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[tmva][sofie] Several Fixes for parsing complex models #22150

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[tmva][sofie] Several Fixes for parsing complex models #22150

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bf80eb7
[tmva][sofie] Fixes for parsing mlpf model
lmoneta May 5, 2026
b94d6ab
[tmva][sofie] Fix a bug in Cast operator in case of casting to bool
lmoneta May 7, 2026
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6 changes: 5 additions & 1 deletion tmva/sofie/inc/TMVA/RModel.hxx
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Expand Up @@ -45,6 +45,8 @@ private:
MemoryPoolInfo fIntermediateMemoryInfo; ///<! intermediate memory info (transient)
std::unordered_map<std::string_view, size_t> fIntermediateTensorFrequencyLookup; ///<! lookup table for intermediate tensor frequency (transient)

std::string fExtraCodeForDimShapes; // extra code needed for initialization of dynamic parameters (e.g. number of non zero elements in NonZero operator)
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@guitargeek guitargeek May 5, 2026

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We can't add new non-transient class members without updating the class version

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@lmoneta lmoneta May 7, 2026

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Now version 0 is used to flag that RModel is not made persistent


public:
/**
Default constructor. Needed to allow serialization of ROOT objects. See
Expand Down Expand Up @@ -108,6 +110,7 @@ public:

void AddShapeTensor(const std::string & name, const std::vector<Dim> & shapeValues, bool scalar = false);

void AddExtraCodeForDimShapes(const std::string & code) { fExtraCodeForDimShapes += code; }

// add and initialize subgraph to the model
void InitializeSubGraph(std::shared_ptr<RModel> graph);
Expand Down Expand Up @@ -239,7 +242,8 @@ public:
bool UseVDT() const { return fUseVDT;}

// Use the ClassDef macro to allow definition of custom streaming
ClassDefNV(RModel, 3);
// Use Version 0 since we don't support for time being ROOT I/O streaming of RModel objects
ClassDefNV(RModel, 4);
};

// need to implement here templated member functions and its specialization
Expand Down
11 changes: 8 additions & 3 deletions tmva/sofie/inc/TMVA/ROperator_Cast.hxx
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Expand Up @@ -66,8 +66,9 @@ public:
if (!fIsOutputConstant)
model.AddIntermediateTensor(fNY, fType, fShape);
if (model.Verbose()) {
std::cout << "Cast : " << ConvertTypeToString(inputType) << " " << fNX << " -> " << ConvertTypeToString(fType) << " for " << fNY
<< " shape " << ConvertDimShapeToString(fShape);
std::cout << "Cast : " << ConvertTypeToString(inputType) << " " << fNX << " -> " << ConvertTypeToString(fType);
if (fType == ETensorType::BOOL) std::cout << " (converted from BOOL) ";
std::cout << " for " << fNY << " shape " << ConvertDimShapeToString(fShape);
if (fIsOutputConstant) std::cout << " (constant) ";
std::cout << std::endl;
}
Expand All @@ -87,7 +88,11 @@ public:

out << SP << "for (int id = 0; id < " << length << " ; id++){\n";

out << SP << SP << "tensor_" << fNY << "[id] = static_cast<"<< ConvertTypeToString(fType) << ">(tensor_" << fNX << "[id]);\n";
// need to handle bool case separatly since casting to uint8 will not give right result
if (fType == ETensorType::BOOL)
out << SP << SP << "tensor_" << fNY << "[id] = (tensor_" << fNX << "[id] != 0) ? 1 : 0;\n";
else
out << SP << SP << "tensor_" << fNY << "[id] = static_cast<"<< ConvertTypeToString(fType) << ">(tensor_" << fNX << "[id]);\n";

out << SP << "}\n";
return out.str();
Expand Down
20 changes: 14 additions & 6 deletions tmva/sofie/inc/TMVA/ROperator_NonZero.hxx
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Expand Up @@ -19,6 +19,7 @@ private:

std::string fNX;
std::string fNY;
std::string fNonZeroParam; // name of the parameter used to store the number of non zero elements when output is not constant
std::vector<Dim> fShapeX;
std::vector<Dim> fShapeY;

Expand Down Expand Up @@ -93,7 +94,15 @@ public:
fShapeY[0] = fShapeX.size();

// identify as -1 since we will declare maximum as size of input
fShapeY[1] = Dim{std::string("v_NonZero_") + fNX, static_cast<size_t>(-1)};
// we will compute at run time the actual number of non zero and rearrange the output vector accordingly
fNonZeroParam = "v_NonZero_" + fNX;
fShapeY[1] = Dim{fNonZeroParam, static_cast<size_t>(-1)};

// declare the parameter for number of non zero elements, used when output is not constant
auto inputLength = ConvertDimShapeToLength(fShapeX);
std::string codeDecl = SP + "size_t " + fNonZeroParam + " = " + inputLength + ";\n";
codeDecl += SP + "fV_NonZero_" + fNX + " = " + fNonZeroParam + ";\n";
model.AddExtraCodeForDimShapes(codeDecl);

model.AddIntermediateTensor(fNY, ETensorType::INT64, fShapeY);
if (model.Verbose()) {
Expand All @@ -104,13 +113,12 @@ public:

std::string GenerateSessionMembersCode(std::string /*opName*/) override {
if (fIsOutputConstant) return "";
// define output value used as max non zero with max size = input shape * N
auto inputLength = ConvertDimShapeToLength(fShapeX);
std::stringstream out;
out << SP << "size_t fV_NonZero_" << fNX << " = " << inputLength << ";\n";
out << SP << "size_t fV_NonZero_" << fNX << " = 0;\n";
return out.str();
}


std::string Generate(std::string opName) override {
if (fIsOutputConstant) {
return "";
Expand All @@ -127,9 +135,9 @@ public:
inputLength = ConvertShapeToLength(intShapeX);

size_t dims = fShapeX.size();
out << "\n//------ NonZero\n";
out << "\n//------ NonZero -> " << ConvertDimShapeToString(fShapeY) << "\n";

std::string vnonzero = "v_NonZero_" + fNX;
std::string vnonzero = fNonZeroParam;

