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 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_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_elemwise_collapse():
""" Test collapsing under many broadcast and strided pattern """
for dtype1 in ["int16", "float32", "int8"]:
for dtype2 in ["int16", "float32", "int8"]:
for shape1_, shape2_, expected in [
# 1d to test this special case
((40, ), (40, ), 0),
((40, ), (1, ), 1),
# No broadcastable dimensions
((4, 5, 6, 9), (4, 5, 6, 9), 0),
# All inputs have one(and the same) broadcastable dimension
((1, 4, 5, 9), (1, 4, 5, 9), 0),
((4, 1, 5, 9), (4, 1, 5, 9), 0),
((4, 5, 1, 9), (4, 5, 1, 9), 0),
((4, 5, 9, 1), (4, 5, 9, 1), 0),
# One inputs have one broadcastable dimension
((1, 5, 6, 9), (4, 5, 6, 9), 2),
((4, 1, 6, 9), (4, 5, 6, 9), 3),
((4, 5, 1, 9), (4, 5, 6, 9), 3),
((4, 5, 6, 1), (4, 5, 6, 9), 2),
# One inputs have two broadcastable dimension
((1, 1, 6, 9), (4, 5, 6, 9), 2),
((1, 5, 1, 9), (4, 5, 6, 9), 4),
((1, 5, 6, 1), (4, 5, 6, 9), 3),
((4, 1, 1, 9), (4, 5, 6, 9), 3),
((4, 1, 6, 1), (4, 5, 6, 9), 4),
((4, 5, 1, 1), (4, 5, 6, 9), 2),
# One inputs have tree broadcastable dimension
((1, 1, 1, 9), (4, 5, 6, 9), 2),
((1, 1, 6, 1), (4, 5, 6, 9), 3),
((1, 5, 1, 1), (4, 5, 6, 9), 3),
((4, 1, 1, 1), (4, 5, 6, 9), 2),
# One scalar
((1, 1, 1, 1), (4, 5, 6, 9), 1),
# One scalar, the other 1 broadcast dims
((1, 1, 1, 1), (4, 5, 6, 1), 1),
]:
scalar_cpu = rand((1, ) * len(shape1_), dtype=dtype1)
scalar_gpu = gpu_ndarray.GpuNdArrayObject(scalar_cpu)
scalar_gpu1 = MyGpuNdArray(scalar_gpu)
for shape1, shape2 in [(shape1_, shape2_), (shape2_, shape1_)]:
a_cpu = rand(shape1, dtype=dtype1)
a = gpu_ndarray.GpuNdArrayObject(a_cpu)
a1 = MyGpuNdArray(a)
b_cpu = rand(shape2, dtype=dtype2)
b = gpu_ndarray.GpuNdArrayObject(b_cpu)
b1 = MyGpuNdArray(b)
assert len(shape1) == len(shape2)
o_shape = []
for i in range(len(shape1)):
o_shape.append(max(shape1[i], shape2[i]))
o = gpu_ndarray.empty(o_shape, dtype=(a_cpu + b_cpu).dtype)
# 1.1 Check direct collapse
nd_collaps, info = elemwise_collapses([a, b], [o])
assert nd_collaps == expected, (shape1, shape2, nd_collaps,
expected, info)
# 1.2 Check computation are still valid
f = MyGpuNdArray.gen_fct(theano.tensor.add, [a1, b1],
len(shape1))
out = f([a1, b1])
out2 = f([a1, b1], out=out)
assert out is out2
assert numpy.allclose(numpy.asarray(f([a1, b1])),
a_cpu + b_cpu)
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(a1, b1)),
a_cpu + b_cpu)
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.add(a1, b1)), a_cpu + b_cpu)
assert MyGpuNdArray.add(a1, b1, out=out2) is out2
# 1.3 Check work without collaping
f = MyGpuNdArray.gen_fct(theano.tensor.add, [a1, b1],
len(shape1),
collapse=False)
out = f([a1, b1])
out2 = f([a1, b1], out=out)
assert out is out2
assert numpy.allclose(numpy.asarray(f([a1, b1])),
a_cpu + b_cpu)
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(a1, b1)),
a_cpu + b_cpu)
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.add(a1, b1)), a_cpu + b_cpu)
