SOFIE_common.hxx

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#ifndef TMVA_SOFIE_SOFIE_COMMON
#define TMVA_SOFIE_SOFIE_COMMON
 
#include "TMVA/RTensor.hxx"
 
#include "ROOT/RSpan.hxx"
 
#include <stdexcept>
#include <type_traits>
#include <cstdint>
#include <cstring>
#include <complex>
#include <string>
#include <vector>
#include <map>
#include <memory>
#include <regex>
#include <set>
#include <sstream>
#include <iostream>
#include <iomanip>
#include <cassert>
#include <limits>
 
namespace TMVA {
namespace Experimental {
namespace SOFIE {
 
enum class ETensorType{
   UNDEFINED = 0, FLOAT = 1, UINT8 = 2, INT8 = 3, UINT16 = 4, INT16 = 5, INT32 = 6, INT64 = 7, STRING = 8, BOOL = 9, //order sensitive
    FLOAT16 = 10, DOUBLE = 11, UINT32 = 12, UINT64 = 13, COMPLEX64 = 14, COMPLEX28 = 15, BFLOAT16 = 16
};
 
enum class EActivationType{
   UNDEFINED = 0, RELU = 1, SOFTMAX = 2, SIGMOID = 3, LEAKYRELU = 4, TANH = 5, ELU = 6
};
 
constexpr size_t GetTypeSize(ETensorType type) {
    switch (type) {
        case ETensorType::FLOAT:     return sizeof(float);
        case ETensorType::DOUBLE:    return sizeof(double);
        case ETensorType::UINT8:     return sizeof(uint8_t);
        case ETensorType::INT8:      return sizeof(int8_t);
        case ETensorType::UINT16:    return sizeof(uint16_t);
        case ETensorType::INT16:     return sizeof(int16_t);
        case ETensorType::INT32:     return sizeof(int32_t);
        case ETensorType::INT64:     return sizeof(int64_t);
        case ETensorType::UINT32:    return sizeof(uint32_t);
        case ETensorType::UINT64:    return sizeof(uint64_t);
        case ETensorType::BOOL:      return sizeof(bool);
        case ETensorType::STRING:    return sizeof(std::string);
        default: return 0;
    }
}
 
typedef std::int64_t int_t;
 
std::string ConvertTypeToString(ETensorType type);
ETensorType ConvertStringToType(std::string type);
 
// find if a string represents a number
bool IsInteger(const std::string & s);
 
struct Dim{
   bool isParam = false;
   size_t dim = 0;
   std::string param;
 
    // default constructor (for I/O)
   Dim() {}
 
   // constructor for a parametric dimension with the option to pass a default dim value
   // We use -1 for dim to indicate that the param dimension is an expression (e.g. "d1+d2")
   // in case the string represents a number make Dim not parametric
   Dim(const std::string & p, size_t d = 0) : isParam(true), dim(d), param(p)
   {
      if (IsInteger(p)) {
            isParam = false;
            dim = std::stoi(p);
      }
   }
 
   // constructor for a non-parametric dimension
   Dim(size_t d) : dim(d) {}
 
   std::string GetVal() const {
      // cast to int64_t for negative shape values
      return (isParam) ? param : std::to_string(static_cast<int64_t>(dim));
   }
 
   std::ostream& operator<< (std::ostream& os) const {
      os << GetVal();
      return os;
   }
 
   bool operator==(const Dim& rhs) const {
       return (isParam && rhs.isParam) ? param == rhs.param : dim == rhs.dim;
   }
   bool operator!=(const Dim& rhs) const {
       return !(*this == rhs);
   }
};
 
//bool operator==(const Dim& lhs, const Dim& rhs);
inline std::ostream & operator<< (std::ostream &os, const Dim &d) {
   os << d.GetVal();
   return os;
}
 
struct InputTensorInfo{
   ETensorType type;
   std::vector<Dim> shape;
};
 
struct TensorInfo{
   ETensorType type;
   std::vector<size_t> shape;
};
 
struct DynamicTensorInfo{
   ETensorType type;
   std::vector<Dim> shape;
};
 
// template traits for Tensor Shape
template <typename T>
struct TensorShape {};
template<>
struct TensorShape<Dim> {
   static bool IsDim() { return true; }
};
template<>
struct TensorShape<size_t> {
   static bool IsDim() { return false; }
};
 
