def test_not_lazy_if_inplace(self):
# Tests that if the outputs are scalars and the graph is big,
# we disable the inplace opt to speed up optimization
x = tensor.vector('x', dtype=self.dtype)
y = tensor.vector('y', dtype=self.dtype)
c = tensor.iscalar('c')
mode = theano.compile.get_mode(self.mode).excluding(
# Disable many opt to keep the graph big enough to disable
# the opt.
'fusion',
'local_add_canonizer',
'inplace',
'constant_folding',
'constant_folding')
y2 = reduce(lambda x, y: x + y, [y] + list(range(200)))
f = theano.function([c, x, y], ifelse(c, x, y2), mode=mode)
# For not inplace ifelse
self.assertFunctionContains1(f, IfElse(1))
rng = numpy.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200)
ylen = rng.randint(200)
vx = numpy.asarray(rng.uniform(size=(xlen, )), self.dtype)
vy = numpy.asarray(rng.uniform(size=(ylen, )), self.dtype)
assert numpy.allclose(vx, f(1, vx, vy))
assert numpy.allclose(vy + sum(range(200)), f(0, vx, vy))
def local_gpua_lazy_ifelse(node, context_name):
if node.op.gpu:
return
c = node.inputs[0]
inps = []
for v in node.inputs[1:]:
if isinstance(v.type, (tensor.TensorType, GpuArrayType)):
inps.append(as_gpuarray_variable(v, context_name))
else:
inps.append(v)
return IfElse(node.op.n_outs, gpu=True)(c, *inps, return_list=True)
def get_ifelse(self, n):
if theano.config.mode == "FAST_COMPILE":
return IfElse(n)
else:
return IfElse(n, as_view=True)
def local_gpua_lazy_ifelse(node, context_name):
if node.op.gpu:
return
c = node.inputs[0]
inps = [as_gpuarray_variable(v, context_name) for v in node.inputs[1:]]
return IfElse(node.op.n_outs, gpu=True)(c, *inps, return_list=True)
