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Merged
guitargeek merged 1 commit into from
Mar 10, 2026
Merged

[tmva][sofie] Remove ConvertShapeToString and ConvertDynamicShapeToLength #21509

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8 changes: 4 additions & 4 deletions tmva/sofie/inc/TMVA/ROperator_BasicBinary.hxx
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Expand Up @@ -127,10 +127,10 @@ public:
}
if (dynamicInputs & 1 && model.Verbose())
std::cout << BinaryOperatorTrait<T, Op>::Name() << " : input " << fNA << " is dynamic "
<< ConvertShapeToString(fDimShapeA) << std::endl;
<< ConvertDimShapeToString(fDimShapeA) << std::endl;
if (dynamicInputs & 2 && model.Verbose())
std::cout << BinaryOperatorTrait<T, Op>::Name() << " : input " << fNB << " is dynamic "
<< ConvertShapeToString(fDimShapeB) << std::endl;
<< ConvertDimShapeToString(fDimShapeB) << std::endl;

// check if need to broadcast at initialization time if shapes are known and different
// (we could broadcast the tensor tensor to maximum values of dynamic shapes - to be done)
Expand Down Expand Up @@ -259,8 +259,8 @@ public:

model.AddIntermediateTensor(fNY, model.GetTensorType(fNA), fDimShapeY);
if (model.Verbose()) {
std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << ConvertShapeToString(fDimShapeA) << " , "
<< ConvertShapeToString(fDimShapeB) << " --> " << ConvertShapeToString(fDimShapeY) << std::endl;
std::cout << BinaryOperatorTrait<T, Op>::Name() << " : " << ConvertDimShapeToString(fDimShapeA) << " , "
<< ConvertDimShapeToString(fDimShapeB) << " --> " << ConvertDimShapeToString(fDimShapeY) << std::endl;
}
}
}
Expand Down
6 changes: 3 additions & 3 deletions tmva/sofie/inc/TMVA/ROperator_BasicNary.hxx
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Expand Up @@ -194,9 +194,9 @@ public:
if (model.Verbose()) {
std::cout << NaryOperatorTraits<T, Op>::Name() << " : ";
if (fNInputs.size() == 2)
std::cout << ConvertShapeToString(fShapeInputs[0]) << " , "
<< ConvertShapeToString(fShapeInputs[1]);
std::cout << " --> " << ConvertShapeToString(fDimShapeY) << std::endl;
std::cout << ConvertDimShapeToString(fShapeInputs[0]) << " , "
<< ConvertDimShapeToString(fShapeInputs[1]);
std::cout << " --> " << ConvertDimShapeToString(fDimShapeY) << std::endl;
}
}

Expand Down
2 changes: 1 addition & 1 deletion tmva/sofie/inc/TMVA/ROperator_BatchNormalization.hxx
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Expand Up @@ -111,7 +111,7 @@ public:

if (fShapeX.size() < 2 || fShapeX.size() > 4) {
throw
std::runtime_error("TMVA SOFIE BatchNormalization Op input tensor " + fNX + " fnx has wrong shape : " + ConvertShapeToString(fShapeX));
std::runtime_error("TMVA SOFIE BatchNormalization Op input tensor " + fNX + " fnx has wrong shape : " + ConvertDimShapeToString(fShapeX));
}

fShapeY = fShapeX;
Expand Down
6 changes: 3 additions & 3 deletions tmva/sofie/inc/TMVA/ROperator_Comparision.hxx
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Expand Up @@ -276,9 +276,9 @@ public:

