ROperator_BasicBinary.hxx

Go to the documentation of this file.

#ifndef TMVA_SOFIE_ROperator_BasicBinary
#define TMVA_SOFIE_ROperator_BasicBinary
 
#include "TMVA/SOFIE_common.hxx"
#include "TMVA/ROperator.hxx"
#include "TMVA/RModel.hxx"
 
#include <sstream>
 
namespace TMVA{
namespace Experimental{
namespace SOFIE{
 
enum EBasicBinaryOperator { Add, Sub, Mul, Div, Pow };
 
template <typename T, EBasicBinaryOperator Op1>
struct BinaryOperatorTrait {};
 
template <typename T>
struct BinaryOperatorTrait<T, Add> {
   static const std::string Name() { return "Add"; }
   static std::string Op(const std::string & t1, const std::string t2) { return t1 + " + " + t2; }
   static T Func(T t1, T t2) {return  t1 + t2;}
};
 
template <typename T>
struct BinaryOperatorTrait<T, Sub> {
   static const std::string Name() { return "Sub"; }
   static std::string Op(const std::string & t1, const std::string t2) { return t1 + " - " + t2; }
   static T Func (T t1, T t2) { return t1 - t2;}
};
 
template <typename T>
struct BinaryOperatorTrait<T, Mul> {
   static const std::string Name() { return "Mul"; }
   static std::string Op(const std::string & t1, const std::string t2) { return t1 + " * " + t2; }
   static T Func (T t1, T t2) { return  t1 * t2;}
};
 
template <typename T>
struct BinaryOperatorTrait<T, Div> {
   static const std::string Name() { return "Div"; }
   static std::string Op(const std::string & t1, const std::string t2) { return t1 + " / " + t2; }
   static T Func (T t1, T t2) { return t1/t2;}
};
 
template <typename T>
struct BinaryOperatorTrait<T, Pow> {
   static const std::string Name() { return "Pow"; }
   static std::string Op(const std::string & t1, const std::string t2) { return "std::pow(" + t1 + "," + t2 + ")"; }
   static T Func (T t1, T t2) { return std::pow(t1,t2);}
};
 
template <typename T>
struct TensorType {};
template<>
struct TensorType<float> {
   static const std::string Name() { return "float"; }
};
template<>
struct TensorType<double> {
   static const std::string Name() { return "double"; }
};
template<>
struct TensorType<int64_t> {
   static const std::string Name() { return "int64_t"; }
};
template<>
struct TensorType<int32_t> {
   static const std::string Name() { return "int32_t"; }
};
template<>
struct TensorType<uint32_t> {
   static const std::string Name() { return "uint32_t"; }
};
template<>
struct TensorType<uint64_t> {
   static const std::string Name() { return "uint64_t"; }
};
 
template<typename T, EBasicBinaryOperator Op>
class ROperator_BasicBinary final : public ROperator{
private:
 
   std::string fNA;
   std::string fNB;
   std::string fNBroadcastedA;
   std::string fNBroadcastedB;
   std::string fNY;
 
   std::vector<size_t> fShapeA;
   std::vector<size_t> fShapeB;
   std::vector<size_t> fShapeY;
 
public:
   ROperator_BasicBinary(){}
   ROperator_BasicBinary(std::string nameA, std::string nameB, std::string nameY):
      fNA(UTILITY::Clean_name(nameA)), fNB(UTILITY::Clean_name(nameB)), fNY(UTILITY::Clean_name(nameY)){}
 
   // type of output given input
   std::vector<ETensorType> TypeInference(std::vector<ETensorType> input) override {
      return input;
   }
 
   // shape of output tensors given input tensors
   std::vector<std::vector<size_t>> ShapeInference(std::vector<std::vector<size_t>> input) override {
      // assume now inputs have same shape (no broadcasting)
      auto ret = std::vector<std::vector<size_t>>(1, input[0]); // return vector size 1 with first input
      return ret;
   }
 
