feat: SOFIE information in ROOT Manual (#874)

manual/tmva/index.md

@@ -140,3 +140,59 @@ You can, for example, plot input variable distributions.
    caption="Example plots for input variable distributions."
 %}
 
+## SOFIE
+
+SOFIE (System for Optimized Fast Inference code Emit) generates C++ functions easily invokable for the fast inference of trained neural network models. It takes ONNX model files as inputs and produces C++ header files that can be included and utilized in a “plug-and-go” style.
+
+This is a new development in TMVA and is currently in early experimental stage. Bug reports and suggestions for improvements are warmly welcomed.
+
+#### Prerequisite
+- Protobuf 3.0 or higher (for input of ONNX model files)
+- BLAS or Eigen (for execution of the generated code for inference)
+
+#### Installation
+Build ROOT with the cmake option tmva-sofie enabled.
+
+```
+cmake ../root -Dtmva-sofie=ON
+make -j8
+```
+
+#### Usage
+SOFIE works in a parser-generator working architecture. With SOFIE, the user gets an ONNX, Keras and a PyTorch parser for translating models in respective formats into SOFIE's internal representation.
+
+From ROOT command line, or in a ROOT macro, we can proceed with an ONNX model:
+```
+using namespace TMVA::Experimental;
+SOFIE::RModelParser_ONNX parser;
+SOFIE::RModel model = parser.Parse(“./example_model.onnx”);
+model.Generate();
+model.OutputGenerated(“./example_output.hxx”);
+```
+And an C++ header file and a .dat file containing the model weights will be generated. You can also use
+```
+model.PrintRequiredInputTensors();
+```
+to check the required size and type of input tensor for that particular model, and use
+```
+model.PrintInitializedTensors();
+```
+to check the tensors (weights) already included in the model.
+
+To use the generated inference code:
+```
+#include "example_output.hxx"
+float input[INPUT_SIZE];
+
+// Generated header file shall contain a Session class which requires initialization to load the corresponding weights.
+TMVA_SOFIE_example_model::Session s("example_model.dat")
+
+// Once instantiated the session object's infer method can be used
+std::vector<float> out = s.infer(input);
+```
+With the default settings, the weights are contained in a separate binary file, but if the user instead wants them to be in the generated header file itself, they can use approproiate generation options.
+```
+model.Generate(Options::kNoWeightFile);
+```
+Other such options includes Options::kNoSession (for not generating the Session class, and instead keeping the infer function independent).
+SOFIE also supports generating inference code with RDataFrame as inputs, refer to the tutorials for examples.