forked from mindspore-Ecosystem/mindspore
mindir:support switchlayer
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b69f492f25
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93a1956978
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@ -121,30 +121,6 @@ using CompileGraphs = compile::CompileGraphs;
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using abstract::AnalysisResult;
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using mindspore::abstract::AnalysisContextPtr;
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// Some operators are not defined.
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inline bool ResetCNodeFromLoad(const AnfNodePtr &node) {
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if (node->isa<CNode>() && node->cast<CNodePtr>()->get_load_flag()) {
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// Process partial("DeadNode",args) when the graph is loaded.
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auto operatorPtr = node->cast<CNodePtr>()->input(0);
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// Set abstract of switch(c,f,t) to null
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auto prim = GetValueNode<PrimitivePtr>(operatorPtr);
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if (IsPrimitiveEquals(prim::kPrimSwitch, prim) || IsPrimitiveEquals(prim::kPrimSwitchLayer, prim) ||
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IsPrimitiveEquals(prim::kPrimPartial, prim)) {
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node->set_abstract(nullptr);
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return true;
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}
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// If the operator is not a primitive, the abstract will been set to null.
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// Because there are not some operators in front end, the abstract of primitive should be reserved.
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if (prim == nullptr) {
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node->set_abstract(nullptr);
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return true;
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}
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// Previous inferred value
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return true;
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}
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return false;
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}
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abstract::AnalysisResult AbstractAnalyze(const ResourcePtr &res, const FuncGraphPtr &func_graph,
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const abstract::AbstractBasePtrList &args_spec, bool clear) {
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MS_LOG(DEBUG) << "AbstractAnalyze start";
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@ -156,17 +132,22 @@ abstract::AnalysisResult AbstractAnalyze(const ResourcePtr &res, const FuncGraph
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engine->Clear();
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for (auto &node : manager->all_nodes()) {
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MS_EXCEPTION_IF_NULL(node);
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const AbstractBasePtr &prev_inferred = node->abstract();
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// Handle previous inferred value for CNode if is loaded from MindIR
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if (res->is_load() && ResetCNodeFromLoad(node)) {
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if (res->is_load()) {
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// If the primitive is not defined in front end,keep the inferred value loaded from MindIR.
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auto primitive = GetCNodePrimitive(node);
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if (primitive != nullptr && abstract::GetPrimEvaluator(primitive, engine) == nullptr) {
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MS_LOG(INFO) << "The primitive is not defined in front end. Primitive: " << primitive->ToString();
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continue;
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}
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}
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const AbstractBasePtr &prev_inferred = node->abstract();
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// Keep previous inferred value for ValueNode if the inferred value is not AbstractFunction.
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if (!node->isa<ValueNode>() || (prev_inferred != nullptr && prev_inferred->isa<abstract::AbstractFunction>())) {
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node->set_abstract(nullptr);
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MS_LOG(DEBUG) << "Abstract of node " << node->ToString() << " is set to nullptr";
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MS_LOG(DEBUG) << "Abstract of node " << node->DebugString() << " is set to nullptr";
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}
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}
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}
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@ -275,7 +256,9 @@ void CheckRootInputShapeAndType(const ResourcePtr &res, const FuncGraphPtr &load
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MS_EXCEPTION_IF_NULL(root_type);
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MS_EXCEPTION_IF_NULL(loaded_type);
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if (root_shape->shape() != loaded_shape->shape()) {
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auto shapeEqu = (root_shape->shape() == loaded_shape->shape()) ||
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(root_shape->shape().size() <= 1 && loaded_shape->shape().size() <= 1);
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if (!shapeEqu) {
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MS_EXCEPTION(ValueError) << "The " << index
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<< " th input shape differ from loaded graph. Input shape: " << root_shape->ToString()
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<< ", input shape of loaded graph: " << loaded_shape->ToString();
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@ -531,8 +514,8 @@ bool OptimizeAction(const ResourcePtr &res, const std::vector<PassItem> &passes)
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auto func_graph = res->func_graph();
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MS_EXCEPTION_IF_NULL(func_graph);
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func_graph->DumpFuncGraph(fg_name);
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ExportIR(fg_name + ".dat", func_graph);
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DumpIR(fg_name + ".ir", func_graph);
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ExportIR(fg_name + ".dat", func_graph);
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MS_LOG(DEBUG) << "Dump " << fg_name << " func graph.";
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}
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counter++;
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@ -359,7 +359,6 @@ void AnalysisEngine::Clear() {
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root_context_ = nullptr;
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}
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namespace {
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EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr &engine) {
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// Custom Primitive with python infer_shape, infer_type
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MS_EXCEPTION_IF_NULL(prim);
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@ -396,7 +395,8 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
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engine->prim_py_evaluators_[prim_py] = evaluator;
