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from __future__ import print_function
import os
import sys
import numpy
import pytest
from pygpu import gpuarray
if numpy.__version__ < '1.6.0':
skip_single_f = True
else:
skip_single_f = False
dtypes_all = ["float32", "float64",
"int8", "int16", "uint8", "uint16",
"int32", "int64", "uint32", "uint64"]
dtypes_no_complex = dtypes_all
# Sometimes int8 is just a source of trouble (like with overflows)
dtypes_no_complex_big = ["float32", "float64", "int16", "uint16",
"int32", "int64", "uint32", "uint64"]
def get_env_dev():
for name in ['GPUARRAY_TEST_DEVICE', 'DEVICE']:
if name in os.environ:
return os.environ[name]
raise RuntimeError(
"No test device specified. Specify one using the DEVICE "
"or GPUARRAY_TEST_DEVICE environment variables.")
context = gpuarray.init(get_env_dev())
print("*** Testing for", context.devname, file=sys.stderr)
def guard_devsup(func):
def f(*args, **kwargs):
try:
func(*args, **kwargs)
except gpuarray.UnsupportedException as e:
pytest.skip("operation not supported")
return f
def rand(shape, dtype):
r = numpy.random.randn(*shape) * 10
if r.dtype.startswith('u'):
r = numpy.absolute(r)
return r.astype(dtype)
def check_flags(x, y):
assert isinstance(x, gpuarray.GpuArray)
if y.size == 0 and y.flags["C_CONTIGUOUS"] and y.flags["F_CONTIGUOUS"]:
# Different numpy version have different value for
# C_CONTIGUOUS in that case.
pass
elif x.flags["C_CONTIGUOUS"] != y.flags["C_CONTIGUOUS"]:
# Numpy 1.10 can set c/f contiguous more frequently by
# ignoring strides on dimensions of size 1.
assert x.flags["C_CONTIGUOUS"] is True, (x.flags, y.flags)
assert x.flags["F_CONTIGUOUS"] is False, (x.flags, y.flags)
assert y.flags["C_CONTIGUOUS"] is False, (x.flags, y.flags)
# That depend of numpy version.
# assert y.flags["F_CONTIGUOUS"] is True, (x.flags, y.flags)
else:
if not (skip_single_f and x.shape == ()):
# Numpy below 1.6.0 does not have a consistent handling of
# f-contiguous for 0-d arrays
if not any([s == 1 for s in x.shape]):
# Numpy 1.10 can set f contiguous more frequently by
# ignoring strides on dimensions of size 1.
assert x.flags["F_CONTIGUOUS"] == y.flags["F_CONTIGUOUS"], (
x.flags, y.flags)
else:
assert x.flags["F_CONTIGUOUS"]
assert x.flags["WRITEABLE"] == y.flags["WRITEABLE"], (x.flags, y.flags)
# Don't check for OWNDATA since it is always true for a GpuArray
assert x.flags["ALIGNED"] == y.flags["ALIGNED"], (x.flags, y.flags)
assert x.flags["WRITEBACKIFCOPY"] == y.flags["WRITEBACKIFCOPY"], (x.flags,
y.flags)
def check_meta_only(x, y):
assert isinstance(x, gpuarray.GpuArray)
assert x.shape == y.shape
assert x.dtype == y.dtype
if y.size != 0:
assert x.strides == y.strides
def check_content(x, y):
assert isinstance(x, gpuarray.GpuArray)
assert numpy.allclose(numpy.asarray(x), numpy.asarray(y))
def check_meta(x, y):
check_meta_only(x, y)
check_flags(x, y)
def check_all(x, y):
check_meta(x, y)
check_content(x, y)
def check_meta_content(x, y):
check_meta_only(x, y)
check_content(x, y)
def gen_gpuarray(shape_orig, dtype='float32', offseted_outer=False,
offseted_inner=False, sliced=1, order='c', nozeros=False,
incr=0, ctx=None, cls=None):
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
low = 0.0
if nozeros:
low = 1.0
a = numpy.random.uniform(low, 10.0, shape)
a += incr
a = numpy.asarray(a, dtype=dtype)
b = gpuarray.array(a, context=ctx, cls=cls)
assert order in ['c', 'f']
if order == 'f' and len(shape) > 0:
a = numpy.asfortranarray(a)
b = gpuarray.asfortranarray(b)
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:]
if offseted_inner and len(shape) > 0:
# The b[..., 1:] act as the test for this subtensor case.
b = b[..., 1:]
a = a[..., 1:]
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)), (a, numpy.asarray(b))
return a, b
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