def testMutate0():
lib = mkDefaultLib()
# region #+ Add recogFive to library
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive', recogDigitType, None))
# endregion
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkListSort(mkBoolTensorSort([1, 1]))
sort = mkFuncSort(inType, outType)
prog = PPFuncApp(fn=PPVar(name='lib.map_l'),
args=[PPVar(name='lib.recogFive')])
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
print(repr_py(newProg))
isort = inferType(newProg, lib)
assert isort is not None
assert sort == isort
def testInfer7():
lib = mkDefaultLib()
# region #+ Add recogFive to library
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive', recogDigitType, None))
# endregion
prog = PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(
args=[
PPFuncSort(
args=[
PPListSort(param_sort=PPSortVar(name='A_1'))
],
rtpe=PPListSort(param_sort=PPSortVar(name='B')))
],
rtpe=PPListSort(param_sort=PPTensorSort(
param_sort=PPBool(),
shape=[PPDimConst(
value=1), PPDimConst(value=1)])))),
PPVar(name='lib.map_l')
])
sortInferred = inferType(prog, lib)
assert sortInferred is None
def testMutate2():
"""
Unkprog
"""
lib = mkDefaultLib()
addRecogFive(lib)
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = \
PPTermUnk(name='nn_fun_x_906', sort=sort)
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
# print(repr_py(newProg))
# print('newProg: ', newProg)
# print('newProgReprPy: ', repr_py(newProg))
isort = inferType(newProg, lib)
if isort is None:
print(repr_py(newProg))
print('newProg: ', newProg)
continue
assert sort == isort
def testInfer5():
lib = mkDefaultLib()
# region #+ Add recogFive to library
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive', recogDigitType, None))
# endregion
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = PPVar(name='lib.recogFive')
sortInferred = inferType(prog, lib)
print(repr_py_sort(sort))
print(sort)
print(repr_py_sort(sortInferred))
print(sortInferred)
assert sort == sortInferred
def testInfer2():
lib = mkDefaultLib()
sort = mkFuncSort(mkRealTensorSort([1, 1]), mkBoolTensorSort([1, 1]),
mkRealTensorSort([1, 1]))
prog = PPTermUnk(
name='nn_fun_x_906',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]),
PPTensorSort(param_sort=PPBool(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)])))
sortInferred = inferType(prog, lib)
print(repr_py_sort(sort))
print(sort)
print(repr_py_sort(sortInferred))
print(sortInferred)
assert sort == sortInferred
def testInfer8():
lib = mkDefaultLib()
addRecogFive(lib)
prog = PPVar(name='lib.map_l')
sortInferred = inferType(prog, lib)
print(repr_py_sort(sortInferred))
def func(term):
nonlocal cid
cid += 1
try:
sort = inferType(term, lib)
except ValueError as e:
print('ValueError: %s' % e)
print('prog: %s' % str(prog))
sort = None
if sort is not None:
idTermSorts.append((cid, term, sort))
return None
def testInfer3():
lib = mkDefaultLib()
sort = mkRealTensorSort([1, 1])
prog = PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=1)])
sortInferred = inferType(prog, lib)
print(repr_py_sort(sort))
print(sort)
print(repr_py_sort(sortInferred))
print(sortInferred)
assert sort == sortInferred
def mutate_rec(term):
nonlocal cid
cid += 1
newTerm = None
if cid < rid:
if isinstance(term, PPFuncApp):
childs = deconstructProg(term)
childSorts = getChildSorts(term, lib)
newChilds = []
for c, cs in zip(childs, childSorts):
if isinstance(cs, PPDimVar):
# Do not mutate DimVars that are direct function params. Example: zeros
nc = c
else:
nc = mutate_rec(c)
newChilds.append(nc)
newTerm = constructProg(term, newChilds)
else:
childs = deconstructProg(term)
newChilds = [mutate_rec(c) for c in childs]
newTerm = constructProg(term, newChilds)
elif cid == rid:
try:
tpe = inferType(term, lib)
if tpe is not None:
N = 10
for i in range(N):
maxDepth = progDepth(term) + 2
newTerm = pg.genProg(tpe, maxDepth)
if newTerm is not None:
