RModel.cxx

Go to the documentation of this file.

#include <limits>
#include <algorithm>
#include <cctype>
#include <memory>
#include <string>
#include <cstdlib>
 
#ifdef SOFIE_SUPPORT_ROOT_BINARY
#include "TFile.h"
#endif
 
#include "TMVA/RModel.hxx"
#include "TMVA/SOFIE_common.hxx"
 
namespace TMVA::Experimental::SOFIE {
 
namespace {
 
const std::string SP = "   ";
 
void ReplaceAll(std::string &str, const std::string &from, const std::string &to)
{
   size_t pos = 0;
   while ((pos = str.find(from, pos)) != std::string::npos) {
      str.replace(pos, from.length(), to);
      pos += to.length();
   }
}
 
bool IsIdentifierChar(char c)
{
   return std::isalnum(static_cast<unsigned char>(c)) || c == '_';
}
 
// Returns true if s is a valid C++ identifier (can be used as a variable name).
// Dim::param can be either a plain name (e.g. "W") or a computed expression
// (e.g. "((W+-3)/2+1)"); only the former can be used as a C++ variable name.
bool IsIdentifier(const std::string &s)
{
   if (s.empty() || std::isdigit(static_cast<unsigned char>(s[0])))
      return false;
   for (char c : s)
      if (!IsIdentifierChar(c))
         return false;
   return true;
}
 
// Get the data member name corresponding to a tensor with a given name.
std::string TensorMember(std::string const &name)
{
   return "tensor_" + name;
}
 
} // namespace
 
std::underlying_type_t<Options> operator|(Options opA, Options opB) {
    return static_cast<std::underlying_type_t<Options>>(opA) | static_cast<std::underlying_type_t<Options>>(opB);
}
std::underlying_type_t<Options> operator|(std::underlying_type_t<Options> opA, Options opB) {
    return opA | static_cast<std::underlying_type_t<Options>>(opB);
}
 
 
std::vector<size_t> RModel::GetTensorShape(const std::string & name) const {
    auto f = fReadyInputTensorInfos.find(name);
    if (f != fReadyInputTensorInfos.end()) {
        return f->second.shape;
    }
    auto f2 = fInitializedTensors.find(name);
    if (f2 != fInitializedTensors.end()) {
        return f2->second.shape();
    }
    auto f3 = fInputTensorInfos.find(name);
    if (f3 != fInputTensorInfos.end()) {
        throw std::runtime_error("TMVA SOFIE tensor [" + name + "] is an input tensor with unspecified dimension parameter");
    }
    auto f4 = fIntermediateTensorInfos.find(name);
    if (f4 != fIntermediateTensorInfos.end()) {
        return f4->second.shape;
    }
    // case of shape tensors
    auto f5 = fShapeTensors.find(name);
    if (f5 != fShapeTensors.end()) {
      // shape is vector of size 1 with size of shape values or just a scalar
      if (f5->second.second)  // check scalar flag
         return std::vector<size_t>{};
      else
         return std::vector<size_t>{f5->second.first.size()};
    }
 
    if (fDynamicTensorInfos.find(name) != fDynamicTensorInfos.end())
      throw std::runtime_error("TMVA SOFIE tensor [" + name + "] is a dynamic tensor. Use GetDynamicTensorShape instead of GetTensorShape");
 
   if (fIsSubGraph && fParentGraph)
      return fParentGraph->GetTensorShape(name);
 
    throw std::runtime_error("TMVA SOFIE tensor [" + name + "] for which the shape is requested is not found");
}
 
std::vector<Dim> RModel::GetDimTensorShape(const std::string & name) const {
   if (auto f = fDynamicTensorInfos.find(name); f != fDynamicTensorInfos.end()) {
      return f->second.shape;
   }
   if (auto f = fInputTensorInfos.find(name); f != fInputTensorInfos.end()) {
      return f->second.shape;
   }
   // in case is not a dynamic tensor convert normal shape to Dim one
   // for this we need to return the vector by value
   return ConvertShapeToDim(GetTensorShape(name));
}
std::vector<Dim> RModel::GetDynamicTensorShape(const std::string & name) const {
   if (auto f = fDynamicTensorInfos.find(name); f != fDynamicTensorInfos.end()) {
      return f->second.shape;
   }
   if (auto f = fInputTensorInfos.find(name); f != fInputTensorInfos.end()) {
      return f->second.shape;
   }
   // throw error if shape is not dynamic
   if (!IsDynamicTensor(name))
      throw std::runtime_error("TMVA SOFIE tensor [" + name + "] for which the shape is requested is not dynamic");
 
   throw std::runtime_error("TMVA SOFIE tensor [" + name + "] for which the shape is requested is not found");
}
 
ETensorType RModel::GetTensorType(std::string name) const {
    auto f = fReadyInputTensorInfos.find(name);
    if (f != fReadyInputTensorInfos.end()) {
        return f->second.type;
    }
    auto f2 = fInitializedTensors.find(name);
    if (f2 != fInitializedTensors.end()) {
        return f2->second.type();
    }
    auto f3 = fInputTensorInfos.find(name);
    if (f3 != fInputTensorInfos.end()) {
        return f3->second.type;
    }
    auto f4 = fIntermediateTensorInfos.find(name);
    if (f4 != fIntermediateTensorInfos.end()) {
        return f4->second.type;
    }
    auto f5 = fDynamicTensorInfos.find(name);
    if (f5 != fDynamicTensorInfos.end()){
      return f5->second.type;
    }
    // case of shape tensor type is INT64
    if (fShapeTensors.find(name) != fShapeTensors.end()){
      return ETensorType::INT64;
    }
 
    if (fIsSubGraph && fParentGraph)
      return fParentGraph->GetTensorType(name);
 
    throw std::runtime_error("TMVA SOFIE tensor [" + name + "] for which the type is requested is not found, model name: " + fName);
}
 
bool RModel::CheckIfTensorAlreadyExist(std::string tensor_name) {
    if (fReadyInputTensorInfos.find(tensor_name) != fReadyInputTensorInfos.end())  return true;
    if (fInputTensorInfos.find(tensor_name) != fInputTensorInfos.end()) return true;
    if (fInitializedTensors.find(tensor_name) != fInitializedTensors.end()) return true;
    if (fIntermediateTensorInfos.find(tensor_name) != fIntermediateTensorInfos.end()) return true;
    if (fDynamicTensorInfos.find(tensor_name) != fDynamicTensorInfos.end()) return true;
    if (fShapeTensors.find(tensor_name) != fShapeTensors.end()) return true;
    if (fIsSubGraph && fParentGraph) return fParentGraph->CheckIfTensorAlreadyExist(tensor_name);
    return false;
}
 
void RModel::AddInputTensorInfo(std::string input_name, ETensorType type, std::vector<Dim> shape) {
    input_name = UTILITY::Clean_name(input_name);
    if (CheckIfTensorAlreadyExist(input_name)) {
        throw std::runtime_error("TMVA-SOFIE: input tensor with name " + input_name + " already exists \n");
    }
 
    InputTensorInfo inputInfo { type, shape };
    fInputTensorInfos[input_name] = inputInfo;
}
 
void RModel::AddInputTensorInfo(std::string input_name, ETensorType type, std::vector<size_t> shape) {
    input_name = UTILITY::Clean_name(input_name);
    if (CheckIfTensorAlreadyExist(input_name)) {
        throw std::runtime_error("TMVA-SOFIE: input tensor with name " + input_name + " already exists \n");
    }
    TensorInfo inputInfo { type, shape };
    fReadyInputTensorInfos[input_name] = inputInfo;
}
 
void RModel::AddInputTensorName(std::string input_name) {
    fInputTensorNames.emplace_back(UTILITY::Clean_name(input_name));
}
 
void RModel::AddOperator(std::unique_ptr<ROperator> op, int order_execution)
{
   AddBlasRoutines(op->GetBlasRoutines());
   auto libs = op->GetStdLibs();
   auto op_input_tensors = op->GetOpInputTensors();
   for (auto &stdlib : libs) {
      AddNeededStdLib(stdlib);
   }
   if (order_execution >= 0) {
      fOperators.insert(fOperators.begin() + order_execution, std::move(op));
   } else {
      fOperators.push_back(std::move(op));
      order_execution = fOperators.size() - 1;
   }
 
   // storing the last usage of tensors which are input to the operator
   // (excluding tensors which are inputs to the model or the initialized (weights) tensors)
   // We call this function during parsing so we don't have yet initialized the operators
   for (size_t index = 0; index < op_input_tensors.size(); index++) {
      if (!IsInitializedTensor(UTILITY::Clean_name(std::string(op_input_tensors[index]))) &&
          std::find(fInputTensorNames.begin(), fInputTensorNames.end(),
                    UTILITY::Clean_name(std::string(op_input_tensors[index]))) == fInputTensorNames.end()) {
 
         fIntermediateTensorFrequencyLookup[op_input_tensors[index]] = order_execution;
         if (Verbose())
            std::cout << "adding order execution for " << op_input_tensors[index] << " order " << order_execution
                      << std::endl;
      }
   }
}
 
void RModel::AddInitializedTensor(std::string tensor_name, ETensorType type, std::vector<std::size_t> shape, std::shared_ptr<void> data) {
    tensor_name = UTILITY::Clean_name(tensor_name);
    //NB: own data
    if (CheckIfTensorAlreadyExist(tensor_name)) {
        throw std::runtime_error("TMVA-SOFIE: initialized tensor with name " + tensor_name + " already exists \n");
    }
    InitializedTensor new_tensor {type, shape, data};
    fInitializedTensors[tensor_name] = new_tensor;
}
 
void RModel::AddInitializedTensor(const std::string &tensor_name, ETensorType tensor_type,
                                  const std::vector<std::size_t> &shape, void *raw_data)
{
   size_t size = ConvertShapeToLength(shape);
   auto itemsize = GetTypeSize(tensor_type);
   std::shared_ptr<void> data(malloc(size * itemsize), free);
   std::memcpy(data.get(), raw_data, size * itemsize);
   AddInitializedTensor(tensor_name, tensor_type, shape, data);
}
 
void RModel::AddConstantTensor(std::string tensor_name, ETensorType type, std::vector<std::size_t> shape, std::shared_ptr<void> data) {
    tensor_name = UTILITY::Clean_name(tensor_name);
    //NB: own data
    if (CheckIfTensorAlreadyExist(tensor_name)) {
        throw std::runtime_error("TMVA-SOFIE: constant tensor with name " + tensor_name + " already exists \n");
    }
    InitializedTensor new_tensor {type, shape, data, true};   // add here flag to specify is a constant tensor
    fInitializedTensors[tensor_name] = new_tensor;
}
 
