TMVA_SOFIE_PyTorch.C File Reference

Detailed Description

This macro provides a simple example for the parsing of PyTorch .pt file into RModel object and further generating the .hxx header files for inference.

 
using namespace TMVA::Experimental;
 
TString pythonSrc = "\
import torch\n\
import torch.nn as nn\n\
\n\
model = nn.Sequential(\n\
           nn.Linear(32,16),\n\
           nn.ReLU(),\n\
           nn.Linear(16,8),\n\
           nn.ReLU()\n\
           )\n\
\n\
criterion = nn.MSELoss()\n\
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)\n\
\n\
x=torch.randn(2,32)\n\
y=torch.randn(2,8)\n\
\n\
for i in range(500):\n\
    y_pred = model(x)\n\
    loss = criterion(y_pred,y)\n\
    optimizer.zero_grad()\n\
    loss.backward()\n\
    optimizer.step()\n\
\n\
model.eval()\n\
m = torch.jit.script(model)\n\
torch.jit.save(m,'PyTorchModel.pt')\n";
 
 
void TMVA_SOFIE_PyTorch(){
 
    //Running the Python script to generate PyTorch .pt file
    TMVA::PyMethodBase::PyInitialize();
 
    TMacro m;
    m.AddLine(pythonSrc);
    m.SaveSource("make_pytorch_model.py");
    gSystem->Exec(TMVA::Python_Executable() + " make_pytorch_model.py");
 
    //Parsing a PyTorch model requires the shape and data-type of input tensor
    //Data-type of input tensor defaults to Float if not specified
    std::vector<size_t> inputTensorShapeSequential{2,32};
    std::vector<std::vector<size_t>> inputShapesSequential{inputTensorShapeSequential};
 
    //Parsing the saved PyTorch .pt file into RModel object
    SOFIE::RModel model = SOFIE::PyTorch::Parse("PyTorchModel.pt",inputShapesSequential);
 
    //Generating inference code
    model.Generate();
    model.OutputGenerated("PyTorchModel.hxx");
 
    //Printing required input tensors
    std::cout<<"\n\n";
    model.PrintRequiredInputTensors();
 
    //Printing initialized tensors (weights)
    std::cout<<"\n\n";
    model.PrintInitializedTensors();
 
    //Printing intermediate tensors
    std::cout<<"\n\n";
    model.PrintIntermediateTensors();
 
    //Checking if tensor already exist in model
    std::cout<<"\n\nTensor \"0weight\" already exist: "<<std::boolalpha<<model.CheckIfTensorAlreadyExist("0weight")<<"\n\n";
    std::vector<size_t> tensorShape = model.GetTensorShape("0weight");
    std::cout<<"Shape of tensor \"0weight\": ";
    for(auto& it:tensorShape){
        std::cout<<it<<",";
    }
    std::cout<<"\n\nData type of tensor \"0weight\": ";
    SOFIE::ETensorType tensorType = model.GetTensorType("0weight");
    std::cout<<SOFIE::ConvertTypeToString(tensorType);
 
    //Printing generated inference code
    std::cout<<"\n\n";
    model.PrintGenerated();
}

 
 
 
Model requires following inputs:
Fully Specified Tensor name: input1 type: float shape: [2,32]
 
 
Model initialized the following tensors:
Tensor name: "2bias" type: float shape: [8]
Tensor name: "0weight"  type: float shape: [16,32]
Tensor name: "2weight"  type: float shape: [8,16]
Tensor name: "0bias" type: float shape: [16]
 
 
Model specify the following intermediate tensors:
Tensor name: "result3"  type: float shape: [2,8]
Tensor name: "2biasbcast"  type: float shape: [2,8]
Tensor name: "input2"   type: float shape: [2,8]
Tensor name: "input0"   type: float shape: [2,16]
Tensor name: "result"   type: float shape: [2,16]
Tensor name: "0biasbcast"  type: float shape: [2,16]
 
 
Tensor "0weight" already exist: true
 
Shape of tensor "0weight": 16,32,
 
Data type of tensor "0weight": float
 
//Code generated automatically by TMVA for Inference of Model file [PyTorchModel.pt] at [Mon Sep 11 20:40:20 2023] 
 
