[tmva][sofie] Update in README list of new implemented operators

Fix also sone compilation wanrings and remove some commented code

tmva/sofie/README.md

@@ -82,6 +82,8 @@ Here is the updated list of supported ONNX operators
 - [x] ConstantOfShape
 - [x] Conv
 - [x] ConvTranspose
+- [x] Cos
+- [x] Einsum
 - [x] Elu
 - [x] Equal
 - [x] Erf
@@ -110,9 +112,14 @@ Here is the updated list of supported ONNX operators
 - [x] Min
 - [x] Mul
 - [x] Neg
+- [x] Pad
 - [x] Pool
 - [x] Pow
 - [x] Range
+- [x] RandomNormal
+- [x] RandomNormalLike
+- [x] RandomUniform
+- [x] RandomUniformLike
 - [x] Reciprocal
 - [x] ReduceMean
 - [x] ReduceProd
@@ -123,6 +130,7 @@ Here is the updated list of supported ONNX operators
 - [x] RNN
 - [x] Selu
 - [x] Sigmoid
+- [x] Sin
 - [x] Slice
 - [x] Softmax
 - [x] Split
@@ -133,6 +141,7 @@ Here is the updated list of supported ONNX operators
 - [x] TopK
 - [x] Transpose
 - [x] Unsqueeze
+- [x] Where
 
 The above operators are supported for tensors of the following types:
 

tmva/sofie/inc/TMVA/ROperator_BasicBinary.hxx

@@ -180,24 +180,17 @@ public:
       size_t length = ConvertShapeToLength(fShapeY);
       std::string typeName = TensorType<T>::Name();
       // Broadcast A if it's uninitialized
+      // use broadcasting function where we pass an already allocated tensor to minimize memory allocations
       if (fShapeA != fShapeY) {
          out << SP << "// Broadcasting uninitialized tensor " << fNA << "\n";
-         //out << SP << "{\n";
          out << SP  << "TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<" << typeName << ">(tensor_" << fNA << ", " << ConvertShapeToString(fShapeA) << ", " << ConvertShapeToString(fShapeY)
                          << ", fTensor_" << fNBroadcastedA << ");\n";
-         //out << SP << SP << "std::copy(data, data + " << length << ", tensor_" << fNBroadcastedA << ");\n";
-         //out << SP << SP << "delete[] data;\n";
-         //out << SP << "}\n";
       }
       // Broadcast B if it's uninitialized
       if (fShapeB != fShapeY) {
          out << SP << "// Broadcasting uninitialized tensor " << fNB << "\n";
-         //out << SP << "{\n";
          out << SP << "TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<" << typeName << ">(tensor_" << fNB << ", " << ConvertShapeToString(fShapeB) << ", " << ConvertShapeToString(fShapeY)
                    << ", fTensor_" << fNBroadcastedB << ");\n";
-         //out << SP << SP << "std::copy(data, data + " << length << ", tensor_" << fNBroadcastedB << ");\n";
-         //out << SP << SP << "delete[] data;\n";
-         //out << SP << "}\n";
       }
       const std::string& nameA = fNBroadcastedA.empty()? fNA : fNBroadcastedA;
       const std::string& nameB = fNBroadcastedB.empty()? fNB : fNBroadcastedB;

tmva/sofie/inc/TMVA/ROperator_Einsum.hxx

@@ -6,6 +6,7 @@
 #include "TMVA/RModel.hxx"
 
 #include <sstream>
+#include <cassert>
 
 namespace TMVA{
 namespace Experimental{
@@ -230,8 +231,8 @@ public:
       if (fGemmType.empty()) {
       int outDims = fShapeY.size();
       int inDims = fSumLabels.length();
-      assert(outDims == fOutputLabels.size());
-      assert(inDims == fSumDims.size());
+      assert(outDims == int(fOutputLabels.size()));
+      assert(inDims == int(fSumDims.size()));
       for (int i = 0; i < outDims; i++) {
          for (int j = 0; j < i; j++) out << SP;
          std::string l {fOutputLabels[i]};

tmva/sofie/inc/TMVA/ROperator_Gemm.hxx

@@ -182,9 +182,6 @@ namespace SOFIE{
             }
          }
 
-            //   throw std::runtime_error("TMVA SOFIE Gemm Op Input Tensors have not compatible shapes. A " +
-            //    ConvertDynamicShapeToString(fShapeA) + " B " + ConvertDynamicShapeToString(fShapeB) );
-
          fShapeY = DynamicShapeInference({fShapeA, fShapeB})[0];
          std::vector<size_t> shapeY;
          if (!fIsDynamic) {

tmva/sofie/inc/TMVA/ROperator_Split.hxx

@@ -108,20 +108,15 @@ public:
          throw std::runtime_error("TMVA SOFIE Operator Split called to Generate without being initialized first");
       }
 
-      // compute input and output strides
       auto input_strides =  UTILITY::ComputeStrideFromShape(fInputShape);
-      std::vector<std::vector<size_t>> output_strides;
-      for (size_t i = 0; i < fOutputShapes.size(); i++) {
-         output_strides.emplace_back( UTILITY::ComputeStrideFromShape(fOutputShapes[i]));
-      }
 
       // generate now the code for split
       std::stringstream out;
       out << "\n" << SP << "//------ Split\n";
       out << SP << "size_t " << OpName << "_axis_offset = 0;\n";
       // unroll the loop on split outputs
       for (size_t i = 0; i < fNYs.size(); i++)  {
-         int length = ConvertShapeToLength(fOutputShapes[i]);
+         size_t length = ConvertShapeToLength(fOutputShapes[i]);
          auto output_strides = UTILITY::ComputeStrideFromShape(fOutputShapes[i]);
 
          out << SP << "for (int id = 0; id < " << length << " ; id++){\n";

tmva/sofie/inc/TMVA/SOFIE_common.hxx

@@ -157,7 +157,7 @@ public:
    bool IsConstantTensor() const { return fConstant;}
    // query if tensor needs to be written in a weight file. Constant tensors are not written in a file
    bool IsWeightTensor() const { return !fConstant && !fIsNotWritable;}
-
+   // set not writable initialized tensors - i.e. tensor that must not be written in a file
    void SetNotWritable() { fIsNotWritable = true;}
 
    template <class T = void>