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mindspore/mindspore/ccsrc/backend/session/session_basic.cc

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/**
* 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 "backend/session/session_basic.h"
#include <utility>
#include <algorithm>
#include <unordered_map>
#include <unordered_set>
#include "pipeline/jit/parse/data_converter.h"
#include "ir/manager.h"
#include "ir/param_value.h"
#include "backend/kernel_compiler/common_utils.h"
#include "frontend/operator/ops.h"
#include "common/trans.h"
#include "utils/config_manager.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/kernel_compiler/oplib/oplib.h"
#include "backend/optimizer/common/common_backend_optimization.h"
#include "backend/optimizer/pass/const_input_to_attr_registry.h"
#include "backend/optimizer/common/helper.h"
#include "common/utils.h"
#include "ir/dtype.h"
#include "ir/anf.h"
#include "ir/func_graph_cloner.h"
#if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
#include "frontend/parallel/ps/worker.h"
#include "frontend/parallel/ps/common.h"
#include "frontend/parallel/ps/util.h"
#endif
namespace mindspore {
namespace session {
static std::shared_ptr<std::map<ValuePtr, ParameterPtr>> python_paras;
void ClearPythonParasMap() { python_paras = nullptr; }
namespace {
const int kSummaryGetItem = 2;
ValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
if (node == nullptr) {
return nullptr;
}
auto parameter = node->cast<ParameterPtr>();
if (parameter == nullptr || !parameter->has_default()) {
return nullptr;
}
return parameter->default_param();
}
BaseRef CreateOneTensor(const AnfNodePtr &node, size_t output_index, const KernelGraph &graph,
const std::vector<tensor::TensorPtr> &input_tensors) {
MS_EXCEPTION_IF_NULL(node);
MS_LOG(INFO) << "Create tensor for output[" << node->DebugString() << "] index[" << output_index << "]";
// if node is a value node, no need sync addr from device to host
if (!AnfAlgo::OutputAddrExist(node, output_index)) {
if (node->isa<ValueNode>()) {
auto value_node = node->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
return value_node->value();
}
if (node->isa<Parameter>()) {
for (size_t input_idx = 0; input_idx < graph.inputs().size(); input_idx++) {
if (input_idx >= input_tensors.size()) {
MS_LOG(EXCEPTION) << "Input idx:" << input_idx << "out of range:" << input_tensors.size();
}
if (graph.inputs()[input_idx] == node) {
return input_tensors[input_idx];
}
}
MS_LOG(EXCEPTION) << "Parameter : " << node->DebugString() << " has no output addr";
}
}
// if proccess reach here,it remarks item_with_index is a real node(Parameter,or executable CNode)
auto address = AnfAlgo::GetMutableOutputAddr(node, output_index);
MS_EXCEPTION_IF_NULL(address);
auto shape = AnfAlgo::GetOutputInferShape(node, output_index);
TypeId type_id = kNumberTypeFloat32;
type_id = AnfAlgo::GetOutputInferDataType(node, output_index);
std::vector<int> temp_shape;
if (graph.IsInternalOutput(node, output_index)) {
temp_shape.emplace_back(1);
tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
tensor->set_device_address(address);
tensor->set_dirty(false);
return tensor;
}
(void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape));
tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
// if in paynative mode,data only copyed to host when user want to print data
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
if (ms_context->execution_mode() == kPynativeMode || ms_context->device_target() == kGPUDevice) {
tensor->set_device_address(address);
tensor->set_dirty(false);
} else if (!address->SyncDeviceToHost(trans::GetRuntimePaddingShape(node, output_index),
LongToSize(tensor->data().nbytes()), tensor->data_type(), tensor->data_c())) {
MS_LOG(INFO) << "Output sync device to host error!!!";
tensor->set_dirty(false);
}
return tensor;
}
BaseRef CreateTensorForOutput(const AnfNodePtr &anf, const KernelGraph &graph,
const std::vector<tensor::TensorPtr> &input_tensors) {
MS_EXCEPTION_IF_NULL(anf);
MS_LOG(INFO) << "Create tensor for output[" << anf->DebugString() << "]";
auto item_with_index = AnfAlgo::VisitKernelWithReturnType(anf, 0);
MS_EXCEPTION_IF_NULL(item_with_index.first);
MS_LOG(INFO) << "Create tensor for output after visit:" << item_with_index.first->DebugString();
// special handle for maketuple
if (AnfAlgo::CheckPrimitiveType(item_with_index.first, prim::kPrimMakeTuple)) {
auto cnode = item_with_index.first->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
VectorRef ret;
for (size_t i = 1; i < cnode->inputs().size(); ++i) {
auto out = CreateTensorForOutput(cnode->input(i), graph, input_tensors);
ret.push_back(out);
}
return ret;
}
// if is graph return nothing ,the function should return a null anylist
size_t size = AnfAlgo::GetOutputTensorNum(item_with_index.first);
if (size == 0) {
return VectorRef();
}
return CreateOneTensor(item_with_index.first, item_with_index.second, graph, input_tensors);
}
ValueNodePtr CreateNewValueNode(const AnfNodePtr &anf, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf);
MS_EXCEPTION_IF_NULL(graph);
auto value_node = anf->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
auto value = value_node->value();
MS_EXCEPTION_IF_NULL(value);
if (value->isa<None>()) {
return nullptr;
}
auto new_value_node = graph->NewValueNode(value_node);
graph->FrontBackendlMapAdd(anf, new_value_node);
graph->AddValueNodeToGraph(new_value_node);
return new_value_node;
}
size_t LoadCtrlInputTensor(const std::shared_ptr<KernelGraph> &graph, std::vector<tensor::TensorPtr> *inputs) {
MS_EXCEPTION_IF_NULL(graph);
MS_LOG(INFO) << "Load kInputCtrlTensors";
auto inputs_params = graph->input_ctrl_tensors();
if (inputs_params == nullptr) {
return 0;
}
if (inputs_params->size() < 2) {
MS_LOG(EXCEPTION) << "Illegal inputs_params size";
}
auto tensor = (*inputs_params)[0];
MS_EXCEPTION_IF_NULL(tensor);
auto *val = static_cast<int32_t *>(tensor->data_c());