// template traits for Tensor type
template <typename T>
struct TensorType {};
template<>
struct TensorType<float> {
   static const std::string Name() { return "float"; }
};
template<>
struct TensorType<double> {
   static const std::string Name() { return "double"; }
};
template<>
struct TensorType<int64_t> {
   static const std::string Name() { return "int64_t"; }
};
template<>
struct TensorType<int32_t> {
   static const std::string Name() { return "int32_t"; }
};
template<>
struct TensorType<uint32_t> {
   static const std::string Name() { return "uint32_t"; }
};
template<>
struct TensorType<uint64_t> {
   static const std::string Name() { return "uint64_t"; }
};
template<>
struct TensorType<bool> {
   static const std::string Name() { return "bool"; }
};
template<>
struct TensorType<int8_t> {
   static const std::string Name() { return "int8_t"; }
};
template<>
struct TensorType<uint8_t> {
   static const std::string Name() { return "uint8_t"; }
};
 
struct TensorMemoryInfo {
   std::string_view tensor_name;
   size_t tensor_size;
 
   TensorMemoryInfo split(const std::string_view new_name, size_t new_size) {
        if (new_size > tensor_size) {
            throw std::invalid_argument("New size exceeds available tensor size.");
        }
        tensor_size -= new_size;
        return TensorMemoryInfo{new_name, new_size};
   }
 
    // Method to merge another struct into this one
   void merge(const TensorMemoryInfo& other) {
        tensor_size += other.tensor_size;
   }
};
 
struct MemoryPoolInfo {
 
   // ordered map with chunk_idx as key and TensorMemoryInfo as value
   std::map<size_t, TensorMemoryInfo> total_stack;
 
   // ordered map with chunk_idx as key and chunk_size as value
   std::map<size_t, size_t> available_stack;
};
 
std::vector<Dim> ConvertShapeToDim(const std::vector<size_t> & shape);
 
std::vector<size_t> ConvertShapeToInt(const std::vector<Dim> & shape);
 
std::size_t ConvertShapeToLength(const std::vector<size_t> & shape);
 
std::string ConvertShapeToString(const std::vector<size_t> & shape);
std::string ConvertDimShapeToString(const std::vector<Dim> & shape);
 
std::string ConvertDimShapeToLength(const std::vector<Dim> & shape);
 
 
template<class T>
std::string ConvertValToString(T value) {
   std::stringstream ret;
   ret << std::to_string(value);
   return ret.str();
}
// float specialization
template<>
inline std::string ConvertValToString<float>(float value) {
   std::stringstream ret;
   // special case for infinity and Nan
   if (std::isinf(value))
         ret << (value > 0 ? "std::numeric_limits<float>::infinity()" :
                                  "-std::numeric_limits<float>::infinity()");
   else if (std::isnan(value))
         ret << "std::numeric_limits<float>::quiet_NaN()";
   else {
      ret << std::setprecision(std::numeric_limits<float>::max_digits10);
      ret << value;
   }
   return ret.str();
}
// double specialization
template<>
inline std::string ConvertValToString<double>(double value) {
   std::stringstream ret;
   // special case for infinity and Nan
   if (std::isinf(value))
         ret << (value > 0 ? "std::numeric_limits<double>::infinity()" :
                                  "-std::numeric_limits<double>::infinity()");
   else if (std::isnan(value))
         ret << "std::numeric_limits<double>::quiet_NaN()";
   else {
      ret << std::setprecision(std::numeric_limits<double>::max_digits10);
      ret << value;
   }
   return ret.str();
}
// int64_t specialization for INT64_MIN
template<>
inline std::string ConvertValToString<int64_t>(int64_t value) {
   std::stringstream ret;
   if (value == INT64_MIN)
      ret << "INT64_MIN";
   else
      ret << std::to_string(value);
   return ret.str();
}
 
 
// convert list of values in a string taking into account the precision
template<class T>
std::string ConvertValuesToString(size_t n, const T * data, size_t maxprint = -1) {
   std::stringstream ret;
   ret << "{ ";
   for (size_t i = 0; i < std::min(n,maxprint); i++) {
      ret << ConvertValToString(data[i]);
      if (i < n-1) ret << ", ";
      if (i < n-1 && i == maxprint-1) ret << "..... ";
   }
   ret << "}";
   return ret.str();
}
template<class T>
std::string ConvertValuesToString(const std::vector<T> & data, size_t maxprint = 5) {
  return ConvertValuesToString(data.size(), data.data(), maxprint);
}
 
class InitializedTensor {
public:
   InitializedTensor() = default;
   InitializedTensor(ETensorType type, std::span<std::size_t> shape, std::shared_ptr<void> data, bool typeConstant = false)
      : fConstant(typeConstant), fType{type}, fShape{shape.begin(), shape.end()}, fData{data}
   {
   }
 
