def test_opfromgraph(self):
# as with the scan tests above, insert foreign inputs into the
# inner graph.
outer = tensor.scalar("outer")
shared = theano.shared(np.array(1.0, dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1.0, name="constant")
z = outer * (shared + constant)
# construct the inner graph
a = tensor.scalar()
b = tensor.scalar()
r = a + b
r.tag.replacement = z * (a - b)
# construct the outer graph
c = tensor.scalar()
d = tensor.scalar()
u = theano.OpFromGraph([a, b], [r])(c, d)
t = z * u
(v, ) = map_variables(self.replacer, [t])
t2 = z * v
f = theano.function([c, d, outer], [t, t2])
for m, n in itertools.combinations(range(10), 2):
assert f(m, n, outer=0.5) == [m + n, m - n]
# test that the unsupported case of replacement with a shared
# variable with updates crashes
shared.update = shared + 1
with pytest.raises(NotImplementedError):
map_variables(self.replacer, [t])
def test_scan(self):
x = tensor.vector('x')
# we will insert a subgraph involving these variables into the inner
# graph of scan. since they were not previously in the inner graph,
# they are like non_sequences to scan(). scan() infers these and
# imports them into the inner graph properly, and map_variables()
# should do this as well.
outer = tensor.scalar("outer")
shared = theano.shared(
numpy.array(1., dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1, name="constant")
# z will equal 1 so multiplying by it doesn't change any values
z = outer * (shared + constant)
def step(x, a):
r = a + x
r.tag.replacement = z * (a - x)
return r
s, _ = theano.scan(step, sequences=x,
outputs_info=[numpy.array(0.)])
# ensure z is owned by the outer graph so map_variables() will need to
# jump through additional hoops to placate FunctionGraph.
t = z * s
s2, = map_variables(self.replacer, [t])
t2 = z * s2
f = theano.function([x, outer], [t, t2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), outer=0.5)
assert numpy.array_equal(rval, [[1, 3, 6], [-1, -3, -6]])
def test_scan(self):
x = tensor.vector('x')
# we will insert a subgraph involving these variables into the inner
# graph of scan. since they were not previously in the inner graph,
# they are like non_sequences to scan(). scan() infers these and
# imports them into the inner graph properly, and map_variables()
# should do this as well.
outer = tensor.scalar("outer")
shared = theano.shared(
numpy.array(1., dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1, name="constant")
# z will equal 1 so multiplying by it doesn't change any values
z = outer * (shared + constant)
def step(x, a):
r = a + x
r.tag.replacement = z * (a - x)
return r
s, _ = theano.scan(step, sequences=x,
outputs_info=[numpy.array(0.)])
# ensure z is owned by the outer graph so map_variables() will need to
# jump through additional hoops to placate FunctionGraph.
t = z * s
s2, = map_variables(self.replacer, [t])
t2 = z * s2
f = theano.function([x, outer], [t, t2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), outer=0.5)
assert numpy.array_equal(rval, [[1, 3, 6], [-1, -3, -6]])
def test_opfromgraph(self):
# as with the scan tests above, insert foreign inputs into the
# inner graph.
outer = tensor.scalar("outer")
shared = theano.shared(
numpy.array(1., dtype=theano.config.floatX),
name="shared")
constant = tensor.constant(1., name="constant")
z = outer * (shared + constant)
# construct the inner graph
a = tensor.scalar()
b = tensor.scalar()
r = a + b
r.tag.replacement = z * (a - b)
# construct the outer graph
c = tensor.scalar()
d = tensor.scalar()
u = theano.OpFromGraph([a, b], [r])(c, d)
t = z * u
v, = map_variables(self.replacer, [t])
t2 = z * v
f = theano.function([c, d, outer], [t, t2])
for m, n in itertools.combinations(range(10), 2):
assert f(m, n, outer=0.5) == [m + n, m - n]
# test that the unsupported case of replacement with a shared
# variable with updates crashes
shared.update = shared + 1
self.assertRaises(NotImplementedError,
map_variables, self.replacer, [t])
def test_scan_with_shared_update(self):
x = tensor.vector("x")
# counts how many times its value is used
counter = theano.shared(0, name="shared")
counter.update = counter + 1
def step(x, a):
r = a + x
# introducing a shared variable with an update into the
# inner graph is unsupported and the code must crash rather
# than silently produce the wrong result.
r.tag.replacement = counter * (a - x)
return r
s, _ = theano.scan(step, sequences=x, outputs_info=[np.array(0.0)])
with pytest.raises(NotImplementedError):
map_variables(self.replacer, [s])
def test_leaf(self):
a = tensor.scalar("a")
b = tensor.scalar("b")
c = tensor.scalar("c")
b.tag.replacement = c
u = a + b
v, = map_variables(self.replacer, [u])
assert u.owner.inputs == [a, b]
assert v.owner.inputs == [a, c]
def test_leaf(self):
a = tensor.scalar("a")
b = tensor.scalar("b")
c = tensor.scalar("c")
b.tag.replacement = c
u = a + b
v, = map_variables(self.replacer, [u])
assert u.owner.inputs == [a, b]
assert v.owner.inputs == [a, c]
def test_leaf_inside_scan(self):
x = tensor.vector('x')
y = tensor.scalar('y')
z = tensor.scalar('z')
y.tag.replacement = z
s, _ = theano.scan(lambda x: x * y, sequences=x)
s2, = map_variables(self.replacer, [s])
f = theano.function([x, y, z], [s, s2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), y=1, z=2)
assert numpy.array_equal(rval, [[1, 2, 3], [2, 4, 6]])
def test_leaf_inside_scan(self):
x = tensor.vector('x')
y = tensor.scalar('y')
z = tensor.scalar('z')
y.tag.replacement = z
s, _ = theano.scan(lambda x: x * y, sequences=x)
s2, = map_variables(self.replacer, [s])
f = theano.function([x, y, z], [s, s2])
rval = f(x=numpy.array([1, 2, 3], dtype=numpy.float32), y=1, z=2)
assert numpy.array_equal(rval, [[1, 2, 3], [2, 4, 6]])
def apply_transforms(variables, reason, hyperparameters):
# tag all variables with their `id` so we can determine identity
# in the aftermath of cloning.
tag_with_ids(deep_ancestors(variables))
# want identical replacements for variables that were identical
# before cloning madness.
memory = dict()
def fn(var):
# get a clone that doesn't have the transforms
var, transforms = pop_transforms(var, reason)
for transform in transforms:
try:
newvar = memory[transform][var.tag.original_id]
except KeyError:
newvar = transform(var, **hyperparameters)
tag_with_ids(deep_ancestors([newvar]))
memory.setdefault(
transform, dict())[var.tag.original_id] = newvar
var = newvar
return var
from theano.scan_module.scan_utils import map_variables
return map_variables(fn, variables)
