OpenMP / Pybind11: Accessing Python object in for loop returns interned string error

Question

I am trying to use OpenMP on a list of Python objects by using Pybind11 in C++. I transform this list in an std::vector of Python objects (as explained in this post) and then try to access them in a parallelized for loop. However, when invoking the attributes of any python object in the vector in the for loop, I get the error:

Fatal Python error: deletion of interned string failed
Thread 0x00007fd282bc7700 (most recent call first):
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)

My questions are: What is the deletion of interned string error ? and how to avoid it in OpenMP ?

I have read here that the problem is with respect to the copy of the string, so I tried to refer to the string with a pointer but it didn't help. Also, the problem doesn't come from a conversion problem in Pybind, because if I remove the #pragma omp clause, the code works perfectly.

C++ Code

#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <pybind11/stl.h>
#include <omp.h>
#include <chrono>
#include <thread>

namespace py = pybind11;

py::object create_seq(
  py::object self
  ){

  std::vector<py::object> dict = self.cast<std::vector<py::object>>();

  #pragma omp parallel for
  for(unsigned int i=0; i<dict.size(); i++) {
    dict[i].attr("attribute") = 2;
  }

  return self;
}

PYBIND11_MODULE(error, m){

    m.doc() = "pybind11 module for iterating over generations";

    m.def("create_seq", &create_seq,
      "the function which creates a sequence");

}

Python Code

import error

class test():
    def __init__(self):
        self.attribute = None

if __name__ == '__main__':
    dict = {}
    for i in range(50):
        dict[i] = test()
    pop = error.create_seq(list(dict.values()))

Compiled with:

g++ -O3 -Wall -shared -std=c++14 -fopenmp -fPIC `python3 -m pybind11 --includes` openmp.cpp -o error.so

Answer 1

I was able to find a solution, but I think I am just doing a single threaded work with multiple threads. I used a #pragma omp ordered in the following way:

std::vector<py::object> dict = self.cast<std::vector<py::object>>();
  #pragma omp parallel for ordered schedule(dynamic)
  for(unsigned int i=0; i<dict.size(); i++) {
    py::object genome = dict[i];
    std::cout << i << std::endl;
    #pragma omp ordered
    genome.attr("fitness")=2; 
    }

And this works

EDIT

I controlled the execution time with and without parallelization and it's the same