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return evaluator;
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}
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MS_LOG(EXCEPTION) << "The primitive with python evaluator should be a python primitive.";
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MS_LOG(ERROR) << "The primitive with python evaluator should be a python primitive.";
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return nullptr;
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}
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// return a default evaluator
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@ -416,11 +416,10 @@ EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr
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}
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}
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if (evaluator == nullptr) {
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MS_LOG(EXCEPTION) << "The evaluator of the primitive is not defined (" << prim->name() << ").";
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MS_LOG(DEBUG) << "The evaluator of the primitive is not defined (" << prim->name() << ").";
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}
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return evaluator;
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}
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} // namespace
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EvaluatorPtr AnalysisEngine::_GetEvaluatorFor(const std::shared_ptr<PrimitiveAbstractClosure> &func) {
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MS_EXCEPTION_IF_NULL(func);
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@ -430,6 +429,9 @@ EvaluatorPtr AnalysisEngine::_GetEvaluatorFor(const std::shared_ptr<PrimitiveAbs
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}
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auto primitive = func->prim();
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auto evaluator = GetPrimEvaluator(primitive, shared_from_this());
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if (evaluator == nullptr) {
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MS_LOG(EXCEPTION) << "The evaluator of the primitive is not defined (" << primitive->name() << ").";
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}
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evaluators_[func] = evaluator;
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return evaluator;
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}
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@ -1012,7 +1014,9 @@ AbstractBasePtr FromValueInside(const ValuePtr &value, bool broaden) {
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EvalResultPtr EvalOnePrim(const PrimitivePtr &primitive, const AbstractBasePtrList &arg_specs) {
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auto evaluator = GetPrimEvaluator(primitive, nullptr);
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MS_EXCEPTION_IF_NULL(evaluator);
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if (evaluator == nullptr) {
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MS_LOG(EXCEPTION) << "The evaluator of the primitive is not defined (" << primitive->name() << ").";
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}
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if (!evaluator->isa<TrivialPrimEvaluator>()) {
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MS_LOG(EXCEPTION) << "Prim " << primitive->ToString() << " should build a TrivialPrimEvaluator, but "
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<< evaluator->ToString();
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@ -347,7 +347,7 @@ template <typename T>
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AbstractBasePtr FromValue(const T &value, bool broaden = false) {
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return FromValueInside(MakeValue(value), broaden);
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}
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EvaluatorPtr GetPrimEvaluator(const PrimitivePtr &prim, const AnalysisEnginePtr &engine);
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EvalResultPtr EvalOnePrim(const PrimitivePtr &p, const AbstractBasePtrList &arg_specs);
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} // namespace abstract
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} // namespace mindspore
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@ -137,10 +137,11 @@ class IrExportBuilder {
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mind_ir::ModelProto model_;
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mind_ir::NodeProto *last_node_{nullptr};
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std::list<FuncGraphPtr> todo_;
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std::map<AnfNodePtr, size_t> node_index_map_;
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std::map<AnfNodePtr, std::string> node_index_map_;
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std::set<std::string> nodeName_;
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size_t node_index_{0};
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size_t shape_index_{0};
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bool top_graph{true};
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};
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using IrExporterPtr = std::shared_ptr<IrExporter>;
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@ -185,9 +186,11 @@ void IrExportBuilder::BuildModel(const FuncGraphPtr &func_graph, bool save_tenso
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nodeName_.clear();
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// Build the main funcGraph
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nodeName_.insert(func_graph->ToString());
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top_graph = true;
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BuildFuncGraph(func_graph, graph_proto, save_tensor_data);
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std::set<FuncGraphPtr> graphVisited;
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graphVisited.insert(func_graph);
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top_graph = false;
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while (!todo_.empty()) {
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FuncGraphPtr fg = todo_.back();
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todo_.pop_back();
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@ -204,6 +207,7 @@ void IrExportBuilder::BuildModel(const FuncGraphPtr &func_graph, bool save_tenso
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}
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// Release resource
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nodeName_.clear();
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node_index_map_.clear();
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}
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void IrExportBuilder::BuildFuncGraph(const FuncGraphPtr &func_graph, mind_ir::GraphProto *const graph_proto,
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@ -227,8 +231,8 @@ void IrExportBuilder::BuildParameters(const FuncGraphPtr &func_graph, mind_ir::G
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MS_LOG(EXCEPTION) << "Parameter: '" << item->ToString() << "' could not cast to parameter.";
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}
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std::string param_name = GetUniqueNodeName(param);
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if (param->has_default()) {
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MS_LOG(DEBUG) << "Parameter: '" << item->ToString() << "' has default.";
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if (top_graph && param->has_default()) {
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MS_LOG(DEBUG) << "Parameter: '" << item->DebugString() << "' has default. address: " << (size_t)param.get();