   void Initialize(RModel& model) override {
      // input must be a graph input, or already initialized intermediate tensor
      if (!model.CheckIfTensorAlreadyExist(fNA)){
         throw std::runtime_error(std::string("TMVA SOFIE Binary Op Input Tensor ") + fNA + "is not found in model");
      }
      if (!model.CheckIfTensorAlreadyExist(fNB)) {
         throw std::runtime_error(std::string("TMVA SOFIE Binary Op Input Tensor ") + fNB + "is not found in model");
      }
      fShapeA = model.GetTensorShape(fNA);
      fShapeB = model.GetTensorShape(fNB);
      bool broadcast = !UTILITY::AreSameShape(fShapeA, fShapeB);
      if (broadcast) {
         // Y is the common shape of A and B
         fShapeY = UTILITY::UnidirectionalBroadcastShape(fShapeA, fShapeB);
         bool broadcastA = !UTILITY::AreSameShape(fShapeA, fShapeY);
         bool broadcastB = !UTILITY::AreSameShape(fShapeB, fShapeY);
         // Broadcast A to Y
         if (broadcastA) {
            fNBroadcastedA = "Broadcasted" + fNA + "to" + fNY;
            if (model.IsInitializedTensor(fNA)) {
               auto data = model.GetInitializedTensorData(fNA);
               std::shared_ptr<void> broadcastedData(
                  UTILITY::UnidirectionalBroadcast<T>(static_cast<T *>(data.get()), fShapeA, fShapeY),
                  std::default_delete<T[]>());
               // Update the data and the shape of A
               model.AddConstantTensor(fNBroadcastedA, model.GetTensorType(fNA), fShapeY, broadcastedData);
               fShapeA = fShapeY;
            } else {
               // Add an intermediate tensor for broadcasting A
               model.AddIntermediateTensor(fNBroadcastedA, model.GetTensorType(fNA), fShapeY);
            }
         }
         // Broadcast B to Y
         if (broadcastB) {
            fNBroadcastedB = "Broadcasted" + fNB + "to" + fNY;
            if (model.IsInitializedTensor(fNB)) {
               auto data = model.GetInitializedTensorData(fNB);
               std::shared_ptr<void> broadcastedData(
                  UTILITY::UnidirectionalBroadcast<T>(static_cast<T *>(data.get()), fShapeB, fShapeY),
                  std::default_delete<T[]>());
               // do not update tensor B but add broadcasted one (since it can be input to some other operators)
               model.AddConstantTensor(fNBroadcastedB, model.GetTensorType(fNB), fShapeY, broadcastedData);
               fShapeB = fShapeY;
            } else {
               // Add an intermediate tensor for broadcasting B
               model.AddIntermediateTensor(fNBroadcastedB, model.GetTensorType(fNB), fShapeY);
            }
         }
      } else {
         fShapeY = fShapeA;
      }
      // check case of constant  output (if all inputs are defined)
      if (model.IsInitializedTensor(fNA) && model.IsInitializedTensor(fNB)) {
         auto dataA = static_cast<T *>(model.GetInitializedTensorData(fNA).get());
         auto dataB = static_cast<T *>(model.GetInitializedTensorData(fNB).get());
         std::vector<T> dataY(ConvertShapeToLength(fShapeY));
         for (size_t i = 0; i < dataY.size(); i++)
            dataY[i] = BinaryOperatorTrait<T,Op>::Func(dataA[i], dataB[i]);
         model.AddConstantTensor<T>(fNY, fShapeY, dataY.data());
         // flag tensors to not be written in a fil
         model.SetNotWritableInitializedTensor(fNA);
         model.SetNotWritableInitializedTensor(fNB);
         fIsOutputConstant = true;
         if (model.Verbose())
            std::cout << "Binary op ---> " << fNY << "  " << ConvertShapeToString(fShapeY) << " : "
               << ConvertValuesToString(dataY) << std::endl;
      }
      else {
        model.AddIntermediateTensor(fNY, model.GetTensorType(fNA), fShapeY);
      }
   }
 
   std::string GenerateInitCode() override {
      std::stringstream out;
      return out.str();
   }
 
   std::string Generate(std::string OpName) override {
 
      if (fIsOutputConstant) return "";
 
      OpName = "op_" + OpName;
 
      if (fShapeY.empty()) {
         throw std::runtime_error("TMVA SOFIE Binary Op called to Generate without being initialized first");
      }
      std::stringstream out;
      out << SP << "\n//------ " << BinaryOperatorTrait<T,Op>::Name() << "\n";
      size_t length = ConvertShapeToLength(fShapeY);
      std::string typeName = TensorType<T>::Name();
      // Broadcast A if it's uninitialized
      if (fShapeA != fShapeY) {
         out << SP << "// Broadcasting uninitialized tensor " << fNA << "\n";
         out << SP << "{\n";
         out << SP << SP << typeName << "* data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<" << typeName << ">(tensor_" << fNA << ", " << ConvertShapeToString(fShapeA) << ", " << ConvertShapeToString(fShapeY) << ");\n";
         out << SP << SP << "std::copy(data, data + " << length << ", tensor_" << fNBroadcastedA << ");\n";
         out << SP << SP << "delete[] data;\n";
         out << SP << "}\n";
      }
      // Broadcast B if it's uninitialized
      if (fShapeB != fShapeY) {
         out << SP << "// Broadcasting uninitialized tensor " << fNB << "\n";
         out << SP << "{\n";
         out << SP << SP << typeName << "* data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<" << typeName << ">(tensor_" << fNB << ", " << ConvertShapeToString(fShapeB) << ", " << ConvertShapeToString(fShapeY) << ");\n";
         out << SP << SP << "std::copy(data, data + " << length << ", tensor_" << fNBroadcastedB << ");\n";
         out << SP << SP << "delete[] data;\n";
         out << SP << "}\n";
      }
      const std::string& nameA = fNBroadcastedA.empty()? fNA : fNBroadcastedA;
      const std::string& nameB = fNBroadcastedB.empty()? fNB : fNBroadcastedB;
      out << SP << "for (size_t id = 0; id < " << length << " ; id++){\n";
      out << SP << SP << "tensor_" << fNY << "[id] = "  << BinaryOperatorTrait<T,Op>::Op( "tensor_" + nameA + "[id]" , "tensor_" + nameB + "[id]") <<  " ;\n";
      out << SP << "}\n";
      return out.str();
   }
 
   std::vector<std::string> GetStdLibs() override {
      if (Op == EBasicBinaryOperator::Pow) {
         return { std::string("cmath") };
      } else {
         return {};
      }
   }
};
 
}//SOFIE
}//Experimental
}//TMVA
 
 
#endif //TMVA_SOFIE_ROperator_BasicBinary