def test_replace_output(self):
# replacing the whole graph
x, y, z = inputs()
e = op1(op2(x, y), z)
g = FunctionGraph([x, y, z], [e])
PatternOptimizer((op1, (op2, "1", "2"), "3"),
(op4, "3", "2")).optimize(g)
assert str(g) == "FunctionGraph(Op4(z, y))"
def test_2(self):
x, y, z = map(MyVariable, "xyz")
e = op1(op1(op3(x, y)))
g = FunctionGraph([x, y, z], [e])
# print g
opt = EquilibriumOptimizer(
[
PatternSub((op1, (op2, "x", "y")), (op4, "x", "y")),
PatternSub((op3, "x", "y"), (op4, "x", "y")),
PatternSub((op4, "x", "y"), (op5, "x", "y")),
PatternSub((op5, "x", "y"), (op6, "x", "y")),
PatternSub((op6, "x", "y"), (op2, "x", "y")),
],
max_use_ratio=10,
)
opt.optimize(g)
assert str(g) == "FunctionGraph(Op2(x, y))"
def test_nested_out_pattern(self):
x, y, z = inputs()
e = op1(x, y)
g = FunctionGraph([x, y, z], [e])
PatternOptimizer(
(op1, "1", "2"),
(op4, (op1, "1"), (op2, "2"), (op3, "1", "2"))).optimize(g)
assert str(g) == "FunctionGraph(Op4(Op1(x), Op2(y), Op3(x, y)))"
def test_no_recurse(self):
# if the out pattern is an acceptable in pattern
# and that the ignore_newtrees flag is True,
# it should do the replacement and stop
x, y, z = inputs()
e = op1(op2(x, y), z)
g = FunctionGraph([x, y, z], [e])
PatternOptimizer((op2, "1", "2"), (op2, "2", "1"), ign=True).optimize(g)
assert str(g) == "[Op1(Op2(y, x), z)]"
def test_unification_1(self):
x, y, z = inputs()
e = op1(op2(x, x), z) # the arguments to op2 are the same
g = FunctionGraph([x, y, z], [e])
PatternOptimizer(
(op1, (op2, "1", "1"), "2"), # they are the same in the pattern
(op4, "2", "1"),
).optimize(g)
# So the replacement should occur
assert str(g) == "[Op4(z, x)]"
def test_identical_constant_args(self):
x = MyVariable("x")
y = Constant(MyType(), 2, name="y")
z = Constant(MyType(), 2, name="z")
with config.change_flags(compute_test_value="off"):
e1 = op1(y, z)
g = FunctionGraph([x, y, z], [e1])
MergeOptimizer().optimize(g)
strg = str(g)
assert strg == "FunctionGraph(Op1(y, y))" or strg == "FunctionGraph(Op1(z, z))"
def test_unification_2(self):
x, y, z = inputs()
e = op1(op2(x, y), z) # the arguments to op2 are different
g = FunctionGraph([x, y, z], [e])
PatternOptimizer(
(op1, (op2, "1", "1"), "2"), # they are the same in the pattern
(op4, "2", "1"),
).optimize(g)
# The replacement should NOT occur
assert str(g) == "FunctionGraph(Op1(Op2(x, y), z))"
def test_constant_merging(self):
x = MyVariable("x")
y = Constant(MyType(), 2, name="y")
z = Constant(MyType(), 2, name="z")
e = op1(op2(x, y), op2(x, y), op2(x, z))
g = FunctionGraph([x, y, z], [e])
MergeOptimizer().optimize(g)
strg = str(g)
assert (strg == "FunctionGraph(Op1(*1 -> Op2(x, y), *1, *1))"
or strg == "FunctionGraph(Op1(*1 -> Op2(x, z), *1, *1))")
def test_pickle(self):
var1 = op1()
var2 = op2()
var3 = op1(var1)
var4 = op2(var3, var2)
func = FunctionGraph([var1, var2], [var4])
s = pickle.dumps(func)
new_func = pickle.loads(s)
assert all(
type(a) == type(b) for a, b in zip(func.inputs, new_func.inputs))
assert all(
type(a) == type(b) for a, b in zip(func.outputs, new_func.outputs))
assert all(
type(a.op) is type(b.op) # noqa: E721
for a, b in zip(func.apply_nodes, new_func.apply_nodes))
assert all(a.type == b.type