void RModel::AddShapeTensor(const std::string & name, const std::vector<Dim> & shape_values, bool scalar){
   auto tensor_name = UTILITY::Clean_name(name);
   if (fShapeTensors.count(tensor_name) != 0) {
      throw std::runtime_error("TMVA-SOFIE: shape tensor with name " + tensor_name + " already exists \n");
   }
   fShapeTensors[tensor_name] = std::make_pair(shape_values, scalar);
}
 
void RModel::AddAliasTensor(const std::string & name, const std::string & origin){
   // add an alias tensor to origin
   auto tensor_name = UTILITY::Clean_name(name);
   auto origin_name = UTILITY::Clean_name(origin);
   if (fAliasTensors.count(tensor_name) != 0) {
      throw std::runtime_error("TMVA-SOFIE: alias tensor with name " + tensor_name + " already exists \n");
   }
   fAliasTensors[tensor_name] = origin_name;
}
 
bool RModel::IsShapeTensor(const std::string & tensor_name) const {
   return fShapeTensors.count(tensor_name) != 0;
}
 
bool RModel::IsAliasTensor(const std::string & tensor_name) const {
   return fAliasTensors.count(tensor_name) != 0;
}
 
const std::vector<Dim> & RModel::GetShapeTensorValues(const std::string & tensor_name) const {
   //if (!IsShapeTensor(tensor_name) ) return std::vector<Dim>{};
   return fShapeTensors.at(tensor_name).first;
}
 
bool RModel::IsInitializedTensor(const std::string& tensorName) const {
    std::string name = UTILITY::Clean_name(tensorName);
    return fInitializedTensors.find(name) != fInitializedTensors.end();
}
bool RModel::IsConstantTensor(const std::string& tensorName) const {
   // a constant tensor is an initialized tensor but has the constant flag set
    std::string name = UTILITY::Clean_name(tensorName);
    auto itr = fInitializedTensors.find(name);
    if (itr == fInitializedTensors.end()) return false;
    return itr->second.IsConstantTensor();
}
 
// dynamic tensors include also Dim input tensors
bool RModel::IsDynamicTensor(const std::string& tensorName) const {
   std::string name = UTILITY::Clean_name(tensorName);
   bool ret = fDynamicTensorInfos.find(name) != fDynamicTensorInfos.end();
   return (ret) ? true : IsDimInputTensor(tensorName);
}
bool RModel::IsDimInputTensor(const std::string& tensorName) const {
   std::string name = UTILITY::Clean_name(tensorName);
   return fInputTensorInfos.find(name) != fInputTensorInfos.end();
}
bool RModel::IsReadyInputTensor(const std::string& tensorName) const {
   std::string name = UTILITY::Clean_name(tensorName);
   return fReadyInputTensorInfos.find(name) != fReadyInputTensorInfos.end();
}
 
// generic addition of a tensor
void RModel::AddIntermediateTensor(std::string tensor_name, ETensorType type, std::vector<Dim> dim_shape) {
   auto int_shape = ConvertShapeToInt(dim_shape);
   if (!int_shape.empty())
      AddIntermediateTensor(tensor_name, type, int_shape);
   else
      AddDynamicTensor(tensor_name, type, dim_shape);
}
 
void RModel::AddIntermediateTensor(std::string tensor_name, ETensorType type, std::vector<std::size_t> shape) {
    tensor_name = UTILITY::Clean_name(tensor_name);
    if (CheckIfTensorAlreadyExist(tensor_name)) {
        throw std::runtime_error("TMVA-SOFIE: intermediate tensor with name " + tensor_name + " already exists \n");
    }
    TensorInfo new_tensor {type, shape};
    fIntermediateTensorInfos[tensor_name] = new_tensor;
}
 
void RModel::AddDynamicTensor(std::string tensor_name, ETensorType type, std::vector<Dim> shape){
   tensor_name = UTILITY::Clean_name(tensor_name);
   if (CheckIfTensorAlreadyExist(tensor_name)){
      throw std::runtime_error("TMVA-SOFIE: intermediate tensor with name " + tensor_name + " already exists \n");
   }
   DynamicTensorInfo new_tensor {type, shape};
   fDynamicTensorInfos[tensor_name] = new_tensor;
   // store shape parameter if not existing
   for (auto &d : shape) {
      if (d.isParam) {
         if (d.dim != size_t(-1)) {
            AddShapeParam(d.param, d.dim);
         }
      }
   }
}
 
void RModel::AddShapeParam(const std::string & param, size_t default_value) {
   if (fShapeParams.count(param) == 0) {
      fShapeParams[param] = std::to_string(default_value);
      // add also in the vector list (used to keep the order)
      fDimShapeNames.push_back(param);
   }
}
 
void RModel::AddOutputTensorNameList(std::vector<std::string> outputtensornames) {
    fOutputTensorNames.clear();
    for(auto& it : outputtensornames) {
        fOutputTensorNames.emplace_back(UTILITY::Clean_name(it));
    }
}
 
void RModel::UpdateOutputTensorList(std::vector<std::string> curr_output_tensors, std::vector<std::string> new_output_tensors) {
    for(auto& it:curr_output_tensors) {
        fOutputTensorNames.erase(std::remove(fOutputTensorNames.begin(), fOutputTensorNames.end(), it), fOutputTensorNames.end());
    }
    fOutputTensorNames.insert(fOutputTensorNames.end(), new_output_tensors.begin(), new_output_tensors.end());
}
 
void RModel::UpdateInitializedTensor(std::string tensor_name, ETensorType type, std::vector<std::size_t> shape, std::shared_ptr<void> data) {
    tensor_name = UTILITY::Clean_name(tensor_name);
    if (!CheckIfTensorAlreadyExist(tensor_name)) {
        throw std::runtime_error("TMVA-SOFIE: tensor " + tensor_name + " not found when trying to update it");
    }
    InitializedTensor new_tensor {type, shape, data};
    fInitializedTensors[tensor_name] = new_tensor;
}
 
std::shared_ptr<void> RModel::GetInitializedTensorData(std::string tensor_name) {
    auto f = fInitializedTensors.find(tensor_name);
    if (f == fInitializedTensors.end()) {
        throw std::runtime_error("TMVA-SOFIE: tensor " + tensor_name + " not found when trying to get its data");
    } else {
        return f->second.sharedptr();
    }
}
 
void RModel::SetNotWritableInitializedTensor(const std::string & tensor_name) {
      auto t = fInitializedTensors.find(tensor_name);
      if (t == fInitializedTensors.end()) {
         throw std::runtime_error("TMVA-SOFIE: initialized tensor " + tensor_name + " not found when trying to get its info");
      }
      t->second.SetNotWritable();
   }
 
std::string RModel::AllocateIntermediateMemory(std::span<const std::string_view> op_output_tensors)
{
   std::stringstream code;
 
   if (fVerbose) {
      std::cout << "Total chunks allocated\n";
      for (auto chunk = fIntermediateMemoryInfo.total_stack.begin(); chunk != fIntermediateMemoryInfo.total_stack.end(); ++chunk) {
         std::cout << "..... chunk " << chunk->first << " size " << chunk->second.tensor_size << " " << chunk->second.tensor_name << std::endl;
      }
   }
 
   auto declareIntermediateTensor = [this, &code](std::string const &name, size_t size, size_t location) {
      std::string typeName = ConvertTypeToString(GetTensorType(name));
      code << "\n // Allocating memory for intermediate tensor " << name << " with size " << size << " bytes";
      code << "\n"
           << typeName << "* " << TensorMember(name) << " = reinterpret_cast<" << typeName
           << "*>(fIntermediateMemoryPool.data() + " << location << ");\n";
   };
 
   if (fVerbose) std::cout << "*** AllocateIntermediateMemory: Loop on op output tensors\n";
   // order output tensors by size
   std::vector<TensorMemoryInfo> ordered_output_tensors;
 
   for (auto &it : op_output_tensors) {
      auto name = std::string(it);
      if (GetTensorType(name) == ETensorType::BOOL || fInitializedTensors.find(name) != fInitializedTensors.end() ||
          fDynamicTensorInfos.find(name) != fDynamicTensorInfos.end())
         continue;
 
      // case of alias tensor
      if (IsAliasTensor(name)) {
         continue;
      }
 
      auto tensor_size = GetTypeSize(GetTensorType(name)) * ConvertShapeToLength(GetTensorShape(name));
      // important fill the pair in the ordered output tensors with the string view and not the string
      TensorMemoryInfo tmi = {it, tensor_size};
      ordered_output_tensors.push_back(tmi);
   }
   std::sort(ordered_output_tensors.begin(), ordered_output_tensors.end(),
             [](const TensorMemoryInfo &a, const TensorMemoryInfo &b) { return a.tensor_size > b.tensor_size; });
 
   for (auto &it : ordered_output_tensors) {
      bool allocated = false;
      std::string name = std::string{it.tensor_name};
      size_t tensor_size = it.tensor_size;
      if (fVerbose)
         std::cout << "output tensor " << name << " size " << tensor_size << std::endl;
 
      for (auto chunk = fIntermediateMemoryInfo.available_stack.begin();
           chunk != fIntermediateMemoryInfo.available_stack.end();) {
 
         if (fVerbose) std::cout << ".. available chunk " << chunk->first << " with size = " << chunk->second;
         // check if available memory chunks can accommodate the tensor
         if (chunk->second >= tensor_size) {
            // need to use here string_view (i.e it.tensor_name)
            // split returns the new chunk with size of new tensor. The free chunk is before the used one
            auto new_chunk = fIntermediateMemoryInfo.total_stack[chunk->first].split(it.tensor_name, tensor_size);
            auto new_chunk_location = chunk->first + chunk->second - tensor_size;
            fIntermediateMemoryInfo.total_stack[new_chunk_location] = new_chunk;
 
            declareIntermediateTensor(name, tensor_size, new_chunk_location);
            chunk->second -= tensor_size;
 
            allocated = true;
 
            if (fVerbose) std::cout << " is re-used and split in a new of size " << new_chunk.tensor_size << " at " << new_chunk_location;
 
            if (chunk->second == 0) {
               if (fVerbose) std::cout << " and deleted since size matches";
               fIntermediateMemoryInfo.available_stack.erase(chunk);
            }
            if (fVerbose) std::cout << std::endl;
            break;
         } else if (chunk->first == fIntermediateMemoryInfo.available_stack.rbegin()->first &&
                    fIntermediateMemoryInfo.total_stack.rbegin()->first == chunk->first) {
            // case last available chunk is the last in the memory, we can increase that one
            fIntermediateMemoryInfo.total_stack[chunk->first] = {it.tensor_name, tensor_size};
            declareIntermediateTensor(name, tensor_size, chunk->first);
            fIntermediateMemoryInfo.available_stack.erase(chunk);
            allocated = true;
            if (fVerbose) std::cout << " is extended  with a bigger one of size " << tensor_size << std::endl;
            break;
         }
         ++chunk;
         if (fVerbose) std::cout << std::endl;
      }
 
      if (!allocated) {
         size_t chunk_idx = fIntermediateMemoryInfo.total_stack.empty()
                               ? 0
                               : fIntermediateMemoryInfo.total_stack.rbegin()->first +
                                    fIntermediateMemoryInfo.total_stack.rbegin()->second.tensor_size;
 
         fIntermediateMemoryInfo.total_stack[chunk_idx] = it;
 
         declareIntermediateTensor(name, tensor_size, chunk_idx);
 
         if (fVerbose) std::cout << "no chunk available - add in total stack a new chunk with size of tensor and idx : " << chunk_idx
                   << std::endl;
      }
   }
   return code.str();
}
 
void RModel::CheckAndFlushIntermediateMemory(std::span<const std::string_view> op_input_tensors, const size_t& op_idx){
   if (fVerbose) std::cout << "*** CheckAndFlushIntermediateMemory: Loop on input tensors for op " << op_idx << "\n";
   //print available chunks
   if (fVerbose) std::cout << "available chunks before freeing them : \n";
   for (auto chunk = fIntermediateMemoryInfo.available_stack.begin();
        chunk != fIntermediateMemoryInfo.available_stack.end(); chunk++) {
      if (fVerbose) std::cout << "-- free chunk " << chunk->first <<  " size = " << chunk->second << std::endl;
   }
   for (auto &iv : op_input_tensors) {
      // last occurrence of the tensor is reached => flush it from memory
      if (fVerbose) std::cout << ".. input tensors : " << iv;
 
