def local_inplace_remove0(node):
"""
Optimization to insert inplace versions of Remove0.
"""
if isinstance(node.op, 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,
failure_callback=gof.TopoOptimizer.warn_inplace),
60, 'fast_run', 'inplace')
@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
register_specialize(local_csm_properties_csm)
csm_grad, csm_grad_c,
usmm_csc_dense, usmm)
from theano.sparse import basic as sparse
from basic import _is_sparse_variable
# This is tested in tests/test_basic.py:UsmmTests
local_usmm = gof.opt.PatternSub(
(theano.tensor.sub, 'z',
(theano.tensor.mul,
{'pattern': 'alpha',
'constraint': lambda expr: numpy.all(expr.type.broadcastable)},
(sparse._dot, 'x', 'y'))),
(usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z'))
register_specialize(local_usmm, name="local_usmm")
# This is tested in tests/test_opt.py:test_local_csm_grad_c
@gof.local_optimizer([csm_grad(None)])
def local_csm_grad_c(node):
""" csm_grad(None) -> csm_grad_c """
if node.op == csm_grad(None):
return [csm_grad_c(*node.inputs)]
return False
register_specialize(local_csm_grad_c)
# This is tested in tests/test_opt.py:test_local_csm_properties_csm
@gof.local_optimizer([csm_properties])
def local_csm_properties_csm(node):
return [new_node]
return False
theano.compile.optdb.register(
'local_inplace_remove0',
gof.TopoOptimizer(local_inplace_remove0,
failure_callback=gof.TopoOptimizer.warn_inplace), 60,
'fast_run', 'inplace')
@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
register_specialize(local_csm_properties_csm)