def test_merge_with_weird_eq():
"""numpy arrays don't compare equal like other python objects"""
#SCALAR CASE
x = T.constant(numpy.asarray(1), name='x')
y = T.constant(numpy.asarray(1), name='y')
g = Env([x, y], [x + y])
MergeOptimizer().optimize(g)
assert len(g.nodes) == 1
node = list(g.nodes)[0]
assert len(node.inputs) == 2
assert node.inputs[0] is node.inputs[1]
#NONSCALAR CASE
# This was created to test TensorConstantSignature
x = T.constant(numpy.ones(5), name='x')
y = T.constant(numpy.ones(5), name='y')
g = Env([x, y], [x + y])
MergeOptimizer().optimize(g)
assert len(g.nodes) == 1
node = list(g.nodes)[0]
assert len(node.inputs) == 2
assert node.inputs[0] is node.inputs[1]
def test_usage_loop_through_views_2():
x, y, z = inputs()
e0 = transpose_view(transpose_view(sigmoid(x)))
e = dot(add_in_place(x,y), transpose_view(e0))
g = Env([x,y,z], [e])
consistent(g) # because sigmoid can do the copy
g.replace(e0, x)
inconsistent(g) # we cut off the path to the sigmoid
def test_misc_2():
x, y, z = inputs()
tv = transpose_view(x)
e = add_in_place(x, tv)
g = Env([x,y], [e], False)
inconsistent(g)
g.replace(tv, x)
inconsistent(g)
def test_usage_loop_through_views():
x, y, z = inputs()
aip = add_in_place(x, y)
e = dot(aip, transpose_view(x))
g = Env([x,y,z], [e], False)
inconsistent(g)
g.replace_validate(aip, add(x, z))
consistent(g)
def test_value_repl_2():
x, y, z = inputs()
sy = sigmoid(y)
e = add_in_place(x, sy)
g = Env([x,y], [e], False)
consistent(g)
g.replace(sy, transpose_view(MyValue("abc")))
consistent(g)
def test_indirect_2():
x, y, z = inputs()
e0 = transpose_view(x)
e = dot(sigmoid(add_in_place(x, y)), e0)
g = Env([x,y,z], [e], False)
inconsistent(g)
new_e0 = add(e0, y)
g.replace(e0, new_e0)
consistent(g)
def test_indestructible_through_views():
x, y, z = inputs()
x.tag.indestructible = True
tv = transpose_view(x)
e = add_in_place(tv, y)
g = Env([x,y,z], [e], False)
inconsistent(g)
g.replace_validate(tv, sigmoid(x))
consistent(g)
def test_indestructible():
x, y, z = inputs()
x.tag.indestructible = True
x = copy(x)
assert x.tag.indestructible # checking if indestructible survives the copy!
e = add_in_place(x, y)
g = Env([x,y,z], [e], False)
inconsistent(g)
g.replace_validate(e, add(x, y))
consistent(g)
def test_repair_destroy_path():
x, y, z = inputs()
e1 = transpose_view(transpose_view(x))
e2 = transpose_view(transpose_view(e1))
e3 = add_in_place(e2, y)
e4 = add_in_place(e1, z)
g = Env([x,y,z], [e3, e4], False)
inconsistent(g)
g.replace(e2, transpose_view(x))
inconsistent(g)
def test_long_destroyers_loop():
x, y, z = inputs()
e = dot(dot(add_in_place(x,y), add_in_place(y,z)), add(z,x))
g = Env([x,y,z], [e])
consistent(g)
OpSubOptimizer(add, add_in_place).optimize(g)
consistent(g)
assert str(g) != "[Dot(Dot(AddInPlace(x, y), AddInPlace(y, z)), AddInPlace(z, x))]" # we don't want to see that!
e2 = dot(dot(add_in_place(x,y), add_in_place(y,z)), add_in_place(z,x))
try:
g2 = Env(*graph.clone([x,y,z], [e2]))
raise Exception("Shouldn't have reached this point.")
