/usr/lib/python2.7/dist-packages/pyopencl/compyte/ndarray/test_gpu_ndarray.py is in python-pyopencl 2016.1+git20161130-1.
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from __future__ import print_function
import copy
import numpy
import pygpu_ndarray as gpu_ndarray
from six.moves import map
enable_double = True
enable_double = False
dtypes_all = ["float32",
"int8", "int16", "int32", "int64",
"uint8", "uint16", "uint32", "uint64",
"complex64",
]
dtypes_no_complex = ["float32",
"int8", "int16", "int32", "int64",
"uint8", "uint16", "uint32", "uint64",
]
if enable_double:
dtypes_all += ["float64", "complex128"]
dtypes_no_complex += ["float64"]
def check_flags(x, y):
assert x.flags["C_CONTIGUOUS"] == y.flags["C_CONTIGUOUS"]
assert x.flags["F_CONTIGUOUS"] == y.flags["F_CONTIGUOUS"]
assert x.flags["WRITEABLE"] == y.flags["WRITEABLE"]
assert x.flags["OWNDATA"] == y.flags["OWNDATA"]
assert x.flags["ALIGNED"] == y.flags["ALIGNED"]
assert x.flags["UPDATEIFCOPY"] == y.flags["UPDATEIFCOPY"]
def check_meta(x, y):
assert x.shape == y.shape
assert x.dtype == y.dtype
assert x.strides == y.strides
check_flags(x, y)
def check_all(x, y):
check_meta(x, y)
assert numpy.allclose(numpy.asarray(x), numpy.asarray(y))
def gen_gpu_nd_array(shape_orig, dtype='float32', offseted_outer=False,
offseted_inner=False, sliced=1, order='c'):
if sliced is True:
sliced = 2
elif sliced is False:
sliced = 1
shape = numpy.asarray(shape_orig).copy()
if sliced != 1 and len(shape) > 0:
shape[0] *= numpy.absolute(sliced)
if offseted_outer and len(shape) > 0:
shape[0] += 1
if offseted_inner and len(shape) > 0:
shape[-1] += 1
a = numpy.random.rand(*shape) * 10
if dtype.startswith("u"):
a = numpy.absolute(a)
a = numpy.asarray(a, dtype=dtype)
assert order in ['c', 'f']
if order == 'f' and len(shape) > 0:
a = numpy.asfortranarray(a)
b = gpu_ndarray.GpuNdArrayObject(a)
if order == 'f' and len(shape) > 0 and b.size > 1:
assert b.flags['F_CONTIGUOUS']
if offseted_outer and len(shape) > 0:
b = b[1:]
a = a[1:]
assert b.offset != 0
if offseted_inner and len(shape) > 0:
# The b[..., 1:] act as the test for this subtensor case.
b = b[..., 1:]
a = a[..., 1:]
assert b.offset != 0
if sliced != 1 and len(shape) > 0:
a = a[::sliced]
b = b[::sliced]
if False and shape_orig == ():
assert a.shape == (1,)
assert b.shape == (1,)
else:
assert a.shape == shape_orig, (a.shape, shape_orig)
assert b.shape == shape_orig, (b.shape, shape_orig)
assert numpy.allclose(a, numpy.asarray(b))
return a, b
def product(*args, **kwds):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = list(map(tuple, args)) * kwds.get('repeat', 1)
result = [[]]
for pool in pools:
result = [x + [y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
def test_transfer():
for shp in [(), (5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted in [True, False]:
a, b = gen_gpu_nd_array(shp, dtype, offseted)
c = numpy.asarray(b)
assert numpy.allclose(c, a)
assert a.shape == b.shape == c.shape
assert a.strides == b.strides == c.strides
assert a.dtype == b.dtype == c.dtype == dtype
assert c.flags.c_contiguous
def test_transfer_not_contiguous():
"""
Test transfer when the input on the CPU is not contiguous
TODO: test when the input on the gpu is not contiguous
"""
for shp in [(5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
a = numpy.random.rand(*shp) * 10
a = a[::-1]
b = gpu_ndarray.GpuNdArrayObject(a)
c = numpy.asarray(b)
assert numpy.allclose(c, a)
assert a.shape == b.shape == c.shape
