def grad(self, inp, grads):
(x, ) = inp
(gz, ) = grads
res = true_div(-1.0, expm1(-x))
# Correct gradient at 0.0 to be -inf
res = switch(isinf(res), -np.inf, res)
return [gz * res]
def test_straightforward(self):
x, y, z = floats("xyz")
e = mul(add(x, y), true_div(x, y))
C = Composite([x, y], [e])
c = C.make_node(x, y)
# print c.c_code(['x', 'y'], ['z'], dict(id = 0))
g = FunctionGraph([x, y], [c.out])
fn = DualLinker().accept(g).make_function()
assert fn(1.0, 2.0) == 1.5
def test_with_constants(self):
x, y, z = floats("xyz")
e = mul(add(70.0, y), true_div(x, y))
C = Composite([x, y], [e])
c = C.make_node(x, y)
assert "70.0" in c.op.c_code(c, "dummy", ["x", "y"], ["z"], dict(id=0))
# print c.c_code(['x', 'y'], ['z'], dict(id = 0))
g = FunctionGraph([x, y], [c.out])
fn = DualLinker().accept(g).make_function()
assert fn(1.0, 2.0) == 36.0
def test_true_div(self):
# true_div's upcast policy is not exactly "upgrade_to_float",
# so the test is a little bit different
x_range = list(range(-127, 128))
y_range = list(range(-127, 0)) + list(range(1, 127))
xi = int8("xi")
yi = int8("yi")
xf = Scalar(aesara.config.floatX)("xf")
yf = Scalar(aesara.config.floatX)("yf")
ei = true_div(xi, yi)
fi = aesara.function([xi, yi], ei)
ef = true_div(xf, yf)
ff = aesara.function([xf, yf], ef)
for x_val in x_range:
for y_val in y_range:
outi = fi(x_val, y_val)
outf = ff(x_val, y_val)
assert outi.dtype == outf.dtype, "incorrect dtype"
assert np.allclose(outi, outf), "insufficient precision"
def test_mul_add_true():
x, y, z = floats("xyz")
e = mul(add(x, y), true_div(x, y))
g = FunctionGraph([x, y], [e])
fn = DualLinker().accept(g).make_function()
assert fn(1.0, 2.0) == 1.5
