ROperator_BasicBinary.hxx

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#ifndef TMVA_SOFIE_ROperator_BasicBinary
#define TMVA_SOFIE_ROperator_BasicBinary
 
#include "TMVA/SOFIE_common.hxx"
#include "TMVA/ROperator.hxx"
#include "TMVA/RModel.hxx"
 
#include <algorithm> // for std::all_of
#include <sstream>
 
namespace TMVA {
namespace Experimental {
namespace SOFIE {
 
enum EBasicBinaryOperator { Add, Sub, Mul, Div, Pow, Mod, FMod };
 
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 BinaryOperatorTrait<T, Mod> {
   static const std::string Name() { return "Mod"; }
   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, FMod> {
   static const std::string Name() { return "FMod"; }
   static std::string Op(const std::string & t1, const std::string t2) { return "std::fmod(" + t1 + "," + t2 + ")"; }
   static T Func(T t1, T t2) { return std::fmod(t1, t2); }
};
 
template <typename T, EBasicBinaryOperator Op>
class ROperator_BasicBinary final : public ROperator {
private:
   int fBroadcastFlag = 0;
   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;
 
   std::vector<Dim> fDimShapeA;
   std::vector<Dim> fDimShapeB;
   std::vector<Dim> fDimShapeY;
 
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))
   {
      fInputTensorNames = {fNA, fNB};
      fOutputTensorNames = {fNY};
   }
 
   // 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");
      }
      int dynamicInputs = 0;
      if (model.IsDynamicTensor(fNA)) {
         fDimShapeA = model.GetDynamicTensorShape(fNA);
         dynamicInputs |= 1;
      } else {
         fShapeA = model.GetTensorShape(fNA);
         fDimShapeA = ConvertShapeToDim(fShapeA);
      }
      if (model.IsDynamicTensor(fNB)) {
         dynamicInputs |= 2;
         fDimShapeB = model.GetDynamicTensorShape(fNB);
      } else {
         fShapeB = model.GetTensorShape(fNB);
         fDimShapeB = ConvertShapeToDim(fShapeB);
      }
      if (dynamicInputs & 1 && model.Verbose())
         std::cout << BinaryOperatorTrait<T, Op>::Name() << " : input " << fNA << " is dynamic "
                   << ConvertDimShapeToString(fDimShapeA) << std::endl;
      if (dynamicInputs & 2 && model.Verbose())
         std::cout << BinaryOperatorTrait<T, Op>::Name() << " : input " << fNB << " is dynamic "
                   << ConvertDimShapeToString(fDimShapeB) << std::endl;
 