break
# return same term if not able to generate a random term after N attempts.
if newTerm is None:
newTerm = term
except ValueError as e:
print('ValueError: %s' % e)
print('term: %s' % str(term))
newTerm = term
elif cid > rid:
newTerm = term
return newTerm
def testInfer9():
lib = mkDefaultLib()
addRecogFive(lib)
sort = mkFuncSort(mkListSort(mkRealTensorSort([1, 1, 28, 28])),
mkListSort(mkBoolTensorSort([1, 1])))
prog = PPFuncApp(
fn=PPVar(name='lib.map_l'),
args=[
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(name='Unk',
sort=PPFuncSort(args=[PPSortVar(name='C')],
rtpe=PPTensorSort(
param_sort=PPBool(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
])
],
rtpe=PPSortVar(name='C'))),
PPVar(name='lib.recogFive')
])
])
])
sortInferred = inferType(prog, lib)
assert sort == sortInferred
def testInfer13():
# lib.repeat(10, Unk)
prog = PPFuncApp(
fn=PPVar(name='lib.repeat'),
args=[
PPIntConst(value=10),
PPTermUnk(
name='Unk',
sort=PPFuncSort(
args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))))
])
prog = PPFuncApp(fn=PPVar(name='lib.repeat'),
args=[
PPIntConst(value=10),
PPTermUnk(name='Unk',
sort=PPFuncSort(args=[PPInt()],
rtpe=PPInt()))
])
lib = mkDefaultLib()
sortInferred = inferType(prog, lib)
print(sortInferred)
def testInfer1():
lib = mkDefaultLib()
# region #+ Add recogFive to library
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive', recogDigitType, None))
# endregion
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = \
PPFuncApp(fn=PPVar(name='lib.compose'),
args=[PPFuncApp(fn=PPVar(name='lib.fold_l'),
args=[PPTermUnk(name='nn_fun_x_906',
sort=PPFuncSort(
args=[PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]),
PPTensorSort(param_sort=PPBool(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)])],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))),
PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=1)])]),
PPFuncApp(fn=PPVar(name='lib.map_l'), args=[PPVar(name='lib.recogFive')])])
sortInferred = inferType(prog, lib)
print(repr_py_sort(sort))
print(sort)
print(repr_py_sort(sortInferred))
print(sortInferred)
assert sort == sortInferred
def testInfer6():
lib = mkDefaultLib()
inType = mkListSort(mkBoolTensorSort([1, 1]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = PPFuncApp(fn=PPVar(name='lib.fold_l'),
args=[
PPTermUnk(name='nn_fun_x_906',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
])
sortInferred = inferType(prog, lib)
print(repr_py_sort(sort))
print(sort)
print(repr_py_sort(sortInferred))
print(sortInferred)
assert sort == sortInferred
def testMutate1():
lib = mkDefaultLib()
addRecogFive(lib)
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = \
PPFuncApp(fn=PPVar(name='lib.compose'),
args=[PPFuncApp(fn=PPVar(name='lib.fold_l'),
args=[PPTermUnk(name='nn_fun_x_906',
sort=PPFuncSort(
args=[PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]),
PPTensorSort(param_sort=PPBool(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)])],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))),
PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=1)])]),
PPFuncApp(fn=PPVar(name='lib.map_l'), args=[PPVar(name='lib.recogFive')])])
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
# print(repr_py(newProg))
# print('newProg: ', newProg)
# print('newProgReprPy: ', repr_py(newProg))
isort = inferType(newProg, lib)
if isort is None:
print(repr_py(newProg))
print('newProg: ', newProg)
continue
assert sort == isort
def testInfer12_1():
lib = None
# lib.compose(lib.compose(lib.compose(Unk, lib.compose(Unk, lib.compose(lib.map_l, lib.map_l))), Unk),
# lib.map_l(lib.recogFive))
prog1 = PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(name='Unk',
sort=PPFuncSort(
args=[PPSortVar(name='C')],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPFuncSort(
args=[
PPListSort(
param_sort=PPListSort(
param_sort=
PPSortVar(
name='A_1_2')))
],
rtpe=PPListSort(
param_sort=PPListSort(
param_sort=PPSortVar(
name='B_1'))))
],
rtpe=PPSortVar(
name='C'))),
PPFuncApp(fn=PPVar(name='lib.compose'),
args=[
PPVar(name='lib.map_l'),
PPVar(name='lib.map_l')
])
])
]),
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(
value=1),
PPDimConst(value=1)
]))
],
rtpe=PPFuncSort(
args=[PPSortVar(name='A_1_2')],
rtpe=PPSortVar(name='B_1'))))
]),
PPFuncApp(fn=PPVar(name='lib.map_l'),
args=[PPVar(name='lib.recogFive')])
])
prog1 = PPFuncApp(fn=PPVar(name='lib.map_l'),
args=[PPVar(name='lib.recogFive')])
lib = mkDefaultLib()
addRecogFive(lib)
sortInferred = inferType(prog1, lib)
print(sortInferred)
sort = PPFuncSort(args=[
PPFuncSort(args=[
PPListSort(param_sort=PPListSort(param_sort=PPListSort(
param_sort=PPSortVar(name='A_1'))))
],
rtpe=PPListSort(param_sort=PPListSort(param_sort=PPListSort(
param_sort=PPSortVar(name='B')))))
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))
print(repr_py_sort(sort))
def testInfer11():
lib = mkDefaultLib()
addRecogFive(lib)
sort = mkFuncSort(mkListSort(mkRealTensorSort([1, 1, 28, 28])),
mkListSort(mkBoolTensorSort([1, 1])))
prog = PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(
args=[PPSortVar(name='B_1')],
rtpe=PPListSort(
param_sort=PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(
value=1),
PPDimConst(value=1)
])))),
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPFuncSort(args=[
PPListSort(param_sort=PPSortVar(name='A'))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='B_1_2')))
],
rtpe=PPSortVar(name='B_1')))
]),
PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPVar(name='lib.map_l'),
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(
value=1),
PPDimConst(
value=1),
PPDimConst(
value=28),
PPDimConst(
value=28)
]))
],
rtpe=PPFuncSort(
args=[PPSortVar(name='A')],
rtpe=PPSortVar(name='B_1_2'))))
])
])
sortInferred = inferType(prog, lib)
# prog is type checked but currently inferType only works for concrete types.
assert sortInferred == sort
