MNN/tools/converter/source/optimizer/Program.cpp

141 lines
4.5 KiB
C++

//
// Program.cpp
// MNNConverter
//
// Created by MNN on 2019/09/15.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "Program.hpp"
#include <MNN/expr/ExprCreator.hpp>
#include <unordered_map>
#include <unordered_set>
#define MNN_OPEN_TIME_TRACE
#include <MNN/AutoTime.hpp>
using namespace MNN::Express;
using namespace MNN;
#define UP_DIV(x) (((x) + 3) / 4)
#include "MNN_generated.h"
namespace MNN {
namespace Express {
void Program::removeDeadNodes() {
std::unordered_set<Expr*> validExprs;
std::unordered_set<Variable*> removingNodes;
auto exprList = Variable::getExecuteOrder(mOutputs);
for (const EXPRP& expr : exprList) {
validExprs.insert(expr.get());
}
for (const auto& it : mVars) {
VARP var = it.second;
EXPRP expr = var->expr().first;
if (!validExprs.count(expr.get())) {
removingNodes.insert(var.get());
}
}
if (removingNodes.empty()) {
return;
}
std::map<int, VARP> validVars;
for (const auto& it : mVars) {
if (!removingNodes.count(it.second.get())) {
validVars.emplace(it.first, it.second);
}
}
mVars.swap(validVars);
std::vector<VARP> validOutputs;
for (const auto& sinkNode : mOutputs) {
if (!removingNodes.count(sinkNode.get())) {
validOutputs.emplace_back(sinkNode);
}
}
mOutputs.swap(validOutputs);
}
void Program::createUnit(std::map<int, VARP>& varMap, std::vector<int>& inputIndexes, const std::vector<std::unique_ptr<OpT>>& oplists,
MNN::OpT* op, const MNN::NetT* net, std::set<OpT*>& invalidSet, std::set<int>& extraInputIndexes) {
if (invalidSet.find(op) != invalidSet.end()) {
return;
}
std::vector<VARP> inputVars;
auto outputIndexes = op->outputIndexes;
for (int j = 0; j < outputIndexes.size(); ++j) {
if (varMap.find(outputIndexes[j]) != varMap.end()) {
// Don't support multi op output to one index
return;
}
}
invalidSet.insert(op);
for (auto input : op->inputIndexes) {
if (varMap.find(input) == varMap.end()) {
for (int j = 0; j < oplists.size(); ++j) {
for (auto outputIndex : oplists[j]->outputIndexes) {
if (outputIndex == input) {
createUnit(varMap, inputIndexes, oplists, oplists[j].get(), net, invalidSet, extraInputIndexes);
}
}
}
if (varMap.find(input) == varMap.end()) {
extraInputIndexes.insert(input);
// MNN_PRINT("Don't find input %d - %s for %s, turn to input\n", input, net->tensorName[input].c_str(),
// op->name.c_str());
auto newInput = _Input({-1});
newInput->setName(net->tensorName[input]);
varMap[input] = newInput;
}
}
inputVars.emplace_back(varMap[input]);
}
auto expr = Expr::create(op, inputVars, outputIndexes.size());
expr->setName(op->name);
for (int j = 0; j < outputIndexes.size(); ++j) {
if (op->type == OpType_Input) {
inputIndexes.emplace_back(outputIndexes[j]);
}
auto newVar = Variable::create(expr, j);
newVar->setName(net->tensorName[outputIndexes[j]]);
varMap[outputIndexes[j]] = newVar;
}
}
void Program::input(const std::unordered_map<std::string, VARP>& inputs) {
for (auto& it : mVars) {
VARP var = it.second;
if (var->expr().first->inputType() != VARP::INPUT) {
continue;
}
if (inputs.count(var->name())) {
VARP input = inputs.at(var->name());
var->input(input);
}
}
}
std::shared_ptr<Program> Program::create(const MNN::NetT* net, bool supportExtra) {
std::map<int, VARP> varMap;
std::vector<int> inputIndexes;
std::set<int> extraInputIndexes;
for (int index = 0; index < net->oplists.size(); ++index) {
std::set<OpT*> invalidSet;
createUnit(varMap, inputIndexes, net->oplists, net->oplists[index].get(), net, invalidSet, extraInputIndexes);
}
std::set<VARP> outputs;
for (auto& iter : varMap) {
if (iter.second->linkNumber() == 0) {
outputs.insert(iter.second);
}
}
std::shared_ptr<Program> newProgram(new Program);
Program& program = *newProgram;
program.mVars = varMap;
for (auto output : outputs) {
program.mOutputs.emplace_back(output);
}
return newProgram;
}
} // namespace Express
} // namespace MNN