MS_EXCEPTION_IF_NULL(val);
*val = 0;
tensor->set_dirty(true);
// set loop_count to zero
MS_EXCEPTION_IF_NULL(inputs);
inputs->push_back(tensor);
auto epoch_tensor = (*inputs_params)[1];
MS_EXCEPTION_IF_NULL(epoch_tensor);
auto *epoch_val = static_cast<int32_t *>(epoch_tensor->data_c());
MS_EXCEPTION_IF_NULL(epoch_val);
*epoch_val = graph->current_epoch();
epoch_tensor->set_dirty(true);
inputs->push_back(epoch_tensor);
MS_LOG(INFO) << "Load epoch_val:" << *epoch_val;
graph->set_current_epoch(graph->current_epoch() + 1);
return inputs_params->size();
}
ValueNodePtr ConstructRunOpValueNode(const std::shared_ptr<KernelGraph> &graph, const tensor::TensorPtr &input_tensor) {
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(input_tensor);
auto value_node = std::make_shared<ValueNode>(input_tensor);
MS_EXCEPTION_IF_NULL(value_node);
// construct abstract of value node
auto type_of_tensor = input_tensor->Dtype();
auto shape_of_tensor = input_tensor->shape();
auto abstract = std::make_shared<abstract::AbstractTensor>(type_of_tensor, shape_of_tensor);
value_node->set_abstract(abstract);
// add value node to graph
auto input_value_node = graph->NewValueNode(value_node);
graph->AddValueNodeToGraph(input_value_node);
return input_value_node;
}
ParameterPtr ConstructRunOpParameter(const std::shared_ptr<KernelGraph> &graph, const tensor::TensorPtr &input_tensor,
int tensor_mask) {
MS_EXCEPTION_IF_NULL(graph);
auto param = graph->NewParameter();
MS_EXCEPTION_IF_NULL(param);
if (tensor_mask == kParameterWeightTensorMask) {
param->set_default_param(input_tensor);
}
// set the kernel info of parameter
auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
MS_EXCEPTION_IF_NULL(input_tensor);
auto device_address = std::dynamic_pointer_cast<device::DeviceAddress>(input_tensor->device_address());
if (device_address == nullptr) {
kernel_build_info_builder->SetOutputsFormat(std::vector<std::string>{kOpFormat_DEFAULT});
TypeId param_init_data_type = AnfAlgo::IsParameterWeight(param) ? kTypeUnknown : input_tensor->data_type();
kernel_build_info_builder->SetOutputsDeviceType(std::vector<TypeId>{param_init_data_type});
} else {
kernel_build_info_builder->SetOutputsFormat(std::vector<std::string>{device_address->format()});
kernel_build_info_builder->SetOutputsDeviceType(std::vector<TypeId>{device_address->type_id()});
}
AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), param.get());
// construct abstract of parameter
auto type_of_tensor = input_tensor->Dtype();
auto shape_of_tensor = input_tensor->shape();
auto abstract = std::make_shared<abstract::AbstractTensor>(type_of_tensor, shape_of_tensor);
param->set_abstract(abstract);
return param;
}
void DumpGraphOutput(const Any &any, size_t recurse_level = 0) {
MS_LOG(INFO) << "Graph outputs:";
const size_t max_deep = 10;
if (recurse_level > max_deep) {
MS_LOG(INFO) << "Recurse too deep";
return;
}
std::string tab_str;
for (size_t i = 0; i < recurse_level; i++) {
tab_str = tab_str.append(" ");
}
if (any.is<AnyList>()) {
(void)tab_str.append("{");
MS_LOG(INFO) << tab_str;
auto any_list = any.cast<AnyList>();
for (auto &it : any_list) {
DumpGraphOutput(it, recurse_level + 1);
}
(void)tab_str.append("}");
MS_LOG(INFO) << tab_str;
}
(void)tab_str.append(any.ToString());
MS_LOG(INFO) << tab_str;
}
bool ExistSummaryNode(const KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto ret = graph->get_return();
MS_EXCEPTION_IF_NULL(ret);
auto all_nodes = DeepLinkedGraphSearch(ret);
for (auto &n : all_nodes) {
if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
return true;
}
}
return false;
}
} // namespace
GraphId SessionBasic::graph_sum_ = 0;
KernelGraphPtr SessionBasic::GetGraph(mindspore::GraphId graph_id) const {
auto it = graphs_.find(graph_id);
if (it == graphs_.end()) {
MS_LOG(WARNING) << "Can't find graph " << graph_id;
return nullptr;
}
return it->second;
}
void SessionBasic::InitInternalOutputParameter(const AnfNodePtr &out_node, const AnfNodePtr &parameter) {
auto graph_id = GetGraphIdByNode(out_node);
if (graph_id == kInvalidGraphId) {
return;
}
auto node_graph = GetGraph(graph_id);
if (node_graph == nullptr) {
return;
}
MS_LOG(INFO) << "Init parameter with pre graph output node: " << out_node->DebugString();
auto ref_node = node_graph->GetInternalOutputByFrontNode(out_node);
if (ref_node == nullptr) {
MS_LOG(INFO) << "No corresponding internal output for output node";
return;
}
size_t output_idx = 0;
if (AnfAlgo::CheckPrimitiveType(out_node, prim::kPrimTupleGetItem)) {
output_idx = AnfAlgo::GetTupleGetItemOutIndex(out_node->cast<CNodePtr>());
}
auto real_kernel = AnfAlgo::VisitKernel(ref_node, output_idx);
auto ref_real_node = real_kernel.first;
auto ref_real_node_index = real_kernel.second;
if (ref_real_node->isa<CNode>() && node_graph->IsInternalOutput(ref_real_node, ref_real_node_index)) {
auto kernel_info = ref_real_node->kernel_info();
if (kernel_info == nullptr || !kernel_info->has_build_info()) {
MS_LOG(INFO) << "No kernel info";
return;
}
if (!opt::IsNopNode(ref_real_node) && !AnfAlgo::OutputAddrExist(ref_real_node, ref_real_node_index)) {
MS_LOG(INFO) << "No kernel address";
return;
}
auto address = AnfAlgo::GetMutableOutputAddr(ref_real_node, ref_real_node_index);
auto format = AnfAlgo::GetOutputFormat(ref_real_node, ref_real_node_index);
auto type = AnfAlgo::GetOutputDeviceDataType(ref_real_node, ref_real_node_index);
auto d_kernel_info = std::make_shared<device::KernelInfo>();
MS_EXCEPTION_IF_NULL(d_kernel_info);
parameter->set_kernel_info(d_kernel_info);
kernel::KernelBuildInfo::KernelBuildInfoBuilder builder;
builder.SetOutputsDeviceType({type});
builder.SetOutputsFormat({format});
d_kernel_info->set_select_kernel_build_info(builder.Build());
AnfAlgo::SetOutputAddr(address, 0, parameter.get());
AnfAlgo::SetOutputInferTypeAndShape({type}, {AnfAlgo::GetOutputInferShape(parameter, 0)}, parameter.get());
}
}
std::vector<AnfNodePtr> SessionBasic::CreateParameterFromTuple(const AnfNodePtr &node, bool valid_input,
KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(graph);
std::vector<AnfNodePtr> parameters;
std::vector<AnfNodePtr> pre_graph_out = {node};
// If a cnode is a call, it's input0 is a cnode too, so it doesn't have primitive
if (!AnfAlgo::IsRealKernel(node)) {
pre_graph_out = AnfAlgo::GetAllOutput(node, {prim::kPrimTupleGetItem});
}
auto valid_inputs = graph->MutableValidInputs();
MS_EXCEPTION_IF_NULL(valid_inputs);
auto graph_inputs = graph->MutableInputs();
MS_EXCEPTION_IF_NULL(graph_inputs);
auto create_parameter = [&](const AbstractBasePtr &abstract) -> void {
auto parameter = graph->NewParameter();
MS_EXCEPTION_IF_NULL(parameter);
parameter->set_abstract(abstract);
auto new_parameter = graph->NewParameter(parameter);
parameters.push_back(new_parameter);
valid_inputs->push_back(valid_input);
graph_inputs->push_back(new_parameter);
};
for (const auto &out_node : pre_graph_out) {
MS_EXCEPTION_IF_NULL(out_node);
auto abstract = out_node->abstract();
MS_EXCEPTION_IF_NULL(abstract);
// create multiple parameters if is a tuple output real kernel
if (abstract->isa<abstract::AbstractTuple>() && !AnfAlgo::CheckPrimitiveType(out_node, prim::kPrimTupleGetItem)) {
auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
MS_LOG(INFO) << "Tuple_size [" << tuple_abstract->size() << "]";
for (size_t output_idx = 0; output_idx < tuple_abstract->size(); output_idx++) {
create_parameter((*tuple_abstract)[output_idx]);
}
continue;
}
// create single parameter if is a abstract real kernel
create_parameter(out_node->abstract());
InitInternalOutputParameter(out_node, parameters[parameters.size() - 1]);
}
return parameters;
}
ParameterPtr SessionBasic::CreateNewParameterFromParameter(const AnfNodePtr &anf, bool valid_input,
KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf);
if (!anf->isa<Parameter>()) {
MS_LOG(EXCEPTION) << "Anf[" << anf->DebugString() << "] is not a parameter";
}
MS_EXCEPTION_IF_NULL(graph);
auto param_value = GetParamDefaultValue(anf);
auto valid_inputs = graph->MutableValidInputs();
MS_EXCEPTION_IF_NULL(valid_inputs);
auto graph_inputs = graph->MutableInputs();
MS_EXCEPTION_IF_NULL(graph_inputs);
ParameterPtr new_parameter = nullptr;
// if parameter's python parameter has been exist a backend parameter, reuse the exist parameter
if (python_paras == nullptr) {
python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
}
auto iter = python_paras->find(param_value);
if (iter != python_paras->end()) {
new_parameter = iter->second;
} else {
TraceManager::DebugTrace(std::make_shared<TraceCopy>(anf->debug_info()));
new_parameter = graph->NewParameter(anf->cast<ParameterPtr>());
if (param_value != nullptr) {
(*python_paras)[param_value] = new_parameter;
}
TraceManager::EndTrace();
}
graph_inputs->push_back(new_parameter);
valid_inputs->push_back(valid_input);
return new_parameter;
}
AnfNodePtr SessionBasic::CreateNewParameterFromCNode(const AnfNodePtr &anf, bool valid_input, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf);
MS_EXCEPTION_IF_NULL(graph);
MS_LOG(INFO) << "Create a new parameter from cnode[" << anf->DebugString() << "]";
auto parameters = CreateParameterFromTuple(anf, valid_input, graph);
if (parameters.empty()) {
MS_LOG(EXCEPTION) << "No parameter exist!!";
}
if (parameters.size() == 1) {
return parameters[0];
}
std::vector<AnfNodePtr> make_tuple_input = {NewValueNode(prim::kPrimMakeTuple)};
(void)std::copy(parameters.begin(), parameters.end(), std::back_inserter(make_tuple_input));
auto make_tuple = graph->NewCNode(make_tuple_input);
MS_EXCEPTION_IF_NULL(make_tuple);
MS_LOG(INFO) << "New make tuple [" << make_tuple->DebugString() << "] of parameters";
return make_tuple;
}
CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, bool valid_input, KernelGraph *graph,
bool *from_other_graph,
std::unordered_map<AnfNodePtr, AnfNodePtr> *other_graph_cnode) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(from_other_graph);
MS_EXCEPTION_IF_NULL(other_graph_cnode);
*from_other_graph = false;
// get primitive of old node
std::vector<AnfNodePtr> cnode_inputs;
auto prim = AnfAlgo::GetCNodePrimitive(cnode);
if (prim != nullptr) {
// push attr to inputs[0] of new cnode
cnode_inputs.push_back(std::make_shared<ValueNode>(std::make_shared<Primitive>(*prim)));
} else {
auto fg = AnfAlgo::GetCNodeFuncGraphPtr(cnode);
MS_EXCEPTION_IF_NULL(fg);
auto new_fg = BasicClone(fg);
cnode_inputs.push_back(std::make_shared<ValueNode>(new_fg));
}
auto origin_inputs = cnode->inputs();
bool optimize_depend = false;
bool optimize_control_depend = false;
if (IsPrimitiveCNode(cnode, prim::kPrimDepend) && origin_inputs.size() == 3 &&
origin_inputs[kRealInputIndexInDepend]->isa<ValueNode>()) {
optimize_depend = true;
}
if (IsPrimitiveCNode(cnode, prim::kPrimControlDepend) && origin_inputs.size() == 3) {
optimize_control_depend = true;
}
// if has multiple depends,only select first depend as parameter
for (size_t input_idx = 1; input_idx < origin_inputs.size(); input_idx++) {
auto anf = origin_inputs[input_idx];
MS_EXCEPTION_IF_NULL(anf);
// anf has been created before
if (graph->GetBackendAnfByFrontAnf(anf) != nullptr) {
cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(anf));
continue;
} else if (other_graph_cnode->find(anf) != other_graph_cnode->end()) {
cnode_inputs.push_back((*other_graph_cnode)[anf]);
continue;
} else if (anf->isa<ValueNode>() && !IsValueNode<FuncGraph>(anf)) {
// if input is a value node,
auto new_value_node = CreateNewValueNode(anf, graph);
if (new_value_node != nullptr) {
cnode_inputs.emplace_back(new_value_node);
}
continue;
} else if (anf->isa<Parameter>()) {
auto new_parameter = CreateNewParameterFromParameter(anf, valid_input, graph);
cnode_inputs.push_back(new_parameter);
if (GetGraphIdByNode(anf) == kInvalidGraphId) {
graph->FrontBackendlMapAdd(anf, new_parameter);
} else {
(*other_graph_cnode)[anf] = new_parameter;
}
continue;
