CSx = CSC
mul_s_d_csx = mul_s_d_csc
elif svar.type.format == 'csr':
CSx = CSR
mul_s_d_csx = mul_s_d_csr
else:
raise NotImplemented()
c_data = mul_s_d_csx(csm_data(svar), csm_indices(svar),
csm_indptr(svar), dvar)
return [CSx(c_data, csm_indices(svar), csm_indptr(svar),
csm_shape(svar))]
return False
register_specialize(local_mul_s_d)
class MulSDCSC(gof.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, a_data, a_indices, a_indptr, b):
assert b.type.ndim == 2
return gof.Apply(self, [a_data, a_indices, a_indptr, b],
[tensor.tensor(b.dtype, (False,))])
#def perform(self, node, (a_data, a_indices, a_indptr, b), (out,)):
# This is tested in tests/test_opt.py:test_local_csm_properties_csm
@gof.local_optimizer([csm_properties])
def local_csm_properties_csm(node):
"""if we find csm_properties(CSM(*args)), then we can replace that with the
*args directly"""
if node.op == csm_properties:
csm, = node.inputs
if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
# csm.owner.inputs could be broadcastable. In that case, we have
# to adjust the broadcasting flag here.
ret_var = [theano.tensor.patternbroadcast(i, o.broadcastable)
for i, o in izip(csm.owner.inputs, node.outputs)]
return ret_var
return False
sparse.register_specialize(local_csm_properties_csm)
# This is tested in tests/test_basic.py:test_remove0
@gof.local_optimizer([None])
def local_inplace_remove0(node):
"""
Optimization to insert inplace versions of Remove0.
"""
if isinstance(node.op, sparse.Remove0) and not node.op.inplace:
new_op = node.op.__class__(inplace=True)
new_node = new_op(*node.inputs)
return [new_node]
return False
theano.compile.optdb.register('local_inplace_remove0',
gof.TopoOptimizer(local_inplace_remove0,
CSx = CSR
mul_s_d_csx = mul_s_d_csr
else:
raise NotImplemented()
c_data = mul_s_d_csx(csm_data(svar), csm_indices(svar),
csm_indptr(svar), dvar)
return [
CSx(c_data, csm_indices(svar), csm_indptr(svar), csm_shape(svar))
]
return False
register_specialize(local_mul_s_d)
class MulSDCSC(gof.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, a_data, a_indices, a_indptr, b):
assert b.type.ndim == 2
return gof.Apply(self, [a_data, a_indices, a_indptr, b],
[tensor.tensor(b.dtype, (False, ))])
def c_code_cache_version(self):
