def test_may_share_memory_scipy():
a = scipy.sparse.csc_matrix(scipy.sparse.eye(5, 3))
b = scipy.sparse.csc_matrix(scipy.sparse.eye(4, 3))
as_ar = lambda a: theano._asarray(a, dtype='int32')
for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
(a, a.data, True), (a, a.indptr, True), (a, a.indices, True), (a, as_ar(a.shape), False),
(a.data, a, True), (a.indptr, a, True), (a.indices, a, True), (as_ar(a.shape), a, False),
(b, b.data, True), (b, b.indptr, True), (b, b.indices, True), (b, as_ar(b.shape), False),
(b.data, b, True), (b.indptr, b, True), (b.indices, b, True), (as_ar(b.shape), b, False),
(b.data, a, False), (b.indptr, a, False), (b.indices, a, False), (as_ar(b.shape), a, False),
]:
assert may_share_memory(a_, b_) == rep
assert may_share_memory(b_, a_) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False)]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def test_may_share_memory():
a = numpy.random.rand(5, 4)
b = numpy.random.rand(5, 4)
va = a.view()
vb = b.view()
ra = a.reshape((4, 5))
rb = b.reshape((4, 5))
ta = a.T
tb = b.T
for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
(a, a[0], True), (a, a[:, 0], True), (a, a.T, True),
(a, (0,), False), (a, 1, False), (a, None, False),
(a, va, True), (b, vb, True), (va, b, False), (a, vb, False),
(a, ra, True), (b, rb, True), (ra, b, False), (a, rb, False),
(a, ta, True), (b, tb, True), (ta, b, False), (a, tb, False),
]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False), ]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def test_may_share_memory_scipy():
a = scipy.sparse.csc_matrix(scipy.sparse.eye(5, 3))
b = scipy.sparse.csc_matrix(scipy.sparse.eye(4, 3))
def as_ar(a):
return theano._asarray(a, dtype='int32')
for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
(a, a.data, True), (a, a.indptr, True),
(a, a.indices, True), (a, as_ar(a.shape), False),
(a.data, a, True), (a.indptr, a, True),
(a.indices, a, True), (as_ar(a.shape), a, False),
(b, b.data, True), (b, b.indptr, True),
(b, b.indices, True), (b, as_ar(b.shape), False),
(b.data, b, True), (b.indptr, b, True),
(b.indices, b, True), (as_ar(b.shape), b, False),
(b.data, a, False), (b.indptr, a, False),
(b.indices, a, False), (as_ar(b.shape), a, False)]:
assert may_share_memory(a_, b_) == rep
assert may_share_memory(b_, a_) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False)]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def may_share_memory_core(a, b):
va = a.view()
vb = b.view()
ra = a.reshape((4, 5))
rb = b.reshape((4, 5))
ta = a.T
tb = b.T
for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
(a, a[0], True), (a, a[:, 0], True), (a, a.T, True),
(a, (0,), False), (a, 1, False), (a, None, False),
(a, va, True), (b, vb, True), (va, b, False),
(a, vb, False), (a, ra, True), (b, rb, True),
(ra, b, False), (a, rb, False), (a, ta, True),
(b, tb, True), (ta, b, False), (a, tb, False)]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False), ]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def test_may_share_memory_cuda():
from theano.misc.may_share_memory import may_share_memory
a = cuda.CudaNdarray(numpy.zeros((3, 4), dtype="float32"))
b = cuda.CudaNdarray(numpy.zeros((3, 4), dtype="float32"))
na = numpy.zeros((3, 4))
nb = numpy.zeros((3, 4))
va = a.view()
vb = b.view()
ra = a.reshape((4, 3))
rb = b.reshape((4, 3))
# can't test the transpose as ta._strides = is not implemented
# manual transpose of a
# ta = a.reshape((4,3))
# ta._strides = (ta._strides[1],ta._strides[0])#not implemented
# elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
# ta.gpudata += ta.size*elem_size
for a_, b_, rep in [
(a, a, True),
(b, b, True),
(a, b, False),
(a, na, False),
(b, nb, False),
(na, b, False),
(nb, a, False),
(a, va, True),
(b, vb, True),
(va, b, False),
(a, vb, False),
(a, ra, True),
(b, rb, True),
(ra, b, False),
(a, rb, False),
]:
assert may_share_memory(a_, b_) == rep
assert may_share_memory(b_, a_) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False)]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def test_may_share_memory_cuda():
from theano.misc.may_share_memory import may_share_memory
a = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
b = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
na = numpy.zeros((3, 4))
nb = numpy.zeros((3, 4))
va = a.view()
vb = b.view()
ra = a.reshape((4, 3))
rb = b.reshape((4, 3))
# can't test the transpose as ta._strides = is not implemented
# manual transpose of a
# ta = a.reshape((4,3))
# ta._strides = (ta._strides[1],ta._strides[0])#not implemented
# elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
# ta.gpudata += ta.size*elem_size
for a_, b_, rep in [
(a, a, True),
(b, b, True),
(a, b, False),
(a, na, False),
(b, nb, False),
(na, b, False),
(nb, a, False),
(a, va, True),
(b, vb, True),
(va, b, False),
(a, vb, False),
(a, ra, True),
(b, rb, True),
(ra, b, False),
(a, rb, False),
]:
