TMVA_SOFIE_RDataFrame.py File Reference

Detailed Description

Example of inference with SOFIE and RDataFrame, of a model trained with Keras.

First, generate the input model by running TMVA_Higgs_Classification.C.

This tutorial parses the input model and runs the inference using ROOT's JITing capability.

 
import ROOT
from os.path import exists
 
ROOT.TMVA.PyMethodBase.PyInitialize()
 
 
# check if the input file exists
modelFile = "Higgs_trained_model.h5"
 
if not exists(modelFile):
    raise FileNotFoundError("You need to run TMVA_Higgs_Classification.C to generate the Keras trained model")
 
# parse the input Keras model into RModel object
model = ROOT.TMVA.Experimental.SOFIE.PyKeras.Parse(modelFile)
 
# generating inference code
model.Generate()
model.OutputGenerated("Higgs_trained_model.hxx")
model.PrintGenerated()
 
# compile using ROOT JIT trained model
print("compiling SOFIE model and functor....")
ROOT.gInterpreter.Declare('#include "Higgs_trained_model.hxx"')
modelName = "Higgs_trained_model"
ROOT.gInterpreter.Declare('auto sofie_functor = TMVA::Experimental::SofieFunctor<7,TMVA_SOFIE_'+modelName+'::Session>(0);')
 
# run inference over input data
inputFile = "http://root.cern/files/Higgs_data.root"
df1 = ROOT.RDataFrame("sig_tree", inputFile)
h1 = df1.Define("DNN_Value", "sofie_functor(rdfslot_,m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)").Histo1D(("h_sig", "", 100, 0, 1),"DNN_Value")
 
df2 = ROOT.RDataFrame("bkg_tree", inputFile)
h2 = df2.Define("DNN_Value", "sofie_functor(rdfslot_,m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)").Histo1D(("h_bkg", "", 100, 0, 1),"DNN_Value")
 
# run over the input data once, combining both RDataFrame graphs.
ROOT.RDF.RunGraphs([h1, h2]);
 
print("Number of signal entries",h1.GetEntries())
print("Number of background entries",h2.GetEntries())
 
h1.SetLineColor(ROOT.kRed)
h2.SetLineColor(ROOT.kBlue)
 
c1 = ROOT.TCanvas()
ROOT.gStyle.SetOptStat(0)
 
h2.DrawClone()
h1.DrawClone("SAME")

Model has not a defined batch size, assume is 1 - input shape for tensor dense_input : { 1 , 7 }
//Code generated automatically by TMVA for Inference of Model file [Higgs_trained_model.h5] at [Wed Apr 17 10:22:06 2024] 
 
#ifndef ROOT_TMVA_SOFIE_HIGGS_TRAINED_MODEL
#define ROOT_TMVA_SOFIE_HIGGS_TRAINED_MODEL
 
#include <algorithm>
#include <vector>
#include <cmath>
#include "TMVA/SOFIE_common.hxx"
#include <fstream>
 
namespace TMVA_SOFIE_Higgs_trained_model{
namespace BLAS{
   extern "C" void sgemv_(const char * trans, const int * m, const int * n, const float * alpha, const float * A,
                          const int * lda, const float * X, const int * incx, const float * beta, const float * Y, const int * incy);
   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 Session {
std::vector<float> fTensor_dense4bias0 = std::vector<float>(2);
float * tensor_dense4bias0 = fTensor_dense4bias0.data();
std::vector<float> fTensor_dense3bias0 = std::vector<float>(64);
float * tensor_dense3bias0 = fTensor_dense3bias0.data();
std::vector<float> fTensor_dense3kernel0 = std::vector<float>(4096);
float * tensor_dense3kernel0 = fTensor_dense3kernel0.data();
std::vector<float> fTensor_dense2bias0 = std::vector<float>(64);
float * tensor_dense2bias0 = fTensor_dense2bias0.data();
std::vector<float> fTensor_dense4kernel0 = std::vector<float>(128);
float * tensor_dense4kernel0 = fTensor_dense4kernel0.data();
std::vector<float> fTensor_dense2kernel0 = std::vector<float>(4096);
float * tensor_dense2kernel0 = fTensor_dense2kernel0.data();
std::vector<float> fTensor_dense1bias0 = std::vector<float>(64);
float * tensor_dense1bias0 = fTensor_dense1bias0.data();
std::vector<float> fTensor_dense1kernel0 = std::vector<float>(4096);
float * tensor_dense1kernel0 = fTensor_dense1kernel0.data();
std::vector<float> fTensor_densebias0 = std::vector<float>(64);
float * tensor_densebias0 = fTensor_densebias0.data();
std::vector<float> fTensor_densekernel0 = std::vector<float>(448);
float * tensor_densekernel0 = fTensor_densekernel0.data();
 