except InconsistencyError:
pass
def test_multiple(self):
# it should replace all occurrences of the pattern
x, y, z = inputs()
e = op1(op2(x, y), op2(x, y), op2(y, z))
g = Env([x, y, z], [e])
PatternOptimizer((op2, '1', '2'), (op4, '1')).optimize(g)
assert str(g) == "[Op1(Op4(x), Op4(x), Op4(y))]"
def test_nested_out_pattern(self):
x, y, z = inputs()
e = op1(x, y)
g = Env([x, y, z], [e])
PatternOptimizer((op1, '1', '2'),
(op4, (op1, '1'), (op2, '2'), (op3, '1', '2'))).optimize(g)
assert str(g) == "[Op4(Op1(x), Op2(y), Op3(x, y))]"
def test_aliased_inputs_replacement():
x, y, z = inputs()
tv = transpose_view(x)
tvv = transpose_view(tv)
sx = sigmoid(x)
e = add_in_place(x, tv)
g = Env([x, y], [e], False)
inconsistent(g)
g.replace(tv, sx)
consistent(g)
g.replace(sx, tv)
inconsistent(g)
g.replace(tv, tvv)
inconsistent(g)
g.replace(tv, sx)
consistent(g)
def test_merge_outputs(self):
x, y, z = inputs()
e1 = op3(op2(x, y))
e2 = op3(op2(x, y))
g = Env([x, y, z], [e1, e2])
MergeOptimizer().optimize(g)
assert str(g) == "[*1 -> Op3(Op2(x, y)), *1]"
def test_match_same(self):
x, y, z = inputs()
e = op1(x, x)
g = Env([x, y, z], [e])
PatternOptimizer((op1, 'x', 'y'),
(op3, 'x', 'y')).optimize(g)
assert str(g) == "[Op3(x, x)]"
def test_expand(self):
x, y, z = inputs()
e = op1(op1(op1(x)))
g = Env([x, y, z], [e])
PatternOptimizer((op1, '1'),
(op2, (op1, '1')), ign=True).optimize(g)
assert str(g) == "[Op2(Op1(Op2(Op1(Op2(Op1(x))))))]"
def test_nested_odd(self):
x, y, z = inputs()
e = op1(op1(op1(op1(op1(x)))))
g = Env([x, y, z], [e])
PatternOptimizer((op1, (op1, '1')),
'1').optimize(g)
assert str(g) == "[Op1(x)]"
def test_multi(self):
x, y, z = inputs()
e0 = op1(x, y)
e = op3(op4(e0), e0)
g = Env([x, y, z], [e])
PatternOptimizer((op4, (op1, 'x', 'y')), (op3, 'x', 'y')).optimize(g)
assert str(g) == "[Op3(Op4(*1 -> Op1(x, y)), *1)]"
def test_replace_subgraph(self):
# replacing inside the graph
x, y, z = inputs()
e = op1(op2(x, y), z)
g = Env([x, y, z], [e])
PatternOptimizer((op2, '1', '2'), (op1, '2', '1')).optimize(g)
assert str(g) == "[Op1(Op1(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 = Env([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_unification_2(self):
x, y, z = inputs()
e = op1(op2(x, y), z) # the arguments to op2 are different
g = Env([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) == "[Op1(Op2(x, y), z)]"
def test_replace_output(self):
# replacing the whole graph
x, y, z = inputs()
e = op1(op2(x, y), z)
g = Env([x, y, z], [e])
PatternOptimizer((op1, (op2, '1', '2'), '3'),
(op4, '3', '2')).optimize(g)
assert str(g) == "[Op4(z, y)]"
def test_constant_unification(self):
x = Constant(MyType(), 2, name='x')
y = MyVariable('y')
z = Constant(MyType(), 2, name='z')
e = op1(op1(x, y), y)
g = Env([y], [e])
PatternOptimizer((op1, z, '1'), (op2, '1', z)).optimize(g)
assert str(g) == "[Op1(Op2(y, z), y)]"
def test_multi_destroyers():
x, y, z = inputs()
e = add(add_in_place(x, y), add_in_place(x, y))
try:
g = Env([x,y,z], [e])
raise Exception("Shouldn't have reached this point.")
