def test_may_share_memory():
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 SparseType.may_share_memory(a_, b_) == rep
def test_sparse_shared_memory():
# Note : There are no inplace ops on sparse matrix yet. If one is
# someday implemented, we could test it here.
a = random_lil((3, 4), 'float32', 3).tocsr()
m1 = random_lil((4, 4), 'float32', 3).tocsr()
m2 = random_lil((4, 4), 'float32', 3).tocsr()
x = SparseType('csr', dtype='float32')()
y = SparseType('csr', dtype='float32')()
sdot = theano.sparse.structured_dot
z = sdot(x * 3, m1) + sdot(y * 2, m2)
f = theano.function(
[theano.In(x, mutable=True),
theano.In(y, mutable=True)],
z,
mode='FAST_RUN')
def f_(x, y, m1=m1, m2=m2):
return numpy.dot(x * 3, m1) + numpy.dot(y * 2, m2)
assert SparseType.may_share_memory(a, a) # This is trivial
result = f(a, a)
result_ = f_(a, a)
assert (result_.todense() == result.todense()).all()
def test_may_share_memory():
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 SparseType.may_share_memory(a_, b_) == rep
def test_sparse_shared_memory():
# Note : There are no inplace ops on sparse matrix yet. If one is someday implemented, we could test it here.
a = random_lil((3,4), 'float32', 3).tocsr()
m1 = random_lil((4,4), 'float32', 3).tocsr()
m2 = random_lil((4,4), 'float32', 3).tocsr()
x = SparseType('csr', dtype='float32')()
y = SparseType('csr', dtype='float32')()
sdot = theano.sparse.structured_dot
z = sdot(x*3,m1) + sdot(y*2, m2)
f = theano.function([theano.In(x,mutable=True),theano.In(y,mutable = True)],z, mode='FAST_RUN')
def f_(x,y,m1=m1,m2=m2):
return numpy.dot(x*3,m1) + numpy.dot(y*2,m2)
assert SparseType.may_share_memory(a,a) #This is trivial
result = f(a,a)
result_ = f_(a,a)
assert (result_.todense() == result.todense()).all()