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split tuple parameter to parameters 

add function trans tuple to maketuple
This commit is contained in:
root 2020-07-20 19:30:44 +08:00 committed by chenfei
parent 49053e7f83
commit 1b6f85dec8
7 changed files with 177 additions and 159 deletions
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2
mindspore/ccsrc/backend/optimizer/common/common_backend_optimization.cc
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@ -46,8 +46,8 @@ void BackendCommonOptimization(const std::shared_ptr<session::KernelGraph> &kern
auto common_pm = std::make_shared<PassManager>("common_pm"); auto common_pm = std::make_shared<PassManager>("common_pm");
common_pm->AddPass(std::make_shared<ConvertConstInputToAttr>()); common_pm->AddPass(std::make_shared<ConvertConstInputToAttr>());
common_pm->AddPass(std::make_shared<ConstToAttrStridedSliceGradPass>()); common_pm->AddPass(std::make_shared<ConstToAttrStridedSliceGradPass>());
common_pm->AddPass(std::make_shared<ConvertConstInputToTensorInput>());
common_pm->AddPass(std::make_shared<ConvertTupleOutputToMaketuple>()); common_pm->AddPass(std::make_shared<ConvertTupleOutputToMaketuple>());
common_pm->AddPass(std::make_shared<ConvertConstInputToTensorInput>());
common_pm->AddPass(std::make_shared<ConvertTupleInputToDynamicInput>()); common_pm->AddPass(std::make_shared<ConvertTupleInputToDynamicInput>());
optimizer->AddPassManager(common_pm); optimizer->AddPassManager(common_pm);
(void)optimizer->Optimize(kernel_graph); (void)optimizer->Optimize(kernel_graph);

5
mindspore/ccsrc/backend/optimizer/pass/convert_const_input_to_tensor_input.cc
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@ -139,7 +139,10 @@ AnfNodePtr ProcessGraphKernelOp(const AnfNodePtr &node) {
const AnfNodePtr ConvertConstInputToTensorInput::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const AnfNodePtr ConvertConstInputToTensorInput::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const EquivPtr &) const { const EquivPtr &) const {
if (node == nullptr || func_graph == nullptr || !AnfAlgo::IsRealCNodeKernel(node)) { if (node == nullptr || func_graph == nullptr || AnfAlgo::CheckPrimitiveType(node, prim::kPrimTupleGetItem)) {
return nullptr;
}
if (!node->isa<CNode>()) {
return nullptr; return nullptr;
} }
if (AnfAlgo::IsGraphKernel(node)) { if (AnfAlgo::IsGraphKernel(node)) {

88
mindspore/ccsrc/backend/optimizer/pass/convert_tuple_output_to_maketuple.cc
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@ -17,6 +17,7 @@
#include <algorithm> #include <algorithm>
#include <memory> #include <memory>
#include <unordered_map>
#include "backend/session/anf_runtime_algorithm.h" #include "backend/session/anf_runtime_algorithm.h"
#include "backend/optimizer/common/helper.h" #include "backend/optimizer/common/helper.h"
@ -25,68 +26,26 @@
namespace mindspore { namespace mindspore {
namespace opt { namespace opt {
namespace { namespace {
CNodePtr ConvertTupleOuputToPlantInputs(const FuncGraphPtr &graph, const AnfNodePtr &input_node) { AnfNodePtr ConvertTupleInputToMakeTuple(const FuncGraphPtr &graph, const AnfNodePtr &tuple_anf,
std::unordered_map<AnfNodePtr, AnfNodePtr> *transed_nodes) {
MS_EXCEPTION_IF_NULL(tuple_anf);
MS_EXCEPTION_IF_NULL(graph); MS_EXCEPTION_IF_NULL(graph);
if (!AnfAlgo::IsTupleOutput(input_node)) { MS_EXCEPTION_IF_NULL(transed_nodes);
MS_LOG(EXCEPTION) << "Cannot using the function to convert a not tuple output node to maketuple!";
if (!AnfAlgo::IsTupleOutput(tuple_anf)) {
return tuple_anf;
} }
