forked from huawei/mindspore2022
377 lines
14 KiB
C++
377 lines
14 KiB
C++
/**
|
|
* Copyright 2019-2020 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 "debug/anf_ir_utils.h"
|
|
#include "backend/session/gpu_session.h"
|
|
#include "runtime/device/gpu/kernel_info_setter.h"
|
|
#include "runtime/device/gpu/gpu_kernel_build.h"
|
|
#include "runtime/device/gpu/gpu_kernel_runtime.h"
|
|
#include "runtime/device/gpu/gpu_stream_assign.h"
|
|
#include "backend/optimizer/common/optimizer.h"
|
|
#include "backend/optimizer/common/pass_manager.h"
|
|
#include "backend/optimizer/common/helper.h"
|
|
#include "backend/optimizer/pass/communication_op_fusion.h"
|
|
#include "backend/optimizer/pass/getitem_tuple.h"
|
|
#include "backend/optimizer/gpu/adam_weight_decay_fusion.h"
|
|
#include "backend/optimizer/gpu/adam_fusion.h"
|
|
#include "backend/optimizer/gpu/replace_bn_cast_fusion.h"
|
|
#include "backend/optimizer/gpu/replace_bn_grad_cast_fusion.h"
|
|
#include "backend/optimizer/gpu/replace_momentum_cast_fusion.h"
|
|
#include "backend/optimizer/gpu/replace_addn_fusion.h"
|
|
#include "runtime/device/kernel_runtime_manager.h"
|
|
#include "common/utils.h"
|
|
#include "common/trans.h"
|
|
#include "utils/context/ms_context.h"
|
|
#include "utils/base_ref_extends.h"
|
|
#include "debug/tensor_load.h"
|
|
|
|
namespace mindspore {
|
|
namespace session {
|
|
namespace gpu {
|
|
using AnfAlgo = mindspore::session::AnfRuntimeAlgorithm;
|
|
|
|
void GPUSession::SelectKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
for (const auto &kernel_node : kernel_graph->execution_order()) {
|
|
MS_EXCEPTION_IF_NULL(kernel_node);
|
|
device::gpu::SetKernelInfo(kernel_node);
|
|
}
|
|
}
|
|
|
|
void GPUSession::StartKernelRT() const {
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
if (!runtime_instance->Init()) {
|
|
MS_LOG(EXCEPTION) << "GPU start kernel runtime failed";
|
|
}
|
|
}
|
|
|
|
void GPUSession::Optimize(const std::shared_ptr<KernelGraph> &kernel_graph) {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto optimizer = std::make_shared<opt::GraphOptimizer>();
|
|
auto pm = std::make_shared<opt::PassManager>();
|
|
pm->AddPass(std::make_shared<opt::AdamWeightDecayFusion>());
|
|
pm->AddPass(std::make_shared<opt::AdamFusion>());
|
|
pm->AddPass(std::make_shared<opt::ReplaceBNCastFusion>());
|
|
pm->AddPass(std::make_shared<opt::ReplaceBNGradCastFusion>());
|
|
pm->AddPass(std::make_shared<opt::ReplaceMomentumCastFusion>());
|
|
pm->AddPass(std::make_shared<opt::ReplaceAddNFusion>());
|
|
optimizer->AddPassManager(pm);
|
|
(void)optimizer->Optimize(kernel_graph);
|
|
kernel_graph->SetExecOrderByDefault();
|
|
}
|
|
|
|
void GPUSession::HardwareOptimize(const std::shared_ptr<KernelGraph> &kernel_graph) {
|
|
auto optimizer = std::make_shared<opt::GraphOptimizer>();
|
|
auto pm = std::make_shared<opt::PassManager>();
|
|
pm->AddPass(std::make_shared<opt::AllReduceFusion>());
|
|
pm->AddPass(std::make_shared<opt::GetitemTuple>());
|
|
optimizer->AddPassManager(pm);
|
|
(void)optimizer->Optimize(kernel_graph);
|
|
kernel_graph->SetExecOrderByDefault();
|
|
}
|
|
|
|
void GPUSession::AssignStream(const std::shared_ptr<KernelGraph> &kernel_graph) {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
device::gpu::AssignGpuStream(kernel_graph);
|
|
}
|
|
|
|
void GPUSession::BuildKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
