rval = rval * rval
return rval
@gradient(4.0, 5.0, 2.0)
def test_while_2(x, y, z):
rval = 0
# Cannot compare to 0 or finite diff is unstable
while x > -0.1:
rval = rval + y
x = x - z
return rval
@gradient([1.0, 2.0, 3.0, 4.0],
pipeline=standard_debug_pipeline.configure(validate=False))
def test_list_while(xs):
y = 1.0
index = 0
while index < len(xs):
y = y * xs[index]
index = index + 1
return y
@gradient([1.0, 2.0, 3.0, 4.0],
pipeline=standard_pipeline,
backend=backend_all)
def test_list_for(xs):
y = 1
for x in xs:
return rval
@gradient(4.0, 5.0, 2.0, backend=backend_all)
def test_while_2(x, y, z):
rval = 0
# Cannot compare to 0 or finite diff is unstable
while x > -0.1:
rval = rval + y
x = x - z
return rval
@gradient(
[1.0, 2.0, 3.0, 4.0],
pipeline=standard_debug_pipeline.configure(validator=None),
backend=backend_except("relay"),
)
def test_list_while(xs):
# Fail with relay:
# myia.pipeline.ressources.default_convert receives
# a tvm.runtime.ADT and then fails because it cannot
# look for attribute __to_myia__
# TypeError: runtime.ADT is not registered via TVM_REGISTER_NODE_TYPE
y = 1.0
index = 0
while index < len(xs):
y = y * xs[index]
index = index + 1
return y
@grad_test(
2.0, )
def test_pow10(x):
v = x
j = 0
while j < 3:
i = 0
while i < 3:
v = v * x
i = i + 1
j = j + 1
return v
@grad_test(([1.0, 2.0, 3.0, 4.0], ),
pipeline=standard_debug_pipeline.configure(validate=False))
def test_list_while(xs):
y = 1.0
index = 0
while index < len(xs):
y = y * xs[index]
index = index + 1
return y
@grad_test(([1.0, 2.0, 3.0, 4.0], ), pipeline=standard_pipeline)
def test_list_for(xs):
y = 1
for x in xs:
y = y * x
return y
@mt(
infer_scalar(i32, result=i32),
infer_scalar(1, result=i64),
infer_scalar(-1, result=InferenceError(_msg)),
)
def test_assert_msg(x):
assert x >= 0, "x must be positive"
return x ** 0.5
@mt(
infer_scalar(i32, result=i32),
run_debug(4.5, result=4.5 ** 0.5),
run_debug(-5, result=Exception),
gradient(4.5, pipeline=standard_debug_pipeline.configure(validator=None)),
)
def test_raise(x):
if x >= 0:
return x ** 0.5
else:
raise Exception("sqrt of negative number")
@infer_scalar(i32, result=InferenceError(_msg))
def test_raise_unconditional(x):
raise Exception("I don't like your face")
@infer_scalar(i32, result=i32)
def test_raise_multiple(x):
@mt(
infer_scalar(i32, result=i32),
infer_scalar(1, result=i64),
infer_scalar(-1, result=InferenceError(_msg)),
)
def test_assert_msg(x):
assert x >= 0, 'x must be positive'
return x**0.5
@mt(
infer_scalar(i32, result=i32),
run_debug(4.5, result=4.5**0.5),
run_debug(-5, result=Exception),
gradient(4.5, pipeline=standard_debug_pipeline.configure(validate=False)),
)
def test_raise(x):
if x >= 0:
return x**0.5
else:
raise Exception("sqrt of negative number")
@infer_scalar(i32, result=InferenceError(_msg))
def test_raise_unconditional(x):
raise Exception("I don't like your face")
@infer_scalar(i32, result=i32)
def test_raise_multiple(x):