//--- declare and allocate the intermediate tensors
std::vector<float> fTensor_dense4Sigmoid0 = std::vector<float>(2);
float * tensor_dense4Sigmoid0 = fTensor_dense4Sigmoid0.data();
std::vector<float> fTensor_dense2bias0bcast = std::vector<float>(64);
float * tensor_dense2bias0bcast = fTensor_dense2bias0bcast.data();
std::vector<float> fTensor_denseDense = std::vector<float>(64);
float * tensor_denseDense = fTensor_denseDense.data();
std::vector<float> fTensor_dense4Dense = std::vector<float>(2);
float * tensor_dense4Dense = fTensor_dense4Dense.data();
std::vector<float> fTensor_dense1bias0bcast = std::vector<float>(64);
float * tensor_dense1bias0bcast = fTensor_dense1bias0bcast.data();
std::vector<float> fTensor_dense1Dense = std::vector<float>(64);
float * tensor_dense1Dense = fTensor_dense1Dense.data();
std::vector<float> fTensor_densebias0bcast = std::vector<float>(64);
float * tensor_densebias0bcast = fTensor_densebias0bcast.data();
std::vector<float> fTensor_dense2Relu0 = std::vector<float>(64);
float * tensor_dense2Relu0 = fTensor_dense2Relu0.data();
std::vector<float> fTensor_denseRelu0 = std::vector<float>(64);
float * tensor_denseRelu0 = fTensor_denseRelu0.data();
std::vector<float> fTensor_dense1Relu0 = std::vector<float>(64);
float * tensor_dense1Relu0 = fTensor_dense1Relu0.data();
std::vector<float> fTensor_dense3bias0bcast = std::vector<float>(64);
float * tensor_dense3bias0bcast = fTensor_dense3bias0bcast.data();
std::vector<float> fTensor_dense2Dense = std::vector<float>(64);
float * tensor_dense2Dense = fTensor_dense2Dense.data();
std::vector<float> fTensor_dense3Relu0 = std::vector<float>(64);
float * tensor_dense3Relu0 = fTensor_dense3Relu0.data();
std::vector<float> fTensor_dense3Dense = std::vector<float>(64);
float * tensor_dense3Dense = fTensor_dense3Dense.data();
std::vector<float> fTensor_dense4bias0bcast = std::vector<float>(2);
float * tensor_dense4bias0bcast = fTensor_dense4bias0bcast.data();
 
 
Session(std::string filename ="Higgs_trained_model.dat") {
 
//--- reading weights from file
   std::ifstream f;
   f.open(filename);
   if (!f.is_open()) {
      throw std::runtime_error("tmva-sofie failed to open file for input weights");
   }
   std::string tensor_name;
   size_t length;
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense4bias0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense4bias0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 2) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 2 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense4bias0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense3bias0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense3bias0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 64) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 64 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense3bias0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense3kernel0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense3kernel0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 4096) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 4096 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense3kernel0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense2bias0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense2bias0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 64) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 64 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense2bias0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense4kernel0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense4kernel0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 128) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 128 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense4kernel0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense2kernel0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense2kernel0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 4096) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 4096 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense2kernel0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense1bias0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense1bias0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 64) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 64 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense1bias0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_dense1kernel0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_dense1kernel0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 4096) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 4096 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_dense1kernel0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_densebias0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_densebias0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 64) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 64 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_densebias0[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_densekernel0" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_densekernel0 , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 448) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 448 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
   for (size_t i = 0; i < length; ++i)
      f >> tensor_densekernel0[i];
   f.close();
 
//---- allocate the intermediate dynamic tensors
//--- broadcast bias tensor densebias0for Gemm op
   {
      float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_densebias0,{ 64 }, { 1 , 64 });
      std::copy(data, data + 64, tensor_densebias0bcast);
      delete [] data;
   }
//--- broadcast bias tensor dense1bias0for Gemm op
   {
      float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense1bias0,{ 64 }, { 1 , 64 });
      std::copy(data, data + 64, tensor_dense1bias0bcast);
      delete [] data;
   }
//--- broadcast bias tensor dense2bias0for Gemm op
   {
      float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense2bias0,{ 64 }, { 1 , 64 });
      std::copy(data, data + 64, tensor_dense2bias0bcast);
      delete [] data;
   }
//--- broadcast bias tensor dense3bias0for Gemm op
   {
      float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense3bias0,{ 64 }, { 1 , 64 });
      std::copy(data, data + 64, tensor_dense3bias0bcast);
      delete [] data;
   }
//--- broadcast bias tensor dense4bias0for Gemm op
   {
      float * data = TMVA::Experimental::SOFIE::UTILITY::UnidirectionalBroadcast<float>(tensor_dense4bias0,{ 2 }, { 1 , 2 });
      std::copy(data, data + 2, tensor_dense4bias0bcast);
      delete [] data;
   }
}
 
std::vector<float> infer(float* tensor_denseinput){
 
//--------- Gemm
   char op_0_transA = 'n';
   char op_0_transB = 'n';
   int op_0_m = 1;
   int op_0_n = 64;
   int op_0_k = 7;
   float op_0_alpha = 1;
   float op_0_beta = 1;
   int op_0_lda = 7;
   int op_0_ldb = 64;
   std::copy(tensor_densebias0bcast, tensor_densebias0bcast + 64, tensor_denseDense);
   BLAS::sgemm_(&op_0_transB, &op_0_transA, &op_0_n, &op_0_m, &op_0_k, &op_0_alpha, tensor_densekernel0, &op_0_ldb, tensor_denseinput, &op_0_lda, &op_0_beta, tensor_denseDense, &op_0_n);
 