} else if (optimize_depend && input_idx == kDependAttachNodeIndex) {
cnode_inputs.push_back(origin_inputs[kRealInputIndexInDepend]);
continue;
} else if (optimize_control_depend) {
cnode_inputs.push_back(NewValueNode(MakeValue(input_idx)));
} else {
*from_other_graph = true;
// the input node is a cnode from other graph
auto parameter_from_cnode = CreateNewParameterFromCNode(anf, valid_input, graph);
cnode_inputs.push_back(parameter_from_cnode);
(*other_graph_cnode)[anf] = parameter_from_cnode;
}
}
TraceManager::DebugTrace(std::make_shared<TraceCopy>(cnode->debug_info()));
auto new_cnode = graph->NewCNode(cnode_inputs);
TraceManager::EndTrace();
return new_cnode;
}
CNodePtr SessionBasic::CreateSwitchInput(const AnfNodePtr &node_input, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(node_input);
MS_EXCEPTION_IF_NULL(graph);
// switch input generalizes partial
if (AnfAlgo::CheckPrimitiveType(node_input, prim::kPrimPartial) ||
AnfAlgo::CheckPrimitiveType(node_input, prim::kPrimCall)) {
return node_input->cast<CNodePtr>();
}
if (node_input->isa<CNode>()) {
MS_LOG(EXCEPTION) << "If switch input is " << node_input->DebugString() << ", it mast be partial or call.";
}
std::vector<AnfNodePtr> partial_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimPartial->name()))};
if (node_input->isa<ValueNode>() && IsValueNode<FuncGraph>(node_input)) {
partial_inputs.emplace_back(node_input);
auto partial_node = graph->NewCNode(partial_inputs);
return partial_node;
}
KernelGraphPtr kernel_graph = NewKernelGraph();
MS_EXCEPTION_IF_NULL(kernel_graph);
kernel_graph->set_output(graph->GetBackendAnfByFrontAnf(node_input));
partial_inputs.emplace_back(std::make_shared<ValueNode>(kernel_graph));
auto partial_node = graph->NewCNode(partial_inputs);
return partial_node;
}
CNodePtr SessionBasic::HandleSwitchInputs(const AnfNodePtr &anf_node, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(graph);
auto node = anf_node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(node);
if (node->inputs().size() < kSwitchInputSize) {
MS_LOG(EXCEPTION) << "Switch input size less than " << kSwitchInputSize;
}
auto primitive = NewValueNode(std::make_shared<Primitive>(prim::kPrimSwitch->name()));
std::vector<AnfNodePtr> switch_inputs = {primitive, node->input(1)};
for (size_t index = 2; index < node->inputs().size(); index++) {
auto input = CreateSwitchInput(node->input(index), graph);
switch_inputs.emplace_back(input);
}
auto switch_node = graph->NewCNode(switch_inputs);
return switch_node;
}
std::vector<AnfNodePtr> SessionBasic::CreateSwitchOrPartialNode(const CNodePtr &cnode, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(graph);
// create primitive of cnode:call(partial or switch)
std::vector<AnfNodePtr> cnode_inputs = {
graph->NewValueNode(NewValueNode(std::make_shared<Primitive>(prim::kPrimCall->name())))};
auto attr_input = cnode->input(kAnfPrimitiveIndex);
MS_EXCEPTION_IF_NULL(attr_input);
auto cnode_input = graph->GetBackendAnfByFrontAnf(attr_input);
if (cnode_input == nullptr) {
MS_LOG(EXCEPTION) << "CNode input[0] is CNode:" << attr_input->DebugString()
<< ", but input[0] has not been created.";
}
// if the node is partial, insert the inputs of partial to the call
if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimPartial)) {
auto partial_node = attr_input->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(partial_node);
auto partial_inputs = partial_node->inputs();
std::transform(partial_inputs.begin() + kFirstDataInputIndex, partial_inputs.end(),
std::back_inserter(cnode_inputs), [&graph](const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(graph->GetBackendAnfByFrontAnf(node));
return graph->GetBackendAnfByFrontAnf(node);
});
return cnode_inputs;
} else if (AnfAlgo::CheckPrimitiveType(cnode_input, prim::kPrimSwitch)) {
auto switch_node = HandleSwitchInputs(cnode_input, graph);
cnode_inputs.emplace_back(switch_node);
return cnode_inputs;
}
MS_LOG(EXCEPTION) << "CNode input[0] must be partial or switch.";
}
CNodePtr SessionBasic::CreateNewCNode(const CNodePtr &cnode, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(graph);
std::vector<AnfNodePtr> cnode_inputs;
auto attr_input = cnode->input(kAnfPrimitiveIndex);
MS_EXCEPTION_IF_NULL(attr_input);
if (AnfAlgo::IsGraphKernel(cnode)) {
auto fg = AnfAlgo::GetCNodeFuncGraphPtr(cnode);
MS_EXCEPTION_IF_NULL(fg);
auto new_fg = BasicClone(fg);
cnode_inputs.push_back(std::make_shared<ValueNode>(new_fg));
} else if (IsValueNode<FuncGraph>(attr_input)) {
// create primitive of cnode:call
cnode_inputs = {graph->NewValueNode(NewValueNode(std::make_shared<Primitive>(prim::kPrimCall->name())))};
// create a ValueNode<KernelGraph> as input of cnode:call
if (graph->GetBackendAnfByFrontAnf(attr_input) != nullptr) {
cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(attr_input));
} else {
auto new_value_node = CreateValueNodeKernelGraph(attr_input, graph);
if (new_value_node != nullptr) {
cnode_inputs.emplace_back(new_value_node);
}
}
} else if (attr_input->isa<CNode>()) {
cnode_inputs = CreateSwitchOrPartialNode(cnode, graph);
} else {
// get primitive of old node
auto prim = AnfAlgo::GetCNodePrimitive(cnode);
MS_EXCEPTION_IF_NULL(prim);
// push attr to inputs[0] of new cnode
cnode_inputs = {graph->NewValueNode(NewValueNode(std::make_shared<Primitive>(*prim)))};
}
for (size_t input_idx = 1; input_idx < cnode->inputs().size(); input_idx++) {
auto anf = cnode->input(input_idx);
MS_EXCEPTION_IF_NULL(anf);
// anf has been created before
if (graph->GetBackendAnfByFrontAnf(anf) != nullptr) {
cnode_inputs.emplace_back(graph->GetBackendAnfByFrontAnf(anf));
continue;
} else if (IsValueNode<None>(anf)) {
continue;
}
MS_LOG(EXCEPTION) << "Unexpected input[" << anf->DebugString() << "]";
}
TraceManager::DebugTrace(std::make_shared<TraceCopy>(cnode->debug_info()));
auto new_cnode = graph->NewCNode(cnode_inputs);
TraceManager::EndTrace();
return new_cnode;
}