model.AddIntermediateTensor(fNY, ETensorType::BOOL, fDimShapeY);
if (model.Verbose()) {
std::cout << ComparisionTrait<T, Op>::Name() << " : " << fNX1 << " " << ConvertShapeToString(fDimShapeX1) << " , "
<< fNX2 << " " << ConvertShapeToString(fDimShapeX2) << " --> "
<< fNY << " " << ConvertShapeToString(fDimShapeY) << std::endl;
std::cout << ComparisionTrait<T, Op>::Name() << " : " << fNX1 << " " << ConvertDimShapeToString(fDimShapeX1) << " , "
<< fNX2 << " " << ConvertDimShapeToString(fDimShapeX2) << " --> "
<< fNY << " " << ConvertDimShapeToString(fDimShapeY) << std::endl;
model.PrintIntermediateTensors();
}
}
Expand Down
10 changes: 5 additions & 5 deletions tmva/sofie/inc/TMVA/ROperator_Concat.hxx
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Expand Up @@ -115,7 +115,7 @@
for (size_t i = 0; i < inputs.size(); i++) {
if (i > 0 && inputs[i].size() != inputs[i - 1].size())
throw std::runtime_error("TMVA SOFIE Concat Op - input tensors have different shapes " + fInputs[i] + " : " +
ConvertShapeToString(inputs[i]) + " and " + fInputs[i-1] + " : " + ConvertShapeToString(inputs[i - 1]));
ConvertDimShapeToString(inputs[i]) + " and " + fInputs[i-1] + " : " + ConvertDimShapeToString(inputs[i - 1]));
for (size_t iaxis = 0; iaxis < inputs[i].size(); iaxis++) {
if ((int)iaxis == fAxis) {
// support both integer and params shape for the concatenation axis
Expand All @@ -134,8 +134,8 @@
}
else if ((!inputs[i][iaxis].isParam && !ret[iaxis].isParam) && (inputs[i][iaxis].dim != ret[iaxis].dim)) {
throw std::runtime_error("TMVA SOFIE Concat Op - input tensors have wrong shapes " +
ConvertShapeToString(inputs[i]) + " and " +
ConvertShapeToString(inputs[i - 1]));
ConvertDimShapeToString(inputs[i]) + " and " +
ConvertDimShapeToString(inputs[i - 1]));
}
else if (!inputs[i][iaxis].isParam && ret[iaxis].isParam){
// if shape is not parametric use it
Expand Down Expand Up @@ -240,7 +240,7 @@
model.AddShapeTensor(fOutput,outputData, false); // cannot be a scalar
if (model.Verbose()) {
std::cout << "output of Concat is a shape tensor " << ConvertShapeToString(outputShape) << " : "
<< ConvertShapeToString(outputData) << " (shape)" << std::endl;
<< ConvertDimShapeToString(outputData) << " (shape)" << std::endl;
}
fIsOutputConstant = true;
}
Expand All @@ -256,7 +256,7 @@
std::string Generate(std::string opName) override {
opName = "op_" + opName;
std::stringstream out;
out<<"\n//--------- Concat " << opName << " --> " << fOutput << " " << ConvertShapeToString(fOutputShape) << "\n";
out<<"\n//--------- Concat " << opName << " --> " << fOutput << " " << ConvertDimShapeToString(fOutputShape) << "\n";

if (fIsOutputConstant) return out.str();

Expand Down
6 changes: 3 additions & 3 deletions tmva/sofie/inc/TMVA/ROperator_Constant.hxx
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Expand Up @@ -141,15 +141,15 @@ public:
std::stringstream out;
if (fIsOutputConstant) {
if (fNX.empty())
out << "// ---- Constant (no-op) " << opName << " --> " << fNY << " " << ConvertShapeToString(fDimOutputShape) << "\n";
out << "// ---- Constant (no-op) " << opName << " --> " << fNY << " " << ConvertDimShapeToString(fDimOutputShape) << "\n";
else
out << "// ---- ConstantOfShape (no-op) " << opName << " --> " << fNY << " " << ConvertShapeToString(fDimOutputShape) << "\n";
out << "// ---- ConstantOfShape (no-op) " << opName << " --> " << fNY << " " << ConvertDimShapeToString(fDimOutputShape) << "\n";
return out.str();
}
// Only ConstantOfShape might require generation code
// generate constant tensor according to input

out << "\n//--------- ConstantOfShape " << opName << " --> " << ConvertShapeToString(fDimOutputShape) << "\n";
out << "\n//--------- ConstantOfShape " << opName << " --> " << ConvertDimShapeToString(fDimOutputShape) << "\n";
// set shape values if needed
if (fIsUndefinedInputShape) {
for (size_t i = 0; i < fDimOutputShape.size(); i++) {
Expand Down
8 changes: 4 additions & 4 deletions tmva/sofie/inc/TMVA/ROperator_Conv.hxx
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Expand Up @@ -241,7 +241,7 @@ public:
}
fShapeX = model.GetDimTensorShape(fNX);
if (fShapeX.size() < 3 || fShapeX.size() > 5) {
std::cout << fNX << " : " << ConvertShapeToString(fShapeX) << std::endl;
std::cout << fNX << " : " << ConvertDimShapeToString(fShapeX) << std::endl;
throw
std::runtime_error("TMVA SOFIE Conv Op input data tensor" + fNX + " is not of 3,4 or 5 dimensions");
}
Expand Down Expand Up @@ -325,8 +325,8 @@ public:
fInputTensorNames.emplace_back(imcol);

if (model.Verbose()) {
std::cout << "Conv - " << fDim << " " << fNX << " : " << ConvertShapeToString(fShapeX)
<< " --> " << fNY << " : " << ConvertShapeToString(fShapeY) << std::endl;
std::cout << "Conv - " << fDim << " " << fNX << " : " << ConvertDimShapeToString(fShapeX)
<< " --> " << fNY << " : " << ConvertDimShapeToString(fShapeY) << std::endl;
}
}