      // for alias tensors replace name with its alias
      std::string it{iv};  // convert view to string
      if (IsAliasTensor(it))
         it = fAliasTensors[it];
      if (fIntermediateTensorFrequencyLookup[it] == op_idx) {
         if (fVerbose) std::cout << "  flash condition is met - looping on chunks to find matching one \n";
         for (auto chunk = fIntermediateMemoryInfo.total_stack.begin();
              chunk != fIntermediateMemoryInfo.total_stack.end(); ++chunk) {
            if (fVerbose) std::cout << "---  chunk " << chunk->first << " , " << chunk->second.tensor_name << " size " << chunk->second.tensor_size;
            if (chunk->second.tensor_name == it) {
               if (fVerbose) std::cout << " --  Found chunk corresponding to input tensor:  " << chunk->first;
               // check if nearby chunks in available memory can coalesce
               auto first_greater = fIntermediateMemoryInfo.available_stack.upper_bound(
                  chunk->first); // smallest element greater than the flushed chunk idx
               auto last_smaller = (first_greater == fIntermediateMemoryInfo.available_stack.begin())
                                      ? fIntermediateMemoryInfo.available_stack.end()
                                      : std::prev(first_greater); // largest element smaller than the flushed chunk idx
 
               // check if the next stack entry is actually adjacent in memory
 
               if (last_smaller != fIntermediateMemoryInfo.available_stack.end() &&
                   last_smaller->first + last_smaller->second == chunk->first) {
                  // merge chunk with previous one
                  last_smaller->second += chunk->second.tensor_size;
                  fIntermediateMemoryInfo.total_stack[last_smaller->first].merge(chunk->second);
                  if (fVerbose) std::cout << " is adjacent in memory with previous one - merge ";
                  if (first_greater != fIntermediateMemoryInfo.available_stack.end() &&
                      last_smaller->first + last_smaller->second == first_greater->first) {
                     // merge also with following one
                     last_smaller->second += first_greater->second;
                     fIntermediateMemoryInfo.total_stack[last_smaller->first].merge(
                        fIntermediateMemoryInfo.total_stack[first_greater->first]);
                     // delete merged one in available stack and in total stack
                     fIntermediateMemoryInfo.total_stack.erase(first_greater->first);
                     fIntermediateMemoryInfo.available_stack.erase(first_greater);
                     if (fVerbose) std::cout << " merge also with following that is free ";
                  }
                  fIntermediateMemoryInfo.total_stack.erase(chunk->first);
                  if (fVerbose) std::cout << std::endl;
                  break;
               } else if (first_greater != fIntermediateMemoryInfo.available_stack.end() &&
                          chunk->first + chunk->second.tensor_size == first_greater->first) {
                  // merge with first greater
                  if (fVerbose) std::cout << " is adjacent in memory with following one - merge \n";
                  // cannot modify idx of first_greter. Insert a new one and delete previous one
                  size_t new_size = chunk->second.tensor_size + first_greater->second;
                  size_t first_greater_idx = first_greater->first;
                  fIntermediateMemoryInfo.available_stack.erase(first_greater);
                  // cannot use anymore first_greater
                  fIntermediateMemoryInfo.available_stack.insert({chunk->first, new_size});
                  fIntermediateMemoryInfo.total_stack[chunk->first].merge(
                     fIntermediateMemoryInfo.total_stack[first_greater_idx]);
                  fIntermediateMemoryInfo.total_stack.erase(first_greater_idx);
               } else {
                  fIntermediateMemoryInfo.available_stack.insert({chunk->first, chunk->second.tensor_size});
                  if (fVerbose) std::cout << " insert in the available stack the chunk with size " << chunk->second.tensor_size << std::endl;
               }
               chunk->second.tensor_name = "free";
               break;
            }
         }
      } else {
         if (fVerbose) std::cout << std::endl;
      }
   }
}
 
void RModel::Initialize(int batchSize, bool verbose) {
   std::map<std::string, size_t> inputParams;
   if (batchSize > 0) {
      inputParams["input_size"] = batchSize;
      inputParams["batch_size"] = batchSize;
      inputParams["bs"] = batchSize;
   }
   Initialize(inputParams, verbose);
   fIntermediateMemoryInfo = MemoryPoolInfo();
}
void RModel::Initialize(const std::map<std::string, size_t> & inputParams, bool verbose) {
 
   fVerbose = int(verbose);
 
   if (fIsInitialized) {
      if (verbose)
         std::cout << "Model is already initialized  - skip initialization " << std::endl;
      return;
   }
   fIntermediateTensorInfos.clear();
   fDynamicTensorInfos.clear();
 
 
   // loop on inputs and see if shape can be  full specified
   // if the batch size is provided it can be used to specify the full shape
   // Add the full specified tensors in fReadyInputTensors collection
   auto originalInputTensorInfos = fInputTensorInfos; // need to copy because we may delete elements
   for (auto &input : originalInputTensorInfos) {
      if (verbose) std::cout << "looking at the tensor " << input.first << std::endl;
      // if a parameter (e.g. batch_size) is specified use for converting parametric shape in defined one
      if (!inputParams.empty()) {
         for (auto &d : input.second.shape) {
            if (d.isParam) {
               std::string pname = d.param;
               if (pname == input.first + "_size") pname = "input_size";
               auto itr = inputParams.find(pname);
               if (itr != inputParams.end() ) {
                  d = Dim{ itr->second };
                  if (verbose)
                     std::cout << "Tensor: " << input.first << " - fix parametric shape " << itr->first << " to " << itr->second << std::endl;
               }
            }
         }
      }
      // see if shape now is fully defined
      auto shape = ConvertShapeToInt(input.second.shape);
      if (verbose)
         std::cout << "converting input shape for " << input.first << " " << ConvertShapeToString(shape) << " from "
            << ConvertDimShapeToString(input.second.shape) << std::endl;
      if (!shape.empty()) {
         // case shape is defined (not parametric) we add the tensor in the fReadyInputTensorInfos map and
         // we remove the tensor from the fInputTensorInfo where th eold parametric shape was stored
         fInputTensorInfos.erase(input.first);
         // add to the ready input tensor information the new fixed shape
         AddInputTensorInfo(input.first, input.second.type, shape);
         // check consistency
         assert( fReadyInputTensorInfos.size() + fInputTensorInfos.size() == fInputTensorNames.size());
      }
      // store the parameters of the input tensors
      else {
         // store the found parametric shape parameters
         for (auto &d : input.second.shape) {
            if (d.isParam) {
               if (fShapeParams.count(d.param) == 0) {
                  fDimShapeNames.push_back(d.param);
                  fShapeParams[d.param] = std::to_string(d.dim);
               }
            }
         }
      }
   }
 
   if (verbose) {
      PrintRequiredInputTensors();
      PrintDynamicTensors();
   }
 
   // Go through model and initialize each operator
   int i = 0;
 
   std::vector<size_t> temp_available_stack; // vector stores individual chunks of available memory that maybe reused
 
   // Build set of initialized tensors consumed by at least one runtime operator (need for later)
   std::unordered_set<std::string> runtimeInitializedInputs;
   for(size_t op_idx = 0; op_idx < fOperators.size(); ++op_idx){
      if (verbose) {
         auto& r = *fOperators[op_idx].get();
         std::cout << "Initializing operator " << i << "  " << typeid(r).name() << std::endl;
      }
      fOperators[op_idx]->Initialize(*this);
      for(auto &it:fOperators[op_idx]->GetOpOutputTensors()){
         std::string name = std::string{it};
         // check if tensor is not an initialized or output tensor and it is not already in the list
         if (fIntermediateTensorFrequencyLookup.find(it) == fIntermediateTensorFrequencyLookup.end() &&
             std::find(fOutputTensorNames.begin(), fOutputTensorNames.end(), name) == fOutputTensorNames.end() &&
             fInitializedTensors.find(name) == fInitializedTensors.end())
         {
            fIntermediateTensorFrequencyLookup[it] = op_idx;
         }
      }
      // loop for non-constant operators and flag the inputs which are initialized tensors to make sure they are writable
      if (!fOperators[op_idx]->IsOutputConstant()) {
         for (auto &it : fOperators[op_idx]->GetOpInputTensors()) {
            std::string name = std::string{it};
            if (fInitializedTensors.find(name) != fInitializedTensors.end()) {
               runtimeInitializedInputs.insert(name);
            }
         }
      }
 
      i++;
   }
 
   // loop on initialized tensors and make the integers as constant to be
   // not written in a weight file and check if the tensors flagged as not writable are really not writable,
   // i.e. are not used by non constant operators
   for (auto &it : fInitializedTensors) {
      // check if not-writable tensors are really not writable, i.e. are not used by non constant operators
      if (it.second.IsNotWritable() && runtimeInitializedInputs.find(it.first) != runtimeInitializedInputs.end()) {
         it.second.SetWritable();
         if (verbose) {
            std::cout << "Initialized tensor " << it.first << " is flagged as not writable but is used by non constant operators, set it as writable \n";
         }
      }
      // if the tensor is an integer we can flag it as constant since it will not be written in a weight file and it is considered equivalent as being created from a Constant operator
      // only FLOAT tensors are written in a weight file
      if (it.second.type() !=  ETensorType::FLOAT) {
         it.second.SetConstant();
      }
   }
 