if (input_node->isa<CNode>()) { auto transed_node_it = transed_nodes->find(tuple_anf);
MS_LOG(EXCEPTION) << "The function can only split a parameter or valuenode bug got " << input_node->DebugString(); if (transed_node_it != transed_nodes->end()) {
return transed_node_it->second;
} }
std::vector<AnfNodePtr> convert_inputs = {NewValueNode(prim::kPrimMakeTuple)};
auto kernel_graph = graph->cast<KernelGraphPtr>(); auto kernel_graph = graph->cast<KernelGraphPtr>();
MS_EXCEPTION_IF_NULL(kernel_graph); auto make_tuple = kernel_graph->TransTupleToMakeTuple(tuple_anf);
auto splited_node_list = kernel_graph->SplitTupleOutputNodeToNodeList(input_node); (*transed_nodes)[tuple_anf] = make_tuple;
for (const auto &node : splited_node_list) { // replace graph inputs if input is a parameter
if (AnfAlgo::IsTupleOutput(node)) { kernel_graph->ReplaceGraphInput(tuple_anf, make_tuple);
convert_inputs.emplace_back(ConvertTupleOuputToPlantInputs(graph, node));
continue;
}
convert_inputs.emplace_back(node);
}
auto make_tuple = graph->NewCNode(convert_inputs);
std::vector<abstract::AbstractBasePtr> abstract_list;
auto make_tuple_input_size = AnfAlgo::GetInputTensorNum(make_tuple);
for (size_t index = 0; index < make_tuple_input_size; ++index) {
auto make_tuple_input = AnfAlgo::GetInputNode(make_tuple, index);
MS_EXCEPTION_IF_NULL(make_tuple_input);
abstract_list.emplace_back(make_tuple_input->abstract());
}
make_tuple->set_abstract(std::make_shared<abstract::AbstractTuple>(abstract_list));
return make_tuple; return make_tuple;
} }
CNodePtr ConvertTupleInputToMakeTuple(const FuncGraphPtr &graph, const CNodePtr &cnode_ptr) {
MS_EXCEPTION_IF_NULL(cnode_ptr);
MS_EXCEPTION_IF_NULL(graph);
std::vector<AnfNodePtr> convert_inputs = {cnode_ptr->input(0)};
for (size_t index = 0; index < AnfAlgo::GetInputTensorNum(cnode_ptr); ++index) {
auto input_node = AnfAlgo::GetInputNode(cnode_ptr, index);
if (AnfAlgo::IsTupleOutput(input_node)) {
std::vector<TypeId> types;
std::vector<std::vector<size_t>> shapes;
std::vector<AnfNodePtr> make_tuple_inputs_list = {NewValueNode(prim::kPrimMakeTuple)};
if (input_node->isa<CNode>()) {
for (size_t tuple_out_index = 0; tuple_out_index < AnfAlgo::GetOutputTensorNum(input_node); ++tuple_out_index) {
make_tuple_inputs_list.emplace_back(CreatTupleGetItemNode(graph, input_node, tuple_out_index));
types.push_back(AnfAlgo::GetOutputInferDataType(input_node, tuple_out_index));
shapes.emplace_back(AnfAlgo::GetOutputInferShape(input_node, tuple_out_index));
}
auto make_tuple = graph->NewCNode(make_tuple_inputs_list);
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get());
convert_inputs.emplace_back(make_tuple);
continue;
}
convert_inputs.emplace_back(ConvertTupleOuputToPlantInputs(graph, input_node));
} else {
convert_inputs.push_back(input_node);
}
}
auto new_node = graph->NewCNode(convert_inputs);
new_node->set_abstract(cnode_ptr->abstract());
return new_node;
}
} // namespace } // namespace
const BaseRef ConvertTupleOutputToMaketuple::DefinePattern() const { const BaseRef ConvertTupleOutputToMaketuple::DefinePattern() const {
@ -102,15 +61,22 @@ const AnfNodePtr ConvertTupleOutputToMaketuple::Process(const FuncGraphPtr &func
} }
auto cnode = node->cast<CNodePtr>(); auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode); MS_EXCEPTION_IF_NULL(cnode);
std::unordered_map<AnfNodePtr, AnfNodePtr> transed_nodes;
if (IsPrimitiveCNode(cnode, prim::kPrimTupleGetItem) || IsPrimitiveCNode(cnode, prim::kPrimControlDepend)) { if (IsPrimitiveCNode(cnode, prim::kPrimTupleGetItem) || IsPrimitiveCNode(cnode, prim::kPrimControlDepend)) {