assert MyGpuNdArray.add(a1, b1, out=out2) is out2
# 2.1 What if we add a scalar?
nd_collaps, info = elemwise_collapses([a, b, scalar_gpu],
[o])
if expected == 0:
expected2 = 1
else:
expected2 = expected
assert nd_collaps == expected2, (shape1, shape2,
nd_collaps, expected,
info)
# 2.2 Check computation
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(a1, b1, scalar_gpu1)),
a_cpu + b_cpu + scalar_cpu)
# 3.1 What if one of the dimensions is strided?
broadcast = any(
[True for i in a.shape + b.shape if i == 1])
if expected == 0:
expected2 = 2
else:
expected2 = expected
if len(shape1_) != 4:
continue
if a.shape[0] != 1:
shape = list(shape1)
shape[0] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2, nd_collaps,
expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[1] != 1:
shape = list(shape1)
shape[1] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2, nd_collaps,
expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
pass
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[2] != 1:
shape = list(shape1)
shape[2] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(c_cpu)[::, ::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2, nd_collaps,
expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == expected2, err
pass
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
if a.shape[3] != 1:
shape = list(shape1)
shape[3] *= 2
c_cpu = rand(shape, dtype='float32')
c = gpu_ndarray.GpuNdArrayObject(
c_cpu)[::, ::, ::, ::2]
c1 = MyGpuNdArray(c)
err = ("strided", c.shape, shape2, nd_collaps,
expected, info)
nd_collaps, info = elemwise_collapses([c, b], [o])
if broadcast:
assert nd_collaps >= expected, err
else:
assert nd_collaps == 1, err
pass
assert numpy.allclose(
numpy.asarray(MyGpuNdArray.adds(c1, b1)),
numpy.asarray(c) + b_cpu)
def test_elemwise_mixed_dtype():
to_cpu = numpy.asarray
for dtype1 in ["int16", "float32", "int8"]:
for dtype2 in ["int16", "float32", "int8"]:
dtypeo = str((numpy.zeros(1, dtype=dtype1) +
numpy.zeros(1, dtype=dtype2)).dtype)
#print "dtypes", dtype1, dtype2, "o dtype", dtypeo
#print " Test inside a wrapping python object 2 inputs"
for shape in [(500, ), (50, 5), (5, 6, 7)]:
input_vals = [rand(shape, dtype) for dtype in [dtype1, dtype2]]
del dtype
gpu_vals = [
gpu_ndarray.GpuNdArrayObject(i) for i in input_vals
]
assert all([
numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)
])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = gpu_vals[0] + gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] + input_vals[1])
out = gpu_vals[0] - gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] - input_vals[1])
out = gpu_vals[0] * gpu_vals[1]
assert all_close(to_cpu(out), input_vals[0] * input_vals[1])
if dtypeo.startswith("float"):
# TODO: execute for all dtype
out = gpu_vals[0] / gpu_vals[1]
assert numpy.allclose(to_cpu(out),
input_vals[0] / input_vals[1])
nb_in = 4
#print " Test inside a wrapping python object %d inputs"%nb_in
for shape in [(500, ), (50, 5), (5, 6, 7)]:
input_vals = [
rand(shape, dtype)
for dtype in [dtype1, dtype2, dtype1, dtype2]
]
gpu_vals = [
gpu_ndarray.GpuNdArrayObject(i) for i in input_vals
]
assert all([
numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)
])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = MyGpuNdArray.adds(*gpu_vals)
assert numpy.allclose(to_cpu(out),
reduce(numpy.add, input_vals))
out = MyGpuNdArray.multiplys(*gpu_vals)
assert all_close(to_cpu(out), reduce(numpy.multiply,
input_vals))
#print " Test broadcasting"
for shapes in [
((1, 5), (4, 5)),
((33, 10), (33, 1)),
((33, 1, 5), (33, 10, 1)),
((33, 1, 5), (33, 10, 1), ((1, 10, 5))),
]:
input_vals = [
rand(shape, dtype)
for shape, dtype in zip(shapes, [dtype1, dtype2])
]
gpu_vals = [
gpu_ndarray.GpuNdArrayObject(i) for i in input_vals
]
assert all([
numpy.allclose(to_cpu(ig), i)
for ig, i in zip(gpu_vals, input_vals)
])
gpu_vals = [MyGpuNdArray(x) for x in gpu_vals]
out = MyGpuNdArray.adds(*gpu_vals)
assert numpy.allclose(to_cpu(out),
reduce(numpy.add, input_vals))
out = MyGpuNdArray.multiplys(*gpu_vals)
assert all_close(to_cpu(out), reduce(numpy.multiply,
input_vals))