   // check if there are initialized tensors to write in a weight file
   if (fUseWeightFile) {
      bool modelHasWeights = false;
      for (auto &it : fInitializedTensors) {
         if (it.second.IsWeightTensor()) {
            modelHasWeights = true;
            break;
         }
      }
      if (!modelHasWeights)
         fUseWeightFile = false;
   }
 
   // update fIntermediateTensorFrequencyLookup for alias tensors
   for (auto & it : fAliasTensors) {
      if (fIntermediateTensorFrequencyLookup.find(it.first) == fIntermediateTensorFrequencyLookup.end()) continue;
      if (fIntermediateTensorFrequencyLookup.find(it.second) == fIntermediateTensorFrequencyLookup.end() )
         fIntermediateTensorFrequencyLookup[it.second] = fIntermediateTensorFrequencyLookup[it.first];
      else {
         // take the largest one
         fIntermediateTensorFrequencyLookup[it.second] = std::max(fIntermediateTensorFrequencyLookup[it.second],fIntermediateTensorFrequencyLookup[it.first] );
      }
   }
 
   fIsInitialized = true;
}
 
void RModel::InitializeSubGraph(std::shared_ptr<RModel>  graph) {
   // add the subgraph to the list
   fSubGraphs.push_back(graph);
   //this needs to be done before initializing
   graph->fParentGraph = this;
   graph->fIsSubGraph = true;
 
   graph->Initialize(fBatchSize, fVerbose);
   // set the same options as parent model
   graph->fWeightFile = fWeightFile;
   graph->fUseWeightFile = fUseWeightFile;
   graph->fUseSession = fUseSession;
   // add needed blas routines and libs
   std::vector<std::string> blasRoutines;
   for (auto & e : graph->fNeededBlasRoutines)
      blasRoutines.push_back(e);
   AddBlasRoutines(blasRoutines);
   for (auto e : graph->fNeededStdLib)
      AddNeededStdLib(e);
   // helper functions used by the subgraph must be emitted in the top-level
   // header, so propagate them to the parent model
   for (auto const &h : graph->GetNeededHelperFunctions())
      AddNeededHelperFunction(h);
 
   // add parent input tensors to current graph
   for (auto & name : fInputTensorNames)
      graph->fInputTensorNames.emplace_back(name);
 
   // clean graph name
   graph->fName = UTILITY::Clean_name(graph->fName);
 
}
 
// Function to generate the code for declaring and initializing constant tensors
// This is for tensors which are not part of weight files and can be created from the Constant operator
template <typename T>
std::string GenerateConstantTensorCode(const std::pair<std::string, InitializedTensor> &t)
{
   std::stringstream strs;
   std::string type = ConvertTypeToString(t.second.type());
   size_t length = ConvertShapeToLength(t.second.shape());
   // avoid using stack sizes for constant tensors to reduce compilation time
   // also for weights which can be broadcasted do not use stack but allocate as a std::vector
   bool allocateOnStack = (length > 100 || t.second.IsWeightTensor()) ? false : true;
 
   const T *data = t.second.data<T>();
 
   // and check if all values are the same
   bool sameData = false;
 
   // for non stack allocation check if data are the same
   if (!allocateOnStack && length > 1) {
      size_t idx = 1;
      do {
         sameData = (data[idx] == data[idx - 1]);
         idx++;
      } while (sameData && idx < length);
   }
   if (allocateOnStack) {
      strs << type << " fTensor_" << t.first << "[" << length << "] = " << ConvertValuesToString(length, data) << ";\n";
      strs << type << " * " << TensorMember(t.first) << " = fTensor_" + t.first + ";\n";
   } else {
      strs << "std::vector<" << type << "> fTensor_" << t.first << " = ";
      if (sameData)
         strs << "std::vector<" << type << ">(" << length << ", " << ConvertValToString(data[0]) << ");\n";
      else {
         strs << ConvertValuesToString(length, data) << ";\n";
      }
      strs << type << " * " << TensorMember(t.first) << " = fTensor_" + t.first + ".data();\n";
   }
   return strs.str();
}
 
void RModel::GenerateInitializedTensorInfo()
{
   if (!fInitializedTensors.empty())
      fGC += "// initialized (weights and constant) tensors\n";
 
   // here are constant tensor or initialized ones which are not weights (e.g. int64_t tensors )
   for (auto &i : fInitializedTensors) {
      if (i.second.IsNotWritable())  continue;
      size_t length = ConvertShapeToLength(i.second.shape());
      if (!fUseWeightFile || i.second.IsConstantTensor() || !i.second.IsWeightTensor() || i.second.type() != ETensorType::FLOAT ) {
         if (i.second.type() == ETensorType::FLOAT) {
            // check if NaN of Inf are inside tensor data
            bool hasInfOrNaN = false;
            const float *data = i.second.data<float>();
            for (size_t idx = 0; idx < length; idx++) {
               if (std::is_floating_point<float>::value) {
                  if (std::isinf(data[idx]) || std::isnan(data[idx])) {
                     hasInfOrNaN = true;
                     break;
                  }
               }
            }
            if (hasInfOrNaN)
               AddNeededStdLib("limits");
            fGC += GenerateConstantTensorCode<float>(i);
            fConstantTensorSize += length * sizeof(float);
         } else if (i.second.type() == ETensorType::INT64) {
            fGC += GenerateConstantTensorCode<int64_t>(i);
            fConstantTensorSize += length * sizeof(int64_t);
         } else if (i.second.type() == ETensorType::INT32) {
            fGC += GenerateConstantTensorCode<int32_t>(i);
            fConstantTensorSize += length * sizeof(int32_t);
         }  else if (i.second.type() == ETensorType::BOOL || i.second.type() == ETensorType::UINT8 ) {
            fGC += GenerateConstantTensorCode<uint8_t>(i);
            fConstantTensorSize += length * sizeof(uint8_t);
         }
 
 
      } else {
         // case of tensors which are read from a file
         if (i.second.type() == ETensorType::FLOAT) {
            fGC += "std::vector<float> fTensor_" + i.first + " = std::vector<float>(" + std::to_string(length) + ");\n";
            fGC += "float * " + TensorMember(i.first) + " = fTensor_" + i.first + ".data();\n";
            fWeightsTensorSize += length * sizeof(float);
         }
      }
   }
}
 
void RModel::GenerateIntermediateMemoryPool() {
   if (fIntermediateMemoryInfo.total_stack.empty()) return;
   fGC += "\n//--- Allocating session memory pool to be used for allocating intermediate tensors\n";
 
   // char memory block is allocated since char takes 1 byte, thus easier to allocate tensors
   // of other data types
   auto const &totalStack = fIntermediateMemoryInfo.total_stack;
   const size_t memPoolSize = totalStack.rbegin()->first + totalStack.rbegin()->second.tensor_size;
   fGC += "std::vector<char> fIntermediateMemoryPool = std::vector<char>(" + std::to_string(memPoolSize) + ");\n\n";
}
 
void RModel::GenerateIntermediateTensorInfo() {
   if (!fIntermediateTensorInfos.empty()) {
      std::string tensor_declaration_block = "";
      for (auto &i : fIntermediateTensorInfos) {
         bool  is_alias = (IsAliasTensor(i.first));
         if (i.second.type == ETensorType::BOOL && !is_alias) {
               tensor_declaration_block += "std::vector<std::uint8_t> fTensor_" + i.first + " = std::vector<std::uint8_t>(" + std::to_string(ConvertShapeToLength(i.second.shape)) + ");\n";
               tensor_declaration_block += "std::uint8_t * " + TensorMember(i.first) + " = fTensor_" + i.first + ".data();\n";
               continue;
         }
         bool is_extended = (fOptimizationLevel == OptimizationLevel::kExtended);
         bool not_in_freq_map =
            (fIntermediateTensorFrequencyLookup.find(i.first) == fIntermediateTensorFrequencyLookup.end());
         bool not_in_output_names =
            (std::find(fOutputTensorNames.begin(), fOutputTensorNames.end(), i.first) == fOutputTensorNames.end());
 
         if (((not_in_freq_map && not_in_output_names) || (!not_in_freq_map && !is_extended && not_in_output_names) ) && !is_alias) {
            size_t length = ConvertShapeToLength(i.second.shape);
 
            if (i.second.type == ETensorType::FLOAT) {
               tensor_declaration_block += "std::vector<float> fTensor_" + i.first + " = std::vector<float>(" + std::to_string(length) + ");\n";
               tensor_declaration_block += "float * " + TensorMember(i.first) + " = fTensor_" + i.first + ".data();\n";
               fOtherTensorSize += 4 * length;
            }
            else if (i.second.type == ETensorType::DOUBLE) {
               tensor_declaration_block += "std::vector<double> fTensor_" + i.first + " = std::vector<double>(" + std::to_string(length) + ");\n";
               tensor_declaration_block += "double * " + TensorMember(i.first) + " = fTensor_" + i.first + ".data();\n";
               fOtherTensorSize += 8 * length;
            }
            else if (i.second.type == ETensorType::INT64) {
               tensor_declaration_block += "std::vector<int64_t> fTensor_" + i.first + " = std::vector<int64_t>(" + std::to_string(length) + ");\n";
               tensor_declaration_block += "int64_t * " + TensorMember(i.first) + " = fTensor_" + i.first + ".data();\n";
               fOtherTensorSize += 8 * length;
            }
         }
         if (is_alias) {
             tensor_declaration_block += ConvertTypeToString(i.second.type) + " * " + TensorMember(i.first) + " = nullptr;\n";
         }
 
      }
 
      if (tensor_declaration_block.length()) {
         fGC += "\n//--- declare and allocate the intermediate tensors\n" + tensor_declaration_block;
      }
   }
   // add also the dynamic tensors (only declarations, allocation will be done later)
   if (!fDynamicTensorInfos.empty()) {
      fGC += "//--- declare the dynamic tensors\n";
      for (auto &i : fDynamicTensorInfos) {
         fGC += ConvertTypeToString(i.second.type) + " * " + TensorMember(i.first) + " = nullptr;\n";
      }
      fGC += "//--- dynamic tensors pool\n";
      fGC += "std::vector<char> fDynamicMemoryPool;\n";
   }
}
 
// generate code for specific operator declarations  to be defined in the Session class
void RModel::GenerateOperatorDeclarations() {
   std::string strcode;
   for (auto & op : fOperators) {
      strcode += op->GenerateDeclCode();
   }
   if (strcode.empty()) return;
   fGC += "\n//---- operator declarations \n";
   fGC += strcode;
   fGC += "\n";
}
 
void RModel::GenerateDynamicTensorInfo()
{
   // generate code for allocating dynamic tensors using the greedy memory allocations
   if (fDynamicTensorInfos.empty())
      return;
 
   if (fVerbose) {
      std::cout << "generating code for dynamic tensor management" << std::endl;
      PrintDynamicTensors();
   }
 