return nullptr; return nullptr;
} }
if (std::any_of(cnode->inputs().begin() + 1, cnode->inputs().end(), [](const AnfNodePtr &node) { bool cnode_input_changed = false;
return node->Type() != nullptr && AnfAlgo::IsRealKernel(node) && AnfAlgo::IsTupleOutput(node); for (size_t i = 0; i < cnode->inputs().size(); ++i) {
})) { const auto &input = cnode->inputs()[i];
return ConvertTupleInputToMakeTuple(func_graph, cnode); if (input->Type() != nullptr && AnfAlgo::IsRealKernel(input) && AnfAlgo::IsTupleOutput(input) &&
!AnfAlgo::CheckPrimitiveType(input, prim::kPrimCall)) {
cnode->set_input(i, ConvertTupleInputToMakeTuple(func_graph, input, &transed_nodes));
cnode_input_changed = true;
} }
return nullptr; }
auto kernel_graph = func_graph->cast<KernelGraphPtr>();
MS_EXCEPTION_IF_NULL(kernel_graph);
return cnode_input_changed ? kernel_graph->NewCNode(cnode) : nullptr;
} }
} // namespace opt } // namespace opt
} // namespace mindspore } // namespace mindspore

7
mindspore/ccsrc/backend/session/ascend_session.cc
View File

@ -1810,7 +1810,7 @@ void AscendSession::CreateMultiBranchOutput(NotNull<KernelGraphPtr> graph, NotNu
// create a parameter to store the output of multiple branch and set the parameter as the condition graph's output // create a parameter to store the output of multiple branch and set the parameter as the condition graph's output
// auto multi_output_param = graph->NewParameter(); // auto multi_output_param = graph->NewParameter();
auto origin_inputs = graph->inputs(); auto origin_inputs = graph->inputs();
auto output_param = CreateNewParameterFromCNode(node, true, graph.get().get()); auto output_param = graph->TransTupleToMakeTuple(graph->NewParameter(node->abstract()));
MS_EXCEPTION_IF_NULL(graph->MutableInputs()); MS_EXCEPTION_IF_NULL(graph->MutableInputs());
graph->MutableInputs()->operator=(origin_inputs); graph->MutableInputs()->operator=(origin_inputs);
graph->AddChildGraphResult(output_param); graph->AddChildGraphResult(output_param);
@ -1828,9 +1828,8 @@ void AscendSession::CreateMultiBranchOutput(NotNull<KernelGraphPtr> graph, NotNu
if (child_graph->get_output_null()) { if (child_graph->get_output_null()) {
continue; continue;
} }
auto graph_output = child_graph->output(); AscendControlParser::InsertMultipleAssignToGraph(NOT_NULL(child_graph), nullptr,
AscendControlParser::InsertMultipleAssignToGraph(NOT_NULL(child_graph), nullptr, NOT_NULL(graph_output), NOT_NULL(child_graph->output()), NOT_NULL(output_param));
NOT_NULL(output_param));
} }
} }
} }

203
mindspore/ccsrc/backend/session/kernel_graph.cc
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@ -406,78 +406,6 @@ ParameterPtr KernelGraph::NewParameter(const abstract::AbstractBasePtr &abstract
return new_parameter; return new_parameter;
} }
std::vector<AnfNodePtr> KernelGraph::SplitTupleParameterToNodeList(const ParameterPtr &parameter) {
MS_EXCEPTION_IF_NULL(parameter);
std::vector<AnfNodePtr> convert_nodes_list;
auto abstract = parameter->abstract();
MS_EXCEPTION_IF_NULL(abstract);
if (!abstract->isa<abstract::AbstractTuple>()) {
MS_LOG(EXCEPTION) << "Multiple output Parameter's output must be a tuple abstract but got " << abstract->ToString();
}
auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
for (size_t index = 0; index < tuple_abstract->size(); ++index) {
auto new_parameter = this->NewParameter((*tuple_abstract)[index]);
SetKernelInfoForNode(new_parameter);
convert_nodes_list.emplace_back(new_parameter);
}
auto new_inputs = std::make_shared<std::vector<AnfNodePtr>>();