ValueNodePtr SessionBasic::CreateValueNodeKernelGraph(const AnfNodePtr &anf, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf);
MS_EXCEPTION_IF_NULL(graph);
auto value_node = anf->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
auto sub_func_graph = AnfAlgo::GetValueNodeFuncGraph(anf);
MS_EXCEPTION_IF_NULL(sub_func_graph);
if (front_backend_graph_map_.find(sub_func_graph) == front_backend_graph_map_.end()) {
MS_LOG(EXCEPTION) << "FuncGraph: " << sub_func_graph->ToString() << " has not been transformed to KernelGraph.";
}
auto sub_kernel_graph = front_backend_graph_map_[sub_func_graph];
ValueNodePtr new_value_node = std::make_shared<ValueNode>(sub_kernel_graph);
new_value_node->set_abstract(value_node->abstract());
// create new kernel_info of new value_node
auto kernel_info = std::make_shared<device::KernelInfo>();
kernel_info->SetFeatureMapFlag(false);
new_value_node->set_kernel_info(kernel_info);
// create kernel_build_info for new value node
auto kernel_build_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info_builder->Build(), new_value_node.get());
AnfAlgo::SetGraphId(graph->graph_id(), new_value_node.get());
graph->FrontBackendlMapAdd(anf, new_value_node);
return new_value_node;
}
ParameterPtr SessionBasic::CreateNewParameter(const AnfNodePtr &anf, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(anf);
MS_EXCEPTION_IF_NULL(graph);
if (!anf->isa<Parameter>()) {
MS_LOG(EXCEPTION) << "Anf[" << anf->DebugString() << "] is not a parameter";
}
auto param_value = GetParamDefaultValue(anf);
ParameterPtr new_parameter = nullptr;
if (python_paras == nullptr) {
python_paras = std::make_shared<std::map<ValuePtr, ParameterPtr>>();
}
auto iter = python_paras->find(param_value);
if (iter != python_paras->end()) {
new_parameter = iter->second;
} else {
TraceManager::DebugTrace(std::make_shared<TraceCopy>(anf->debug_info()));
new_parameter = graph->NewParameter(anf->cast<ParameterPtr>());
if (param_value != nullptr) {
(*python_paras)[param_value] = new_parameter;
}
TraceManager::EndTrace();
}
return new_parameter;
}
KernelGraphPtr SessionBasic::ConstructKernelGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
std::unordered_map<AnfNodePtr, AnfNodePtr> other_graph_cnode;
auto graph = NewKernelGraph();
MS_EXCEPTION_IF_NULL(graph);
MS_LOG(INFO) << "Create graph: " << graph->graph_id();
size_t from_other_graph_depend_num = 0;
for (const auto &node : lst) {
MS_EXCEPTION_IF_NULL(node);
MS_LOG(DEBUG) << "Start create new cnode, node = " << node->DebugString();
if (!node->isa<CNode>()) {
MS_LOG(EXCEPTION) << "Node " << node->DebugString() << " is not CNode";
}
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
// create a new cnode object
bool from_other_graph = false;
// only first depend from other graph can create
bool valid_input = true;
if (from_other_graph_depend_num != 0 && AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend)) {
valid_input = false;
}
auto new_cnode = CreateNewCNode(cnode, valid_input, graph.get(), &from_other_graph, &other_graph_cnode);
if (AnfAlgo::CheckPrimitiveType(node, prim::kPrimDepend) && from_other_graph) {
from_other_graph_depend_num++;
}
MS_EXCEPTION_IF_NULL(new_cnode);
new_cnode->set_abstract(cnode->abstract());
new_cnode->set_scope(cnode->scope());
// record map relations between anf from ME and new anf node used in backend
graph->FrontBackendlMapAdd(node, new_cnode);
}
// add a make_tuple at the end of graph as output
graph->set_output(ConstructOutput(outputs, graph));
MS_EXCEPTION_IF_NULL(context_);
FuncGraphManagerPtr manager = MakeManager({graph});
if (manager) {
manager->AddFuncGraph(graph);
graph->set_manager(manager);
}
graph->SetExecOrderByDefault();
if (ExistSummaryNode(graph.get())) {
graph->set_summary_node_exist(true);
}
opt::BackendCommonOptimization(graph);
return graph;
}
void SessionBasic::CreateCNodeKernelGraph(const AnfNodePtr node, KernelGraphPtr graph) {
MS_EXCEPTION_IF_NULL(node);
MS_EXCEPTION_IF_NULL(graph);
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
// create a new cnode object
auto new_cnode = CreateNewCNode(cnode, graph.get());
MS_EXCEPTION_IF_NULL(new_cnode);
new_cnode->set_abstract(cnode->abstract());
new_cnode->set_fullname_with_scope(cnode->fullname_with_scope());
new_cnode->set_scope(cnode->scope());
graph->FrontBackendlMapAdd(node, new_cnode);
if (AnfAlgo::CheckPrimitiveType(new_cnode, prim::kPrimReturn)) {
graph->set_return(new_cnode);
}
}
std::shared_ptr<KernelGraph> SessionBasic::ConstructKernelGraph(const FuncGraphPtr &func_graph,
std::vector<KernelGraphPtr> *all_out_graph) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(all_out_graph);
auto node_list = TopoSort(func_graph->get_return());
auto graph = NewKernelGraph();
MS_EXCEPTION_IF_NULL(graph);
front_backend_graph_map_[func_graph] = graph;
MS_LOG(INFO) << "Create graph: " << graph->graph_id();
bool is_trace_back = false;
for (const auto &node : node_list) {
MS_EXCEPTION_IF_NULL(node);
MS_LOG(DEBUG) << "Start create new cnode, node = " << node->DebugString();
if (node->isa<Parameter>()) {
auto graph_inputs = graph->MutableInputs();
MS_EXCEPTION_IF_NULL(graph_inputs);
auto new_parameter = CreateNewParameter(node, graph.get());
graph_inputs->push_back(new_parameter);
graph->FrontBackendlMapAdd(node, new_parameter);
continue;
} else if (node->isa<ValueNode>()) {
if (!IsValueNode<FuncGraph>(node)) {
// if input is a common value node,
(void)CreateNewValueNode(node, graph.get());
} else {
// if input is a ValueNode<FuncGraph>
FuncGraphPtr child_graph = AnfAlgo::GetValueNodeFuncGraph(node);
if (front_backend_graph_map_.find(child_graph) != front_backend_graph_map_.end()) {
is_trace_back = true;
} else {
(void)ConstructKernelGraph(child_graph, all_out_graph);
}
(void)CreateValueNodeKernelGraph(node, graph.get());
}
continue;
} else {
CreateCNodeKernelGraph(node, graph);
}
}
// if a graph jump back unconditionally, return op of this graph will never be executed, so output is null.
