Reikna FFT produces unexpected transposed result when either input or output is Fortran-ordered

reikna_bug_fortran_order.py

import itertools
import numpy as np
from numpy.fft import fftn as npfftn
from numpy.testing import assert_array_almost_equal
import pyopencl as cl
import pyopencl.array
from reikna import cluda
from reikna.fft import FFT as reikna_FFT

# Select device
platform = 0
device = 0

# Init OpenCL
cl_platforms = cl.get_platforms()
cl_devices = cl_platforms[platform].get_devices(cl.device_type.GPU)
cl_device = cl_devices[device]
cl_context = cl.Context(devices=[cl_device])
cl_queue = cl.CommandQueue(cl_context, properties=cl.command_queue_properties.PROFILING_ENABLE)

# Input initialized with random data.
data_shape = (2, 2)
np.random.seed(1)
np_array_C = np.random.random(data_shape).astype(dtype=np.complex64)
np_array_F = np.asfortranarray(np_array_C)

# Output initialized with zeros.
np_result_C = np.zeros_like(np_array_C, dtype=np.complex64)
np_result_F = np.zeros_like(np_array_F, dtype=np.complex64)

axes = (1, 0)

for np_in, np_out in itertools.product([np_array_C, np_array_F], [np_result_C, np_result_F]):

	# Reset input data on device.
	cl_data_in  = cl.array.to_device(cl_queue, np_in)
	# Reset output data on device.
	cl_data_out = cl.array.to_device(cl_queue, np_out)

	# Prepare FFT.
	reikna_thread = cluda.ocl_api().Thread(cl_queue)
	prepared = reikna_FFT(cl_data_in, axes=axes).compile(reikna_thread, fast_math=True)

	# Call FFT.
	prepared(cl_data_out, cl_data_in, inverse=False)

	cl_to_compare = cl_data_out
	npfft_to_compare = npfftn(np_in, axes=axes)

	assert_array_almost_equal(cl_to_compare.get(), npfft_to_compare, decimal=1)