# We copy a to a c contiguous array before the transfer
assert (-a.strides[0],) + a.strides[1:] == b.strides == c.strides
assert a.dtype == b.dtype == c.dtype
assert c.flags.c_contiguous
def test_transfer_fortran():
for shp in [(5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
a = numpy.random.rand(*shp) * 10
a_ = numpy.asfortranarray(a)
if len(shp) > 1:
assert a_.strides != a.strides
a = a_
b = gpu_ndarray.GpuNdArrayObject(a)
c = numpy.asarray(b)
assert a.shape == b.shape == c.shape
assert a.dtype == b.dtype == c.dtype
assert a.flags.f_contiguous
assert c.flags.f_contiguous
assert a.strides == b.strides == c.strides
assert numpy.allclose(c, a)
def test_ascontiguousarray():
for shp in [(), (5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted_o in [True, False]:
for offseted_i in [True, True]:
for sliced in [1, 2, -1, -2]:
for order in ['f', 'c']:
#print shp, dtype, offseted_o, offseted_i,
#print sliced, order
cpu, gpu = gen_gpu_nd_array(shp, dtype, offseted_o,
offseted_i,
sliced, order)
a = numpy.ascontiguousarray(cpu)
b = gpu_ndarray.ascontiguousarray(gpu)
# numpy upcast with a view to 1d scalar.
if (sliced != 1 or shp == () or
(offseted_i and len(shp) > 1)):
assert b is not gpu
if sliced == 1 and not offseted_i:
assert ((a.data is cpu.data) ==
(b.bytes is gpu.bytes))
else:
assert b is gpu
assert a.shape == b.shape
assert a.dtype == b.dtype
assert a.flags.c_contiguous
assert b.flags['C_CONTIGUOUS']
assert a.strides == b.strides
assert numpy.allclose(cpu, a)
assert numpy.allclose(cpu, b)
def test_asfortranarray():
for shp in [(), (5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted_outer in [True, False]:
for offseted_inner in [True, False]:
for sliced in [1, 2, -1, -2]:
for order in ['f', 'c']:
#print shp, dtype, offseted_outer, offseted_inner, sliced, order
cpu, gpu = gen_gpu_nd_array(shp, dtype,
offseted_outer,
offseted_inner,
sliced,
order)
a = numpy.asfortranarray(cpu)
b = gpu_ndarray.asfortranarray(gpu)
# numpy upcast with a view to 1d scalar.
if (sliced != 1 or shp == () or
(offseted_outer and len(shp) > 1) or
(order != 'f' and len(shp) > 1)):
assert b is not gpu
if (sliced == 1 and not offseted_outer and
order != 'c'):
assert ((a.data is cpu.data) ==
(b.bytes is gpu.bytes))
else:
assert b is gpu
pass
assert a.shape == b.shape
assert a.dtype == b.dtype
assert a.flags.f_contiguous
if shp != ():
assert b.flags['F_CONTIGUOUS']
assert a.strides == b.strides
assert numpy.allclose(cpu, a)
assert numpy.allclose(cpu, b)
def test_zeros():
for shp in [(), (0,), (5,),
(0, 0), (1, 0), (0, 1), (6, 7),
(0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
(4, 8, 9), (1, 8, 9)]:
for order in ["C", "F"]:
for dtype in dtypes_all:
x = numpy.zeros(shp, dtype, order)
y = gpu_ndarray.zeros(shp, dtype, order)
check_all(x, y)
x = gpu_ndarray.zeros(()) # no dtype and order param
y = numpy.zeros(())
check_meta(x, y)
try:
gpu_ndarray.zeros()
assert False
except TypeError:
pass
def test_empty():
for shp in [(), (0,), (5,),
(0, 0), (1, 0), (0, 1), (6, 7),
(0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
(4, 8, 9), (1, 8, 9)]:
for order in ["C", "F"]:
for dtype in dtypes_all:
x = numpy.empty(shp, dtype, order)
y = gpu_ndarray.empty(shp, dtype, order)
check_meta(x, y)
x = gpu_ndarray.empty(()) # no dtype and order param
y = numpy.empty(())
check_meta(x, y)
try:
gpu_ndarray.empty()
assert False
except TypeError:
pass
def test_mapping_getitem_ellipsis():
for shp in [(5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted in [True, False]:
a, a_gpu = gen_gpu_nd_array(shp, dtype, offseted)
b = a_gpu[...]