graph->set_output_null(is_trace_back);
AddParameterToGraphInputs(func_graph->parameters(), graph.get());
graph->SetExecOrderByDefault();
if (ExistSummaryNode(graph.get())) {
graph->set_summary_node_exist(true);
}
all_out_graph->push_back(graph);
return graph;
}
void SessionBasic::AddParameterToGraphInputs(const std::vector<AnfNodePtr> &parameters, KernelGraph *graph) {
MS_EXCEPTION_IF_NULL(graph);
auto graph_inputs = graph->MutableInputs();
MS_EXCEPTION_IF_NULL(graph_inputs);
graph_inputs->clear();
for (auto &parameter : parameters) {
MS_EXCEPTION_IF_NULL(parameter);
auto backend_parameter = graph->GetBackendAnfByFrontAnf(parameter);
if (backend_parameter == nullptr) {
// for example "def f(x,y,z) {return x + y}", parameter z in unused
auto new_parameter = CreateNewParameter(parameter, graph);
graph_inputs->push_back(new_parameter);
MS_LOG(INFO) << "Can't find parameter:" << parameter->DebugString();
continue;
}
MS_LOG(INFO) << "Graph[" << graph->graph_id() << "],parameter:" << parameter->DebugString();
graph_inputs->push_back(backend_parameter);
}
}
namespace {
bool TensorNeedSync(const AnfNodePtr &parameter, const tensor::TensorPtr &tensor) {
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
auto device_address = AnfAlgo::GetMutableOutputAddr(parameter, 0);
if (ms_context->enable_pynative_infer()) {
return tensor->device_address().get() == nullptr || tensor->device_address() != device_address;
}
if (tensor->is_dirty()) {
return true;
}
if (tensor->device_address() != device_address) {
(void)tensor->data_sync();
return true;
}
return false;
}
} // namespace
// run graph steps
void SessionBasic::LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
const std::vector<tensor::TensorPtr> &inputs_const) const {
std::vector<tensor::TensorPtr> inputs(inputs_const);
size_t input_ctrl_size = 2;
MS_EXCEPTION_IF_NULL(kernel_graph);
if (kernel_graph->input_ctrl_tensors()) {
input_ctrl_size = LoadCtrlInputTensor(kernel_graph, &inputs);
}
std::vector<AnfNodePtr> input_nodes;
for (const auto &input_node : kernel_graph->inputs()) {
auto params = AnfAlgo::GetAllOutput(input_node);
std::copy(params.begin(), params.end(), std::back_inserter(input_nodes));
}
if ((inputs.size() + input_ctrl_size) - 2 != input_nodes.size()) {
MS_LOG(EXCEPTION) << "Tensor input:" << inputs.size() << " is not equal graph inputs:" << input_nodes.size()
<< ", input_ctrl_size:" << input_ctrl_size;
}
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];
if (TensorNeedSync(input_node, tensor) && input_node->isa<Parameter>() && AnfAlgo::OutputAddrExist(input_node, 0)) {
auto device_address = AnfAlgo::GetMutableOutputAddr(input_node, 0);
if (ms_context->execution_mode() == kPynativeMode ||
AnfAlgo::IsParameterWeight(input_node->cast<ParameterPtr>())) {
tensor->set_device_address(device_address);
}
MS_EXCEPTION_IF_NULL(device_address);
if (!device_address->SyncHostToDevice(trans::GetRuntimePaddingShape(input_node, 0),
LongToSize(tensor->data().nbytes()), tensor->data_type(),
tensor->data_c())) {
MS_LOG(EXCEPTION) << "SyncHostToDevice failed.";
}
}
tensor->set_dirty(false);
}
}
void SessionBasic::UpdateOutputs(const std::shared_ptr<KernelGraph> &kernel_graph, VectorRef *const outputs,
const std::vector<tensor::TensorPtr> &input_tensors) const {
MS_EXCEPTION_IF_NULL(kernel_graph);
MS_EXCEPTION_IF_NULL(outputs);
auto anf_outputs = kernel_graph->outputs();
for (auto &item : anf_outputs) {
MS_EXCEPTION_IF_NULL(item);
MS_LOG(INFO) << "Update output[" << item->DebugString() << "]";
outputs->emplace_back(CreateTensorForOutput(item, *kernel_graph, input_tensors));
}
}
void SessionBasic::RegisterSummaryCallBackFunc(const CallBackFunc &callback) {
MS_EXCEPTION_IF_NULL(callback);
summary_callback_ = callback;
}
void SessionBasic::Reorder(std::vector<CNodePtr> *node_list) { AnfAlgo::ReorderExecList(NOT_NULL(node_list)); }
void SessionBasic::SetSummaryNodes(KernelGraph *graph) {
MS_LOG(DEBUG) << "Update summary Start";
MS_EXCEPTION_IF_NULL(graph);
if (!graph->summary_node_exist()) {
return;
}
auto summary = graph->summary_nodes();
auto apply_list = TopoSort(graph->get_return());
for (auto &n : apply_list) {
MS_EXCEPTION_IF_NULL(n);
if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
auto cnode = n->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (cnode->inputs().size() <= kSummaryGetItem) {
MS_LOG(EXCEPTION) << "The node Summary should have 2 inputs at least!";
}
auto node = cnode->input(kSummaryGetItem);
MS_EXCEPTION_IF_NULL(node);
auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node, 0, true);
MS_EXCEPTION_IF_NULL(item_with_index.first);
if (!AnfAlgo::IsRealKernel(item_with_index.first)) {
MS_LOG(EXCEPTION) << "Unexpected node:" << item_with_index.first->DebugString();
}
summary[n->fullname_with_scope()] = item_with_index;
}
}
graph->set_summary_nodes(summary);
MS_LOG(DEBUG) << "Update summary end size: " << summary.size();
}
void SessionBasic::Summary(KernelGraph *graph) {
if (summary_callback_ == nullptr) {
return;
}
MS_EXCEPTION_IF_NULL(graph);
bool exist_summary = graph->summary_node_exist();
if (!exist_summary) {
return;
}
SetSummaryNodes(graph);
auto summary_outputs = graph->summary_nodes();
std::map<std::string, tensor::TensorPtr> params_list;
// fetch outputs apply kernel in session & run callback functions