      // check if need to broadcast at initialization time if shapes are known and different
      // (we could broadcast the tensor tensor to maximum values of dynamic shapes - to be done)
      // case of known shapes
      // if shapes are known find the output shape from broadcasting
      if (dynamicInputs == 0) {
         auto ret = UTILITY::MultidirectionalBroadcastShape(fShapeA, fShapeB);
         fBroadcastFlag = ret.first;
         fShapeY = ret.second;
         auto  lengthY = ConvertShapeToLength(fShapeY);
         if (model.IsConstantTensor(fNA) && model.IsConstantTensor(fNB)) {
            bool broadcast = fBroadcastFlag > 0;
            if (broadcast) {
               // Y is the common shape of A and B
               bool broadcastA = fBroadcastFlag & 2;
               bool broadcastB = fBroadcastFlag & 1;
               // Broadcast A to Y
               if (broadcastA) {
                  fNBroadcastedA = "Broadcasted" + fNA + "to" + fNY;
                  auto data = model.GetInitializedTensorData(fNA);
                  std::shared_ptr<void> broadcastedData(
                     UTILITY::UnidirectionalBroadcast(static_cast<T *>(data.get()), fShapeA, fShapeY),
                     std::default_delete<T[]>());
                  if (model.Verbose())
                     std::cout << "broadcasted data A " << ConvertShapeToString(fShapeY) << " : "
                               << ConvertValuesToString(ConvertShapeToLength(fShapeY),
                                                        static_cast<T *>(broadcastedData.get()))
                               << std::endl;
                  // Update the data and the shape of A
                  model.AddConstantTensor(fNBroadcastedA, model.GetTensorType(fNA), fShapeY, broadcastedData);
                  fShapeA = fShapeY;
                  fDimShapeA = ConvertShapeToDim(fShapeA);
               }
               // Broadcast B to Y
               if (broadcastB) {
                  fNBroadcastedB = "Broadcasted" + fNB + "to" + fNY;
                  auto data = model.GetInitializedTensorData(fNB);
                  if (model.Verbose())
                     std::cout << "data B " << ConvertShapeToString(fShapeB) << " : "
                               << ConvertValuesToString(ConvertShapeToLength(fShapeB), static_cast<T *>(data.get()))
                               << std::endl;
                  std::shared_ptr<void> broadcastedData(
                     UTILITY::UnidirectionalBroadcast(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)
                  if (model.Verbose())
                     std::cout << "broadcasted data B " << ConvertShapeToString(fShapeY) << " : "
                               << ConvertValuesToString(ConvertShapeToLength(fShapeY),
                                                        static_cast<T *>(broadcastedData.get()))
                               << std::endl;
                  model.AddConstantTensor(fNBroadcastedB, model.GetTensorType(fNB), fShapeY, broadcastedData);
                  fShapeB = fShapeY;
                  fDimShapeB = ConvertShapeToDim(fShapeB);
               }
            } else {
               fShapeY = fShapeA;
            }
            // tensors are constant: perform here the binary operation
 
            const std::string &nameA = fNBroadcastedA.empty() ? fNA : fNBroadcastedA;
            const std::string &nameB = fNBroadcastedB.empty() ? fNB : fNBroadcastedB;
            auto dataA = static_cast<T *>(model.GetInitializedTensorData(nameA).get());
            auto dataB = static_cast<T *>(model.GetInitializedTensorData(nameB).get());
            std::vector<T> dataY(lengthY);
            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 the generated code or weight file
            model.SetNotWritableInitializedTensor(nameA);
            model.SetNotWritableInitializedTensor(nameB);
            fIsOutputConstant = true;
            if (model.Verbose()) {
               std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << fNA << "  " << ConvertShapeToString(fShapeA)
                         << " , " << fNB << "  " << ConvertShapeToString(fShapeB) << " ---> " << fNY << "  "
                         << ConvertShapeToString(fShapeY) << " : " << ConvertValuesToString(dataY) << std::endl;
            }
         } else if (((model.IsShapeTensor(fNA) && model.IsShapeTensor(fNB)) ||
                    (model.IsShapeTensor(fNA) && model.IsInitializedTensor(fNB)) ||
                    (model.IsShapeTensor(fNB) && model.IsInitializedTensor(fNA)))
                     && (fShapeA.size() <=1 && fShapeB.size() <=1 &&  model.GetTensorType(fNA) == ETensorType::INT64)) {
            // case of shape tensors ( tensors are of rank 0 or 1  )
            std::vector<Dim> dimValA;
            std::vector<Dim> dimValB;
            if (model.IsShapeTensor(fNA))
               dimValA = model.GetShapeTensorValues(fNA);
            if (model.IsShapeTensor(fNB))
               dimValB = model.GetShapeTensorValues(fNB);
            // adjust for broadcasting - repet values until it reaches shapes of Y
            if (!fShapeY.empty() && fShapeY[0] > 1) {
               if (dimValA.size() == 1) dimValA = std::vector<Dim>( fShapeY[0], dimValA[0]);
               if (dimValB.size() == 1) dimValB = std::vector<Dim>( fShapeY[0], dimValB[0]);
            }
 
            auto convertDataToDim = [&](const std::string & name, const std::vector<size_t> & shape, std::vector<Dim> & dimValues) {
               auto data = static_cast<int64_t *>(model.GetInitializedTensorData(name).get());
               dimValues.resize(lengthY);
               for (size_t i = 0; i < lengthY; i++) {
                  if (!shape.empty() && lengthY == shape[0])
                     dimValues[i] = Dim{ static_cast<size_t>(data[i])};
                  else // case dataA is a scalar
                     dimValues[i] = Dim{ static_cast<size_t>(data[0])};
               }
            };
            if (model.IsInitializedTensor(fNA)) {
               convertDataToDim(fNA,fShapeA,dimValA);
            } else if (model.IsInitializedTensor(fNB)) {
               convertDataToDim(fNB,fShapeB,dimValB);
            }
 