   ETensorType const &type() const { return fType; }
   std::vector<std::size_t> const &shape() const { return fShape; }
   std::shared_ptr<void> const &sharedptr() const { return fData; }
   // query if tensor comes from a Constant operator
   bool IsConstantTensor() const { return fConstant;}
   // query if tensor needs to be written in a weight file. Constant tensors are not written in a separate file
   bool IsWeightTensor() const { return !fConstant && !fIsNotWritable;}
   // check if a Tensor is Writable (need to be written in the file or in the generated code (e.g. as a constant tensor)
   // if an initialized tensors is used in a constant operator at compile time does not need to be written and can be omitted in
   // the generated code
   bool IsNotWritable() const { return fIsNotWritable; }
   // set not writable initialized tensors - i.e. tensor that must not be written in a file
   void SetNotWritable() { fIsNotWritable = true;}
   // set writable initialized tensors - i.e. tensor that must be written in a file
   void SetWritable() { fIsNotWritable = false;}
   // set as constant (needed for non-float initialized tensors)
   void SetConstant() { fConstant = true;}
 
   template <class T = void>
   T const *data() const
   {
      return static_cast<T const *>(fData.get());
   }
 
private:
   bool  fConstant = false;      ///< Flag specifying if tensor is a Constant one (coming from a Constant operator)
   bool  fIsNotWritable = false; ///< Flag to indicate that tensor values do not need to be written as weight or generated code
   ETensorType fType;               ///< Encodes the type of the data
   std::vector<std::size_t> fShape; ///< The shape of the data in terms of elements in each dimension
   std::shared_ptr<void> fData;     ///<! Transient shared data
};
 
template <typename T>
ETensorType GetTemplatedType(T /*obj*/ ){
   if (std::is_same<T, float>::value) return ETensorType::FLOAT;
   if (std::is_same<T, uint8_t>::value) return ETensorType::UINT8;
   if (std::is_same<T, int8_t>::value) return ETensorType::INT8;
   if (std::is_same<T, uint16_t>::value) return ETensorType::UINT16;
   if (std::is_same<T, int16_t>::value) return ETensorType::INT16;
   if (std::is_same<T, int32_t>::value) return ETensorType::INT32;
   if (std::is_same<T, int64_t>::value) return ETensorType::INT64;
   if (std::is_same<T, std::string>::value) return ETensorType::STRING;
   if (std::is_same<T, bool>::value) return ETensorType::BOOL;
   //float16 unimplemented
   if (std::is_same<T, double>::value) return ETensorType::DOUBLE;
   if (std::is_same<T, uint32_t>::value) return ETensorType::UINT32;
   if (std::is_same<T, uint64_t>::value) return ETensorType::UINT64;
   //complex 64, 28, bfloat 16 unimplemented
}
 
namespace UTILITY{
 
 
 
// clean operator and tensor names
std::string Clean_name(std::string input_tensor_name);
 
// Check if two shapes are equal
bool AreSameShape(const std::vector<size_t>&, const std::vector<size_t>&);
bool AreSameShape(const std::vector<size_t>&, const std::vector<Dim>&);
bool AreSameShape(const std::vector<Dim>&, const std::vector<Dim>&);
 
 
// Multidirectional broadcast a list of tensors to the same shape
std::vector<size_t> MultidirectionalBroadcastShape(std::vector<std::vector<size_t>>);
 
// Multidirectional broadcast two shapes to the same shape
 
std::pair<int, std::vector<size_t>> MultidirectionalBroadcastShape(std::vector<size_t> &, std::vector<size_t> &);
std::vector<size_t> UnidirectionalBroadcastShape(std::vector<size_t> &, std::vector<size_t> &);
 
std::pair<int, std::vector<Dim>> MultidirectionalBroadcastShape(std::vector<Dim> &, std::vector<Dim> &);
 
 
 
template<typename T>
T* BroadcastConvBias(const T* data, const size_t channel, const std::vector<size_t>& targetShape) {
   size_t size = targetShape.size();
   if (targetShape[1] != channel) {
      std::stringstream ss;
      ss << "TMVA::SOFIE - Error broadcasting Conv Bias of shape {";
      ss << std::to_string(channel);
      ss << "} to ";
      ss << ConvertShapeToString(targetShape);
      throw
         std::runtime_error(ss.str());
   }
 
   size_t targetLength = ConvertShapeToLength(targetShape);
   T* newData = new T[targetLength];
 
   if (targetLength == channel) {
      std::copy(data, data + channel, newData);
      return newData;
   }
 