device::gpu::GpuBuild(kernel_graph);
|
|
}
|
|
|
|
void GPUSession::AllocateMemory(KernelGraph *kernel_graph) const {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
runtime_instance->AssignMemory(kernel_graph);
|
|
}
|
|
|
|
void GPUSession::RunOpAllocateMemory(const ValuePtr &pre_output_value,
|
|
const std::vector<tensor::TensorPtr> &input_tensors,
|
|
KernelGraph *kernel_graph) const {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
runtime_instance->RunOpAssignMemory(pre_output_value, input_tensors, kernel_graph);
|
|
}
|
|
|
|
void GPUSession::RunOpClearMemory(KernelGraph *kernel_graph) const {
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
runtime_instance->RunOpClearMemory(kernel_graph);
|
|
}
|
|
|
|
void GPUSession::LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
|
|
const std::vector<tensor::TensorPtr> &inputs_const) const {
|
|
std::vector<tensor::TensorPtr> inputs(inputs_const);
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto input_nodes = kernel_graph->inputs();
|
|
auto ms_context = MsContext::GetInstance();
|
|
MS_EXCEPTION_IF_NULL(ms_context);
|
|
|
|
for (size_t i = 0; i < inputs.size(); ++i) {
|
|
auto tensor = inputs[i];
|
|
MS_EXCEPTION_IF_NULL(tensor);
|
|
auto input_node = input_nodes[i];
|
|
MS_EXCEPTION_IF_NULL(input_node);
|
|
if (input_node->isa<Parameter>() && AnfAlgo::OutputAddrExist(input_node, 0)) {
|
|
auto pk_node = input_node->cast<ParameterPtr>();
|
|
auto device_address = AnfAlgo::GetMutableOutputAddr(pk_node, 0);
|
|
auto tensor_address = std::dynamic_pointer_cast<device::DeviceAddress>(tensor->device_address());
|
|
bool need_sync = false;
|
|
if (ms_context->enable_pynative_infer()) {
|
|
if (tensor_address == nullptr || tensor_address != device_address) {
|
|
need_sync = true;
|
|
}
|
|
} else if (tensor->is_dirty() || tensor_address == nullptr) {
|
|
need_sync = true;
|
|
} else if (tensor_address != device_address) {
|
|
if (tensor_address->DeviceType() == device_address->DeviceType()) {
|
|
AnfAlgo::SetOutputAddr(tensor_address, 0, pk_node.get());
|
|
} else {
|
|
need_sync = true;
|
|
}
|
|
}
|
|
if (need_sync) {
|
|
tensor->set_device_address(device_address);
|
|
MS_EXCEPTION_IF_NULL(device_address);
|
|
if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(pk_node, 0),
|
|
LongToSize(tensor->data().nbytes()), tensor->data_type(),
|
|
tensor->data_c())) {
|
|
MS_LOG(EXCEPTION) << "SyncHostToDevice failed.";
|
|
}
|
|
}
|
|
}
|
|
tensor->set_dirty(false);
|
|
}
|
|
}
|
|
|
|
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())) {
|
|
#else
|
|
if (!runtime_instance->Run(kernel_graph.get())) {
|
|
#endif
|
|
MS_LOG(EXCEPTION) << "GPU execute graph failed!";
|
|
}
|
|
}
|
|
|
|
GraphId GPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
|
|
// Construct graph, if successfully, graph_sum_ + 1
|
|
auto graph_id = graph_sum_;
|
|
auto graph = ConstructKernelGraph(lst, outputs);
|
|
MS_EXCEPTION_IF_NULL(graph);
|
|
// Prepare ms context info for dump .pb graph
|
|
auto context_ptr = MsContext::GetInstance();
|
|
MS_EXCEPTION_IF_NULL(context_ptr);
|
|
bool save_graphs = context_ptr->save_graphs_flag();
|
|
// Optimize
|
|
Optimize(graph);
|
|
// Select kernel build info
|
|
SelectKernel(graph);
|
|
#if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
|
|
// Assign parameter keys.