   // the generated code uses the TensorLifeInfo / OrganizeMemory inference helpers
   AddNeededHelperFunction("DynamicMemory");
 
   std::stringstream out;
   out << "//  dynamic tensor memory management\n";
   out << SP << "std::vector<TensorLifeInfo> dynamicTensorInfos;\n";
   out << SP << "dynamicTensorInfos.reserve(" << fDynamicTensorInfos.size() << ");\n";
 
   // loop on all the operators to find begin/end life of the tensors
   int op_index = 0;
   std::vector<std::pair<std::string, ETensorType>> tensors;
   tensors.reserve(fDynamicTensorInfos.size());
   for (auto & op : fOperators) {
      // loop on output tensors -
      for (auto &it : op->GetOpOutputTensors()) {
         if (fVerbose) {
            auto op_ptr = op.get();
            std::cout << "Looping on operator " << op_index << "   " << typeid(*op_ptr).name() << std::endl;
         }
         // check if is a dynamic tensor and not an alias tensor or output tensor
         std::string name = std::string(it);
         if ( fDynamicTensorInfos.find(name) != fDynamicTensorInfos.end() && !IsAliasTensor(name)
              && std::find(fOutputTensorNames.begin(), fOutputTensorNames.end(), name) == fOutputTensorNames.end()) {
            auto tensor_size =  ConvertDimShapeToLength(GetDimTensorShape(name));
            auto type = GetTensorType(name);
            size_t type_size = GetTypeSize(type);
            int begin = op_index;
            int end = fOperators.size();
            // look for end
            auto it_lookup = fIntermediateTensorFrequencyLookup.find(name);
            if (it_lookup != fIntermediateTensorFrequencyLookup.end())
               end = it_lookup->second + 1;  // end is last time used + 1
            // // some tensors (like xcol in convolutions) are just used within the operators
            // if (end == 0 && begin > 0) end = begin+1;
 
            if (begin> end) {
               std::cout << "op " << op_index << "tensor_" << name << " begin " << begin << "  "  << " end " << end << std::endl;
               throw std::runtime_error("TMVA-SOFIE: RModel::GenerateDynamicTensorInfo: tensor_" + name + " has end before begin");
            }
 
            // write in code
            out << SP << "dynamicTensorInfos.push_back( {" << begin << ", " << end << ", " << type_size << "* (" << tensor_size << ") });"
                << " // tensor_" << name << std::endl;
            tensors.push_back({name,type});
         }
      }
      op_index++; // increment operator index
   }
   out << "\n" << SP << "auto memory_result = OrganizeMemory(dynamicTensorInfos);\n\n";
   out << "//  allocating now the memory\n";
   out << SP << "fDynamicMemoryPool = std::vector<char>(memory_result.total_bytes);\n";
   out << SP << "int idx = 0;\n";
   for (auto & it : tensors) {
      out << SP << "tensor_" << it.first << " = reinterpret_cast<" << ConvertTypeToString(it.second) << " *>(fDynamicMemoryPool.data() + memory_result.offsets[idx++]);\n";
   }
   // check that all dynamic tensors are covered
   bool missingTensor = false;
   for (auto &i : fDynamicTensorInfos) {
      if (IsAliasTensor(i.first)) continue;
      if (std::find(fOutputTensorNames.begin(), fOutputTensorNames.end(), i.first) != fOutputTensorNames.end()) continue;
      if (std::find(tensors.begin(), tensors.end(), std::pair<std::string,ETensorType>{i.first, i.second.type}) == tensors.end()) {
         std::cout << "Dynamic tensors " << i.first << " is not in list of operator input/output " << std::endl;
         missingTensor = true;
      }
   }
   if (missingTensor)
      throw std::runtime_error("TMVA-SOFIE: RModel::GenerateDynamicTensorInfo - some tensors are not in input/output list");
 
   fGC += out.str();
}
 
/// Check if a given parameter is used for the shape of an input tensor.
bool RModel::IsInputTensorShapeParam(std::string const &paramName) const
{
   for (auto &name : fInputTensorNames) {
      if (IsDimInputTensor(name)) {
         auto shape = GetDynamicTensorShape(name);
         for (auto &d : shape) {
            if (d.param == paramName)
               return true;
         }
      }
   }
   return false;
}
 
/// Collects all identifiers starting with "tensor_" in the input code,
/// provided that the occurrence is not immediately preceded by a
/// character that is valid in a C++ identifier. Excludes input and output tensor names.
/// Returns a deduplicated std::vector<std::string>.
std::vector<std::string> RModel::CollectTensorMemberNames(const std::string &input)
{
   const std::string target = "tensor_";
 
   std::vector<std::string> result;
 
   for (size_t i = 0; i < input.size();) {
 
      bool doCollect = false;
 
      if (i + target.size() <= input.size() && input.compare(i, target.size(), target) == 0 &&
          (i == 0 || !IsIdentifierChar(input[i - 1]))) {
 
         doCollect = true;
 
         std::size_t j = i + target.size();
 
         // Extend to full identifier
         while (j < input.size() && IsIdentifierChar(input[j]))
            ++j;
 
         std::string fullName = input.substr(i, j - i);
 
         // Exclude input tensor names
         for (std::string const &name : fInputTensorNames) {
            if (fullName == target + name) {
               doCollect = false;
               break;
            }
         }
 
         // Exclude output tensor names
         if (doCollect) {
            for (std::string const &name : fOutputTensorNames) {
               if (fullName == target + name) {
                  doCollect = false;
                  break;
               }
            }
         }
 
         if (doCollect) {
            result.push_back(fullName);
         }
 
         i = j; // advance past the identifier
      } else {
         ++i;
      }
   }
 
   // Deduplicate (order not preserved)
   std::sort(result.begin(), result.end());
   result.erase(std::unique(result.begin(), result.end()), result.end());
 
   return result;
}
 
std::string RModel::GenerateInferSignature(bool isdecl) {
   // generate the infer signature given the inputs: eg. "float * tensor1, float * tensor2"
   // if (decl = false) generate only calling signature (tensor1,tensor2,....)
   std::string rGC;
   std::unordered_map<std::string, int> inputParams;
   int i_input = 0;
   for (auto &name : fInputTensorNames) {
      // if is a dynamic tensor pass initial parameters
      if (IsDimInputTensor(name)) {
         auto shape = GetDynamicTensorShape(name);
         for (auto &d : shape) {
            std::string pName = d.param;
            // need to check if the input parameters is already existing in another input tensor
            if (d.isParam && inputParams.count(pName) == 0) {
               if (isdecl) rGC += "size_t ";
               rGC += d.param + ",";
               inputParams[pName] = i_input;
            }
         }
      }
      if (isdecl) {
         std::string type = ConvertTypeToString(GetTensorType(name));
         if (type == "other")
            throw std::runtime_error("TMVA-SOFIE: input tensor " + name +
                                     " is of a data type which is not yet supported.");
         rGC += type + " const* ";
      }
      rGC += "tensor_" + name + ",";
      i_input++;
   }
 
   if (fInputTensorNames.size() > 0) rGC.pop_back();// remove last ","
   return rGC;
}
 
namespace {
 
std::string typeForOutput(ETensorType t) {
   // The std::vector<bool> is a special type that is not wrapping continuous memory.
   // We don't want to use it as a return type.
   if (t == ETensorType::BOOL) t = ETensorType::UINT8;
   return ConvertTypeToString(t);
}
 
std::string memberNameForDimShape(std::string name)
{
   if (!name.empty()) {
      name[0] = std::toupper(static_cast<unsigned char>(name[0]));
   }
   name = "f" + name;
   return name;
}
 
}
 
void RModel::GenerateOutput()
{
   size_t outputSize = fOutputTensorNames.size();
   // assume output types are all the same
 
   bool sameOutputTypes = true;
   std::string inferReturnType; // type return by infer function
   ETensorType eFirstOutputType = GetTensorType(*fOutputTensorNames.begin());
   fGC += "\n\n";
   if (outputSize == 1) {
      fGC += "std::vector<" + typeForOutput(eFirstOutputType) + ">";
   } else {
      // if all output types are the same we return an std::vector - otherwise a tuple
      for (std::string const &name : fOutputTensorNames) {
         if (GetTensorType(name) != eFirstOutputType)
            sameOutputTypes = false;
      }
      if (sameOutputTypes)
         fGC += "std::vector<std::vector<" + typeForOutput(eFirstOutputType) + ">>";
      else {
         inferReturnType = "std::tuple<";
         for (size_t i = 0; i < outputSize; i++) {
            inferReturnType += "std::vector<" + typeForOutput(GetTensorType(fOutputTensorNames[i])) + ">";
            if (i < outputSize - 1)
               inferReturnType += ",";
         }
         inferReturnType += ">";
         fGC += inferReturnType;
      }
   }
 
   fGC += " infer(" + GenerateInferSignature() + "){\n";
 
   std::string doInferArgs = GenerateInferSignature(false);
   if (!doInferArgs.empty())
      doInferArgs += ",";
   for (std::string const &name : fOutputTensorNames) {
      bool isDynamic = fDynamicTensorInfos.count(name) > 0;
      std::string n;
      if(!isDynamic) {
         n = std::to_string(ConvertShapeToLength(GetTensorShape(name)));
      } else {
         std::string dimLen = ConvertDimShapeToLength(GetDynamicTensorShape(name));
         // Use the session member (fXxx) when any dim is a runtime-computed identifier
         // (e.g. NonZero count). For expression-type dims derived from input shapes
         // (e.g. "((W+-3)/2+1)"), use the expression directly.
         // for input shape parameters we don't need to use the session member since it is passed as argument to the infer function and it is not a runtime computed value
         bool hasRuntimeParam = false;
         for (auto const &dim : GetDynamicTensorShape(name)) {
            if (dim.isParam && IsIdentifier(dim.param) && !IsInputTensorShapeParam(dim.param))
               hasRuntimeParam = true;
         }
         n = hasRuntimeParam ? memberNameForDimShape(dimLen) : dimLen;
      }
      std::string outputName = "output_tensor_" + name;
      fGC += SP + "std::vector<" + typeForOutput(GetTensorType(name)) + " > " + outputName + "(" + n + ");\n";
      doInferArgs += " " + outputName + ".data(),";
      if(isDynamic) {
         for (auto const &dim : GetDynamicTensorShape(name)) {
            if (dim.isParam && !IsInputTensorShapeParam(dim.param) && IsIdentifier(dim.param)) {
               fGC += SP + "size_t " + dim.param + " = 0;\n";
               doInferArgs += " " + dim.param + ",";
            }
         }
      }
   }
   if (!doInferArgs.empty())
      doInferArgs.back() = ' ';
 
