RuntimeError: variable impl does not have is_contiguous Pytorch C++ extension

ex.cpp

#include <torch/extension.h>
#include <cmath>
#include <iostream>
#include <vector>


at::Tensor ex_forward(
    at::Tensor input
    ) {
    auto n_samples = input.size(0);
    auto n_features = input.size(1);
    auto G = n_features / 2;
    auto M = 2;

    at::Tensor temp = at::zeros({n_samples, G, 2});
    at::Tensor slice1 = input.slice(1, 0, n_features, 2) + input.slice(1, 1, n_features, 2);
    at::Tensor slice2 = input.slice(1, 0, n_features, 2) - input.slice(1, 1, n_features, 2);
    temp = at::stack({slice1, slice2}, 2);

    auto res = temp;
    for (auto dumb_idx = 0; dumb_idx < std::log2(n_features) + 1; dumb_idx++) {
        temp = at::zeros({n_samples, G / 2, M * 2});
        slice1 = res.slice(2, 0, M, 2).slice(1, 0, G, 2);
        slice2 = res.slice(2, 0, M, 2).slice(1, 1, G, 2);
        auto mesh1 = at::meshgrid({at::_cast_Long(at::arange(0, n_samples, 1)), at::_cast_Long(at::arange(0, G/2, 1)), at::_cast_Long(at::arange(0, 2 * M, 4))});
        auto mesh2 = at::meshgrid({at::_cast_Long(at::arange(0, n_samples, 1)), at::_cast_Long(at::arange(0, G/2, 1)), at::_cast_Long(at::arange(1, 2 * M, 4))});
        temp.index_put_(mesh1, slice1 + slice2);
        temp.index_put_(mesh2, slice1 - slice2);
        slice1 = res.slice(2, 1, M, 2).slice(1, 0, G, 2);
        slice2 = res.slice(2, 1, M, 2).slice(1, 1, G, 2);
        mesh1 = at::meshgrid({at::_cast_Long(at::arange(0, n_samples, 1)), at::_cast_Long(at::arange(0, G/2, 1)), at::_cast_Long(at::arange(2, 2 * M, 4))});
        mesh2 = at::meshgrid({at::_cast_Long(at::arange(0, n_samples, 1)), at::_cast_Long(at::arange(0, G/2, 1)), at::_cast_Long(at::arange(3, 2 * M, 4))});
        temp.index_put_(mesh1, slice1 - slice2);
        temp.index_put_(mesh2, slice1 + slice2);
        res = temp;
        G = G / 2;
        M = M * 2;
    }
    at::Tensor output = temp.select(1, 0); // select index 0 along dim 1
    return output * pow(std::sqrt(n_features), -1);
}


PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &ex_forward, "EX forward");
}

setup.py

from setuptools import setup
from torch.utils.cpp_extension import CppExtension, BuildExtension

setup(name='ex',
      ext_modules=[CppExtension('ex', ['ex.cpp'], extra_compile_args=['-g', '-O0'])],
      cmdclass={'build_ext': BuildExtension}
      )

test.py

import torch

import ex


class EXATen(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input):
        outputs = ex.forward(input)
        return outputs


x = torch.randn(3, 8)
y1 = EXATen.apply(x)