for a, b in zip(func.variables, new_func.variables))
def test_pre_greedy_local_optimizer():
empty_fgraph = FunctionGraph([], [])
x = MyVariable("x")
y = MyVariable("y")
c1 = Constant(MyType(), 1, "c1")
c2 = Constant(MyType(), 2, "c2")
o1 = op2(c1, c2)
o3 = op1(c1, y)
o2 = op1(o1, c2, x, o3, o1)
assert o2.owner.inputs[0].owner is not None
assert o2.owner.inputs[4].owner is not None
# This should fold `o1`, because it has only `Constant` arguments, and
# replace it with the `Constant` result
cst = pre_greedy_local_optimizer(empty_fgraph, [constant_folding], o2)
assert cst.owner.inputs[0].owner is None
assert cst.owner.inputs[1] is c2
assert cst.owner.inputs[2] is x
assert cst.owner.inputs[3] is o3
assert cst.owner.inputs[4] is cst.owner.inputs[0]
# We're going to do it again, except this time `o1` is
# in the `fgraph`, so it shouldn't be folded
fg = FunctionGraph([], [o1], clone=False)
o2 = op1(o1, c2, x, o3, o1)
cst = pre_greedy_local_optimizer(fg, [constant_folding], o2)
assert cst.owner.inputs[0] is o1
assert cst.owner.inputs[4] is cst.owner.inputs[0]
# What exactly is this supposed to test?
ms = MakeSlice()(1)
cst = pre_greedy_local_optimizer(empty_fgraph, [constant_folding], ms)
assert isinstance(cst, SliceConstant)
# Make sure constant of slice signature is hashable.
assert isinstance(hash(cst.signature()), int)
def test_pre_constant_merge():
empty_fgraph = FunctionGraph([], [])
x = MyVariable("x")
y = MyVariable("y")
c1 = Constant(MyType(), 1, "c1")
c2 = Constant(MyType(), 1, "c1")
o1 = op2(c1, x)
o2 = op1(o1, y, c2)
assert c1 is not c2
res = pre_constant_merge(empty_fgraph, [o2])
assert [o2] == res
assert o2.owner.inputs[2] is c1
o2 = op1(o1, y, c2)
fg = FunctionGraph([x, y], [o2], clone=False)
assert o2.owner in fg.apply_nodes
res = pre_constant_merge(fg, [o2])
assert res == [o2]
assert o2.owner.inputs[2] is c2
# What is this supposed to test?
ms = MakeSlice()(1)
res = pre_constant_merge(empty_fgraph, [ms])
assert res == [ms]
const_slice = SliceConstant(type=slicetype, data=slice(1, None, 2))
assert isinstance(const_slice, Constant)
adv = AdvancedSubtensor()(tt.matrix(), [2, 3], const_slice)
res = pre_constant_merge(empty_fgraph, adv)
assert res == [adv]
def test_identical_constant_args(self):
x = MyVariable("x")
y = Constant(MyType(), 2, name="y")
z = Constant(MyType(), 2, name="z")
ctv_backup = config.compute_test_value
config.compute_test_value = "off"
try:
e1 = op1(y, z)
finally:
config.compute_test_value = ctv_backup
g = FunctionGraph([x, y, z], [e1])
MergeOptimizer().optimize(g)
strg = str(g)
assert strg == "FunctionGraph(Op1(y, y))" or strg == "FunctionGraph(Op1(z, z))"
def test_contains(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
assert var1 in fg
assert var3 in fg
assert var3.owner in fg
assert var5 in fg
assert var5.owner in fg
def test_replace_bad_state(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
with pytest.raises(MissingInputError):
var0 = MyVariable("var0")
# FIXME TODO XXX: This breaks the state of the `FunctionGraph`,
# because it doesn't check for validity of the replacement *first*.
fg.replace(var1, var0, verbose=True)
def test_replace_circular(self):
"""`FunctionGraph` allows cycles--for better or worse."""