   // cStride = OutDepth * outHeight * outWidth
   size_t cStride = 1;
   for (size_t i = 2; i < size; i++)
      cStride *= targetShape[i];
   // Broadcast each element of the bias to a vector of size cStride and concatenate them
   // into a vector of size channel * cStride
   for (size_t i = 0; i < channel; i++) {
      std::fill(newData + i * cStride, newData + (i + 1) * cStride, data[i]);
   }
   // Broadcast newData[0...channel * cStride) to newData[0...batch * channel * cStride)
   size_t batch = targetShape[0];
   size_t bStride = channel * cStride;
   for (size_t i = 1; i < batch; i++) {
      std::copy(newData, newData + bStride, newData + i * bStride);
   }
   return newData;
}
 
// Broadcast a tensor from shape to targetShape according to numpy broadcasting rules
// See more at https://numpy.org/doc/stable/user/basics.broadcasting.html
// and https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md .
template<typename T, class ConstContT = std::span<const T>>
void BroadcastTensor(ConstContT data, const std::vector<size_t>& shape, const std::vector<size_t>& targetShape, T *broadcastedData) {
   // Size of the shapes (tensor input here have shapes with same sizes, we have already added the needed ones )
   size_t size = shape.size();
   // Current length of the broadcasted tensor
   size_t curLength = data.size();
   // special case when broadcasting last dimensions (initial shapes must be the same)
   if (size > 1 && shape.front() == targetShape.front() && shape.back() == 1) {
      size_t bsize = targetShape.back();
      // compute the size of the data to broadcast
      for (int k = int(size)-2; k >=0; k--) {
         if (shape[k] != 1) break;
         bsize *= targetShape[k];
      }
      for (size_t i = 0; i < curLength; i++) {
         std::fill(broadcastedData + i*bsize, broadcastedData + (i+1)*bsize , data[i]);
      }
      return;
   }
 
   std::copy(data.begin(), data.end(), broadcastedData);
   // Product of the previous dimensions of targetShape
   size_t arrayNum = 1;
   // New broadcasted data: is this needed?
   std::vector<T> newData(ConvertShapeToLength(targetShape));
 
   for (size_t idx = 0; idx < size; idx++) {
      size_t dim = shape[idx];
      size_t targetDim = targetShape[idx];
      if (dim == 1 && targetDim > 1) {
         // Set the new length of the data
         size_t newLength = curLength * targetDim;
         // View the data as a list of arrayNum arrays of size arrayLength
         size_t arrayLength = curLength / arrayNum;
         // Broadcast each array dim times
         if (arrayLength > 1) {
            // If each array has at least two elements
            for (size_t arrayIdx = 0; arrayIdx < arrayNum; arrayIdx++) {
               for (size_t targetIdx = 0; targetIdx < targetDim; targetIdx++) {
                  size_t offset = arrayIdx * arrayLength * targetDim + targetIdx * arrayLength;
                  std::copy(broadcastedData + arrayIdx * arrayLength,
                     broadcastedData + (arrayIdx + 1) * arrayLength,
                     newData.begin() + offset);
               }
            }
         } else {
            // If each array has one element
            for (size_t arrayIdx = 0; arrayIdx < arrayNum; arrayIdx++) {
               std::fill(newData.begin() + arrayIdx * targetDim,
                  newData.begin() + (arrayIdx + 1) * targetDim, broadcastedData[arrayIdx]);
            }
         }
         // Update current length
         curLength = newLength;
         // Update broadcasted data
         std::copy(newData.begin(), newData.begin() + newLength, broadcastedData);
      }
      // Update the number of arrays
      arrayNum *= targetDim;
   }
}
 