assert may_share_memory(a_, b_) == rep
assert may_share_memory(b_, a_) == rep
# test that it raise error when needed.
for a_, b_, rep in [(a, (0, ), False), (a, 1, False), (a, None, False)]:
assert may_share_memory(a_, b_, False) == rep
assert may_share_memory(b_, a_, False) == rep
try:
may_share_memory(a_, b_)
raise Exception("An error was expected")
except TypeError:
pass
try:
may_share_memory(b_, a_)
raise Exception("An error was expected")
except TypeError:
pass
def test_inplace_set_value(self):
"""
We test that if the SharedVariable implement it we do inplace set_value
We also test this for partial inplace modification when accessing the internal of theano.
"""
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
shp = (100//4, 1024) # 100KB
x = np.zeros(shp, dtype=dtype)
x = self.cast_value(x)
x_shared = self.shared_constructor(x, borrow=True)
old_data = x_shared.container.storage[0]
nd = np.ones(shp, dtype=dtype)
if x.__class__.__name__ != 'csr_matrix':
# sparse matrix don't support inplace affectation
x_shared.container.value[:] = nd
assert (np.asarray(x_shared.get_value(borrow=True)) == nd).all()
# This should always share value!
assert may_share_memory(old_data, x_shared.container.storage[0])
assert may_share_memory(old_data, x_shared.get_value(borrow=True, return_internal_type=True))
nd[0] += 1
x_shared.container.value[0] = nd[0]
assert (np.asarray(x_shared.get_value(borrow=True)[0]) == nd[0]).all()
assert (np.asarray(x_shared.get_value(borrow=True)[1:]) == nd[1:]).all()
# This should always share value!
assert may_share_memory(old_data, x_shared.container.storage[0])
assert may_share_memory(old_data, x_shared.get_value(borrow=True, return_internal_type=True))
if x.__class__.__name__ != 'csr_matrix':
# sparse matrix don't support inplace affectation
nd += 1
# THIS DOENS'T DO WHAT WE EXPECT the content of a is
# not updated for GpuArray, but it is for ndarray
x_shared.get_value(borrow=True)[:] = nd
assert may_share_memory(old_data, x_shared.container.storage[0])
x_shared.get_value(borrow=True)
# Test by set_value with borrow=False
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd, borrow=False)
assert np.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_value_inplace
# Test by set_value with borrow=False when new data cast.
# specificaly useful for gpu data
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd), borrow=False)
assert np.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_cast_value_inplace
# Test by set_value with borrow=True
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd.copy(), borrow=True)
assert np.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_value_inplace
# Test by set_value with borrow=True when new data cast.
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd.copy()), borrow=True)
assert np.allclose(self.ref_fct(x_shared.get_value(borrow=True)), self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_cast_value_inplace
def test_inplace_set_value(self):
# We test that if the SharedVariable implement it we do inplace set_value
# We also test this for partial inplace modification when accessing the internal of theano.
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
shp = (100 // 4, 1024) # 100KB
x = np.zeros(shp, dtype=dtype)
x = self.cast_value(x)
x_shared = self.shared_constructor(x, borrow=True)
old_data = x_shared.container.storage[0]
nd = np.ones(shp, dtype=dtype)
if x.__class__.__name__ != "csr_matrix":
# sparse matrix don't support inplace affectation
x_shared.container.value[:] = nd
assert (np.asarray(
x_shared.get_value(borrow=True)) == nd).all()
# This should always share value!
assert may_share_memory(old_data,
x_shared.container.storage[0])
assert may_share_memory(
old_data,
x_shared.get_value(borrow=True, return_internal_type=True))
nd[0] += 1
x_shared.container.value[0] = nd[0]
assert (np.asarray(
x_shared.get_value(borrow=True)[0]) == nd[0]).all()
assert (np.asarray(
x_shared.get_value(borrow=True)[1:]) == nd[1:]).all()
# This should always share value!
assert may_share_memory(old_data,
x_shared.container.storage[0])
assert may_share_memory(
old_data,
x_shared.get_value(borrow=True, return_internal_type=True))
if x.__class__.__name__ != "csr_matrix":
# sparse matrix don't support inplace affectation
nd += 1
# THIS DOENS'T DO WHAT WE EXPECT the content of a is
# not updated for GpuArray, but it is for ndarray
x_shared.get_value(borrow=True)[:] = nd
assert may_share_memory(old_data,
x_shared.container.storage[0])
x_shared.get_value(borrow=True)
# Test by set_value with borrow=False
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd, borrow=False)
assert np.allclose(
self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)),
)
assert (may_share_memory(
old_data,
x_shared.container.storage[0]) == self.set_value_inplace)