|
|
AssignParamKey(graph);
|
|
#endif
|
|
// Start gpu kernel runtime
|
|
StartKernelRT();
|
|
// Dump .pb graph before hardware optimization
|
|
if (save_graphs) {
|
|
DumpIRProto(graph, "before_hwopt_" + std::to_string(graph_id));
|
|
}
|
|
// HardwareOptimize
|
|
HardwareOptimize(graph);
|
|
// Dump .pb graph after hardware optimization
|
|
if (save_graphs) {
|
|
DumpIRProto(graph, "after_hwopt_" + std::to_string(graph_id));
|
|
}
|
|
// Assign CUDA streams
|
|
AssignStream(graph);
|
|
// Hide NoOp from execution graph
|
|
opt::HideNopNode(graph.get());
|
|
// Build kernel if node is cnode
|
|
BuildKernel(graph);
|
|
// Set graph execution order before memory alloc, ensure that memory alloc is according to the reorder graph
|
|
auto execution_order = graph->execution_order();
|
|
Reorder(&execution_order);
|
|
graph->set_execution_order(execution_order);
|
|
// Get summary nodes.
|
|
SetSummaryNodes(graph.get());
|
|
// Remove NoOp from execution graph
|
|
opt::RemoveNopNode(graph.get());
|
|
// Set graph manager.
|
|
MS_EXCEPTION_IF_NULL(context_);
|
|
FuncGraphManagerPtr manager = MakeManager({graph});
|
|
context_->AddManager(manager);
|
|
if (manager) {
|
|
manager->AddFuncGraph(graph);
|
|
graph->set_manager(manager);
|
|
}
|
|
// Alloc memory, including static memory and dynamic memory
|
|
AllocateMemory(graph.get());
|
|
return graph_id;
|
|
}
|
|
|
|
void GPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) {
|
|
auto &kernel_graph = graphs_[graph_id];
|
|
#ifdef ENABLE_DEBUGGER
|
|
PreIterationDbg(kernel_graph);
|
|
#endif
|
|
// Load input data from user input
|
|
LoadInputData(kernel_graph, inputs);
|
|
#if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
|
|
// Initialize parameter server
|
|
if (!ps_init_) {
|
|
InitPSParamAndOptim(kernel_graph, inputs);
|
|
}
|
|
#endif
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
{
|
|
py::gil_scoped_release gil_release;
|
|
// Run graph on GPU
|
|
Execute(kernel_graph);
|
|
}
|
|
#ifdef ENABLE_DEBUGGER
|
|
PostLoadTensor(kernel_graph);
|
|
#endif
|
|
// Get result from GPU
|
|
UpdateOutputs(kernel_graph, outputs, inputs);
|
|
// Summary
|
|
auto context_ptr = MsContext::GetInstance();
|
|
MS_EXCEPTION_IF_NULL(context_ptr);
|
|
if (context_ptr->enable_gpu_summary()) {
|
|
Summary(kernel_graph.get());
|
|
}
|
|
#ifdef ENABLE_DEBUGGER
|
|
PostIterationDbg(kernel_graph);
|
|
#endif
|
|
}
|
|
|
|
void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
|
|
const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) {
|
|
// Check if the graph cache exists.
