def test_allow_input_downcast_int(self):
a = wvector("a") # int16
b = bvector("b") # int8
c = bscalar("c") # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert np.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
g([3], np.array([6], dtype="int16"), 0)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
g([3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert np.all(h([3], [6], 0) == 9)
with pytest.raises(TypeError):
h([3], np.array([6], dtype="int16"), 0)
with pytest.raises(TypeError):
h([3], [312], 0)
def test_cast_float16(self):
f16 = vector(dtype="float16")
f32 = fvector()
i8 = bvector()
f = aesara.function(
[f16, f32, i8],
[
f16.astype("float32"),
f32.astype("float16"),
f32.astype("float64"),
f16.astype("int8"),
f32.astype("int8"),
i8.astype("float16"),
i8.astype("float32"),
],
mode=mode_with_gpu,
)
d1 = (np.random.rand(4) * 10).astype("float16")
d2 = (np.random.rand(5) * 10).astype("float32")
d3 = (np.random.rand(6) * 10).astype("int8")
res = f(d1, d2, d3)
for i, out in enumerate(f.outputs):
dtype = out.variable.dtype
assert res[i].dtype == dtype
inp = out.variable.owner.inputs[0]
if inp.dtype == "float16":
d = d1
elif inp.dtype == "float32":
d = d2
else:
d = d3
assert_allclose(d.astype(dtype), res[i])
def test_param_allow_downcast_int(self):
a = wvector("a") # int16
b = bvector("b") # int8
c = bscalar("c") # int8
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
f([3], np.array([6], dtype="int16"), 1)
# Value too big for a, silently ignored
assert np.all(f([2 ** 20], np.ones(1, dtype="int8"), 1) == 2)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
f([3], [312], 1)
# Value too big for c, raises TypeError
with pytest.raises(TypeError):
f([3], [6], 806)
def test_composite_elemwise_float16(self):
w = bvector()
x = vector(dtype="float16")
y = fvector()
cz = tanh(x + aet.cast(y, "float16"))
o = (
cz
- cz ** 2
+ aet.cast(x, "int16")
+ aet.cast(x, "float32")
+ aet.cast(w, "float16")
- aet.constant(np.float16(1.0))
)
aesara.function([w, x, y], o, mode=mode_with_gpu)
v = vector(dtype="uint8")
w = vector(dtype="float16")
x = vector(dtype="float16")
y = vector(dtype="float16")
z = vector(dtype="float16")
o = aet.switch(v, mul(w, x, y), z)
aesara.function([v, w, x, y, z], o, mode=mode_with_gpu)