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from __future__ import print_function
import functools
import numpy as np
from mpi4py import MPI
from mpi4py_fft.mpifft import PFFT
from mpi4py_fft.pencil import Subcomm
from mpi4py_fft.distarray import newDistArray
from mpi4py_fft import fftw
backends = ['fftw']
try:
import pyfftw
backends.append('pyfftw')
except ImportError:
pass
abstol = dict(f=0.1, d=2e-10, g=1e-10)
def allclose(a, b):
atol = abstol[a.dtype.char.lower()]
return np.allclose(a, b, rtol=0, atol=atol)
def random_like(array):
shape = array.shape
dtype = array.dtype
return np.random.random(shape).astype(dtype)
def random_true_or_false(comm):
r = 0
if comm.rank == 0:
r = np.random.randint(2)
r = comm.bcast(r)
return r
def test_r2r():
N = (5, 6, 7, 8, 9)
assert MPI.COMM_WORLD.Get_size() < 6
dctn = functools.partial(fftw.dctn, type=3)
idctn = functools.partial(fftw.idctn, type=3)
dstn = functools.partial(fftw.dstn, type=3)
idstn = functools.partial(fftw.idstn, type=3)
fft = PFFT(MPI.COMM_WORLD, N, axes=((0,), (1, 2), (3, 4)), grid=(-1,),
transforms={(1, 2): (dctn, idctn), (3, 4): (dstn, idstn)})
A = newDistArray(fft, forward_output=False)
A[:] = np.random.random(A.shape)
C = fftw.aligned_like(A)
B = fft.forward(A)
C = fft.backward(B, C)
assert np.allclose(A, C)
def test_mpifft():
from itertools import product
comm = MPI.COMM_WORLD
dims = (2, 3, 4,)
sizes = (12, 13)
assert MPI.COMM_WORLD.Get_size() < 8, "due to sizes"
types = ''
for t in 'fdg':
if fftw.get_fftw_lib(t):
types += t+t.upper()
grids = {2: (None,),
3: ((-1,), None),
4: ((-1,), None)}
for typecode in types:
for dim in dims:
for shape in product(*([sizes]*dim)):
if dim < 3:
n = min(shape)
if typecode in 'fdg':
n //= 2
n += 1
if n < comm.size:
continue
for grid in grids[dim]:
padding = False
for collapse in (True, False):
for backend in backends:
transforms = None
if dim < 3:
allaxes = [None, (-1,), (-2,),
(-1, -2,), (-2, -1),
(-1, 0), (0, -1),
((0,), (1,))]
elif dim < 4:
allaxes = [None, ((0,), (1, 2)),
((0,), (-2, -1))]
elif dim > 3:
allaxes = [None, ((0,), (1,), (2,), (3,)),
((0,), (1, 2, 3)),
((0,), (1,), (2, 3))]
dctn = functools.partial(fftw.dctn, type=3)
idctn = functools.partial(fftw.idctn, type=3)
if not typecode in 'FDG':
if backend == 'pyfftw':
transforms = {(3,): (pyfftw.builders.rfftn, pyfftw.builders.irfftn),
(2, 3): (pyfftw.builders.rfftn, pyfftw.builders.irfftn),
(1, 2, 3): (pyfftw.builders.rfftn, pyfftw.builders.irfftn),
(0, 1, 2, 3): (pyfftw.builders.rfftn, pyfftw.builders.irfftn)}
else:
transforms = {(3,): (dctn, idctn),
(2, 3): (dctn, idctn),
(1, 2, 3): (dctn, idctn),
(0, 1, 2, 3): (dctn, idctn)}
for axes in allaxes:
# Test also the slab is number interface
_grid = grid
if grid is not None:
ax = -1
if axes is not None:
ax = axes[-1] if isinstance(axes[-1], int) else axes[-1][-1]
_slab = (ax+1) % len(shape)
_grid = [1]*(_slab+1)
_grid[_slab] = 0
_comm = comm
# Test also the comm is Subcomm interfaces
# For PFFT the Subcomm needs to be as long as shape
if len(shape) > 2 and axes is None and grid is None:
_dims = [0] * len(shape)
_dims[-1] = 1 # distribute all but last axis (axes is None)
_comm = comm
if random_true_or_false(comm) == 1:
# then test Subcomm with a MPI.CART argument
_dims = MPI.Compute_dims(comm.Get_size(), _dims)
_comm = comm.Create_cart(_dims)
_dims = None
_comm = Subcomm(_comm, _dims)
#print(typecode, shape, axes, collapse, _grid)
fft = PFFT(_comm, shape, axes=axes, dtype=typecode,
padding=padding, grid=_grid, collapse=collapse,
backend=backend, transforms=transforms)
#if comm.rank == 0:
# grid_ = [c.size for c in fft.subcomm]
# print('grid:{} shape:{} typecode:{} backend:{} axes:{}'
# .format(grid_, shape, typecode, backend, axes))
assert fft.dtype(True) == fft.forward.output_array.dtype
assert fft.dtype(False) == fft.forward.input_array.dtype
assert len(fft.axes) == len(fft.xfftn)
assert len(fft.axes) == len(fft.transfer) + 1
assert (fft.forward.input_pencil.subshape ==
fft.forward.input_array.shape)
assert (fft.forward.output_pencil.subshape ==
fft.forward.output_array.shape)
assert (fft.backward.input_pencil.subshape ==
fft.backward.input_array.shape)
assert (fft.backward.output_pencil.subshape ==
fft.backward.output_array.shape)
assert np.all(np.array(fft.global_shape(True)) == np.array(fft.forward.output_pencil.shape))
assert np.all(np.array(fft.global_shape(False)) == np.array(fft.forward.input_pencil.shape))
ax = -1 if axes is None else axes[-1] if isinstance(axes[-1], int) else axes[-1][-1]
assert fft.forward.input_pencil.substart[ax] == 0
assert fft.backward.output_pencil.substart[ax] == 0
ax = 0 if axes is None else axes[0] if isinstance(axes[0], int) else axes[0][0]
assert fft.forward.output_pencil.substart[ax] == 0
assert fft.backward.input_pencil.substart[ax] == 0
assert fft.dimensions == len(shape)
U = random_like(fft.forward.input_array)
if random_true_or_false(comm) == 1:
F = fft.forward(U)
V = fft.backward(F)
assert allclose(V, U)
else:
fft.forward.input_array[...] = U
fft.forward()
fft.backward()
V = fft.backward.output_array
assert allclose(V, U)
fft.destroy()
padding = [1.5]*len(shape)
for backend in backends:
if dim < 3:
allaxes = [None, (-1,), (-2,),
(-1, -2,), (-2, -1),
(-1, 0), (0, -1),
((0,), (1,))]
elif dim < 4:
allaxes = [None, ((0,), (1,), (2,)),
((0,), (-2,), (-1,))]
elif dim > 3:
allaxes = [None, (0, 1, -2, -1),
((0,), (1,), (2,), (3,))]
for axes in allaxes:
_grid = grid
if grid is not None:
ax = -1
if axes is not None:
ax = axes[-1] if isinstance(axes[-1], int) else axes[-1][-1]
_slab = (ax+1) % len(shape)
_grid = [1]*(_slab+1)
_grid[_slab] = 0
fft = PFFT(comm, shape, axes=axes, dtype=typecode,
padding=padding, grid=_grid, backend=backend)
#if comm.rank == 0:
# grid = [c.size for c in fft.subcomm]
# print('grid:{} shape:{} typecode:{} backend:{} axes:{}'
# .format(grid, shape, typecode, backend, axes))
assert len(fft.axes) == len(fft.xfftn)
assert len(fft.axes) == len(fft.transfer) + 1
assert (fft.forward.input_pencil.subshape ==
fft.forward.input_array.shape)
assert (fft.forward.output_pencil.subshape ==
fft.forward.output_array.shape)
assert (fft.backward.input_pencil.subshape ==
fft.backward.input_array.shape)
assert (fft.backward.output_pencil.subshape ==
fft.backward.output_array.shape)
ax = -1 if axes is None else axes[-1] if isinstance(axes[-1], int) else axes[-1][-1]
assert fft.forward.input_pencil.substart[ax] == 0
assert fft.backward.output_pencil.substart[ax] == 0
ax = 0 if axes is None else axes[0] if isinstance(axes[0], int) else axes[0][0]
assert fft.forward.output_pencil.substart[ax] == 0
assert fft.backward.input_pencil.substart[ax] == 0
U = random_like(fft.forward.input_array)
F = fft.forward(U)
if random_true_or_false(comm) == 1:
Fc = F.copy()
V = fft.backward(F)
F = fft.forward(V)
assert allclose(F, Fc)
else:
fft.backward.input_array[...] = F
fft.backward()
fft.forward()
V = fft.forward.output_array
assert allclose(F, V)
# Test normalization on backward transform instead of default
fft.backward.input_array[...] = F
fft.backward(normalize=True)
fft.forward(normalize=False)
V = fft.forward.output_array
assert allclose(F, V)
fft.destroy()
if __name__ == '__main__':
test_mpifft()
test_r2r()
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