Add const to GradientMachine::eval

Author: reyoung

paddle/gserver/gradientmachines/GradientMachine.h

@@ -186,7 +186,7 @@ class GradientMachine {
   /**
    * evaluate using the given evaluator
    */
-  virtual void eval(Evaluator* evaluator) = 0;
+  virtual void eval(Evaluator* evaluator) const = 0;
 
   std::vector<ParameterPtr>& getParameters() { return parameters_; }
 

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -331,7 +331,7 @@ Evaluator* MultiGradientMachine::makeEvaluator() {
   return threads_[0]->getGradientMachine()->makeEvaluator();
 }
 
-void MultiGradientMachine::eval(Evaluator* evaluator) {
+void MultiGradientMachine::eval(Evaluator* evaluator) const {
   for (auto& thread : threads_) {
     SetDevice device(thread->getDeviceId());
     thread->getGradientMachine()->eval(evaluator);

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -195,7 +195,7 @@ class MultiGradientMachine : public GradientMachine {
 
   virtual Evaluator* makeEvaluator();
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   bool useGpu() const { return useGpu_; }
 

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -181,6 +181,6 @@ Evaluator* MultiNetwork::makeEvaluator() {
   return multiCombinedEvaluator;
 }
 
-void MultiNetwork::eval(Evaluator* evaluator) { evaluator->eval(*this); }
+void MultiNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); }
 
 }  // namespace paddle

paddle/gserver/gradientmachines/MultiNetwork.h

@@ -48,7 +48,7 @@ class MultiNetwork : public NeuralNetwork {
 
   virtual Evaluator* makeEvaluator();
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   const std::vector<std::unique_ptr<NeuralNetwork>>& getSubNetworks() const {
     return subNetworks_;

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -383,7 +383,7 @@ Evaluator* NeuralNetwork::makeEvaluator() {
   return combinedEvaluator;
 }
 
-void NeuralNetwork::eval(Evaluator* evaluator) { evaluator->eval(*this); }
+void NeuralNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); }
 
 void NeuralNetwork::setOutputGrad(const std::vector<Argument>& args) {
   CHECK_GE(outputLayers_.size(), args.size());

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -98,7 +98,7 @@ class NeuralNetwork : public GradientMachine {
 
   virtual Evaluator* makeEvaluator();
 
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
   virtual void resetState();
   virtual void setOutputGrad(const std::vector<Argument>& args);
 

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -593,7 +593,7 @@ void RecurrentGradientMachine::forwardBackward(
   LOG(FATAL) << "should not use this function";
 }
 
-void RecurrentGradientMachine::eval(Evaluator* evaluator) {
+void RecurrentGradientMachine::eval(Evaluator* evaluator) const {
   // call printers frame by frame
   for (int i = 0; i < maxSequenceLength_; ++i) {
     LOG(INFO) << "Recurrent Layer Group eval frame " << i << " begin";

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -63,7 +63,7 @@ class RecurrentGradientMachine : public NeuralNetwork {
                        const UpdateCallback& callback);
 
   virtual void resetState() {}
-  virtual void eval(Evaluator* evaluator);
+  virtual void eval(Evaluator* evaluator) const;
 
   const std::vector<int>& getParameterIds() { return parameterIds_; }