auto old_inputs = inputs();
for (const auto &input_node : old_inputs) {
if (input_node != parameter) {
new_inputs->emplace_back(input_node);
continue;
}
std::copy(convert_nodes_list.begin(), convert_nodes_list.end(), std::back_inserter(*new_inputs));
}
inputs_ = new_inputs;
return convert_nodes_list;
}
std::vector<AnfNodePtr> KernelGraph::SplitTupleOutputNodeToNodeList(const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(node);
if (node->isa<CNode>()) {
MS_LOG(EXCEPTION) << "The function can only split a parameter or valuenode bug got " << node->DebugString();
}
if (node->isa<Parameter>()) {
return SplitTupleParameterToNodeList(node->cast<ParameterPtr>());
}
return SplitTupleValueNodeToNodeList(node->cast<ValueNodePtr>());
}
std::vector<AnfNodePtr> KernelGraph::SplitTupleValueNodeToNodeList(const ValueNodePtr &value_node) {
MS_EXCEPTION_IF_NULL(value_node);
auto node_value = value_node->value();
std::vector<AnfNodePtr> convert_inputs;
if (!node_value->isa<ValueTuple>()) {
MS_LOG(EXCEPTION) << "Multiple output valuenode's value must be a value tuple but got " << node_value->ToString();
}
auto value_tuple = node_value->cast<ValueTuplePtr>();
MS_EXCEPTION_IF_NULL(value_tuple);
auto abstract = value_node->abstract();
if (!abstract->isa<abstract::AbstractTuple>()) {
MS_LOG(EXCEPTION) << "Spilted node's output abstract is not type tuple";
}
auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
if (tuple_abstract->size() != value_tuple->size()) {
MS_LOG(EXCEPTION) << "The node output index [" << value_tuple->size() << "]is outof range "
<< tuple_abstract->size();
}
for (size_t index = 0; index < value_tuple->value().size(); ++index) {
auto new_value_node = std::make_shared<ValueNode>(value_tuple->value()[index]);
new_value_node->set_abstract((*tuple_abstract)[index]);
AddValueNodeToGraph(new_value_node);
SetKernelInfoForNode(new_value_node);
AnfAlgo::SetGraphId(graph_id_, new_value_node.get());
convert_inputs.emplace_back(new_value_node);
}
if (!RemoveValueNodeFromGraph(value_node)) {
MS_LOG(WARNING) << "Failed to remove the value_node " << value_node->DebugString();
}
return convert_inputs;
}
ValueNodePtr KernelGraph::NewValueNode(const ValueNodePtr &value_node) { ValueNodePtr KernelGraph::NewValueNode(const ValueNodePtr &value_node) {
MS_EXCEPTION_IF_NULL(value_node); MS_EXCEPTION_IF_NULL(value_node);
auto new_value_node = MakeValueNode(value_node)->cast<ValueNodePtr>(); auto new_value_node = MakeValueNode(value_node)->cast<ValueNodePtr>();
@ -485,6 +413,110 @@ ValueNodePtr KernelGraph::NewValueNode(const ValueNodePtr &value_node) {
return new_value_node; return new_value_node;
} }
ValueNodePtr KernelGraph::NewValueNode(const AbstractBasePtr &abstract, const ValuePtr &value) {
MS_EXCEPTION_IF_NULL(abstract);
MS_EXCEPTION_IF_NULL(value);
ValueNodePtr new_value_node = std::make_shared<ValueNode>(value);
new_value_node->set_abstract(abstract);
SetKernelInfoForNode(new_value_node);
AnfAlgo::SetGraphId(graph_id(), new_value_node.get());
return new_value_node;
}
AnfNodePtr KernelGraph::TransValueNodeTuple(const AbstractBasePtr abstract, const ValuePtr &value) {
MS_EXCEPTION_IF_NULL(abstract);
MS_EXCEPTION_IF_NULL(value);
if (!abstract->isa<abstract::AbstractTuple>()) {
auto new_value_node = NewValueNode(abstract, value);
AddValueNodeToGraph(new_value_node);
return new_value_node;
}
auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
auto value_tuple = value->cast<ValueTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
MS_EXCEPTION_IF_NULL(value_tuple);
if (tuple_abstract->size() != value_tuple->size()) {
