def test_local_csm_properties_csm():
data = vector()
indices, indptr, shape = (ivector(), ivector(), ivector())
mode = get_default_mode()
mode = mode.including("specialize", "local_csm_properties_csm")
for CS, cast in [
(sparse.CSC, sp.sparse.csc_matrix),
(sparse.CSR, sp.sparse.csr_matrix),
]:
f = aesara.function(
[data, indices, indptr, shape],
sparse.csm_properties(CS(data, indices, indptr, shape)),
mode=mode,
)
assert not any(
isinstance(node.op, (sparse.CSM, sparse.CSMProperties))
for node in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40), config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
def test_local_csm_grad_c():
data = vector()
indices, indptr, shape = (ivector(), ivector(), ivector())
mode = get_default_mode()
if aesara.config.mode == "FAST_COMPILE":
mode = Mode(linker="c|py", optimizer="fast_compile")
mode = mode.including("specialize", "local_csm_grad_c")
for CS, cast in [
(sparse.CSC, sp.sparse.csc_matrix),
(sparse.CSR, sp.sparse.csr_matrix),
]:
cost = aet_sum(sparse.DenseFromSparse()(CS(data, indices, indptr, shape)))
f = aesara.function(
[data, indices, indptr, shape], aesara.grad(cost, data), mode=mode
)
assert not any(
isinstance(node.op, sparse.CSMGrad) for node in f.maker.fgraph.toposort()
)
v = cast(random_lil((10, 40), config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)