|
|
if (run_op_graphs_.find(graph_info) != run_op_graphs_.end()) {
|
|
return;
|
|
}
|
|
// Prepare the graph
|
|
auto kernel_graph = ConstructSingleOpGraph(op_run_info, input_tensors, tensors_mask);
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
SelectKernel(kernel_graph);
|
|
StartKernelRT();
|
|
// Hide NoOp from execution graph
|
|
opt::HideNopNode(kernel_graph.get());
|
|
BuildKernel(kernel_graph);
|
|
run_op_graphs_[graph_info] = kernel_graph;
|
|
}
|
|
|
|
py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
|
|
const std::vector<tensor::TensorPtr> &input_tensors) {
|
|
auto kernel_graph = run_op_graphs_[graph_info];
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
// Remove NoOp from execution graph
|
|
opt::RemoveNopNode(kernel_graph.get());
|
|
RunOpAllocateMemory(op_run_info.value, input_tensors, kernel_graph.get());
|
|
// Execute the computation
|
|
LoadInputData(kernel_graph, input_tensors);
|
|
{
|
|
py::gil_scoped_release gil_release;
|
|
Execute(kernel_graph);
|
|
}
|
|
// Fetch outputs
|
|
VectorRef outputs;
|
|
UpdateOutputs(kernel_graph, &outputs, input_tensors);
|
|
// Trans output to tuple
|
|
auto output_tensors = TransformBaseRefListToTuple(outputs);
|
|
if (!utils::isa<PyObjectRef>(output_tensors) ||
|
|
!py::isinstance<py::tuple>(utils::cast<PyObjectRef>(output_tensors).object_)) {
|
|
MS_EXCEPTION(NotSupportError) << "The output tensors should be a tuple !";
|
|
}
|
|
py::object tuple_obj = utils::cast<PyObjectRef>(output_tensors).object_;
|
|
py::tuple tuple_tensors = py::cast<py::tuple>(tuple_obj);
|
|
RunOpClearMemory(kernel_graph.get());
|
|
return tuple_tensors;
|
|
}
|
|
|
|
#ifdef ENABLE_DEBUGGER
|
|
void GPUSession::Dump(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
#ifdef ENABLE_DUMP_E2E
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
(void)runtime_instance->DumpData(kernel_graph.get(), debugger_.get());
|
|
#endif
|
|
}
|
|
|
|
bool GPUSession::DumpDataEnabledIteration() const {
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
return runtime_instance->DumpDataEnabledIteration();
|
|
}
|
|
|
|
void GPUSession::PreIterationDbg(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
if (debugger_) {
|
|
debugger_->PreExecute(kernel_graph);
|
|
}
|
|
PreLoadTensor(kernel_graph);
|
|
}
|
|
|
|
void GPUSession::PostIterationDbg(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
bool dump_enabled = DumpDataEnabledIteration();
|
|
// debug used for dump
|
|
if (debugger_ && dump_enabled) {
|
|
Dump(kernel_graph);
|
|
}
|
|
if (debugger_) {
|
|
debugger_->PostExecute();
|
|
}
|
|
}
|
|
|
|
void GPUSession::PreLoadTensor(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
bool dump_enabled = DumpDataEnabledIteration();
|
|
if (!(debugger_ && (debugger_->debugger_enabled() || dump_enabled))) {
|
|
return;
|
|
}
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
DebugServices *debug_services = debugger_->debug_services();
|
|
TensorLoader *tensor_loader = debug_services->tensor_loader();
|
|
tensor_loader->EmptyTensor();
|
|
uint32_t iter_num = tensor_loader->GetIterNum();
|
|
tensor_loader->set_iter_num(++iter_num);
|
|
}
|
|
|
|
void GPUSession::PostLoadTensor(const std::shared_ptr<KernelGraph> &kernel_graph) const {
|
|
bool dump_enabled = DumpDataEnabledIteration();
|
|
if (!(debugger_ && (debugger_->debugger_enabled() || dump_enabled))) {
|
|
return;
|
|
}
|
|
MS_EXCEPTION_IF_NULL(kernel_graph);
|
|
auto runtime_instance = device::KernelRuntimeManager::Instance().GetSingleKernelRuntime(kGPUDevice, device_id_);
|
|
MS_EXCEPTION_IF_NULL(runtime_instance);
|
|
DebugServices *debug_services = debugger_->debug_services();
|
|
TensorLoader *tensor_loader = debug_services->tensor_loader();
|
|
tensor_loader->EmptyPrevTensor();
|
|
}
|
|
#endif
|
|
|
|
} // namespace gpu
|
|
} // namespace session
|
|
} // namespace mindspore
|