// loop on input indices
out << SP << "size_t offset_" << opName << " = 0;\n";
Expand Down
14 changes: 9 additions & 5 deletions tmva/sofie/inc/TMVA/ROperator_Softmax.hxx
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Expand Up @@ -62,12 +62,14 @@ public:
}
}

std::string Generate(std::string OpName) override {
OpName = "op_" + OpName;
std::string Generate(std::string opName) override {
opName = "op_" + opName;
if (fShape.empty()) {
throw std::runtime_error("TMVA SOFIE Operator Softmax called to Generate without being initialized first");
}
std::stringstream out;
out << "///------- Softmax " << opName << " ---> " << fNY << " "
<< ConvertDimShapeToString(fShape) << "\n" << std::endl;
size_t size = fShape.size();
auto length_str = ConvertDimShapeToLength(fShape);
size_t axis = fAttrAxis < 0 ? size + fAttrAxis : fAttrAxis;
Expand All @@ -85,7 +87,7 @@ public:
num_rows = "(" + length_str + ") / (" + axis_size + ")";
}

out << "\n" << SP << "//------ SOFTMAX - " << size << " " << length_str << " " << axis << "\n";
out << SP << "//----- softmax axis is last one - " << axis << "\n";
out << SP << "for (int i = 0; i < " << num_rows << "; ++i) {\n";
out << SP << SP << "size_t offset = i * " << axis_size << ";\n";
out << SP << SP << fType << " const * x_ptr = &tensor_" << fNX << "[offset];\n";
Expand All @@ -111,14 +113,15 @@ public:
out << SP << "}\n";

} else {
// generic case for any axis
auto stride = UTILITY::ComputeStrideFromShape(fShape);
size_t k = 0;
std::vector<std::string> l(size);
for (size_t i = 0; i < size; i++) {
if (i != axis) {
for (size_t j = 0; j < k; j++) out << SP;
l[i] = std::string("i") + std::to_string(i);
out << "for (int " << l[i] << " = 0; " << l[i] << " < " << fShape[i] << "; " << l[i] << "++) {\n";
out << SP << "for (int " << l[i] << " = 0; " << l[i] << " < " << fShape[i] << "; " << l[i] << "++) {\n";
k++;
}
}
Expand Down Expand Up @@ -167,7 +170,8 @@ public:
out << "for (int i = 0; i < " << fShape[axis] << "; i++) {\n";
for (size_t j = 0; j < size; j++) out << SP;
out << "size_t id = index + i";
if (stride[axis].GetVal() != "1") out << "*(" << stride[axis] << ");\n";
if (stride[axis].GetVal() != "1") out << "*(" << stride[axis] << ")";
out << ";\n";
for (size_t j = 0; j < size; j++) out << SP;
out << "tensor_" << fNY << "[id] /= sum;\n";
if (fLogSoftmax) {
Expand Down
6 changes: 3 additions & 3 deletions tmva/sofie/inc/TMVA/SOFIE_common.hxx
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Expand Up @@ -231,8 +231,7 @@ std::string ConvertValuesToString(size_t n, const T * data, size_t maxprint = -1
if (std::is_floating_point_v<T>)
ret << std::setprecision(std::numeric_limits<T>::max_digits10) << data[i];
else
// cast in case of boolean (int8)
ret << data[i];
ret << std::to_string(data[i]);

if (i < n-1) ret << ", ";
if (i < n-1 && i == maxprint-1) ret << "..... ";
Expand Down Expand Up @@ -779,7 +778,8 @@ inline void Fill(float *output, float value, int size)
std::fill(output, output + size, value);
}

inline void Copy(float *output, float const *input, int size)
template <class T>
inline void Copy(T *output, T const *input, int size)
{
std::copy(input, input + size, output);
}
Expand Down
41 changes: 40 additions & 1 deletion tmva/sofie/src/RModel.cxx
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Expand Up @@ -1158,6 +1158,7 @@ void RModel::GenerateOutput()
// 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))
Expand Down Expand Up @@ -1304,7 +1305,7 @@ void RModel::GenerateSessionCode()

// storing the parameters for future checking to avoid mismatches
if (!fDimShapeNames.empty()) {
fGC += "\n\n";
fGC += "\n// dynamic shape parameters\n";
std::sort(fDimShapeNames.begin(), fDimShapeNames.end());
for (const auto &p : fDimShapeNames) {
fGC += "size_t " + memberNameForDimShape(p) + ";\n";
Expand Down Expand Up @@ -1361,6 +1362,8 @@ void RModel::GenerateSessionCode()
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";
Expand Down Expand Up @@ -1759,6 +1762,42 @@ void RModel::GenerateRequiredInputTensorInfo()

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 {
Expand Down
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