Expand All @@ -348,7 +348,7 @@ public:
else
out << SP << "{\n";
out << SP << SP << "float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast(tensor_"
<< fNB << ", " << ConvertShapeToString(shape) << ", " << ConvertShapeToString(fShapeY) << ");\n";
<< fNB << ", " << ConvertShapeToString(shape) << ", " << ConvertDimShapeToString(fShapeY) << ");\n";
out << SP << SP << "fTensor_" << fNB << ".resize(" << length << ");\n";
out << SP << SP << "std::copy(data, data + " << length << ", fTensor_" << fNB << ".begin());\n";
out << SP << SP << "tensor_" << fNB << " = fTensor_" << fNB << ".data();\n";
Expand Down
8 changes: 4 additions & 4 deletions tmva/sofie/inc/TMVA/ROperator_Expand.hxx
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Expand Up @@ -120,8 +120,8 @@ public:
}
fType = ConvertTypeToString(model.GetTensorType(fNX));
if (model.Verbose()) {
std::cout << "Expand - input " << fNX << " shape " << ConvertShapeToString(fShapeX) << " --> " << fNY << " shape "
<< ConvertShapeToString(fShapeY) << (fIsOutputConstant ? ConvertValuesToString(model.GetTensorData<T>(fNY)) + " (constant)" : "") << std::endl;
std::cout << "Expand - input " << fNX << " shape " << ConvertDimShapeToString(fShapeX) << " --> " << fNY << " shape "
<< ConvertDimShapeToString(fShapeY) << (fIsOutputConstant ? ConvertValuesToString(model.GetTensorData<T>(fNY)) + " (constant)" : "") << std::endl;
}
}

Expand All @@ -143,7 +143,7 @@ public:
throw std::runtime_error("TMVA SOFIE Expand Op called to Generate without being initialized first");
}
std::stringstream out;
out << SP << "\n//------ Expand " << opName << " --> " << ConvertShapeToString(fShapeY) << "\n";
out << SP << "\n//------ Expand " << opName << " --> " << ConvertDimShapeToString(fShapeY) << "\n";
// need to declare shape parameters for non initialized shapes
if (!fInitializedShape) {
for (size_t i = 0; i < fShapeDim.size(); i++) {
Expand All @@ -153,7 +153,7 @@ public:
// No need to broadcast A if it's an initialized tensor or shapes are the same
if (!fInitialized && fShapeX != fShapeY) {
out << SP << "// Broadcasting uninitialized tensor " << fNX << "\n";
out << SP << "TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast(tensor_" << fNX << ", " << ConvertShapeToString(fShapeX) << ", " << ConvertShapeToString(fShapeY)
out << SP << "TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast(tensor_" << fNX << ", " << ConvertDimShapeToString(fShapeX) << ", " << ConvertDimShapeToString(fShapeY)
<< ", tensor_"<<fNY<<");\n";
}
return out.str();
Expand Down
18 changes: 9 additions & 9 deletions tmva/sofie/inc/TMVA/ROperator_Gather.hxx
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Expand Up @@ -54,8 +54,8 @@ public:
}
fShapeX = model.GetDimTensorShape(fNX);
if (model.Verbose())
std::cout << "Gather - initial shape " << ConvertShapeToString(fShapeX) << " shape of indices "
<< ConvertShapeToString(model.GetDimTensorShape(fNIndices)) << std::endl;
std::cout << "Gather - initial shape " << ConvertDimShapeToString(fShapeX) << " shape of indices "
<< ConvertDimShapeToString(model.GetDimTensorShape(fNIndices)) << std::endl;
// fShapeIndices can be dynamic
fShapeIndices = model.GetDimTensorShape(fNIndices);
size_t q = fShapeIndices.size();
Expand Down Expand Up @@ -86,7 +86,7 @@ public:
}
// Output shape
if (model.Verbose())
std::cout << "Gather: q and r " << q << " " << r << " shape indices " << ConvertShapeToString(fShapeIndices) << std::endl;
std::cout << "Gather: q and r " << q << " " << r << " shape indices " << ConvertDimShapeToString(fShapeIndices) << std::endl;