// interface where we allocate a new array for broadcasted data
template<typename T>
T* CreateBroadcastTensor(const T* data, const std::vector<size_t>& shape, const std::vector<size_t>& targetShape, size_t targetLength) {
   // newShape is an array of size equal to dimension along which we are broadcasting the tensor
   T* broadcastedData = new T[targetLength];
   size_t curLength = ConvertShapeToLength(shape);
   BroadcastTensor<T>({data, curLength}, shape, targetShape, broadcastedData);
   return broadcastedData;
}
// Unidirectional broadcasting shape to targetShape// In unidirectional broadcast - only tensor B can have the shape changed not
// tensor A - otherwise is a multidirectional broadcast
template<typename T>
T* UnidirectionalBroadcast(const T* data, const std::vector<size_t>& shape, const std::vector<size_t>& targetShape) {
   // Prepend shape with ones
   if (shape.size() < targetShape.size()) {
      size_t targetSize = targetShape.size();
      std::vector<size_t> newShape(targetSize, 1);
      size_t offset = targetSize - shape.size();
      std::copy(shape.begin(), shape.end(), newShape.begin() + offset);
      return CreateBroadcastTensor(data, newShape, targetShape, ConvertShapeToLength(targetShape));
   }
   return CreateBroadcastTensor(data, shape, targetShape, ConvertShapeToLength(targetShape));
}
 
// Unidirectional broadcasting shape to targetShape using a passed vector to avoid allocations
template<typename T>
void UnidirectionalBroadcast(const T* data, const std::vector<size_t>& shape, const std::vector<size_t>& targetShape, T *broadcastedData) {
   size_t curLength = ConvertShapeToLength(shape);
   std::span<T> inData(const_cast<T*>(data), curLength);
   // Prepend shape with ones
   if (shape.size() < targetShape.size()) {
      size_t targetSize = targetShape.size();
      std::vector<size_t> newShape(targetSize, 1);
      size_t offset = targetSize - shape.size();
      std::copy(shape.begin(), shape.end(), newShape.begin() + offset);
      BroadcastTensor(inData, newShape, targetShape, broadcastedData);
      return;
   }
   BroadcastTensor(inData, shape, targetShape, broadcastedData);
}
 
/// compute stride of a tensor given its shape (assume layout is row-major)
std::vector<size_t> ComputeStrideFromShape(const std::vector<size_t> & shape);
std::vector<Dim> ComputeStrideFromShape(const std::vector<Dim> & shape);
 
 
}  // end namespace UTILITY
 
namespace BLAS{
extern "C" void sgemm_(const char * transa, const char * transb, const int * m, const int * n, const int * k,
                       const float * alpha, const float * A, const int * lda, const float * B, const int * ldb,
                       const float * beta, float * C, const int * ldc);
}//BLAS
 
 
struct GNN_Data {
      RTensor<float> node_data;      // the node feature data, tensor with shape (num_nodes, num_node_features)
      RTensor<float> edge_data;      // the edge feature data, tensor with shape (num_edges, num_edge_features)
      RTensor<float> global_data;    // the global features, tensor with shape (1, num_global_features)
      RTensor<int> edge_index;       // the edge index (receivers and senders for each edge), tensor with shape (2, num_edges)
                                     // edge_index[0,:] are the receivers and edge_index[1,:] are the senders
 
 
      // need to have default constructor since RTensor has not one
      GNN_Data(): node_data(RTensor<float>({})), edge_data(RTensor<float>({})), global_data(RTensor<float>({})), edge_index(RTensor<int>({})) {}
 
};
 
template<typename T>
TMVA::Experimental::RTensor<T> Concatenate( TMVA::Experimental::RTensor<T> & t1,  TMVA::Experimental::RTensor<T> & t2, int axis = 0)
{
   // concatenate tensor along axis. Shape must be the same except in the dimension of the concatenated axis
   if (t1.GetMemoryLayout() != t2.GetMemoryLayout())
      throw std::runtime_error("TMVA RTensor Concatenate - tensors have different memory layout");
   auto & shape1 = t1.GetShape();
   auto & shape2 = t2.GetShape();
   if (t1.GetSize()/shape1[axis] != t2.GetSize()/shape2[axis]) {
      std::cout << "axis " << axis << " sizes " << t1.GetSize() << " " << t2.GetSize() << "  ";
      std::cout << "shape 1 : " << ConvertShapeToString(t1.GetShape());
      std::cout << " shape 2 : " << ConvertShapeToString(t2.GetShape()) << std::endl;
      throw std::runtime_error("TMVA RTensor Concatenate - tensors have incompatible shapes");
   }
   std::vector<size_t> outShape = shape1;
   outShape[axis] = shape1[axis] + shape2[axis];
   TMVA::Experimental::RTensor<T> tout(outShape, t1.GetMemoryLayout());
   if (t1.GetMemoryLayout() == TMVA::Experimental::MemoryLayout::ColumnMajor) {
      throw std::runtime_error("TMVA RTensor Concatenate is not yet supported for column major tensors");
   }
 
