#ifdef AT_PARALLEL_OPENMP
#include <ATen/Parallel.h>

#include <atomic>

#ifdef TH_BLAS_MKL
#include <mkl.h>
#endif

namespace at {

namespace {
// Number of threads set by the user
std::atomic<int> num_threads{-1};

} // namespace

void init_num_threads() {
  auto nthreads = num_threads.load();
  if (nthreads > 0) {
    set_num_threads(nthreads);
  } else {
#if defined(_OPENMP) && defined(TH_BLAS_MKL)
    // If we are using MKL an OpenMP make sure the number of threads match.
    // Otherwise, MKL and our OpenMP-enabled functions will keep changing the
    // size of the OpenMP thread pool, resulting in worse performance (and memory
    // leaks in GCC 5.4)
    omp_set_num_threads(mkl_get_max_threads());
#endif
  }
}

void set_num_threads(int nthreads) {
  TORCH_CHECK(nthreads > 0, "Expected positive number of threads");
  num_threads.store(nthreads);
#ifdef _OPENMP
  omp_set_num_threads(nthreads);
#endif
#ifdef TH_BLAS_MKL
  mkl_set_num_threads(nthreads);

  // because PyTorch uses OpenMP outside of MKL invocations
  // as well, we want this flag to be false, so that
  // threads aren't destroyed and recreated across every
  // MKL / non-MKL boundary of OpenMP usage
  // See https://github.com/pytorch/pytorch/issues/13757
  mkl_set_dynamic(false);
#endif
}

// Explicitly calling omp_get_max_threads() as the size of the parallel
// region might be different in the new thread;
// Use init_num_threads() during thread initialization to ensure
// consistent size of parallel region in different threads
int get_num_threads() {
#ifdef _OPENMP
  return omp_get_max_threads();
#else
  return 1;
#endif
}

int get_thread_num() {
#ifdef _OPENMP
  return omp_get_thread_num();
#else
  return 0;
#endif
}

bool in_parallel_region() {
#ifdef _OPENMP
  return omp_in_parallel();
#else
  return false;
#endif
}

void intraop_launch(std::function<void()> func) {
  // execute inline in openmp case
  func();
}

} // namespace at
#endif