#ifndef TMVA_SOFIE_PYTORCHMODEL
#define TMVA_SOFIE_PYTORCHMODEL
 
#include<algorithm>
#include<vector>
#include "TMVA/SOFIE_common.hxx"
#include <fstream>
 
namespace TMVA_SOFIE_PyTorchModel{
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_2bias = std::vector<float>(8);
float * tensor_2bias = fTensor_2bias.data();
std::vector<float> fTensor_0weight = std::vector<float>(512);
float * tensor_0weight = fTensor_0weight.data();
std::vector<float> fTensor_2weight = std::vector<float>(128);
float * tensor_2weight = fTensor_2weight.data();
std::vector<float> fTensor_0bias = std::vector<float>(16);
float * tensor_0bias = fTensor_0bias.data();
std::vector<float> fTensor_result3 = std::vector<float>(16);
float * tensor_result3 = fTensor_result3.data();
std::vector<float> fTensor_2biasbcast = std::vector<float>(16);
float * tensor_2biasbcast = fTensor_2biasbcast.data();
std::vector<float> fTensor_input2 = std::vector<float>(16);
float * tensor_input2 = fTensor_input2.data();
std::vector<float> fTensor_input0 = std::vector<float>(32);
float * tensor_input0 = fTensor_input0.data();
std::vector<float> fTensor_result = std::vector<float>(32);
float * tensor_result = fTensor_result.data();
std::vector<float> fTensor_0biasbcast = std::vector<float>(32);
float * tensor_0biasbcast = fTensor_0biasbcast.data();
 
 
Session(std::string filename ="") {
   if (filename.empty()) filename = "PyTorchModel.dat";
   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;
   int length;
   f >> tensor_name >> length;
   if (tensor_name != "tensor_2bias" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_2bias , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 8) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 8 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
    for (int i =0; i < length; ++i) 
       f >> tensor_2bias[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_0weight" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_0weight , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 512) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 512 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
    for (int i =0; i < length; ++i) 
       f >> tensor_0weight[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_2weight" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_2weight , 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 (int i =0; i < length; ++i) 
       f >> tensor_2weight[i];
   f >> tensor_name >> length;
   if (tensor_name != "tensor_0bias" ) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor name; expected name is tensor_0bias , read " + tensor_name;
      throw std::runtime_error(err_msg);
    }
   if (length != 16) {
      std::string err_msg = "TMVA-SOFIE failed to read the correct tensor size; expected size is 16 , read " + std::to_string(length) ;
      throw std::runtime_error(err_msg);
    }
    for (int i =0; i < length; ++i) 
       f >> tensor_0bias[i];
   f.close();
   {
      std::vector<size_t> oldShape = { 16 };
      std::vector<size_t> newShape = { 2 , 16 };
      float * newData_ptr = TMVA::Experimental::SOFIE::UTILITY::Unidirectional_broadcast<float>(tensor_0bias, oldShape, newShape);
      std::copy(newData_ptr, newData_ptr + 32, tensor_0biasbcast);
      delete [] newData_ptr;
   }
   {
      std::vector<size_t> oldShape = { 8 };
      std::vector<size_t> newShape = { 2 , 8 };
      float * newData_ptr = TMVA::Experimental::SOFIE::UTILITY::Unidirectional_broadcast<float>(tensor_2bias, oldShape, newShape);
      std::copy(newData_ptr, newData_ptr + 16, tensor_2biasbcast);
      delete [] newData_ptr;
   }
}
 
std::vector<float> infer(float* tensor_input1){
 
//--------- Gemm
   char op_0_transA = 'n';
   char op_0_transB = 't';
   int op_0_m = 2;
   int op_0_n = 16;
   int op_0_k = 32;
   float op_0_alpha = 1;
   float op_0_beta = 1;
   int op_0_lda = 32;
   int op_0_ldb = 32;
   std::copy(tensor_0biasbcast, tensor_0biasbcast + 32, tensor_input0);
   BLAS::sgemm_(&op_0_transB, &op_0_transA, &op_0_n, &op_0_m, &op_0_k, &op_0_alpha, tensor_0weight, &op_0_ldb, tensor_input1, &op_0_lda, &op_0_beta, tensor_input0, &op_0_n);
 
//------ RELU
   for (int id = 0; id < 32 ; id++){
      tensor_result[id] = ((tensor_input0[id] > 0 )? tensor_input0[id] : 0);
   }
 
//--------- Gemm
   char op_2_transA = 'n';
   char op_2_transB = 't';
   int op_2_m = 2;
   int op_2_n = 8;
   int op_2_k = 16;
   float op_2_alpha = 1;
   float op_2_beta = 1;
   int op_2_lda = 16;
   int op_2_ldb = 16;
   std::copy(tensor_2biasbcast, tensor_2biasbcast + 16, tensor_input2);
   BLAS::sgemm_(&op_2_transB, &op_2_transA, &op_2_n, &op_2_m, &op_2_k, &op_2_alpha, tensor_2weight, &op_2_ldb, tensor_result, &op_2_lda, &op_2_beta, tensor_input2, &op_2_n);
 
//------ RELU
   for (int id = 0; id < 16 ; id++){
      tensor_result3[id] = ((tensor_input2[id] > 0 )? tensor_input2[id] : 0);
   }
   std::vector<float> ret (tensor_result3, tensor_result3 + 16);
   return ret;
}
};
} //TMVA_SOFIE_PyTorchModel
 
#endif  // TMVA_SOFIE_PYTORCHMODEL

Author

Sanjiban Sengupta

Definition in file TMVA_SOFIE_PyTorch.C.