputs, bool mock);
bool AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs, bool mock);
bool AllocKernelOutputDynamicRes(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_outputs, bool mock);
bool AllocKernelWorkspaceDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_workspaces,
bool mock);
void AllocCommunicationOpDynamicRes(const session::KernelGraph *graph);
void AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel);
void AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel);
void AllocCommunicationOpMemory(bool is_need_alloc_memory, bool is_need_free_memory,
const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list);
void FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel);
bool UpdateMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling);
bool AddMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling);
void UpdateHostSwapInQueue(const DeviceAddressPtr device_address, bool mock);
void UpdateHostSwapOutQueue(bool mock);
void ClearSwapInfo(bool mock);
std::unordered_map<uint32_t, MemReuseUtilPtr> mem_reuse_util_map_;
std::unordered_map<uint32_t, MemSwapManagerPtr> mem_swap_map_;
std::unordered_map<uint32_t, bool> is_first_step_map_;
std::unordered_map<uint32_t, std::set<AnfNodePtr>> graph_output_map_;
MemReuseUtilPtr mem_reuse_util_{nullptr};
MemSwapManagerPtr mem_swap_manager_{nullptr};
};
MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime);
} // namespace gpu
} // namespace device
} // namespace mindspore
#endif // MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
#define MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
#include <string>
#include <memory>
#include <vector>
#include <set>
#include <utility>
#include <unordered_map>
#include <unordered_set>
#include "runtime/device/kernel_runtime.h"
#include "runtime/device/kernel_runtime_manager.h"
#include "backend/optimizer/mem_reuse/mem_swap_manager.h"
namespace mindspore {
namespace device {
namespace gpu {
using mindspore::device::memswap::MemSwapManagerPtr;
class GPUKernelRuntime : public KernelRuntime {
public:
GPUKernelRuntime() = default;
~GPUKernelRuntime() override = default;
bool Init() override;
void ReleaseDeviceRes() override;
void ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
const std::unordered_set<ValueNodePtr> &value_nodes,
const std::vector<CNodePtr> &execution_order) override;
void AssignMemory(session::KernelGraph *graph) override;
bool Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger = nullptr) override;
#ifdef ENABLE_DUMP_E2E
bool DumpData(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
#endif
protected:
DeviceAddressPtr CreateDeviceAddress(void *device_ptr, size_t device_size, const string &format,
TypeId type_id) override;
bool SyncStream() override;
private:
GPUKernelRuntime(const GPUKernelRuntime &);
GPUKernelRuntime &operator=(const GPUKernelRuntime &);
bool InitDevice();
bool device_init_{false};
// The related functions and members for using dynamic memory pool.
void InitKernelRefCount(const session::KernelGraph *graph);
void InitKernelOutputAddress(const session::KernelGraph *graph);
void InitKernelWorkspaceAddress(const session::KernelGraph *graph);
void InitMemorySwapInfo(const session::KernelGraph *graph);
void SaveGraphOutputNode(const session::KernelGraph *graph);
bool IsGraphOutput(const session::KernelGraph *graph, const mindspore::AnfNodePtr &kernel) const;
void ClearKernelOutputAddress(const session::KernelGraph *graph);
void ClearKernelWorkspaceAddress(const session::KernelGraph *graph);
void ClearKernelOldOutputAndWorkspace(const session::KernelGraph *graph);
bool RunOneStep(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool SearchMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool RefineMemSwapScheme(const session::KernelGraph *graph, Debugger *debugger = nullptr);
bool LaunchKernelDynamic(const session::KernelGraph *graph, Debugger *debugger = nullptr, bool mock = false,
bool profiling = false);
void LaunchKernelWithTimeProfiling(const AnfNodePtr &kernel, const AddressPtrList &inputs,
const AddressPtrList &workspace, const AddressPtrList &outputs);
bool AttemptMallocMem(const DeviceAddressPtr &device_address, size_t size, bool mock);
bool AllocKernelDynamicRes(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_inputs, AddressPtrList *kernel_workspaces,
AddressPtrList *kernel_outputs, bool mock);
bool AllocKernelInputDynamicRes(const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_inputs, bool mock);
bool AllocKernelOutputDynamicRes(const mindspore::kernel::KernelMod &kernel_mod, const mindspore::AnfNodePtr &kernel,
AddressPtrList *kernel_outputs, bool mock);
bool AllocKernelWorkspaceDynamicRes(const mindspore::kernel::KernelMod &kernel_mod,
const mindspore::AnfNodePtr &kernel, AddressPtrList *kernel_workspaces,
bool mock);
void AllocCommunicationOpDynamicRes(const session::KernelGraph *graph);
void AllocCommunicationOpInputDynamicRes(const mindspore::AnfNodePtr &kernel);
void AllocCommunicationOpOutputDynamicRes(const mindspore::AnfNodePtr &kernel);
void AllocCommunicationOpMemory(bool is_need_alloc_memory, bool is_need_free_memory,
const DeviceAddressPtrList addr_list, size_t total_size,
std::vector<size_t> size_list);
void FreeKernelDynamicRes(const mindspore::AnfNodePtr &kernel);
bool UpdateMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling);
bool AddMemorySwapTask(const AnfNodePtr &kernel, bool mock, bool profiling);
void UpdateHostSwapInQueue(const DeviceAddressPtr device_address, bool mock);
void UpdateHostSwapOutQueue(bool mock);
void ClearSwapInfo(bool mock);
std::unordered_map<uint32_t, MemReuseUtilPtr> mem_reuse_util_map_;
std::unordered_map<uint32_t, MemSwapManagerPtr> mem_swap_map_;
std::unordered_map<uint32_t, bool> is_first_step_map_;
std::unordered_map<uint32_t, std::set<AnfNodePtr>> graph_output_map_;
MemReuseUtilPtr mem_reuse_util_{nullptr};
MemSwapManagerPtr mem_swap_manager_{nullptr};
};
MS_REG_KERNEL_RUNTIME(kGPUDevice, GPUKernelRuntime);
} // namespace gpu
} // namespace device
} // namespace mindspore
#endif // MINDSPORE_CCSRC_RUNTIME_DEVICE_GPU_GPU_KERNEL_RUNTIME_H_
mindspore/ccsrc/runtime/device/kernel_runtime.cc
浏览文件 @ bbafa9db
......@@ -40,7 +40,7 @@ KernelRuntime::~KernelRuntime() {
#endif
}
bool KernelRuntime::Load(session::KernelGraph *graph) { return true; }
bool KernelRuntime::Load(session::KernelGraph *graph, bool is_task_sink) { return true; }
bool KernelRuntime::DumpData(mindspore::session::KernelGraph *graph, Debugger *debugger) {
if (graph != nullptr) {
......
mindspore/ccsrc/runtime/device/kernel_runtime.h
浏览文件 @ bbafa9db
......@@ -59,8 +59,8 @@ class KernelRuntime {
bool DumpDataEnabled();
bool DumpDataEnabledIteration();
virtual bool DumpData(session::KernelGraph *graph, Debugger *debugger = nullptr);
virtual bool Load(session::KernelGraph *graph);
virtual bool Run(session::KernelGraph *graph, Debugger *debugger = nullptr) = 0;
virtual bool Load(session::KernelGraph *graph, bool is_task_sink);
virtual bool Run(session::KernelGraph *graph, bool is_task_sink, Debugger *debugger = nullptr) = 0;
bool LaunchKernel(const session::KernelGraph *graph);
bool LaunchTaskBasedOnSingleKernel(kernel::KernelModPtr kernel_mod_ptr, const AddressPtrList &kernel_inputs,
const AddressPtrList &kernel_outputs,
......
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