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
fg.replace_all([(var3, var4)])
# The following works (and is kind of gross), because `var4` has been
# mutated in-place
assert fg.apply_nodes == {var4.owner, var5.owner}
assert var4.owner.inputs == [var4, var2]
def test_init(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var1)
var4 = op2(var3, var2)
fg = FunctionGraph([var1, var2], [var3, var4], clone=False)
assert fg.inputs == [var1, var2]
assert fg.outputs == [var3, var4]
assert fg.apply_nodes == {var3.owner, var4.owner}
assert fg.update_mapping is None
assert fg.check_integrity() is None
assert fg.variables == {var1, var2, var3, var4}
assert fg.clients(var1) == [(var3.owner, 0)]
assert fg.clients(var2) == [(var4.owner, 1)]
assert fg.clients(var3) == [(var4.owner, 0), ("output", 0)]
assert fg.clients(var4) == [("output", 1)]
def test_remove_client(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
assert fg.variables == {var1, var2, var3, var4, var5}
assert fg.clients(var2) == [
(var3.owner, 0),
(var4.owner, 1),
(var5.owner, 1),
(var5.owner, 2),
]
fg.remove_client(var2, (var4.owner, 1))
assert fg.clients(var2) == [
(var3.owner, 0),
(var5.owner, 1),
(var5.owner, 2),
]
fg.remove_client(var1, (var3.owner, 1))
assert fg.clients(var1) == []
assert var4.owner in fg.apply_nodes
# This next `remove_client` should trigger a complete removal of `var4`'s
# variables and `Apply` node from the `FunctionGraph`.
#
# Also, notice that we already removed `var4` from `var2`'s client list
# above, so, when we completely remove `var4`, `fg.remove_client` will
# attempt to remove `(var4.owner, 1)` from `var2`'s client list again.
# This attempt would previously raise a `ValueError` exception, because
# the entry was not in the list.
fg.remove_client(var4, (var5.owner, 0), reason="testing")
assert var4.owner not in fg.apply_nodes
assert var4.owner.tag.removed_by == ["testing"]
assert not any(o in fg.variables for o in var4.owner.outputs)
def test_replace_test_value(self):
var1 = MyVariable("var1")
var1.tag.test_value = 1
var2 = MyVariable("var2")
var2.tag.test_value = 2
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var4.tag.test_value = np.array([1, 2])
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
var6 = op3()
var6.tag.test_value = np.array(0)
assert var6.tag.test_value.shape != var4.tag.test_value.shape
with pytest.raises(AssertionError, match="The replacement.*"):
fg.replace(var4, var6)
def test_replace(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
with pytest.raises(BadOptimization):
var0 = MyVariable2("var0")
# The types don't match and one cannot be converted to the other
fg.replace(var3, var0)
# Test a basic replacement
fg.replace_all([(var3, var1)])
assert var3 not in fg.variables
assert fg.apply_nodes == {var4.owner, var5.owner}
assert var4.owner.inputs == [var1, var2]
def test_import_node(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
var5 = MyVariable("var5")
var6 = op2(var5)
with pytest.raises(MissingInputError):
fg.import_node(var6.owner)
var6 = op2(var2)
assert not hasattr(var6.owner.tag, "imported_by")
fg.import_node(var6.owner)
assert hasattr(var6.owner.tag, "imported_by")
assert var6 in fg.variables
assert var6.owner in fg.apply_nodes
assert (var6.owner, 0) in var2.clients
def test_match_same(self):
x, y, z = inputs()
e = op1(x, x)
g = FunctionGraph([x, y, z], [e])
PatternOptimizer((op1, "x", "y"), (op3, "x", "y")).optimize(g)
assert str(g) == "FunctionGraph(Op3(x, x))"
def test_check_integrity(self):
var1 = MyVariable("var1")
var2 = MyVariable("var2")
var3 = op1(var2, var1)
var4 = op2(var3, var2)
var5 = op3(var4, var2, var2)
fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
with pytest.raises(Exception, match="The nodes are .*"):
fg.apply_nodes.remove(var5.owner)
fg.check_integrity()
with pytest.raises(Exception, match="Inconsistent clients.*"):
fg.apply_nodes.add(var5.owner)
var2.clients.remove((var5.owner, 1))
fg.check_integrity()
var2.clients.append((var5.owner, 1))
with pytest.raises(Exception, match="The variables are.*"):
fg.variables.remove(var4)
fg.check_integrity()
fg.variables.add(var4)
with pytest.raises(Exception, match="Undeclared input.*"):
var6 = MyVariable2("var6")
var6.fgraph = fg
var6.clients = [(var5.owner, 3)]
fg.variables.add(var6)
var5.owner.inputs.append(var6)
fg.check_integrity()
fg.variables.remove(var6)
var5.owner.inputs.remove(var6)