assert b.bytes == a_gpu.bytes
assert b.strides == a.strides
assert b.shape == a.shape
b_cpu = numpy.asarray(b)
assert numpy.allclose(a, b_cpu)
def test_copy_view():
from ..array import may_share_memory
def check_memory_region(a, a_op, b, b_op):
assert numpy.may_share_memory(a, a_op) == may_share_memory(b, b_op)
if a_op.base is None:
assert b_op.base is None
else:
assert a_op.base is a
if b.base:
# We avoid having a series of object connected by base.
# This is to don't bloc the garbage collection.
assert b_op.base is b.base
else:
assert b_op.base is b
for shp in [(5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted in [False, True]:
# order1 is the order of the original data
for order1 in ['c', 'f']:
# order2 is the order wanted after copy
for order2 in ['c', 'f']:
print(shp, dtype, offseted, order1, order2)
#TODO test copy unbroadcast!
a, b = gen_gpu_nd_array(shp, dtype, offseted,
order=order1)
assert numpy.allclose(a, numpy.asarray(b))
check_flags(a, b)
c = b.copy(order2)
assert numpy.allclose(a, numpy.asarray(c))
check_flags(c, a.copy(order2))
check_memory_region(a, a.copy(order2), b, c)
d = copy.copy(b)
assert numpy.allclose(a, numpy.asarray(d))
check_flags(d, copy.copy(a))
check_memory_region(a, copy.copy(a), b, d)
e = b.view()
assert numpy.allclose(a, numpy.asarray(e))
check_flags(e, a.view())
check_memory_region(a, a.view(), b, e)
f = copy.deepcopy(b)
assert numpy.allclose(a, numpy.asarray(f))
check_flags(f, copy.deepcopy(a))
check_memory_region(a, copy.deepcopy(a), b, f)
g = copy.copy(b.view())
assert numpy.allclose(a, numpy.asarray(g))
check_memory_region(a, copy.copy(a.view()), b, g)
check_flags(g, copy.copy(a.view()))
def test_len():
for shp in [(5,), (6, 7), (4, 8, 9), (1, 8, 9)]:
for dtype in dtypes_all:
for offseted in [True, False]:
a, a_gpu = gen_gpu_nd_array(shp, dtype, offseted)
assert len(a_gpu) == shp[0]
def test_mapping_getitem_w_int():
def _cmp(x, y):
assert x.shape == y.shape
assert x.dtype == y.dtype
assert x.strides == y.strides
assert x.flags["C_CONTIGUOUS"] == y.flags["C_CONTIGUOUS"]
assert x.flags["F_CONTIGUOUS"] == y.flags["F_CONTIGUOUS"]
if x.flags["WRITEABLE"] != y.flags["WRITEABLE"]:
assert x.ndim == 0
assert not x.flags["OWNDATA"]
assert y.flags["OWNDATA"]
else:
assert x.flags["WRITEABLE"] == y.flags["WRITEABLE"]
assert x.flags["OWNDATA"] == y.flags["OWNDATA"]
assert x.flags["ALIGNED"] == y.flags["ALIGNED"]
assert x.flags["UPDATEIFCOPY"] == y.flags["UPDATEIFCOPY"]
x = numpy.asarray(x)
assert x.shape == y.shape
assert x.dtype == y.dtype
assert x.strides == y.strides
if not numpy.all(x == y):
print(x)
print(y)
assert numpy.all(x == y), (x, y)
def _cmpNs(x, y):
"""
Don't compare the stride after the transfer
