logsumexp.cc

logsumexp.cc

#include <numpy/arrayobject.h>
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
#include "xtensor/xarray.hpp"
#include "xtensor/xtensor.hpp"
#include "xtensor/xcontainer.hpp"
#include "xtensor/xbroadcast.hpp"
//#include "xtensor/xbuilder.hpp"
#include "xtensor/xview.hpp"
#include "xtensor/xeval.hpp"
#include "xtensor/xstridedview.hpp"
#include "xtensor-python/pyarray.hpp"
#include "xtensor-python/pytensor.hpp"
#include "xtensor/xio.hpp"
#include <algorithm>            // ?

namespace py = pybind11;

template<class E1>
auto logsumexp1 (E1 const& e1) {
  using value_type = typename std::decay_t<E1>::value_type;
  auto max = xt::amax (e1)();
  return std::move (max + xt::log (xt::sum (xt::exp (e1-max))));
}

template<class E1, class X>
auto logsumexp2 (const E1& e1, X const& axes) {
  using value_type = typename std::decay_t<E1>::value_type;
  auto max = xt::eval(xt::amax(e1, axes));
  auto sv = xt::slice_vector(max);
  for (int i = 0; i < e1.dimension(); i++)
  {
    if (std::find (axes.begin(), axes.end(), i) != axes.end())
      sv.push_back(xt::newaxis());
    else
      sv.push_back(e1.shape()[i]);
  }
  auto max2 = xt::dynamic_view(max, sv);
  return (xt::pyarray<value_type>(max2 + xt::log(xt::sum(xt::exp(e1 - max2), axes))));
}

template<class value_type>
auto normalize (xt::pytensor<value_type,2> const& e1) {
  auto axis = std::vector<size_t>{ e1.dimension() - 1 };
  auto ls = logsumexp2(e1, axis);

  auto sv = xt::slice_vector(ls);
  sv.push_back(xt::all());
  sv.push_back(xt::newaxis());

  auto ls2 = xt::dynamic_view(ls, sv);
  return xt::pyarray<value_type>((e1 - ls2));
}

PYBIND11_PLUGIN (logsumexp) {
  if (_import_array() < 0) {
    PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import");
    return nullptr;
  }
  py::module m("logsumexp", "pybind11 example plugin");

  m.def("logsumexp", [](xt::pyarray<double>const& x) {
      return xt::pyarray<double>(xt::eval(logsumexp1(x)));
    });

  m.def("logsumexp", [](xt::pyarray<double>const& x, std::vector<size_t>const& ax) {
      return logsumexp2 (x, ax);
    });

  m.def("normalize", [](xt::pytensor<double,2>const& x) {
      return normalize(x);
    });

  return m.ptr();
}

test_logsumexp.py

import numpy as np
from xtensor_test.logsumexp import logsumexp, normalize

def logsumexp_py1 (u):
    m = np.max (u)
    return m + np.log (np.sum (np.exp (u - m)))

def logsumexp_py2 (u, axes):
    m = np.max (u, axes)
    slices = [slice(m.shape[i]) if i not in axes else np.newaxis for i in range (len (u.shape))]
    m2 = m[slices]
    #print (m.shape, u.shape, m2)
    return m2 + np.log (np.sum (np.exp (u - m2), axes))

def logsumexp_py (u, axes=None):
    if axes == None:
        return logsumexp_py1 (u)
    else:
        return logsumexp_py2 (u, axes)

u = np.ones (4)
v = logsumexp(u)
print (v)
print (logsumexp_py (u, (0,)))
print (logsumexp (u, (0,)))
print ('logsumexp (np.ones ((2,4))):', logsumexp (np.ones ((2,4))))
print (logsumexp_py (np.ones ((2,4))))
print ('logsumexp_py (np.ones ((2,4)), (1,))):', logsumexp_py (np.ones ((2,4)), (1,)))
print ('logsumexp (np.ones ((2,4)), (1,)):', logsumexp (np.ones ((2,4)), (1,)))
#print (np.ones ((2,4)))
print ('norm:', normalize (np.ones ((2,4))))

from scipy.misc import logsumexp as logsumexp2
print (logsumexp2 (u))
def normalize2 (u):
    print ('ls:', logsumexp2 (u, axis=-1))
    print ('u:', u)
    return u - logsumexp2 (u, axis=-1)[:,np.newaxis]

print ('norm2:', normalize2 (np.ones ((2,4))))