for (auto &output_item : summary_outputs) {
auto node = output_item.second.first;
size_t index = IntToSize(output_item.second.second);
auto address = AnfAlgo::GetOutputAddr(node, index);
auto shape = AnfAlgo::GetOutputInferShape(node, index);
TypeId type_id = AnfAlgo::GetOutputInferDataType(node, index);
std::vector<int> temp_shape;
(void)std::copy(shape.begin(), shape.end(), std::back_inserter(temp_shape));
tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
MS_EXCEPTION_IF_NULL(address);
if (!address->GetPtr()) {
continue;
}
if (!address->SyncDeviceToHost(trans::GetRuntimePaddingShape(node, index), LongToSize(tensor->data().nbytes()),
tensor->data_type(), tensor->data_c())) {
MS_LOG(ERROR) << "Failed to sync output from device to host.";
}
tensor->set_dirty(false);
params_list[output_item.first] = tensor;
}
// call callback function here
summary_callback_(0, params_list);
}
CNodePtr SessionBasic::ConstructOutput(const AnfNodePtrList &outputs, const std::shared_ptr<KernelGraph> &graph) {
MS_EXCEPTION_IF_NULL(graph);
std::vector<AnfNodePtr> output_args;
for (const auto &output : outputs) {
MS_EXCEPTION_IF_NULL(output);
MS_LOG(INFO) << "Output:" << output->DebugString();
}
auto FindEqu = [graph, outputs](const AnfNodePtr &out) -> AnfNodePtr {
auto backend_anf = graph->GetBackendAnfByFrontAnf(out);
if (backend_anf != nullptr) {
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
if (context_ptr->execution_mode() == kPynativeMode) {
return backend_anf;
}
auto front_real_kernel = AnfAlgo::VisitKernel(out, 0);
auto backend_real_kernel = AnfAlgo::VisitKernel(backend_anf, 0);
MS_EXCEPTION_IF_NULL(out);
auto out_func_graph = out->func_graph();
MS_EXCEPTION_IF_NULL(out_func_graph);
auto out_func_graph_manager = out_func_graph->manager();
if (out_func_graph_manager == nullptr) {
return backend_anf;
}
auto node_users = out_func_graph_manager->node_users();
auto users = node_users[out];
bool internal_output = true;
std::string kernel_target = GetCNodeTarget(front_real_kernel.first);
for (auto user : users) {
auto cnode = user.first->cast<CNodePtr>();
if (cnode == nullptr) {
internal_output = false;
break;
}
auto prim = cnode->input(kAnfPrimitiveIndex);
if (prim == nullptr || !prim->isa<ValueNode>()) {
internal_output = false;
break;
}
if (!AnfAlgo::IsRealKernel(user.first) || kernel_target != GetCNodeTarget(user.first)) {
internal_output = false;
break;
}
}
if (internal_output) {
MS_LOG(INFO) << "Internal output1: " << out->DebugString() << "To " << backend_real_kernel.first->DebugString();
graph->AddInternalOutput(out, backend_real_kernel.first);
}
return backend_anf;
}
MS_LOG(EXCEPTION) << "Can't find the node in the equiv map!";
};
output_args.push_back(NewValueNode(prim::kPrimMakeTuple));
(void)std::transform(outputs.begin(), outputs.end(), std::back_inserter(output_args),
[&](const AnfNodePtr &out) -> AnfNodePtr { return FindEqu(out); });
return graph->NewCNode(output_args);
}
void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr<KernelGraph> &graph) {
MS_LOG(INFO) << "Start!";
std::vector<AnfNodePtr> make_tuple_inputs;
make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
MS_EXCEPTION_IF_NULL(graph);
if (AnfRuntimeAlgorithm::GetOutputTensorNum(cnode) > 1) {
for (size_t output_index = 0; output_index < AnfRuntimeAlgorithm::GetOutputTensorNum(cnode); output_index++) {
auto idx = NewValueNode(SizeToInt(output_index));
MS_EXCEPTION_IF_NULL(idx);
auto imm = std::make_shared<Int32Imm>(output_index);
idx->set_abstract(std::make_shared<abstract::AbstractScalar>(imm));
auto getitem = graph->NewCNode({NewValueNode(prim::kPrimTupleGetItem), cnode, idx});
std::vector<TypeId> types = {AnfAlgo::GetOutputInferDataType(cnode, output_index)};
std::vector<std::vector<size_t>> shapes = {AnfAlgo::GetOutputInferShape(cnode, output_index)};
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, getitem.get());
make_tuple_inputs.push_back(getitem);
}
} else {
make_tuple_inputs.push_back(cnode);
}
// create output
auto g_output = graph->NewCNode(make_tuple_inputs);
graph->set_output(g_output);
// set graph manager,which now is only used to get valuenodes and hardware optimizing
MS_EXCEPTION_IF_NULL(context_);
FuncGraphManagerPtr manager = context_->manager();
if (manager != nullptr) {
manager->AddFuncGraph(graph);
graph->set_manager(manager);
}
MS_LOG(INFO) << "Finish!";
}
std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info,
const std::vector<tensor::TensorPtr> &input_tensors,
const std::vector<int> &tensors_mask) {
auto graph = std::make_shared<KernelGraph>();
std::vector<AnfNodePtr> inputs;
// set input[0]
PrimitivePtr op_prim = op_run_info.py_primitive;
MS_EXCEPTION_IF_NULL(op_prim);
inputs.push_back(std::make_shared<ValueNode>(op_prim));
// set input parameter
MS_LOG(INFO) << "Input tensor size: " << input_tensors.size();
if (input_tensors.size() != tensors_mask.size()) {
MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors.size() << " should be equal to tensors mask size "
<< tensors_mask.size();
}
for (size_t i = 0; i < input_tensors.size(); ++i) {
if (tensors_mask[i] == kValueNodeTensorMask) {
auto value_node = ConstructRunOpValueNode(graph, input_tensors[i]);
inputs.push_back(value_node);
continue;
}