# TODO: What if the index value is greater than 1? It will throw an
# `IndexError`, but that doesn't sound like anything we'd want.
with pytest.raises(Exception, match="Inconsistent clients list.*"):
var4.clients.append(("output", 1))
fg.check_integrity()
var4.clients.remove(("output", 1))
with pytest.raises(Exception, match="Client not in FunctionGraph.*"):
var4.clients.append((var6.owner, 0))
fg.check_integrity()
var4.clients.remove((var6.owner, 0))
with pytest.raises(Exception, match="Inconsistent clients list.*"):
var4.clients.append((var3.owner, 0))
fg.check_integrity()
def test_no_merge(self):
x, y, z = inputs()
e = op1(op3(op2(x, y)), op3(op2(y, x)))
g = FunctionGraph([x, y, z], [e])
MergeOptimizer().optimize(g)
assert str(g) == "FunctionGraph(Op1(Op3(Op2(x, y)), Op3(Op2(y, x))))"
def test_deep_merge(self):
x, y, z = inputs()
e = op1(op3(op2(x, y), z), op4(op3(op2(x, y), z)))
g = FunctionGraph([x, y, z], [e])
MergeOptimizer().optimize(g)
assert str(g) == "FunctionGraph(Op1(*1 -> Op3(Op2(x, y), z), Op4(*1)))"
def test_straightforward(self):
x, y, z = inputs()
e = op1(op2(x, y), op2(x, y), op2(x, z))
g = FunctionGraph([x, y, z], [e])
MergeOptimizer().optimize(g)
assert str(g) == "FunctionGraph(Op1(*1 -> Op2(x, y), *1, Op2(x, z)))"
def test_straightforward_2(self):
x, y, z = inputs()
e = op1(op2(x), op3(y), op4(z))
g = FunctionGraph([x, y, z], [e])
OpSubOptimizer(op3, op4).optimize(g)
assert str(g) == "FunctionGraph(Op1(Op2(x), Op4(y), Op4(z)))"
def test_straightforward(self):
x, y, z = inputs()
e = op1(op1(op1(op1(op1(x)))))
g = FunctionGraph([x, y, z], [e])
OpSubOptimizer(op1, op2).optimize(g)
assert str(g) == "FunctionGraph(Op2(Op2(Op2(Op2(Op2(x))))))"
def test_expand(self):
x, y, z = inputs()
e = op1(op1(op1(x)))
g = FunctionGraph([x, y, z], [e])
PatternOptimizer((op1, "1"), (op2, (op1, "1")), ign=True).optimize(g)
assert str(g) == "FunctionGraph(Op2(Op1(Op2(Op1(Op2(Op1(x)))))))"
def test_nested_odd(self):
x, y, z = inputs()
e = op1(op1(op1(op1(op1(x)))))
g = FunctionGraph([x, y, z], [e])
PatternOptimizer((op1, (op1, "1")), "1").optimize(g)
assert str(g) == "FunctionGraph(Op1(x))"
