def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = MRG_RandomStream(np.random.randint(1, 2147462579))
pvals = shared(np.random.rand(10, 20).astype(config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = cast(samples, pvals.dtype)
samples = zero_grad(samples)
cost = tt_sum(samples + pvals)
grad_out = grad(cost, samples)
f = function([], grad_out)
assert not any(
[isinstance(node.op, UndefinedGrad) for node in f.maker.fgraph.apply_nodes]
)
def test_op_removed(self):
x = matrix("x")
y = x * zero_grad(x)
f = aesara.function([x], y)
# need to refer to aesara.zero_grad here,
# aesara.zero_grad is a wrapper function!
assert zero_grad_ not in [node.op for node in f.maker.fgraph.toposort()]
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = RandomStreams(np.random.randint(1, 2147462579))
pvals = aesara.shared(np.random.rand(10, 20).astype(aesara.config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = gradient.zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = aesara.tensor.cast(samples, pvals.dtype)
samples = gradient.zero_grad(samples)
cost = aesara.tensor.sum(samples + pvals)
grad = aesara.tensor.grad(cost, samples)
f = aesara.function([], grad)
aesara.printing.debugprint(f)
assert not any([
isinstance(node.op, gradient.UndefinedGrad)
for node in f.maker.fgraph.apply_nodes
])
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = MRG_RandomStream(np.random.default_rng().integers(1, 2147462579))
pvals = aesara.shared(np.random.random((10, 20)).astype(config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = at.cast(samples, pvals.dtype)
samples = zero_grad(samples)
cost = at_sum(samples + pvals)
grad_res = grad(cost, samples)
f = aesara.function([], grad_res)
assert not any(
isinstance(node.op, UndefinedGrad) for node in f.maker.fgraph.apply_nodes
)
def test_grad(self):
a = np.asarray(self.rng.randn(5, 5), dtype=config.floatX)
x = matrix("x")
expressions_gradients = [
(x * zero_grad(x), x),
(x * zero_grad(exp(x)), exp(x)),
(zero_grad(x), aet.constant(0.0)),
(x**2 * zero_grad(x), 2 * x**2),
]
for expr, expr_grad in expressions_gradients:
g = grad(expr.sum(), x)
# gradient according to aesara
f = aesara.function([x], g, on_unused_input="ignore")
# desired gradient
f2 = aesara.function([x], expr_grad, on_unused_input="ignore")
assert np.allclose(f(a), f2(a))
def test_rop(self):
x = vector()
v = vector()
y = zero_grad(x)
rop = Rop(y, x, v)
f = aesara.function([x, v], rop, on_unused_input="ignore")
a = np.asarray(self.rng.randn(5), dtype=config.floatX)
u = np.asarray(self.rng.randn(5), dtype=config.floatX)
assert np.count_nonzero(f(a, u)) == 0