auto parameter = ConstructRunOpParameter(graph, input_tensors[i], tensors_mask[i]);
inputs.push_back(parameter);
auto mutable_inputs = graph->MutableInputs();
MS_EXCEPTION_IF_NULL(mutable_inputs);
mutable_inputs->push_back(parameter);
}
// set execution order
auto cnode = graph->NewCNode(inputs);
MS_EXCEPTION_IF_NULL(cnode);
// set abstract,which include inferred shapes and types
cnode->set_abstract(op_run_info.abstract);
// set execution order
std::vector<CNodePtr> exe_order = {cnode};
graph->set_execution_order(exe_order);
// set output
CreateOutputNode(cnode, graph);
return graph;
}
BaseRef SessionBasic::TransformBaseRefListToTuple(const BaseRef &base_ref) {
if (utils::isa<VectorRef>(base_ref)) {
auto ref_list = utils::cast<VectorRef>(base_ref);
py::tuple output_tensors(ref_list.size());
for (size_t i = 0; i < ref_list.size(); ++i) {
auto output = TransformBaseRefListToTuple(ref_list[i]); // use pyObjectRef
if (utils::isa<tensor::TensorPtr>(output)) {
auto tensor_ptr = utils::cast<tensor::TensorPtr>(output);
MS_EXCEPTION_IF_NULL(tensor_ptr);
output_tensors[i] = tensor_ptr;
} else if (utils::isa<PyObjectRef>(output)) {
py::object obj = utils::cast<PyObjectRef>(output).object_;
py::tuple tensor_tuple = py::cast<py::tuple>(obj);
output_tensors[i] = tensor_tuple;
} else {
MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
}
}
return output_tensors; // turn tuple to py::object and store in PyObjectRef
} else if (utils::isa<tensor::TensorPtr>(base_ref)) {
return base_ref;
} else {
MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
}
}
KernelGraphPtr SessionBasic::NewKernelGraph() {
auto graph = std::make_shared<KernelGraph>();
graph->set_graph_id(graph_sum_);
graphs_[graph_sum_++] = graph;
return graph;
}
AnfNodePtr SessionBasic::FindPullNode(const AnfNodePtr &push_node, const std::vector<AnfNodePtr> &node_list) {
MS_EXCEPTION_IF_NULL(push_node);
for (auto &node : node_list) {
if (node != nullptr && node->isa<CNode>()) {
for (auto input : node->cast<CNodePtr>()->inputs()) {
if (push_node == AnfAlgo::VisitKernel(input, 0).first) {
if (AnfAlgo::GetCNodeName(node) != kPullOpName) {
MS_LOG(EXCEPTION) << "The edge between Push and Pull node is invalid.";
}
return node;
}
}
}
}
return nullptr;
}
#if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
void SessionBasic::AssignParamKey(const KernelGraphPtr &kernel_graph) {
if (!parallel::ps::Util::IsRoleOfWorker()) {
MS_LOG(INFO) << "Not parameter server mode.";
return;
}
MS_EXCEPTION_IF_NULL(kernel_graph);
std::vector<AnfNodePtr> node_list = TopoSort(kernel_graph->get_return());
for (auto &node : node_list) {
if (node != nullptr && node->isa<CNode>()) {
// Assign key for forward kernel EmbeddingLookup.
// The key will be assigned to embedding table ande Push kernel as well.
if (AnfAlgo::GetCNodeName(node) == kEmbeddingLookupOpName) {
size_t embedding_table_idx = 0;
auto embedding_table = AnfAlgo::GetInputNode(node->cast<CNodePtr>(), embedding_table_idx);
size_t key = parallel::ps::Worker<float>::GetInstance().SetParamKey(embedding_table->fullname_with_scope());
AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node);
} else if (AnfAlgo::GetCNodeName(node) == kPushOpName) {
auto pull_node = FindPullNode(node, node_list);
if (!pull_node) {
MS_LOG(EXCEPTION) << "Assigning parameter key failed: can't find Pull node of the Push node.";
}
// Second input of Pull node is the trainable parameter.
size_t parameter_index = 1;
auto parameter_node = AnfAlgo::GetInputNode(pull_node->cast<CNodePtr>(), parameter_index);
size_t key = parallel::ps::Worker<float>::GetInstance().SetParamKey(parameter_node->fullname_with_scope());
AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), node);
AnfAlgo::SetNodeAttr(kAttrPsKey, MakeValue(key), pull_node);
std::string optimizer_name = AnfAlgo::GetNodeAttr<std::string>(node, kAttrOptimizerType);
parallel::ps::Worker<float>::GetInstance().SetKeyOptimId(key, optimizer_name);
}
}
}
}
void SessionBasic::InitPSParamAndOptim(const KernelGraphPtr &kernel_graph,
const std::vector<tensor::TensorPtr> &inputs_const) {
if (!parallel::ps::Util::IsRoleOfWorker()) {
return;
}
std::vector<tensor::TensorPtr> inputs(inputs_const);
size_t input_ctrl_size = 1;
MS_EXCEPTION_IF_NULL(kernel_graph);
if (kernel_graph->input_ctrl_tensors()) {
input_ctrl_size = LoadCtrlInputTensor(kernel_graph, &inputs);
}
auto input_nodes = kernel_graph->inputs();
if ((inputs.size() + input_ctrl_size) - 1 != input_nodes.size()) {
MS_LOG(EXCEPTION) << "Tensor input:" << inputs.size() << " is not equal graph inputs:" << input_nodes.size()
<< ", input_ctrl_size:" << input_ctrl_size;
}
auto ms_context = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(ms_context);
std::vector<int> shape_init_in_server = {1};
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>();
bool init_in_server = false;
if (tensor->shape_c() == shape_init_in_server) {
MS_LOG(INFO) << "The param need to be initialized in server " << pk_node->fullname_with_scope();
init_in_server = true;
}
mindspore::parallel::ps::Worker<float>::GetInstance().InitPSParamAndOptim(
pk_node->fullname_with_scope(), tensor->data_c(), LongToSize(tensor->data().nbytes()), init_in_server);
}
}
ps_init_ = true;
}
#endif
} // namespace session
} // namespace mindspore
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