There is a copy that have been made on the gpu before the transfer
"""
assert x.shape == y.shape
assert x.dtype == y.dtype
assert x.strides == y.strides
assert x.flags["C_CONTIGUOUS"] == y.flags["C_CONTIGUOUS"]
assert x.flags["F_CONTIGUOUS"] == y.flags["F_CONTIGUOUS"]
assert x.flags["WRITEABLE"] == y.flags["WRITEABLE"]
assert x.flags["ALIGNED"] == y.flags["ALIGNED"]
assert x.flags["OWNDATA"] == y.flags["OWNDATA"]
assert x.flags["UPDATEIFCOPY"] == y.flags["UPDATEIFCOPY"]
x_ = numpy.asarray(x)
assert x_.shape == y.shape
assert x_.dtype == y.dtype
if not numpy.all(x_ == y):
print(x_)
print(y)
assert numpy.all(x_ == y), (x_, y)
pass
def _cmpf(x, *y):
try:
x.__getitem__(y)
except IndexError:
pass
else:
raise Exception("Did not generate out or bound error")
def _cmpfV(x, *y):
try:
if len(y) == 1:
x.__getitem__(*y)
else:
x.__getitem__(y)
except ValueError:
pass
else:
raise Exception("Did not generate value error")
for dtype in dtypes_all:
for offseted in [True, False]:
# test vector
dim = (2,)
a, _a = gen_gpu_nd_array(dim, dtype, offseted)
import sys
init_ref_count = sys.getrefcount(_a)
_cmp(_a[...], a[...])
_cmp(_a[...], a[...])
_cmp(_a[...], a[...])
_cmp(_a[...], a[...])
_cmp(_a[...], a[...])
_cmp(_a[-1], a[-1])
_cmp(_a[1], a[1])
_cmp(_a[0], a[0])
_cmp(_a[::1], a[::1])
_cmpNs(_a[::-1], a[::-1])
_cmp(_a[...], a[...])
_cmpf(_a, 2)
# test scalar
dim = ()
a, _a = gen_gpu_nd_array(dim, dtype, offseted)
_cmp(_a[...], a[...])
_cmpf(_a, 0)
_cmpfV(_a, slice(1))
# test 4d-tensor
dim = (5, 4, 3, 2)
a, _a = gen_gpu_nd_array(dim, dtype, offseted)
_cmpf(_a, slice(-1), slice(-1), 10, -10)
_cmpf(_a, slice(-1), slice(-1), -10, slice(-1))
_cmpf(_a, 0, slice(0, -1, -20), -10)
_cmpf(_a, 10)
_cmpf(_a, (10, 0, 0, 0))
_cmpf(_a, -10)
#test with integer
_cmp(_a[1], a[1])
_cmp(_a[-1], a[-1])
_cmp(_a[numpy.int64(1)], a[numpy.int64(1)])
_cmp(_a[numpy.int64(-1)], a[numpy.int64(-1)])
#test with slice
_cmp(_a[1:], a[1:])
_cmp(_a[1:2], a[1:2])
_cmp(_a[-1:1], a[-1:1])
#test with tuple (mix slice, integer, numpy.int64)
_cmpNs(_a[0, 0, ::numpy.int64(-1), ::-1], a[0, 0, ::-1, ::-1])
_cmpNs(_a[:, :, ::numpy.int64(-1), ::-1], a[:, :, ::-1, ::-1])
_cmpNs(_a[:, :, numpy.int64(1), -1], a[:, :, 1, -1])
_cmpNs(_a[:, :, ::-1, ::-1], a[:, :, ::-1, ::-1])
_cmpNs(_a[:, :, ::-10, ::-10], a[:, :, ::-10, ::-10])
_cmpNs(_a[:, :, 1, -1], a[:, :, 1, -1])
_cmpNs(_a[:, :, -1, :], a[:, :, -1, :])
_cmpNs(_a[:, ::-2, -1, :], a[:, ::-2, -1, :])
_cmpNs(_a[:, ::-20, -1, :], a[:, ::-20, -1, :])
_cmpNs(_a[:, ::-2, -1], a[:, ::-2, -1])
_cmpNs(_a[0, ::-2, -1], a[0, ::-2, -1])
_cmp(_a[-1, -1, -1, -2], a[-1, -1, -1, -2])
#test ellipse
_cmp(_a[...], a[...])
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