if (fShapeY.empty()) {
fShapeY.resize(q + r - 1);
Expand Down Expand Up @@ -128,15 +128,15 @@ public:
// shapeY can be scalar or vector of size1
model.AddShapeTensor(fNY, outputData, fShapeY.size() == 0);
if (model.Verbose())
std::cout << "Gather: " << fNX << " " << ConvertShapeToString(fShapeX) << " -> " << fNY << " with shape " << ConvertShapeToString(fShapeY)
<< " and values " << ConvertShapeToString(outputData) << " (shape) " << std::endl;
std::cout << "Gather: " << fNX << " " << ConvertDimShapeToString(fShapeX) << " -> " << fNY << " with shape " << ConvertDimShapeToString(fShapeY)
<< " and values " << ConvertDimShapeToString(outputData) << " (shape) " << std::endl;
} else {
int64_t value = static_cast<int64_t>(outputData[0].dim);
auto shapeY = ConvertShapeToInt(fShapeY);
model.AddConstantTensor(fNY, shapeY, &value);
fIsOutputConstant = true;
if (model.Verbose())
std::cout << "Gather: " << fNX << " " << ConvertShapeToString(fShapeX) << " -> " << fNY << " with shape " << ConvertShapeToString(fShapeY)
std::cout << "Gather: " << fNX << " " << ConvertDimShapeToString(fShapeX) << " -> " << fNY << " with shape " << ConvertDimShapeToString(fShapeY)
<< " and values {" << value << "} (constant) " << std::endl;
}
}
Expand All @@ -145,15 +145,15 @@ public:
model.AddIntermediateTensor(fNY, model.GetTensorType(fNX), fShapeY);
fType = ConvertTypeToString(model.GetTensorType(fNX));
if (model.Verbose())
std::cout << "Gather: input " << fNX << " " << ConvertShapeToString(fShapeX) << " indices " << fNIndices << ConvertShapeToString(fShapeIndices)
<< " -> " << fNY << " with shape " << ConvertShapeToString(fShapeY) << std::endl;
std::cout << "Gather: input " << fNX << " " << ConvertDimShapeToString(fShapeX) << " indices " << fNIndices << ConvertDimShapeToString(fShapeIndices)
<< " -> " << fNY << " with shape " << ConvertDimShapeToString(fShapeY) << std::endl;
}
}

std::string Generate(std::string opName) override {
opName = "op_" + opName;
std::stringstream out;
out << "//--------- Gather " << opName << " --> " << fNY << " " << ConvertShapeToString(fShapeY) << "\n";
out << "//--------- Gather " << opName << " --> " << fNY << " " << ConvertDimShapeToString(fShapeY) << "\n";
if (fIsOutputConstant) {
// no code to generate here for constant output. Tensor output is defined in Session constructor
out << "//--------------------(constant)----------\n";
Expand Down
20 changes: 10 additions & 10 deletions tmva/sofie/inc/TMVA/ROperator_GatherND.hxx
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Expand Up @@ -44,8 +44,8 @@ public:
}
fShapeX = model.GetDimTensorShape(fNX);
if (model.Verbose())
std::cout << "GatherND - initial shape " << ConvertShapeToString(fShapeX) << " shape of indices "
<< ConvertShapeToString(model.GetDimTensorShape(fNIndices)) << std::endl;
std::cout << "GatherND - initial shape " << ConvertDimShapeToString(fShapeX) << " shape of indices "
<< ConvertDimShapeToString(model.GetDimTensorShape(fNIndices)) << std::endl;
// fShapeIndices can be dynamic
fShapeIndices = model.GetDimTensorShape(fNIndices);
size_t q = fShapeIndices.size();
Expand All @@ -64,8 +64,8 @@ public:
if (fBatchDims > 0) {
for (size_t i = 0; i < fBatchDims; i++) {
if (fShapeX[i] != fShapeIndices[i]) {
std::cout << " input shape " << ConvertShapeToString(fShapeX) << " "
<< " index shape " << ConvertShapeToString(fShapeIndices) << std::endl;
std::cout << " input shape " << ConvertDimShapeToString(fShapeX) << " "
<< " index shape " << ConvertDimShapeToString(fShapeIndices) << std::endl;
throw std::runtime_error("TMVA SOFIE GatherND : invalid input or index shape for " + std::to_string(i));
}
}
Expand All @@ -89,9 +89,9 @@ public:
fShapeY = std::vector<Dim>(fShapeIndices.begin(), fShapeIndices.end() - 1);
fShapeY.insert(fShapeY.end(), fShapeX.begin() + fBatchDims + last_index_shape, fShapeX.end());
if (fShapeY.size() != output_rank) {
std::cout << " input shape " << ConvertShapeToString(fShapeX) << " "
<< " index shape " << ConvertShapeToString(fShapeIndices)
<< " output shape " << ConvertShapeToString(fShapeY)
std::cout << " input shape " << ConvertDimShapeToString(fShapeX) << " "
<< " index shape " << ConvertDimShapeToString(fShapeIndices)
<< " output shape " << ConvertDimShapeToString(fShapeY)
<< " and output rank should be " << output_rank << std::endl;
throw std::runtime_error("TMVA SOFIE GatherND : Something is wrong in initialization ");
}
Expand All @@ -101,8 +101,8 @@ public:
model.AddIntermediateTensor(fNY, model.GetTensorType(fNX), fShapeY);
fType = ConvertTypeToString(model.GetTensorType(fNX));
if (model.Verbose())
std::cout << "GatherND: input " << fNX << " " << ConvertShapeToString(fShapeX) << " indices " << fNIndices << ConvertShapeToString(fShapeIndices)
<< " -> " << fNY << " with shape " << ConvertShapeToString(fShapeY) << std::endl;
std::cout << "GatherND: input " << fNX << " " << ConvertDimShapeToString(fShapeX) << " indices " << fNIndices << ConvertDimShapeToString(fShapeIndices)
<< " -> " << fNY << " with shape " << ConvertDimShapeToString(fShapeY) << std::endl;
}