   auto & stride1 = t1.GetStrides();
   auto & stride2 = t2.GetStrides();
   auto & outStride = tout.GetStrides();
 
   size_t s1 = (axis > 0) ? stride1[axis-1] : t1.GetSize();  // block size to copy from first tensor
   size_t s2 = (axis > 0) ? stride2[axis-1] : t2.GetSize();  // block size to copy from second tensor
   size_t sout = (axis > 0) ? outStride[axis-1] : tout.GetSize();
   size_t nb = t1.GetSize()/s1;
   for (size_t i = 0; i < nb; i++) {
      std::copy(t1.GetData() + i*s1, t1.GetData() + (i+1)*s1, tout.GetData() + i * sout );
      std::copy(t2.GetData() + i*s2, t2.GetData() + (i+1)*s2, tout.GetData() + i * sout + s1 );
   }
 
   return tout;
}
 
 
inline GNN_Data Concatenate(GNN_Data & data1, GNN_Data & data2, int axis = 0) {
   GNN_Data out;
   out.node_data = Concatenate(data1.node_data,data2.node_data, axis);
   out.edge_data = Concatenate(data1.edge_data,data2.edge_data, axis);
   out.global_data = Concatenate<float>(data1.global_data,data2.global_data, axis-1);
   // assume sender/receivers of data1 and data2 are the same
   out.edge_index = data1.edge_index.Copy();
   return out;
}
 
inline GNN_Data Copy(const GNN_Data & data) {
   GNN_Data out;
   out.node_data = RTensor<float>(data.node_data.GetShape());
   out.edge_data = RTensor<float>(data.edge_data.GetShape());
   out.global_data = RTensor<float>(data.global_data.GetShape());
   out.edge_index = RTensor<int>(data.edge_index.GetShape());
   std::copy(data.node_data.GetData(), data.node_data.GetData()+ data.node_data.GetSize(), out.node_data.GetData());
   std::copy(data.edge_data.GetData(), data.edge_data.GetData()+ data.edge_data.GetSize(), out.edge_data.GetData());
   std::copy(data.global_data.GetData(), data.global_data.GetData()+ data.global_data.GetSize(), out.global_data.GetData());
   std::copy(data.edge_index.GetData(), data.edge_index.GetData()+ data.edge_index.GetSize(), out.edge_index.GetData());
   return out;
}
 
 
//Utility functions to generate code
void EmitNestedLoops(std::stringstream &out, size_t loopRank, const std::vector<Dim> shape);
void CloseNestedLoops(std::stringstream &out, size_t loopRank);
 
 
 
/// Source code of the inference helper functions to embed in generated code so
/// that it is standalone and does not need to include TMVA/SOFIE_common.hxx.
struct HelperFunctionsCode {
   std::string includes;    ///< #include directives to place in the header preamble
   std::string definitions; ///< function/type definitions to place inside the generated model namespace
   std::string cladDefinitions; ///< Clad custom-derivative definitions to place at file scope (outside the model
                                ///< namespace) so that Clad discovers them; empty when none are needed
};
 
/// Return the standalone C++ source of the inference helper functions requested
/// in `neededHelpers` (see RModel_Base::AddNeededHelperFunction), resolving
/// their inter-dependencies. Recognised keys are: "Im2col", "Im2col_3d",
/// "col2im", "UnidirectionalBroadcast", "BroadcastConvBias", "Gemm_Call",
/// "Relu", "Fill", "Copy", "ReadTensorFromStream", "InputTensorDims",
/// "DynamicMemory" and "GNN_Data".
///
/// `modelNamespace` (e.g. "TMVA_SOFIE_MyModel") is the generated model namespace;
/// the Clad pullbacks are emitted into clad::custom_derivatives::<modelNamespace>
/// so the model stays differentiable without SOFIE_common.hxx / CladDerivator.h.
///
/// `sgemmAlreadyDeclared`: set true if the caller already emitted the `extern "C"`
/// sgemm_ declaration (fNeededBlasRoutines block), so Gemm_Call skips its own and
/// avoids a duplicate. Default false emits it, keeping the returned code self-contained.
HelperFunctionsCode GenerateHelperFunctionsCode(const std::set<std::string> & neededHelpers,
                                                const std::string & modelNamespace,
                                                bool sgemmAlreadyDeclared = false);
 
 
} // namespace SOFIE
} // namespace Experimental
} // namespace TMVA
 
#endif //TMVA_SOFIE_COMMON