# Test by set_value with borrow=False when new data cast.
# specificaly useful for gpu data
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd), borrow=False)
assert np.allclose(
self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)),
)
assert (may_share_memory(
old_data,
x_shared.container.storage[0]) == self.set_cast_value_inplace)
# Test by set_value with borrow=True
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd.copy(), borrow=True)
assert np.allclose(
self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)),
)
assert (may_share_memory(
old_data,
x_shared.container.storage[0]) == self.set_value_inplace)
# Test by set_value with borrow=True when new data cast.
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd.copy()), borrow=True)
assert np.allclose(
self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)),
)
assert (may_share_memory(
old_data,
x_shared.container.storage[0]) == self.set_cast_value_inplace)
def test_inplace_set_value(self):
"""
We test that if the SharedVariable implement it we do inplace set_value
We also test this for partial inplace modification when accessing the internal of theano.
"""
dtype = self.dtype
if dtype is None:
dtype = theano.config.floatX
shp = (100/4,1024)#100KB
x = numpy.zeros(shp, dtype=dtype)
x = self.cast_value(x)
x_shared = self.shared_constructor(x, borrow=True)
old_data = x_shared.container.storage[0]
nd = numpy.ones(shp, dtype=dtype)
if x.__class__.__name__ != 'csr_matrix':
#sparse matrix don't support inplace affectation
x_shared.container.value[:] = nd
assert (numpy.asarray(x_shared.get_value(borrow=True))==nd).all()
#This should always share value!
assert may_share_memory(old_data, x_shared.container.storage[0])
assert may_share_memory(old_data, x_shared.get_value(borrow=True, return_internal_type=True))
nd[0]+=1
x_shared.container.value[0] = nd[0]
assert (numpy.asarray(x_shared.get_value(borrow=True)[0])==nd[0]).all()
assert (numpy.asarray(x_shared.get_value(borrow=True)[1:])==nd[1:]).all()
#This should always share value!
assert may_share_memory(old_data, x_shared.container.storage[0])
assert may_share_memory(old_data, x_shared.get_value(borrow=True, return_internal_type=True))
if x.__class__.__name__ != 'csr_matrix':
#sparse matrix don't support inplace affectation
nd += 1
#THIS DON't DO WHAT WE EXPECT the contain of a is not updated for CudaNdarray, but it is for ndarray
x_shared.get_value(borrow=True)[:] = nd
#assert (numpy.asarray(x_shared.get_value(borrow=True))!=nd).all()
assert may_share_memory(old_data, x_shared.container.storage[0])
x_shared.get_value(borrow=True)
# Test by .value
# As we know that .value is deprecated, we filter out the warning
warnings.filterwarnings(
action='ignore',
message='The .value property of shared variables is deprecated.'
)
try:
nd += 1
old_data = x_shared.container.storage[0]
x_shared.value = nd
assert numpy.allclose(self.ref_fct(x_shared.value), self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_value_inplace
finally:
# Restore the default behavior.
# TODO There is a cleaner way to do this in Python 2.6, once
# Theano drops support of Python 2.4 and 2.5.
warnings.filterwarnings(
action='default',
message='The .value property of shared variables is deprecated.'
)
# Test by set_value with borrow=False
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd, borrow=False)
assert numpy.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_value_inplace
# Test by set_value with borrow=False when new data casted.
# specificaly useful for gpu data
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd), borrow=False)
assert numpy.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_casted_value_inplace
# Test by set_value with borrow=True
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(nd.copy(), borrow=True)
assert numpy.allclose(self.ref_fct(x_shared.get_value(borrow=True)),
self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_value_inplace
# Test by set_value with borrow=True when new data casted.
nd += 1
old_data = x_shared.container.storage[0]
x_shared.set_value(self.cast_value(nd.copy()), borrow=True)
assert numpy.allclose(self.ref_fct(x_shared.get_value(borrow=True)), self.ref_fct(self.cast_value(nd)))
assert may_share_memory(old_data, x_shared.container.storage[0]) == self.set_casted_value_inplace