            //perform binary operations on shape tensors
            std::vector<Dim> dimValY(lengthY);
            for (size_t i = 0; i < lengthY; i++) {
               if (!dimValA[i].isParam && !dimValB[i].isParam) {
                  size_t d = BinaryOperatorTrait<size_t, Op>::Func(dimValA[i].dim, dimValB[i].dim);
                  dimValY[i] = Dim{d};
               } else {
                  auto res =  BinaryOperatorTrait<T, Op>::Op(dimValA[i].GetVal(), dimValB[i].GetVal());
                  dimValY[i] = Dim{res, static_cast<size_t>(-1)};
               }
            }
            model.AddShapeTensor(fNY,dimValY, fShapeY.empty()); // cannot be a  scalar
            if (model.Verbose()) {
               std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << fNA << "  " << ConvertShapeToString(fShapeA)
                         << " , " << fNB << "  " << ConvertShapeToString(fShapeB) << " ---> " << fNY << "  "
                         << ConvertShapeToString(fShapeY) << " : " << ConvertDimShapeToString(dimValY) << " (shape)" <<  std::endl;
            }
            // no code needs to be generated (flag this as a constant output tensor)
            fIsOutputConstant = true;
 
         } else {
            // case of defined and non-constant tensors
            model.AddIntermediateTensor(fNY, model.GetTensorType(fNA), fShapeY);
            if (model.Verbose()) {
               std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << fNA << "  " << ConvertShapeToString(fShapeA)
                         << " , " << fNB << "  " << ConvertShapeToString(fShapeB) << " ---> " << fNY << "  "
                         << ConvertShapeToString(fShapeY) << std::endl;
            }
            // we convert non-dim shapes to Dim shapes
            fDimShapeY = ConvertShapeToDim(fShapeY);
         }
      } else {
         // case A or B have dynamic shapes. We need to broadcast if shape are not same
         auto ret = UTILITY::MultidirectionalBroadcastShape(fDimShapeA, fDimShapeB);
         fBroadcastFlag = ret.first;
         fDimShapeY = ret.second;
         // case of all parametric shapes and MultiDirectionalBroadcastShape  return the max of the 2
         // need to do before we declare the output tensor shape and the broadcasted ones
         if (ret.first & 4) {
            // check if one of the parameter is an input dimension
            // define function to find this
            auto IsInputDimParam = [&](const std::string &p) {
               auto inputNames = model.GetInputTensorNames();
               for (auto &input : inputNames) {
                  for (auto &i_s : model.GetDimTensorShape(input)) {
                     if (i_s.isParam && i_s.param == p)
                        return true;
                  }
               }
               return false;
            };
            for (size_t i = 0; i < fDimShapeY.size(); i++) {
               auto &s = fDimShapeY[i];
               if (s.isParam && s.param.find("std::max") != std::string::npos) {
                  if (IsInputDimParam(fDimShapeA[i].param)) {
                     // case dim is 1 we indicate that the input parameter is equal to 1
                     if (fDimShapeA[i].dim != 1)
                        s = fDimShapeA[i];
                     else
                        s = fDimShapeB[i];
                  } else if (IsInputDimParam(fDimShapeB[i].param)) {
                     if (fDimShapeB[i].dim != 1)
                        s = fDimShapeB[i];
                     else
                        s = fDimShapeA[i];
                  }
               }
            }
         }
 