MS_LOG(EXCEPTION) << "Abstract size:" << tuple_abstract->size()
<< " is not equal to value size:" << value_tuple->size();
}
std::vector<AnfNodePtr> make_tuple_inputs = {
mindspore::NewValueNode(std::make_shared<Primitive>(prim::kPrimMakeTuple->name()))};
for (size_t index = 0; index < tuple_abstract->size(); ++index) {
make_tuple_inputs.push_back(TransValueNodeTuple((*tuple_abstract)[index], (*value_tuple)[index]));
}
auto make_tuple = NewCNode(make_tuple_inputs);
make_tuple->set_abstract(tuple_abstract);
return make_tuple;
}
AnfNodePtr KernelGraph::TransParameterTuple(const AbstractBasePtr &abstract) {
MS_EXCEPTION_IF_NULL(abstract);
if (!abstract->isa<abstract::AbstractTuple>()) {
return NewParameter(abstract);
}
auto tuple_abstract = abstract->cast<abstract::AbstractTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
std::vector<AnfNodePtr> make_tuple_inputs = {
mindspore::NewValueNode(std::make_shared<Primitive>(prim::kPrimMakeTuple->name()))};
for (size_t index = 0; index < tuple_abstract->size(); ++index) {
make_tuple_inputs.push_back(TransParameterTuple((*tuple_abstract)[index]));
}
auto make_tuple = NewCNode(make_tuple_inputs);
make_tuple->set_abstract(tuple_abstract);
return make_tuple;
}
AnfNodePtr KernelGraph::CreatTupleGetItemNode(const AnfNodePtr &node, size_t output_idx) {
auto idx = mindspore::NewValueNode(SizeToInt(output_idx));
MS_EXCEPTION_IF_NULL(idx);
auto imm = std::make_shared<Int32Imm>(SizeToInt(output_idx));
auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(imm);
idx->set_abstract(abstract_scalar);
AnfNodePtr tuple_getitem = NewCNode({mindspore::NewValueNode(prim::kPrimTupleGetItem), node, idx});
MS_EXCEPTION_IF_NULL(tuple_getitem);
tuple_getitem->set_scope(node->scope());
std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
TypeId origin_type = AnfAlgo::GetOutputInferDataType(node, output_idx);
AnfAlgo::SetOutputInferTypeAndShape({origin_type}, {origin_shape}, tuple_getitem.get());
return tuple_getitem;
}
AnfNodePtr KernelGraph::TransCNodeTuple(const CNodePtr &node) {
MS_EXCEPTION_IF_NULL(node);
std::vector<TypeId> types;
std::vector<std::vector<size_t>> shapes;
std::vector<AnfNodePtr> make_tuple_inputs_list = {mindspore::NewValueNode(prim::kPrimMakeTuple)};
for (size_t tuple_out_index = 0; tuple_out_index < AnfAlgo::GetOutputTensorNum(node); ++tuple_out_index) {
make_tuple_inputs_list.emplace_back(CreatTupleGetItemNode(node, tuple_out_index));
types.push_back(AnfAlgo::GetOutputInferDataType(node, tuple_out_index));
shapes.emplace_back(AnfAlgo::GetOutputInferShape(node, tuple_out_index));
}
auto make_tuple = NewCNode(make_tuple_inputs_list);
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, make_tuple.get());
return make_tuple;
}
AnfNodePtr KernelGraph::TransTupleToMakeTuple(const AnfNodePtr &node) {
MS_EXCEPTION_IF_NULL(node);
if (!AnfAlgo::IsTupleOutput(node)) {
return node;
}
if (node->isa<Parameter>()) {
return TransParameterTuple(node->abstract());
} else if (node->isa<ValueNode>()) {
auto value_node = node->cast<ValueNodePtr>();
MS_EXCEPTION_IF_NULL(value_node);
auto make_tuple = TransValueNodeTuple(value_node->abstract(), value_node->value());
if (RemoveValueNodeFromGraph(value_node)) {
MS_LOG(WARNING) << "Failed to remove the value_node " << value_node->DebugString();
}
return make_tuple;
} else if (node->isa<CNode>()) {
return TransCNodeTuple(node->cast<CNodePtr>());
}
MS_LOG(EXCEPTION) << "Unexpected node:" << node->DebugString();
}
const std::vector<AnfNodePtr> &KernelGraph::inputs() const { const std::vector<AnfNodePtr> &KernelGraph::inputs() const {