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mind_ir::TensorProto *parameter_proto = graph_proto->add_parameter();
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parameter_proto->set_name(param_name);
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SetParamToTensorProto(param, parameter_proto);
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@ -308,7 +312,7 @@ void IrExportBuilder::SetValueInfoProto(const AnfNodePtr &node, mind_ir::ValueIn
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} else if (type->isa<Tuple>()) {
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auto tup_shape = shape->cast<abstract::TupleShapePtr>();
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value_proto->set_denotation(type->type_name() + ":" + std::to_string(tup_shape->shape().size()));
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} else if (type->isa<Number>() || type->isa<String>()) {
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} else if (type->isa<Number>() || type->isa<String>() || type->isa<UMonadType>() || type->isa<IOMonadType>()) {
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value_proto->set_denotation(type->type_name());
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} else {
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MS_LOG(EXCEPTION) << "Value type: " << type->type_name() << " is not supported!";
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@ -541,28 +545,19 @@ std::string IrExportBuilder::GetUniqueNodeName(const AnfNodePtr &node) {
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// Naming anfnode
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// 1. parameter is unique in one func_graph
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// 2. cnode and valuenode may be reduplicative, so add index to identify.
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std::string node_name = "";
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if (node->isa<Parameter>()) {
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node_name = GetNodeName(node);
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} else if (node->isa<CNode>()) {
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auto iter = node_index_map_.find(node);
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if (iter != node_index_map_.end()) {
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node_name = GetNodeName(node) + ":" + std::to_string(iter->second);
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return iter->second;
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} else {
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std::string node_name = GetNodeName(node);
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while (nodeName_.count(node_name) > 0) {
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auto node_idx = GetNodeIndex();
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node_index_map_[node] = node_idx;
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node_name = GetNodeName(node) + ":" + std::to_string(node_idx);
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node_name = node_name + ":" + std::to_string(node_idx);
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}
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} else if (node->isa<ValueNode>()) {
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auto node_idx = GetNodeIndex();
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node_index_map_[node] = node_idx;
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node_name = GetNodeName(node) + ":" + std::to_string(node_idx);
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} else {
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MS_LOG(EXCEPTION) << "Can not support type of node:" << node->ToString();
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}
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MS_LOG(DEBUG) << "Node name: " << node_name;
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node_index_map_[node] = node_name;
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return node_name;
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}
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}
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std::string IrExportBuilder::GetNodeName(const AnfNodePtr &node) {
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std::string node_name = "";
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@ -0,0 +1,103 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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import numpy as np
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import pytest
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import mindspore.context as context
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from mindspore import Tensor, nn
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from mindspore.common import dtype as mstype
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from mindspore.train.serialization import export, load
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class CaseNet(nn.Cell):
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def __init__(self):
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super(CaseNet, self).__init__()
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self.conv = nn.Conv2d(1, 1, 3)
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self.relu = nn.ReLU()
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self.relu1 = nn.ReLU()
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self.softmax = nn.Softmax()
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self.layers1 = (self.relu, self.softmax)
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self.layers2 = (self.conv, self.relu1)
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def construct(self, x, index1, index2):
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x = self.layers1[index1](x)
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x = self.layers2[index2](x)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_switch_layer():
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context.set_context(mode=context.GRAPH_MODE)
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net = CaseNet()
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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export(net, data, idx, idx2, file_name=file_name, file_format='MINDIR')
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assert os.path.exists(mindir_name)
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(data, idx, idx2)
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relu = nn.ReLU()
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true_value = relu(data)
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ret = np.allclose(outputs_after_load.asnumpy(), true_value.asnumpy())
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assert ret
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@pytest.mark.skip(reason="depend on export")
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_export():
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context.set_context(mode=context.GRAPH_MODE)
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net = CaseNet()
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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export(net, data, idx, idx2, file_name=file_name, file_format='MINDIR')
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assert os.path.exists(mindir_name)
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@pytest.mark.skip(reason="depend on export")
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_load():
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context.set_context(mode=context.GRAPH_MODE)
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(data, idx, idx2)
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relu = nn.ReLU()
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true_value = relu(data)
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ret = np.allclose(outputs_after_load.asnumpy(), true_value.asnumpy())
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assert ret
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