         model.AddIntermediateTensor(fNY, model.GetTensorType(fNA), fDimShapeY);
         if (model.Verbose()) {
            std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << ConvertDimShapeToString(fDimShapeA) << " , "
                      << ConvertDimShapeToString(fDimShapeB) << " --> " << ConvertDimShapeToString(fDimShapeY) << std::endl;
         }
      }
   }
 
   std::string GenerateInitCode() override
   {
      std::stringstream out;
      return out.str();
   }
 
   std::string Generate(std::string opName) override
   {
 
      if (fIsOutputConstant)
         return "";
 
      opName = "op_" + opName;
 
      std::stringstream out;
      out << SP << "\n//------ " << opName << "  " << BinaryOperatorTrait<T, Op>::Name() << " --> "
          << ConvertDimShapeToString(fDimShapeY) << "\n";
      auto length = ConvertDimShapeToLength(fDimShapeY);
      std::string typeName = TensorType<T>::Name();
 
      // we need to check if we can broadcast (case flag has bit 4 set)
 
      if (fBroadcastFlag & 4) {
         // need to check if shapes are the same
         auto lengthA = ConvertDimShapeToLength(fDimShapeA);
         auto lengthB = ConvertDimShapeToLength(fDimShapeB);
         out << SP << "if (" << lengthA << "!=" << lengthB << ") {\n";
         // check if A->B or B->A
         // bool broadcastable = true;
         for (size_t i = 0; i < fDimShapeY.size(); i++) {
            if (fBroadcastFlag & 5 && fDimShapeY[i] == fDimShapeA[i] && fDimShapeA[i].dim > 1 &&
                fDimShapeB[i].isParam) {
               // B->A B[i] needs to be 1
               out << SP << SP << "if (" << fDimShapeB[i] << "!= 1)\n";
               out << SP << SP << SP << "throw std::runtime_error(\"SOFIE - Cannot broadcast B->A in operator "
                   << opName << "\");\n";
            }
            if (fBroadcastFlag & 6 && fDimShapeY[i] == fDimShapeB[i] && fDimShapeB[i].dim > 1 &&
                fDimShapeA[i].isParam) {
               // A-> B A[i] needs to be 1
               out << SP << SP << "if (" << fDimShapeA[i] << "!= 1)\n";
               out << SP << SP << SP << "throw std::runtime_error(\"SOFIE - Cannot broadcast A->B in operator "
                   << opName << "\");\n";
            } else if (fDimShapeA[i].isParam && fDimShapeB[i].isParam) {
               // both shapes are parametric and we broadcast to maximum
               // we allocate here output vector
               out << SP << SP << "if (" << fDimShapeA[i] << " != " << fDimShapeB[i] << " && (" << fDimShapeA[i]
                   << " != 1 || " << fDimShapeB[i] << " != 1))\n";
               out << SP << SP << SP << "throw std::runtime_error(\"SOFIE - Cannot broadcast shapes in operator " << opName
                   << "\");\n";
            }
         }
         out << SP << "}\n";
      }
 
      auto stridesA = UTILITY::ComputeStrideFromShape(fDimShapeA);
      auto stridesB = UTILITY::ComputeStrideFromShape(fDimShapeB);
      auto stridesY = UTILITY::ComputeStrideFromShape(fDimShapeY);
 