Expand Down Expand Up @@ -176,7 +176,7 @@ public:
}
opName = "op_" + opName;
std::stringstream out;
out << "//--------- GatherND " << opName << " --> " << ConvertShapeToString(fShapeY) << "\n";
out << "//--------- GatherND " << opName << " --> " << ConvertDimShapeToString(fShapeY) << "\n";
// The shape of the output is q + r - 1
size_t r = fShapeX.size();
// Indices of shape q
Expand Down
6 changes: 3 additions & 3 deletions tmva/sofie/inc/TMVA/ROperator_Gemm.hxx
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Expand Up @@ -264,7 +264,7 @@ namespace SOFIE{
if (model.Verbose()){
std::cout << "Gemm (or MatMul) " << " ---> " << fNY << " shape ";
if (fIsDynamic)
std::cout << ConvertShapeToString(fShapeY) << std::endl;
std::cout << ConvertDimShapeToString(fShapeY) << std::endl;
else
std::cout << ConvertShapeToString(shapeY) << std::endl;
}
Expand All @@ -279,8 +279,8 @@ namespace SOFIE{
throw std::runtime_error("TMVA SOFIE Gemm Op called to Generate without being initialized first");
}
std::stringstream out;
out << "\n//--------- Gemm " << opName << " " << ConvertShapeToString(fShapeA) << " * " << ConvertShapeToString(fShapeB)
<< " -> " << ConvertShapeToString(fShapeY) << "\n";
out << "\n//--------- Gemm " << opName << " " << ConvertDimShapeToString(fShapeA) << " * " << ConvertDimShapeToString(fShapeB)
<< " -> " << ConvertDimShapeToString(fShapeY) << "\n";
// need to consider case A and B have dim > 2 (for MatMul)
int64_t dimA = fShapeA.size();
int64_t dimB = fShapeB.size();
Expand Down
2 changes: 1 addition & 1 deletion tmva/sofie/inc/TMVA/ROperator_LayerNormalization.hxx
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Expand Up @@ -178,7 +178,7 @@ public:
out << SP << "// Broadcasting the bias of LayerNormalization op\n";
out << SP << "{\n";
out << SP << SP << "float* data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast(tensor_";
out << fNB << ", " << ConvertShapeToString(fShapeB) << ", " << ConvertShapeToString(fShapeX) << ");\n";
out << fNB << ", " << ConvertDimShapeToString(fShapeB) << ", " << ConvertDimShapeToString(fShapeX) << ");\n";
out << SP << "std::copy(data, data + " << fLength << ", tensor_" << fNBroadcastedB << ");\n";
out << SP << "delete[] data;\n";
out << SP << "}\n";
Expand Down
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