Remove not used params in GradientMachine::start

Author: reyoung

paddle/gserver/gradientmachines/GradientMachine.h

@@ -212,11 +212,7 @@ class GradientMachine {
    * @note    This function will only been implemented and used in a
    *          multithreaded environment.
    */
-  virtual void start(const TrainerConfig& config,
-                     DataProviderPtr dataProvider) {
-    (void)config;
-    (void)dataProvider;
-  }
+  virtual void start() {}
 
   /**
    * @brief   check  each work-thread whether is failed/error/finish,

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -441,7 +441,7 @@ TrainerThread::TrainerThread(const ModelConfig& config,
 TrainerThread::~TrainerThread() { stop(); }
 
 void TrainerThread::start() {
-  gradientMachine_->start(*(TrainerConfig*)nullptr, (DataProviderPtr) nullptr);
+  gradientMachine_->start();
 
   computeThread_.reset(new std::thread([this]() { computeThread(); }));
 

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -109,10 +109,9 @@ void MultiNetwork::onPassEnd() {
   }
 }
 
-void MultiNetwork::start(const TrainerConfig& config,
-                         DataProviderPtr dataProvider) {
+void MultiNetwork::start() {
   for (auto& subNetwork : subNetworks_) {
-    subNetwork->start(config, dataProvider);
+    subNetwork->start();
   }
 }
 

paddle/gserver/gradientmachines/MultiNetwork.h

@@ -54,7 +54,7 @@ class MultiNetwork : public NeuralNetwork {
     return subNetworks_;
   }
 
-  virtual void start(const TrainerConfig& config, DataProviderPtr dataProvider);
+  virtual void start();
 
   virtual void finish();
 

paddle/gserver/gradientmachines/ParallelNeuralNetwork.cpp

@@ -131,11 +131,7 @@ void ParallelNeuralNetwork::forwardBackward(const std::vector<Argument>& inArgs,
   backward(callback);
 }
 
-void ParallelNeuralNetwork::start(const TrainerConfig& config,
-                                  DataProviderPtr dataProvider) {
-  (void)config;
-  (void)dataProvider;
-
+void ParallelNeuralNetwork::start() {
   for (auto& thread : threads_) {
     thread->start();
   }

paddle/gserver/gradientmachines/ParallelNeuralNetwork.h

@@ -56,7 +56,7 @@ class ParallelNeuralNetwork : public NeuralNetwork {
                        PassType passType,
                        const UpdateCallback &callback = NULL);
 
-  virtual void start(const TrainerConfig &config, DataProviderPtr dataProvider);
+  virtual void start();
 
   void addComputeThread(int deviceId);
 

paddle/gserver/tests/test_NetworkCompare.cpp

@@ -114,7 +114,7 @@ void calcGradient(DataIn& in, DataOut& out, const std::string& configPath) {
       parameters[i]->getBuf(PARAMETER_VALUE)->copyFrom(*in.paraValues[i]);
     }
   }
-  gradientMachine->start(trainer.getConfig(), nullptr);
+  gradientMachine->start();
   gradientMachine->forward(in.inArgs, &outArgs, PASS_TRAIN);
   for (size_t i = 0; i < in.outGrads.size(); i++) {
     // If the all the layers in the config have no parameters, also

paddle/gserver/tests/test_RecurrentGradientMachine.cpp

@@ -28,7 +28,7 @@ class TrainerForTest : public paddle::Trainer {
 public:
   void startTrain() {
     GradientMachine& gm = *this->trainerInternal_.getGradientMachine();
-    gm.start(this->getConfig(), dataProvider_);
+    gm.start();
   }
 
   void finishTrain() {

paddle/trainer/Tester.cpp

@@ -257,7 +257,7 @@ void Tester::test() {
   CHECK(testDataProvider_) << "TestData is not specified";
   testDataProvider_->setSkipShuffle();
   testDataProvider_->reset();
-  gradientMachine_->start(*config_, testDataProvider_);
+  gradientMachine_->start();
 
   // For evaluation
   std::vector<std::string> modelList;

paddle/trainer/Trainer.cpp

@@ -308,7 +308,7 @@ static double genPerturbation(real* d, real* grad, size_t dim) {
 }
 
 real Trainer::checkGradient() {
-  trainerInternal_.getGradientMachine()->start(*config_, dataProvider_);
+  trainerInternal_.getGradientMachine()->start();
   std::vector<ParameterPtr>& parameters =
       trainerInternal_.getGradientMachine()->getNonStaticParameters();
   DataBatch dataBatch;
@@ -390,7 +390,7 @@ void Trainer::startTrain() {
     dataProvider_->reset();
   }
 
-  trainerInternal_.getGradientMachine()->start(*config_, dataProvider_);
+  trainerInternal_.getGradientMachine()->start();
 }
 
 void Trainer::finishTrain() { trainerInternal_.getGradientMachine()->finish(); }