def test_sparse_input_aliasing_affecting_inplace_operations(self):
##
# Note this test will never fail because I am not aware of any
# inplace op on sparse variables
try:
import scipy.sparse as sp
except ImportError:
# The variable enable_sparse will be used to disable the test file.
pass
from theano.sparse import enable_sparse
if not enable_sparse:
pytest.skip("Optional package sparse disabled")
from theano import sparse
# Note: to trigger this bug with theano rev 4586:2bc6fc7f218b,
# you need to make in inputs mutable (so that inplace
# operations are used) and to break the elemwise composition
# with some non-elemwise op (here dot)
x = sparse.SparseType("csc", dtype="float64")()
y = sparse.SparseType("csc", dtype="float64")()
f = theano.function(
[theano.In(x, mutable=True),
theano.In(y, mutable=True)], (x + y) + (x + y))
# Test 1. If the same variable is given twice
# Compute bogus values
m = sp.csc_matrix(
np.asarray(
[
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
],
dtype="float64",
))
bogus_vals = f(m, m)
# Since we used inplace operation v and m may be corrupted
# so we need to recreate them
m = sp.csc_matrix(
np.asarray(
[
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
],
dtype="float64",
))
m_copy = m.copy()
vals = f(m, m_copy)
assert np.allclose(vals.todense(), bogus_vals.todense())
def test_sparse_input_aliasing_affecting_inplace_operations(self):
sp = pytest.importorskip("scipy", minversion="0.7.0")
from theano import sparse
# Note: to trigger this bug with theano rev 4586:2bc6fc7f218b,
# you need to make in inputs mutable (so that inplace
# operations are used) and to break the elemwise composition
# with some non-elemwise op (here dot)
x = sparse.SparseType("csc", dtype="float64")()
y = sparse.SparseType("csc", dtype="float64")()
f = theano.function(
[theano.In(x, mutable=True), theano.In(y, mutable=True)], (x + y) + (x + y)
)
# Test 1. If the same variable is given twice
# Compute bogus values
m = sp.sparse.csc_matrix(
np.asarray(
[
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
],
dtype="float64",
)
)
bogus_vals = f(m, m)
# Since we used inplace operation v and m may be corrupted
# so we need to recreate them
m = sp.sparse.csc_matrix(
np.asarray(
[
[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
],
dtype="float64",
)
)
m_copy = m.copy()
vals = f(m, m_copy)
assert np.allclose(vals.todense(), bogus_vals.todense())
def make_node(self, diag):
diag = tensor.as_tensor_variable(diag)
if diag.type.ndim != 1:
raise TypeError('data argument must be a vector', diag.type)
return gof.Apply(self, [diag],
[sparse.SparseType(dtype=diag.dtype, format='csc')()])
def test_sparse(self):
mySymbolicSparseList = TypedListType(
sparse.SparseType('csr', theano.config.floatX))()
mySymbolicSparse = sparse.csr_matrix()
z = Count()(mySymbolicSparseList, mySymbolicSparse)
f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)
x = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
y = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
self.assertTrue(f([x, y, y], y) == 2)
def test_sparse(self):
sp = pytest.importorskip("scipy")
mySymbolicSparseList = TypedListType(
sparse.SparseType("csr", theano.config.floatX))()
mySymbolicSparse = sparse.csr_matrix()
z = Count()(mySymbolicSparseList, mySymbolicSparse)
f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)
x = sp.sparse.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
y = sp.sparse.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
assert f([x, y, y], y) == 2
def test_sparse(self):
if not scipy_imported:
raise SkipTest('Optional package SciPy not installed')
mySymbolicSparseList = TypedListType(
sparse.SparseType('csr', theano.config.floatX))()
mySymbolicSparse = sparse.csr_matrix()
z = Count()(mySymbolicSparseList, mySymbolicSparse)
f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)
x = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
y = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
self.assertTrue(f([x, y, y], y) == 2)
def test_sparse(self):
if not scipy_imported:
pytest.skip("Optional package SciPy not installed")
mySymbolicSparseList = TypedListType(
sparse.SparseType("csr", theano.config.floatX)
)()
mySymbolicSparse = sparse.csr_matrix()
z = Count()(mySymbolicSparseList, mySymbolicSparse)
f = theano.function([mySymbolicSparseList, mySymbolicSparse], z)
x = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
y = sp.csr_matrix(random_lil((10, 40), theano.config.floatX, 3))
assert f([x, y, y], y) == 2
def test_mul_s_v(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
x = S.SparseType(format, dtype=dtype)()
y = T.vector(dtype=dtype)
f = theano.function([x, y], S2.mul_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
out = f(spmat, mat)
assert numpy.allclose(out.toarray(), spmat.toarray() * mat)
def test_structured_add_s_v(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
x = S.SparseType(format, dtype=dtype)()
y = T.vector(dtype=dtype)
f = theano.function([x, y], S2.structured_add_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
spones = spmat.copy()
spones.data = numpy.ones_like(spones.data)
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
out = f(spmat, mat)
assert numpy.allclose(out.toarray(), spones.multiply(spmat + mat))
def make_node(self, diag):
return gof.Apply(self, [diag],
[sparse.SparseType(dtype=diag.dtype, format='csc')()])