except InconsistencyError, e:
pass
def test_nested_even(self):
# regardless of the order in which we optimize, this
# should work
x, y, z = inputs()
e = op1(op1(op1(op1(x))))
g = Env([x, y, z], [e])
PatternOptimizer((op1, (op1, '1')), '1').optimize(g)
assert str(g) == "[x]"
def test_eq(self):
# replacing the whole graph
x, y, z = inputs()
e = op1(op_y(x, y), z)
g = Env([x, y, z], [e])
PatternOptimizer((op1, (op_z, '1', '2'), '3'),
(op4, '3', '2')).optimize(g)
str_g = str(g)
assert str_g == "[Op4(z, y)]"
def test_multi_destroyers_through_views():
x, y, z = inputs()
e = dot(add(transpose_view(z), y), add(z, x))
g = Env([x,y,z], [e])
consistent(g)
fail = FailureWatch()
OpSubOptimizer(add, add_in_place, fail).optimize(g)
consistent(g)
assert fail.failures == 1 # should have succeeded once and failed once
def test_usage_loop_insert_views():
x, y, z = inputs()
e = dot(add_in_place(x, add(y, z)), sigmoid(sigmoid(sigmoid(sigmoid(sigmoid(x))))))
g = Env([x,y,z], [e])
consistent(g)
fail = FailureWatch()
OpSubOptimizer(sigmoid, transpose_view, fail).optimize(g)
consistent(g)
assert fail.failures == 1 # it must keep one sigmoid in the long sigmoid chain
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 = Env([x, y, z], [e])
MergeOptimizer().optimize(g)
strg = str(g)
assert strg == "[Op1(*1 -> Op2(x, y), *1, *1)]" \
or strg == "[Op1(*1 -> Op2(x, z), *1, *1)]"
def test_ambiguous(self):
# this test should always work with TopoOptimizer and the
# ignore_newtrees flag set to False. Behavior with ignore_newtrees
# = True or with other NavigatorOptimizers may differ.
x, y, z = inputs()
e = op1(op1(op1(op1(op1(x)))))
g = Env([x, y, z], [e])
TopoPatternOptimizer((op1, (op1, '1')), (op1, '1'),
ign=False).optimize(g)
assert str(g) == "[Op1(x)]"
def test_straightforward(self):
x, y, z = inputs()
e0 = dot(y, z)
e = add(add(sigmoid(x), sigmoid(sigmoid(z))), dot(add(x, y), e0))
g = Env([x, y, z], [e])
g.extend(NodeFinder())
assert hasattr(g, 'get_nodes')
for type, num in ((add, 3), (sigmoid, 3), (dot, 2)):
if not len([x for x in g.get_nodes(type)]) == num:
raise Exception("Expected: %i times %s" % (num, type))
new_e0 = add(y, z)
assert e0.owner in g.get_nodes(dot)
assert new_e0.owner not in g.get_nodes(add)
g.replace(e0, new_e0)
assert e0.owner not in g.get_nodes(dot)
assert new_e0.owner in g.get_nodes(add)
for type, num in ((add, 4), (sigmoid, 3), (dot, 1)):
if not len([x for x in g.get_nodes(type)]) == num:
raise Exception("Expected: %i times %s" % (num, type))
def test_misc():
x, y, z = inputs()
e = transpose_view(transpose_view(transpose_view(transpose_view(x))))
g = Env([x,y,z], [e])
consistent(g)
chk = g.checkpoint()
PatternOptimizer((transpose_view, (transpose_view, 'x')), 'x').optimize(g)
assert str(g) == "[x]"
new_e = add(x,y)
g.replace_validate(x, new_e)
assert str(g) == "[Add(x, y)]"
g.replace(new_e, dot(add_in_place(x,y), transpose_view(x)))
assert str(g) == "[Dot(AddInPlace(x, y), TransposeView(x))]"
inconsistent(g)
g.revert(chk)
consistent(g)
assert str(g) == "[TransposeView(TransposeView(TransposeView(TransposeView(x))))]"
def test_destroyers_loop():
# AddInPlace(x, y) and AddInPlace(y, x) should not coexist
x, y, z = inputs()
e1 = add(x, y)
e2 = add(y, x)
g = Env([x,y,z], [e1, e2])
chk = g.checkpoint()
consistent(g)
g.replace_validate(e1, add_in_place(x, y))
consistent(g)
try:
g.replace_validate(e2, add_in_place(y, x))
raise Exception("Shouldn't have reached this point.")
except InconsistencyError:
pass
consistent(g)
g.revert(chk)
g.replace_validate(e2, add_in_place(y, x))
consistent(g)
try:
g.replace_validate(e1, add_in_place(x, y))
raise Exception("Shouldn't have reached this point.")
except InconsistencyError:
pass
consistent(g)