   // verifying if the dynamic parameters are within allowed range
   std::unordered_set<std::string> input_params_checked;
   std::string dynamic_parameters_check = "";
   for (auto &name : fInputTensorNames) {
      if (IsDimInputTensor(name)) {
         auto shape = GetDynamicTensorShape(name);
         for (auto &d : shape) {
            std::string pName = d.param;
            if (d.isParam && input_params_checked.count(pName) == 0) {
               std::string memberName = memberNameForDimShape(d.param);
               dynamic_parameters_check += d.param + " > " + memberName + " || ";
               input_params_checked.insert(pName);
               fGC += SP + "if (" + d.param + " > " + memberName + ") {\n";
               fGC += SP + SP + "throw std::runtime_error(\"TMVA-SOFIE: dynamic input tensor shape parameter " +
                      d.param + " exceeds the initialized maximum allowed shape.\");\n";
               fGC += SP + "}\n";
            }
         }
      }
   }
 
   if (fUseSession) {
      fGC += SP + "doInfer(*this, " + doInferArgs + ");\n";
   } else {
      fGC += SP + "doInfer(" + doInferArgs + ");\n";
   }
 
   // If the output tensors have dynamic sizes, now is the time to set them
   for (std::string const &name : fOutputTensorNames) {
      bool isDynamic = fDynamicTensorInfos.count(name) > 0;
      if (isDynamic) {
         std::string outputName = "output_tensor_" + name;
         auto tensor_size = ConvertDimShapeToLength(GetDimTensorShape(name));
         fGC += SP + outputName + ".resize(" + tensor_size + ");\n";
      }
   }
 
   fGC += SP + "return {";
   for (size_t i = 0; i < fOutputTensorNames.size(); i++) {
      fGC += "output_tensor_" + fOutputTensorNames[i];
      if (i < fOutputTensorNames.size() - 1)
         fGC += ",";
   }
   fGC += "};\n";
   fGC += "}\n"; // end of infer function scope
}
 
void RModel::GenerateSessionCode()
{
   std::string sessionName = !fIsSubGraph ? "Session" : "Session_" + fName;
 
   if (fUseSession && !fIsGNNComponent) {
      //  forward declare session struct
      fGC += "struct " + sessionName + ";\n";
   }
 
   // Determine the signature of the actual inference function
   std::string doInferSignature = GenerateInferSignature();
   if (!doInferSignature.empty())
      doInferSignature += ", ";
   for (auto const &name : fOutputTensorNames) {
      bool isDynamic = fDynamicTensorInfos.count(name) > 0;
      doInferSignature += typeForOutput(GetTensorType(name)) + " *tensor_" + name + ",";
      if(isDynamic) {
         for (auto const &dim : GetDynamicTensorShape(name)) {
            if (dim.isParam && !IsInputTensorShapeParam(dim.param) && IsIdentifier(dim.param))
               doInferSignature += " size_t &" + dim.param + "_output,";
         }
      }
   }
   doInferSignature.back() = ' ';
 
   if (fUseSession) {
      doInferSignature = sessionName + " const &session, " + doInferSignature;
   }
 
   doInferSignature = "inline void doInfer(" + doInferSignature + ")";
 
   if (!fIsGNNComponent) {
      // forward declare inference implementation
      fGC += doInferSignature + ";\n";
   }
 
   // define the Session struct (for GNN this is generated in RModel_GNN)
   if (fUseSession && !fIsGNNComponent) {
      fGC += "struct " + sessionName + " {\n";
   }
 
   // generate code for declaring the initialized tensors
   GenerateInitializedTensorInfo();
 
   if (fOptimizationLevel == OptimizationLevel::kExtended) {
      // evaluate total intermediate memory and position intermediate tensor addresses
      std::string intermediate_memory_alloc_string = "";
      intermediate_memory_alloc_string += "\n// --- Positioning intermediate tensor memory --";
      for (size_t op_idx = 0; op_idx < fOperators.size(); ++op_idx) {
         if (fVerbose) {
            auto op = fOperators[op_idx].get();
            std::cout << "\n******************\n analyzing input/output operator " << op_idx << "  "
                      << typeid(*op).name() << std::endl;
         }
         intermediate_memory_alloc_string += AllocateIntermediateMemory(fOperators[op_idx]->GetOpOutputTensors());
         CheckAndFlushIntermediateMemory(fOperators[op_idx]->GetOpInputTensors(), op_idx);
      }
 
      // to check remaining unused fragments after memory allocation (lesser the better)
      // for (const auto &it: fIntermediateMemoryInfo.available_stack){
      //    std::cout<<"chunk_idx: "<<it.first<<", chunk_size: "<<it.second<<"\n";
      // }
 
      // generate the memory pool to be used by intermediate tensors
      GenerateIntermediateMemoryPool();
 
      // position intermediate tensors
      fGC += intermediate_memory_alloc_string;
   }
 
   // generate the declaring the intermediate tensors
   GenerateIntermediateTensorInfo();
   // generate code for declarations of some specific operators
   GenerateOperatorDeclarations();
 
   // storing the parameters for future checking to avoid mismatches
   if (!fDimShapeNames.empty()) {
      fGC += "\n//   dynamic shape parameters\n";
      auto dimShapeNames = fDimShapeNames;
      std::sort(dimShapeNames.begin(), dimShapeNames.end());
      for (const auto &p : dimShapeNames) {
         fGC += "size_t " + memberNameForDimShape(p) + ";\n";
      }
   }
 
   // add subgraph session
   if (!fSubGraphs.empty()) fGC += "//   subgraph sessions\n";
   for (auto & graph : fSubGraphs) {
      fGC += "Session_" + graph->fName + "  fSession_" + graph->fName + ";\n";
   }
 
   // Generate code for Session constructor
   if (fUseSession) {
      // add here specific operator code that needs to define session data members
      fGC += "\n";
      for (size_t id = 0; id < fOperators.size(); id++) {
         std::string opName = std::to_string(id);
         fGC += fOperators[id]->GenerateSessionMembersCode(opName);
      }
      fGC += "\n";
      // here add initialization and reading of weight tensors
      if (fUseWeightFile) {
         std::string fileName = fName;
         if (fWeightFile == WeightFileType::Text) {
            fileName += ".dat";
         }
         if (fWeightFile == WeightFileType::RootBinary) {
            fileName += ".root";
         }
         fGC += sessionName + "(std::string filename =\"" + fileName + "\"";
      } else {
         // no need to pass weight file since it is not used
         // keep passing a string for compatibility
         fGC += sessionName + "(std::string = \"\"";
      }
      // add initialization of shape parameters
      // assume all parameters are of type size_t
      if (!fDimShapeNames.empty()) {
         // need to use same order as in infer function not alphabetical one
         for (auto &p : fDimShapeNames) {
            fGC += ",\n";
            fGC += "        size_t " + p + " = " + fShapeParams[p];
         }
      }
      fGC += ") {\n";
 
      // initializing dynamic parameters
      if (!fDimShapeNames.empty()) {
         fGC += "\n\n";
         std::sort(fDimShapeNames.begin(), fDimShapeNames.end());
         for (const auto &p : fDimShapeNames) {
            fGC += "   " + memberNameForDimShape(p) + " = " + p + ";\n";
         }
      }
      // add some extra code needed for initialization of dynamic parameters
      fGC += fExtraCodeForDimShapes;
 
      if (fUseWeightFile) {
         fGC += "\n//--- reading weights from file\n";
         ReadInitializedTensorsFromFile(fReadPos);
         fGC += "\n";
         // fUseWeightFile = fUseWeightFile;
      }
 
      // now we have passed the parameters we can allocate the dynamic tensors
      GenerateDynamicTensorInfo();
 
      // add here initialization code  for operator
      for (size_t id = 0; id < fOperators.size(); id++) {
         fGC += fOperators[id]->GenerateInitCode();
      }
 
      fGC += "}\n\n";
 
      // Used to build the tangent Session objects needed to differentiate the
      // generated code with Clad: the derivatives of the (constant) weights
      // are zero, but a default-constructed Session holds the actual values.
      fGC += "// Set all weight and constant tensors to zero. This is useful to create\n"
             "// the tangent Session objects needed to differentiate the generated code\n"
             "// with Clad.\n"
             "void SetWeightsToZero() {\n";
      for (auto &i : fInitializedTensors) {
         // IsNotWritable tensors have no emitted fTensor_ member, and integer
         // tensors are structural (indices, shapes) that carry no tangent.
         if (i.second.IsNotWritable() ||
             (i.second.type() != ETensorType::FLOAT && i.second.type() != ETensorType::DOUBLE))
            continue;
         fGC += "   for (auto &v : fTensor_" + i.first + ") v = 0;\n";
      }
      for (auto &graph : fSubGraphs) {
         fGC += "   fSession_" + graph->fName + ".SetWeightsToZero();\n";
      }
      fGC += "}\n\n";
   }
 
   // generate the inference overload that returns an output struct
   GenerateOutput();
 
   // end of session
   if (fUseSession && !fIsGNNComponent) {
      fGC += "};   // end of Session\n\n";
 
      GenerateRequiredInputTensorInfo();
   }
 
   fGC += doInferSignature + " {\n";
   fGC += "\n";
 
   // generate the inference code
   if (fVerbose)
      std::cout << "Generating main inference code for " << fName << std::endl;
 
   if (fOutputTensorNames.size() == 0)
      throw std::runtime_error("TMVA-SOFIE: output size=0 are not supported");
 
   std::string allOperatorCode;
 
   for (size_t op_idx = 0; op_idx < fOperators.size(); ++op_idx) {
      if (fVerbose)
         std::cout << "Generating code for operator .... " << op_idx << std::endl;
      std::string operatorCode = fOperators[op_idx]->Generate(std::to_string(op_idx));
      allOperatorCode += operatorCode;
   }
 
   // If the generated code users members of the session struct, use the
   // local variable name that we're using for the session:
   ReplaceAll(allOperatorCode, "this->", "session.");
 
   if (fUseSession && !fIsGNNComponent) {
      // Collect all "tensor_*" data members that are not input or output tensors
      std::vector<std::string> tensorMemberNames = CollectTensorMemberNames(allOperatorCode);
      for (auto const& name: tensorMemberNames) {
         fGC += "    auto &" + name + " = session." + name + ";\n";
      }
      fGC += "\n";
   }
 
   fGC += allOperatorCode;
 
   for (auto const& name: fOutputTensorNames) {
      bool isDynamic = fDynamicTensorInfos.count(name) > 0;
      if(isDynamic) {
         for (auto const &dim : GetDynamicTensorShape(name)) {
            if (dim.isParam && !IsInputTensorShapeParam(dim.param) && IsIdentifier(dim.param))
               fGC += "   " + dim.param + "_output = " + dim.param + ";\n";
         }
      }
      if(IsConstantTensor(name)) {
         std::string t = "session.tensor_" + name;
         size_t length = ConvertShapeToLength(fInitializedTensors[name].shape());
         fGC += "    std::copy(" + t + ", " + t + " + " + std::to_string(length) + ", tensor_" + name + ");\n";
      }
   }
   fGC += "\n";
 
   fGC += "}\n";
}
 
void RModel::Generate(std::underlying_type_t<Options> options, int batchSize, long pos, bool verbose)
{
   fVerbose = verbose;
   fBatchSize = batchSize;
   fReadPos = pos;
 