      std::string compute_idx_A, compute_idx_B, compute_idx_Y;
      if (fDimShapeA.empty() ||
          std::all_of(fDimShapeA.begin(), fDimShapeA.end(), [](Dim d) { return d.dim == 1 || d.GetVal() == "1"; })) {
         compute_idx_A = "0";
      } else {
         for (size_t i = 0; i < fDimShapeA.size(); ++i) {
            if (fDimShapeA[i].dim == 1 || fDimShapeA[i].GetVal() == "1")
               continue;
            compute_idx_A += "idx_" + std::to_string(i + (fDimShapeY.size() - fDimShapeA.size()));
            if (stridesA[i].GetVal() != "1")
               compute_idx_A += " * " + stridesA[i].GetVal();
            compute_idx_A += " + ";
         }
         // remove last 3 character " + "
         for (int j = 0; j < 3; j++)
            compute_idx_A.pop_back();
      }
      if (fDimShapeB.empty() ||
          std::all_of(fDimShapeB.begin(), fDimShapeB.end(), [](Dim d) { return d.dim == 1 || d.GetVal() == "1"; })) {
         compute_idx_B = "0";
      } else {
         for (size_t i = 0; i < fDimShapeB.size(); ++i) {
            if (fDimShapeB[i].dim == 1 || fDimShapeB[i].GetVal() == "1")
               continue;
            compute_idx_B += "idx_" + std::to_string(i + (fDimShapeY.size() - fDimShapeB.size()));
            if (stridesB[i].GetVal() != "1")
               compute_idx_B += " * " + stridesB[i].GetVal();
            compute_idx_B += " + ";
         }
          // remove last 3 character " + "
         for (int j = 0; j < 3; j++)
            compute_idx_B.pop_back();
      }
      int nloop = 0;
      if (fDimShapeY.empty() ||
          std::all_of(fDimShapeY.begin(), fDimShapeY.end(), [](Dim d) { return d.dim == 1 || d.GetVal() == "1"; })) {
         compute_idx_Y = "0";
      } else {
         for (size_t i = 0; i < fDimShapeY.size(); ++i) {
            if (fDimShapeY[i].dim != 1 && fDimShapeY[i].GetVal() != "1") {
               nloop++;
               for (int j = 0; j < nloop; j++) out << SP;
               out << "for (size_t idx_" << i << " = 0; idx_" << i << " < " << fDimShapeY[i]
                   << "; ++idx_" << i << "){\n";
               compute_idx_Y += "idx_" + std::to_string(i);
               if (stridesY[i].GetVal() != "1")
                  compute_idx_Y += " * " + stridesY[i].GetVal();
               compute_idx_Y += " + ";
            }
         }
         // remove last 3 characters " + "
         for (int j = 0; j < 3; j++)
            compute_idx_Y.pop_back();
      }
      for (int j = 0; j < nloop + 1; j++) out << SP;
      out << "tensor_" << fNY << "[" << compute_idx_Y << "] = "
          << BinaryOperatorTrait<T, Op>::Op("tensor_" + fNA + "[" + compute_idx_A + "]",
                                            "tensor_" + fNB + "[" + compute_idx_B + "]")
          << " ;\n";
 
      for (int i = nloop; i > 0; i--) {
         for (int j = 0; j < i; j++) out << SP;
         out << "}\n";
      }
      return out.str();
   }
 
   std::vector<std::string> GetStdLibs() override
   {
      if (Op == EBasicBinaryOperator::Pow) {
         return {std::string("cmath")};
      } else {
         return {};
      }
   }
};
 
inline std::unique_ptr<ROperator> createBasicBinary(std::string layerDType, std::string layerType, std::string nameA,
                                                    std::string nameB, std::string nameY)
{
   if (ConvertStringToType(layerDType) != ETensorType::FLOAT) {
      throw std::runtime_error(
         ("TMVA::SOFIE - Unsupported - Operator BasicBinary does not yet support input type " + layerDType).c_str());
   }
   if (layerType == "Add")
      return std::make_unique<ROperator_BasicBinary<float, EBasicBinaryOperator::Add>>(nameA, nameB, nameY);
   if (layerType == "Subtract")
      return std::make_unique<ROperator_BasicBinary<float, EBasicBinaryOperator::Sub>>(nameA, nameB, nameY);
   if (layerType == "Multiply")
      return std::make_unique<ROperator_BasicBinary<float, EBasicBinaryOperator::Mul>>(nameA, nameB, nameY);
 
   throw std::runtime_error(
      ("TMVA::SOFIE - Unsupported - Operator BasicBinary does not yet support layer type " + layerType).c_str());
}
 
} // namespace SOFIE
} // namespace Experimental
} // namespace TMVA
 
#endif // TMVA_SOFIE_ROperator_BasicBinary