MS_EXCEPTION_IF_NULL(inputs_); MS_EXCEPTION_IF_NULL(inputs_);
return *inputs_; return *inputs_;
@ -782,6 +814,23 @@ bool KernelGraph::RemoveValueNodeFromGraph(const ValueNodePtr &value_node) {
return false; return false;
} }
void KernelGraph::ReplaceGraphInput(const AnfNodePtr &old_parameter, const AnfNodePtr &new_parameter) {
// update graph inputs
MS_EXCEPTION_IF_NULL(old_parameter);
MS_EXCEPTION_IF_NULL(new_parameter);
if (old_parameter == new_parameter) {
return;
}
for (size_t i = 0; i < inputs_->size(); i++) {
if ((*inputs_)[i] == old_parameter) {
MS_LOG(INFO) << "Replace input of graph:" << graph_id_ << ", old graph input: " << old_parameter->DebugString()
<< ",new graph input:" << new_parameter->DebugString();
(*inputs_)[i] = new_parameter;
break;
}
}
}
void KernelGraph::ReplaceNode(NotNull<AnfNodePtr> old_anf_node, NotNull<AnfNodePtr> new_anf_node) { void KernelGraph::ReplaceNode(NotNull<AnfNodePtr> old_anf_node, NotNull<AnfNodePtr> new_anf_node) {
MS_EXCEPTION_IF_NULL(inputs_); MS_EXCEPTION_IF_NULL(inputs_);
{ {
@ -805,15 +854,7 @@ void KernelGraph::ReplaceNode(NotNull<AnfNodePtr> old_anf_node, NotNull<AnfNodeP
output_cnode->set_input(i, new_anf_node); output_cnode->set_input(i, new_anf_node);
} }
} }
// update graph inputs ReplaceGraphInput(old_anf_node, new_anf_node);
for (size_t i = 0; i < inputs_->size(); i++) {
if ((*inputs_)[i] == old_anf_node.get()) {
MS_LOG(INFO) << "Replace input of graph:" << graph_id_ << ", old graph input: " << old_anf_node->DebugString()
<< ",new graph input:" << new_anf_node->DebugString();
(*inputs_)[i] = new_anf_node.get();
break;
}
}
} }
// update front to backend map // update front to backend map
FrontBackendlMapUpdate(old_anf_node, new_anf_node); FrontBackendlMapUpdate(old_anf_node, new_anf_node);

12
mindspore/ccsrc/backend/session/kernel_graph.h
View File

@ -49,15 +49,17 @@ class KernelGraph : public FuncGraph {
const std::vector<AnfNodePtr> &inputs() const; const std::vector<AnfNodePtr> &inputs() const;
std::vector<AnfNodePtr> *MutableInputs() const { return inputs_.get(); } std::vector<AnfNodePtr> *MutableInputs() const { return inputs_.get(); }
void ReplaceGraphInput(const AnfNodePtr &old_parameter, const AnfNodePtr &new_parameter);
std::vector<AnfNodePtr> outputs() const; std::vector<AnfNodePtr> outputs() const;
CNodePtr NewCNode(const std::vector<AnfNodePtr> &inputs) override; CNodePtr NewCNode(const std::vector<AnfNodePtr> &inputs) override;
void CreateKernelInfoFromNewParameter(const CNodePtr &cnode); void CreateKernelInfoFromNewParameter(const CNodePtr &cnode);
CNodePtr NewCNode(const CNodePtr &cnode); CNodePtr NewCNode(const CNodePtr &cnode);
ParameterPtr NewParameter(const ParameterPtr &parameter = nullptr); ParameterPtr NewParameter(const ParameterPtr &parameter = nullptr);
ParameterPtr NewParameter(const abstract::AbstractBasePtr &abstract); ParameterPtr NewParameter(const abstract::AbstractBasePtr &abstract);
ValueNodePtr NewValueNode(const ValuePtr &value); ValueNodePtr NewValueNode(const AbstractBasePtr &abstract, const ValuePtr &value);
ValueNodePtr NewValueNode(const ValueNodePtr &value_node = nullptr); ValueNodePtr NewValueNode(const ValueNodePtr &value_node = nullptr);
std::vector<AnfNodePtr> SplitTupleOutputNodeToNodeList(const AnfNodePtr &node); // trans tuple output to maketuple + no_tuple out
AnfNodePtr TransTupleToMakeTuple(const AnfNodePtr &node);
void set_execution_order(const std::vector<CNodePtr> &order) { execution_order_ = order; } void set_execution_order(const std::vector<CNodePtr> &order) { execution_order_ = order; }