   // session flag is used in operator initialize
   if (static_cast<std::underlying_type_t<Options>>(Options::kNoSession) & options) {
      fUseSession = false;
      fWeightFile = WeightFileType::None;
   }
   if (static_cast<std::underlying_type_t<Options>>(Options::kNoWeightFile) & options) {
      fUseWeightFile = false;
      fWeightFile = WeightFileType::None;
   }
   if (static_cast<std::underlying_type_t<Options>>(Options::kRootBinaryWeightFile) & options) {
      fUseWeightFile = true;
      fWeightFile = WeightFileType::RootBinary;
   }
   if (fUseWeightFile && !fUseSession) {
      throw std::runtime_error(
         "TMVA-SOFIE: RModel::Generate: cannot use a separate weight file without generating a Session class");
   }
 
   if (static_cast<std::underlying_type_t<Options>>(Options::kGNN) & options)
      fIsGNN = true;
   if (static_cast<std::underlying_type_t<Options>>(Options::kGNNComponent) & options)
      fIsGNNComponent = true;
 
   // initialize the model including all operators and sub-graphs
   Initialize(batchSize, verbose);
 
   // if having dynamic tensor we need to have a Session
   if (!fDynamicTensorInfos.empty()) {
      fUseSession = true;
      if (verbose)
         std::cout << "Warning: Force having a Session since model has dynamic tensors " << std::endl;
   }
 
   std::string hgname;
   if (!fIsGNNComponent && !fIsSubGraph) {
      fGC.clear();
      GenerateHeaderInfo(hgname);
   }
 
   // generate first code for the subgraphs
   for (auto &graph : fSubGraphs) {
      if (fVerbose)
         std::cout << "generate session code for subgraph " << graph->fName << std::endl;
      graph->GenerateSessionCode();
      fGC += graph->fGC;
   }
 
   if (fVerbose)
      std::cout << "generate Main session code - model  " << fName << std::endl;
 
   // generate main session code
   GenerateSessionCode();
 
   if (!fIsGNNComponent && !fIsSubGraph) {
      fGC += ("} //TMVA_SOFIE_" + fName + "\n");
      fGC += "\n#endif  // " + hgname + "\n";
      // dump the standalone definitions of the helper functions this model uses
      // so that the generated header does not depend on TMVA/SOFIE_common.hxx
      EmitHelperFunctionsCode();
   }
}
 
void RModel::ReadInitializedTensorsFromFile(long pos) {
    // generate the code to read initialized tensors from a text data file
    if (fWeightFile == WeightFileType::Text) {
        // check if there are tensors to write
 
        if (!fUseWeightFile) return;
 
        fGC += "   std::ifstream f;\n";
        fGC += "   f.open(filename);\n";
        fGC += "   if (!f.is_open()) {\n";
        fGC += "      throw std::runtime_error(\"tmva-sofie failed to open file \" + filename + \" for input weights\");\n";
        fGC += "   }\n";
 
        if(fIsGNNComponent) {
            fGC += "   f.seekg(" + std::to_string(pos) + ");\n";
        }
 
        // ReadTensorFromStream is emitted as a standalone helper in the header
        AddNeededHelperFunction("ReadTensorFromStream");
 
        // loop on tensors and parse the file
        for (auto& i: fInitializedTensors) {
            // skip Constant and shape tensors (not written in a file)
            if (!i.second.IsWeightTensor()) continue;
            std::string tensor_name = "tensor_" + i.first;
            if (i.second.type() == ETensorType::FLOAT) {
               std::string length = std::to_string(ConvertShapeToLength(i.second.shape()));
               fGC += "   ReadTensorFromStream(f, " + tensor_name + ", \"" + tensor_name + "\", " + length + ");\n";
            } else {
               throw std::runtime_error("tmva-sofie tensor " + tensor_name + " with type " + ConvertTypeToString(i.second.type()) + " cannot be read from a file");
            }
        }
        fGC += "   f.close();\n";
    }
 
    // generate the code to read initialized tensors from a ROOT data file
    if(fWeightFile == WeightFileType::RootBinary) {
#ifdef SOFIE_SUPPORT_ROOT_BINARY
        fGC += "  {\n";
        fGC += "   std::unique_ptr<TFile> rootFile(TFile::Open(filename.c_str(), \"READ\"));\n";
        fGC += "   if (!rootFile->IsOpen()) {\n";
        fGC += "      throw std::runtime_error(\"tmva-sofie failed to open ROOT file for input weights\");\n";
        fGC += "   }\n";
 
        std::string dirName = fName + "_weights";
        fGC += "   if (!rootFile->GetKey(\"" + dirName + "\")) {\n";
        fGC += "      throw std::runtime_error(\"tmva-sofie failed to open ROOT directory for input weights\");\n";
        fGC += "   }\n";
 
        for (auto &i : fInitializedTensors) {
            // skip Constant and shape tensors
            if (!i.second.IsWeightTensor()) continue;
            fGC += "  {\n";
            std::string tensor_name = "tensor_" + i.first;
            if (i.second.type() == ETensorType::FLOAT) {
               fGC += "      fTensor_" + i.first + " = *reinterpret_cast<std::vector<float>*>(rootFile->Get(\"";
               fGC += dirName + "/" + tensor_name + "\"));\n";
            } else if (i.second.type() == ETensorType::DOUBLE) {
               fGC += "      fTensor_" + i.first + " = *reinterpret_cast<std::vector<double>*>(rootFile->Get(\"";
               fGC += dirName + + "/" + tensor_name + "\"));\n";
            } else if (i.second.type() == ETensorType::INT64) {
               fGC += "      fTensor_" + i.first + " = *reinterpret_cast<std::vector<int64_t>*>(rootFile->Get(\"";
               fGC += dirName + "/" + tensor_name + "\"));\n";
            } else {
               throw std::runtime_error("tmva-sofie tensor " + tensor_name + " with type " + ConvertTypeToString(i.second.type()) + " cannot be read from a ROOT file");
            }
            fGC += "  }\n";
        }
        fGC += "  }\n";
#else
        throw std::runtime_error("SOFIE was not built with ROOT file support.");
#endif // SOFIE_SUPPORT_ROOT_BINARY
    }
}
 
long RModel::WriteInitializedTensorsToFile(std::string filename) {
    // Determine the file extension based on the weight file type
    std::string fileExtension;
    switch (fWeightFile) {
    case WeightFileType::None:
        fileExtension = ".dat";
        break;
    case WeightFileType::RootBinary:
        fileExtension = ".root";
        break;
    case WeightFileType::Text:
        fileExtension = ".dat";
        break;
    }
 
    // If filename is empty, use the model name as the base filename
    if (filename.empty()) {
        filename = fFileName + fileExtension;
    }
 
    // Write the initialized tensors to the file
    if (fWeightFile == WeightFileType::RootBinary) {
#ifdef SOFIE_SUPPORT_ROOT_BINARY
        if(fIsGNNComponent || fIsGNN) {
            throw std::runtime_error("SOFIE-GNN yet not supports writing to a ROOT file.");
        }
        std::unique_ptr<TFile> outputFile(TFile::Open(filename.c_str(), "UPDATE"));
 
        std::string dirName = fName + "_weights";
        // check if directory exists, in case delete to replace with new one
        if (outputFile->GetKey(dirName.c_str()))
            outputFile->rmdir(dirName.c_str());
 
        auto outputDir = outputFile->mkdir(dirName.c_str());
 
        for (const auto& item : fInitializedTensors) {
            // skip Constant tensors and tensors which are not writable (e.g. shape tensors)
            if (!item.second.IsWeightTensor()) continue;
            std::string tensorName = "tensor_" + item.first;
            size_t length = 1;
            length = ConvertShapeToLength(item.second.shape());
            if(item.second.type() == ETensorType::FLOAT) {
               const float* data = item.second.data<float>();
                std::vector<float> tensorDataVector(data, data + length);
               outputDir->WriteObjectAny(&tensorDataVector, "std::vector<float>", tensorName.c_str());
            }
            else if(item.second.type() == ETensorType::DOUBLE) {
               const double* data = item.second.data<double>();
               std::vector<double> tensorDataVector(data, data + length);
               outputDir->WriteObjectAny(&tensorDataVector, "std::vector<double>", tensorName.c_str());
            }
            else if(item.second.type() == ETensorType::INT64) {
               const int64_t* data = item.second.data<int64_t>();
               std::vector<int64_t> tensorDataVector(data, data + length);
               outputDir->WriteObjectAny(&tensorDataVector, "std::vector<int64_t>", tensorName.c_str());
            }
            else {
               throw std::runtime_error("tmva-sofie tensor " + tensorName + " with type " + ConvertTypeToString(item.second.type()) +
                                  " cannot be written to a ROOT file");
            }
        }
        outputFile->Write(filename.c_str());
 
        // this needs to be changed, similar to the text file
        return -1;
 
#else
        throw std::runtime_error("SOFIE was not built with ROOT file support.");
#endif // SOFIE_SUPPORT_ROOT_BINARY
    } else if (fWeightFile == WeightFileType::Text) {
        std::ofstream f;
        if(fIsGNNComponent) {
            // appending all GNN components into the same file
            f.open(filename, std::ios::app);
        } else {
            f.open(filename);
        }
        if (!f.is_open())
            throw
            std::runtime_error("tmva-sofie failed to open file " + filename + " for tensor weight data");
        for (auto& i: fInitializedTensors) {
             // skip Constant tensors and not writable tensors (e.g. shape tensors)
            if (!i.second.IsWeightTensor()) {
               continue;
            }
            size_t length = ConvertShapeToLength(i.second.shape());
            std::string tensor_name = "tensor_" + i.first;
            f << tensor_name << " " << length << "\n";
            if (i.second.type() == ETensorType::FLOAT) {
               const float * data = i.second.data<float>();
               for (size_t idx = 0; idx < length; idx++) {
                  // round to zero sub-normal values
                  float value = data[idx];
                  if (value != 0. && std::abs(value) < std::numeric_limits<float>::min() ) value = 0;
                  // handle non-finite values explicitly
                  if (std::isinf(value))
                     f << (value > 0 ? "inf" : "-inf");
                  else if (std::isnan(value))
                     f << "nan";
                  else
                     f << std::setprecision(std::numeric_limits<float>::max_digits10) << value;
                  f <<  ( (idx < length-1) ? " " : "\n" );
               }
            }
            else {
               throw std::runtime_error("tmva-sofie tensor " + tensor_name + " with type " + ConvertTypeToString(i.second.type()) + " cannot be written to a file");
            }
            if (f.fail())
               throw std::runtime_error("tmva-sofie failed to write tensor data to file for  " + tensor_name);
        }
        long curr_pos = f.tellp();
        f.close();
        return curr_pos;
    } else {
        return -1;
    }
}
 
void RModel::PrintSummary() const {
   std::cout << "Summary of model " << GetName() << std::endl;
   for(size_t op_idx = 0; op_idx < fOperators.size(); ++op_idx){
      auto& r = *fOperators[op_idx].get();
      std::string raw_name =  typeid(r).name();
      // look for ROperator_NAME
      std::string name = raw_name.substr(raw_name.find("ROperator_")+10, raw_name.size());
      std::cout <<  op_idx << "  " << name << "  :  ";
      for (auto & t_in : r.GetOpInputTensors()) std::cout << t_in << "  ";
      std::cout << " ----> ";
      for (auto & t_out : r.GetOpOutputTensors()) std::cout << t_out << "  ";
      std::cout << std::endl;
   }
}
 