//------ RELU
   for (int id = 0; id < 64 ; id++){
      tensor_denseRelu0[id] = ((tensor_denseDense[id] > 0 )? tensor_denseDense[id] : 0);
   }
 
//--------- Gemm
   char op_2_transA = 'n';
   char op_2_transB = 'n';
   int op_2_m = 1;
   int op_2_n = 64;
   int op_2_k = 64;
   float op_2_alpha = 1;
   float op_2_beta = 1;
   int op_2_lda = 64;
   int op_2_ldb = 64;
   std::copy(tensor_dense1bias0bcast, tensor_dense1bias0bcast + 64, tensor_dense1Dense);
   BLAS::sgemm_(&op_2_transB, &op_2_transA, &op_2_n, &op_2_m, &op_2_k, &op_2_alpha, tensor_dense1kernel0, &op_2_ldb, tensor_denseRelu0, &op_2_lda, &op_2_beta, tensor_dense1Dense, &op_2_n);
 
//------ RELU
   for (int id = 0; id < 64 ; id++){
      tensor_dense1Relu0[id] = ((tensor_dense1Dense[id] > 0 )? tensor_dense1Dense[id] : 0);
   }
 
//--------- Gemm
   char op_4_transA = 'n';
   char op_4_transB = 'n';
   int op_4_m = 1;
   int op_4_n = 64;
   int op_4_k = 64;
   float op_4_alpha = 1;
   float op_4_beta = 1;
   int op_4_lda = 64;
   int op_4_ldb = 64;
   std::copy(tensor_dense2bias0bcast, tensor_dense2bias0bcast + 64, tensor_dense2Dense);
   BLAS::sgemm_(&op_4_transB, &op_4_transA, &op_4_n, &op_4_m, &op_4_k, &op_4_alpha, tensor_dense2kernel0, &op_4_ldb, tensor_dense1Relu0, &op_4_lda, &op_4_beta, tensor_dense2Dense, &op_4_n);
 
//------ RELU
   for (int id = 0; id < 64 ; id++){
      tensor_dense2Relu0[id] = ((tensor_dense2Dense[id] > 0 )? tensor_dense2Dense[id] : 0);
   }
 
//--------- Gemm
   char op_6_transA = 'n';
   char op_6_transB = 'n';
   int op_6_m = 1;
   int op_6_n = 64;
   int op_6_k = 64;
   float op_6_alpha = 1;
   float op_6_beta = 1;
   int op_6_lda = 64;
   int op_6_ldb = 64;
   std::copy(tensor_dense3bias0bcast, tensor_dense3bias0bcast + 64, tensor_dense3Dense);
   BLAS::sgemm_(&op_6_transB, &op_6_transA, &op_6_n, &op_6_m, &op_6_k, &op_6_alpha, tensor_dense3kernel0, &op_6_ldb, tensor_dense2Relu0, &op_6_lda, &op_6_beta, tensor_dense3Dense, &op_6_n);
 
//------ RELU
   for (int id = 0; id < 64 ; id++){
      tensor_dense3Relu0[id] = ((tensor_dense3Dense[id] > 0 )? tensor_dense3Dense[id] : 0);
   }
 
//--------- Gemm
   char op_8_transA = 'n';
   char op_8_transB = 'n';
   int op_8_m = 1;
   int op_8_n = 2;
   int op_8_k = 64;
   float op_8_alpha = 1;
   float op_8_beta = 1;
   int op_8_lda = 64;
   int op_8_ldb = 2;
   std::copy(tensor_dense4bias0bcast, tensor_dense4bias0bcast + 2, tensor_dense4Dense);
   BLAS::sgemm_(&op_8_transB, &op_8_transA, &op_8_n, &op_8_m, &op_8_k, &op_8_alpha, tensor_dense4kernel0, &op_8_ldb, tensor_dense3Relu0, &op_8_lda, &op_8_beta, tensor_dense4Dense, &op_8_n);
   for (int id = 0; id < 2 ; id++){
      tensor_dense4Sigmoid0[id] = 1 / (1 + std::exp( - tensor_dense4Dense[id]));
   }
   return fTensor_dense4Sigmoid0;
}
};
} //TMVA_SOFIE_Higgs_trained_model
 
#endif  // ROOT_TMVA_SOFIE_HIGGS_TRAINED_MODEL
TF/Keras Version: 2.13.0
compiling SOFIE model and functor....
Number of signal entries 10000.0
Number of background entries 10000.0

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_RDataFrame.py.