const std::vector<CNodePtr> &execution_order() const { return execution_order_; } const std::vector<CNodePtr> &execution_order() const { return execution_order_; }
void SetExecOrderByDefault(); void SetExecOrderByDefault();
@ -167,8 +169,6 @@ class KernelGraph : public FuncGraph {
// remove value node form graph // remove value node form graph
bool RemoveValueNodeFromGraph(const ValueNodePtr &value_node); bool RemoveValueNodeFromGraph(const ValueNodePtr &value_node);
void SetKernelInfoForNode(const AnfNodePtr &node) const; void SetKernelInfoForNode(const AnfNodePtr &node) const;
std::vector<AnfNodePtr> SplitTupleValueNodeToNodeList(const ValueNodePtr &value_node);
std::vector<AnfNodePtr> SplitTupleParameterToNodeList(const ParameterPtr &parameter);
AnfNodePtr MakeValueNode(const AnfNodePtr &node); AnfNodePtr MakeValueNode(const AnfNodePtr &node);
void VisitNodeDescendants(const AnfNodePtr &node, std::queue<AnfNodePtr> *visit_queue, void VisitNodeDescendants(const AnfNodePtr &node, std::queue<AnfNodePtr> *visit_queue,
std::unordered_set<AnfNodePtr> *visited_nodes); std::unordered_set<AnfNodePtr> *visited_nodes);
@ -181,6 +181,10 @@ class KernelGraph : public FuncGraph {
bool HandleControlDependNode(const AnfNodePtr &node, std::queue<AnfNodePtr> *que, bool HandleControlDependNode(const AnfNodePtr &node, std::queue<AnfNodePtr> *que,
std::unordered_set<AnfNodePtr> *visited_nodes); std::unordered_set<AnfNodePtr> *visited_nodes);
void UpdateControlDependRelations(const std::vector<AnfNodePtr> &depends); void UpdateControlDependRelations(const std::vector<AnfNodePtr> &depends);
AnfNodePtr TransValueNodeTuple(const AbstractBasePtr abstract, const ValuePtr &value);
AnfNodePtr TransParameterTuple(const AbstractBasePtr &abstract);
AnfNodePtr TransCNodeTuple(const CNodePtr &node);
AnfNodePtr CreatTupleGetItemNode(const AnfNodePtr &node, size_t output_idx);
std::shared_ptr<std::vector<AnfNodePtr>> inputs_; std::shared_ptr<std::vector<AnfNodePtr>> inputs_;
std::vector<AnfNodePtr> child_graph_result_; std::vector<AnfNodePtr> child_graph_result_;

17
tests/ut/cpp/pre_activate/pass/convert_const_input_to_tensor_input_test.cc
View File

@ -99,13 +99,18 @@ TEST_F(TestHWConstInputToTensorInput, test_value_tuple_tensor_input) {
EXPECT_NE(ret->input(1)->cast<CNodePtr>(), nullptr); EXPECT_NE(ret->input(1)->cast<CNodePtr>(), nullptr);
auto cnode = ret->input(1)->cast<CNodePtr>()->input(1)->cast<CNodePtr>(); auto cnode = ret->input(1)->cast<CNodePtr>()->input(1)->cast<CNodePtr>();
EXPECT_EQ(AnfAlgo::GetCNodeName(cnode), prim::kPrimDropoutGenMask->name()); EXPECT_EQ(AnfAlgo::GetCNodeName(cnode), prim::kPrimDropoutGenMask->name());
auto input1 = cnode->input(1); std::vector<int> out;
ASSERT_TRUE(input1 != nullptr); for (size_t i = 1; i <= 4; i++) {
EXPECT_TRUE(IsValueNode<tensor::Tensor>(input1)); auto input = cnode->input(i);
auto tensor = input1->cast<ValueNodePtr>()->value()->cast<tensor::TensorPtr>(); ASSERT_TRUE(input != nullptr);
EXPECT_TRUE(IsValueNode<tensor::Tensor>(input));
auto tensor = input->cast<ValueNodePtr>()->value()->cast<tensor::TensorPtr>();
ASSERT_TRUE(tensor != nullptr); ASSERT_TRUE(tensor != nullptr);
auto data = tensor->data_c(); int *data = (int *)(tensor->data_c());
EXPECT_EQ(std::vector<int>((int *)data, (int *)data + 4), std::vector<int>({2, 4, 2, 2})); ASSERT_TRUE(data != nullptr);
out.push_back(*data);
}
EXPECT_EQ(out, std::vector<int>({2, 4, 2, 2}));
} }
} // namespace opt } // namespace opt
} // namespace mindspore } // namespace mindspore