/// To emit the dimensions of the input tensors as a data member of a session,
/// which is helpful when validating the inference inputs.
void RModel::GenerateRequiredInputTensorInfo()
{
   fGC += "\n// Input tensor dimensions\n";
   // SingleDim / TensorDims / makeDims are emitted as standalone helpers
   AddNeededHelperFunction("InputTensorDims");
   bool hasDynamicInputTensors = false;
 
   for (std::size_t iInput = 0; iInput < fInputTensorNames.size(); ++iInput) {
      auto const &name = fInputTensorNames[iInput];
      if (IsDimInputTensor(name)) {
         hasDynamicInputTensors = true;
      }
      std::vector<Dim> shape = GetDimTensorShape(name);
      fGC += "constexpr std::array<SingleDim, " + std::to_string(shape.size()) + "> dim_" + name + "{";
      for (std::size_t iDim = 0; iDim < shape.size(); ++iDim) {
         auto const &dim = shape[iDim];
         if (dim.isParam) {
            fGC += "SingleDim{\"" + dim.GetVal() + "\"}";
         } else {
            fGC += "SingleDim{" + dim.GetVal() + "}";
         }
         if (iDim != shape.size() - 1) {
            fGC += ", ";
         }
      }
      fGC += "};\n";
   }
   fGC += "\nconstexpr std::array<TensorDims, " + std::to_string(fInputTensorNames.size()) + "> inputTensorDims{\n";
   for (std::size_t iInput = 0; iInput < fInputTensorNames.size(); ++iInput) {
      auto const &name = fInputTensorNames[iInput];
      fGC += SP + "makeDims(dim_" + name + ")";
      if (iInput == fInputTensorNames.size() - 1) {
         fGC += "\n";
      } else {
         fGC += ",\n";
      }
   }
   fGC += "};\n";
 
   fGC +=
      "\nconstexpr bool hasDynamicInputTensors{" + std::string{hasDynamicInputTensors ? "true" : "false"} + "};\n\n";
 
   fGC += "\n// Output tensor dimensions\n";
   bool hasDynamicOutputTensors = false;
   for (std::size_t iOutput = 0; iOutput < fOutputTensorNames.size(); ++iOutput) {
      auto const &name = fOutputTensorNames[iOutput];
      if (IsDynamicTensor(name)) {
         hasDynamicOutputTensors = true;
      }
      std::vector<Dim> shape = GetDimTensorShape(name);
      fGC += "constexpr std::array<SingleDim, " + std::to_string(shape.size()) + "> dim_" + name + "{";
      for (std::size_t iDim = 0; iDim < shape.size(); ++iDim) {
         auto const &dim = shape[iDim];
         if (dim.isParam) {
            fGC += "SingleDim{\"" + dim.GetVal() + "\"}";
         } else {
            fGC += "SingleDim{" + dim.GetVal() + "}";
         }
         if (iDim != shape.size() - 1) {
            fGC += ", ";
         }
      }
      fGC += "};\n";
   }
   fGC += "\nconstexpr std::array<TensorDims, " + std::to_string(fOutputTensorNames.size()) + "> outputTensorDims{\n";
   for (std::size_t iOutput = 0; iOutput < fOutputTensorNames.size(); ++iOutput) {
      auto const &name = fOutputTensorNames[iOutput];
      fGC += SP + "makeDims(dim_" + name + ")";
      if (iOutput == fOutputTensorNames.size() - 1) {
         fGC += "\n";
      } else {
         fGC += ",\n";
      }
   }
   fGC += "};\n";
   fGC +=
      "\nconstexpr bool hasDynamicOutputTensors{" + std::string{hasDynamicOutputTensors ? "true" : "false"} + "};\n\n";
}
 
void RModel::PrintRequiredInputTensors() const {
    std::cout << "Model requires following inputs:\n";
    for (auto& inputInfo: fInputTensorInfos) {
        std::cout << "Parametrised Tensor name: " << inputInfo.first << "\t";
        std::cout << "type: " << ConvertTypeToString(inputInfo.second.type) << "\t";
        std::cout << "shape: [";
        for (size_t i = 0; i < inputInfo.second.shape.size(); i++) {
            if (inputInfo.second.shape[i].isParam) {
                std::cout << inputInfo.second.shape[i].param;
            } else {
                std::cout << inputInfo.second.shape[i].dim ;
            }
            if (i < inputInfo.second.shape.size() - 1) std::cout << ",";
        }
        std::cout << "]" << std::endl;
    }
 
    for (auto& inputInfo: fReadyInputTensorInfos) {
        std::cout << "Fully Specified Tensor name: " << inputInfo.first << "\t";
        std::cout << "type: " << ConvertTypeToString(inputInfo.second.type) << "\t";
        std::cout << "shape: [";
        for (size_t i = 0; i < inputInfo.second.shape.size(); i++) {
            std::cout << inputInfo.second.shape[i];
            if (i < inputInfo.second.shape.size() - 1) std::cout << ",";
        }
        std::cout << "]" << std::endl;
    }
    std::cout << "\n";
}
 
void RModel::PrintInitializedTensors() const {
    std::cout << "Model initialized the following tensors:\n";
    for (auto& it: fInitializedTensors) {
        std::cout << "Tensor name: \"" << it.first << "\"\t";
        std::cout << "type: " << ConvertTypeToString(it.second.type()) << "\t";
        std::cout << "shape: [";
        for (size_t i = 0; i < it.second.shape().size(); i++) {
            std::cout << it.second.shape()[i];
            if (i < it.second.shape().size() - 1) std::cout << ",";
        }
        std::cout << "]";
        if (it.second.IsConstantTensor()) std::cout << " (Constant)";
        if (it.second.IsNotWritable()) std::cout << " (Not Writable)";
        std::cout << std::endl;
    }
    std::cout << "\n";
}
 
void RModel::PrintIntermediateTensors() const {
    std::cout << "Model specify the following intermediate tensors:\n";
    for (auto& it: fIntermediateTensorInfos) {
        std::cout << "Tensor name: \"" << it.first << "\"\t";
        std::cout << "type: " << ConvertTypeToString(it.second.type) << "\t";
        std::cout << "shape: [";
        for (size_t i = 0; i < it.second.shape.size(); i++) {
            std::cout << it.second.shape[i];
            if (i < it.second.shape.size() - 1) std::cout << ",";
        }
        std::cout << "]" << std::endl;
    }
    std::cout << "\n";
}
 
void RModel::PrintDynamicTensors() const {
    std::cout << "Model specify the following dynamic tensors:\n";
    for (auto& it: fDynamicTensorInfos) {
        std::cout << "Tensor name: \"" << it.first << "\"\t";
        std::cout << "type: " << ConvertTypeToString(it.second.type) << "\t";
        std::cout << "shape: [";
        for (size_t i = 0; i < it.second.shape.size(); i++) {
            std::cout << it.second.shape[i].GetVal();
            if (i < it.second.shape.size() - 1) std::cout << ",";
        }
        std::cout << "]" << std::endl;
    }
    std::cout << "\n";
}
 
void RModel::PrintOutputTensors() const {
    std::cout << "Model specify the following output tensors:\n";
    for (auto& it: fOutputTensorNames) {
        std::cout << "Tensor name: \"" << it << "\"\t";
        try {
         auto shape = GetDimTensorShape(it);
         std::cout << "with shape: " << ConvertDimShapeToString(shape) << std::endl;
        } catch (...) {
          std::cout << "with shape not yet defined" << std::endl;
        }
    }
    std::cout << "\n";
}
 
void RModel::HeadInitializedTensors(std::string name, int n_print) {
    auto it = fInitializedTensors.find(name);
    if (it == fInitializedTensors.end()) {
        std::cout << "Tensor " << name << " not found in model's initialized tensor list" << std::endl;
        return;
    }
 
    std::cout << "Tensor name: " << it->first << "\t";
    std::cout << "type: " << ConvertTypeToString(it->second.type()) << "\t";
    int length =1;
    std::cout << "shape: [";
    for (size_t i = 0; i < it->second.shape().size(); i++) {
        std::cout << it->second.shape()[i];
        length *= it->second.shape()[i];
        if (i < it->second.shape().size() - 1) std::cout << ",";
    }
    std::cout << "]" << std::endl;
    bool ellipsis = true;
    if (n_print > length) {
        n_print = length;
        ellipsis = false;
    }
 
    std::cout << "data: [" << std::endl;
    if (it->second.type() == ETensorType::FLOAT) {
        auto converted_data = it->second.data<float>();
        for (int i =0; i < n_print; i++) {
            std::cout << converted_data[i];
            if (i < n_print - 1) std::cout << " ,";
        }
    }
    if (ellipsis) std::cout << ", ...";
    std::cout << "]" << std::endl;
 
}
 
void RModel::OutputGenerated(std::string filename, bool append) {
 
    RModel_Base::OutputGenerated(filename, append);
 
    // write weights in a text file
    if (fUseWeightFile) {
        if (!filename.empty()) {
            size_t pos = filename.find(".hxx");
            if (fWeightFile == WeightFileType::Text)
                filename.replace(pos, 4, ".dat");
            if (fWeightFile == WeightFileType::RootBinary)  {
                filename = filename.erase(pos, 4);
                filename += ".root";
            }
        } else {
            filename = fName;
            filename += fWeightFile == WeightFileType::Text ? ".dat" : ".root";
        }
        WriteInitializedTensorsToFile(filename);
    }
}
 
} // namespace SOFIE::Experimental::TMVA