def test_factor():
def model():
a = pyro.sample("a", dist.Normal(0, 1))
pyro.factor("b", torch.tensor(0.0))
pyro.factor("c", a)
actual = get_dependencies(model)
expected = {
"prior_dependencies": {
"a": {
"a": set()
},
"b": {
"b": set()
},
"c": {
"c": set(),
"a": set()
},
},
"posterior_dependencies": {
"a": {
"a": set(),
"c": set()
},
},
}
assert actual == expected
def test_docstring_example_3():
def model_3():
with pyro.plate("p", 5):
a = pyro.sample("a", dist.Normal(0, 1))
pyro.sample("b", dist.Normal(a.sum(), 1), obs=torch.tensor(0.0))
actual = get_dependencies(model_3)
expected = {
"prior_dependencies": {
"a": {
"a": set()
},
"b": {
"a": set(),
"b": set()
},
},
"posterior_dependencies": {
"a": {
"a": {"p"},
"b": set()
},
},
}
assert actual == expected
def test_nested_plate_collider():
# a a b b
# a a b b
# \\ //
# c c
# |
# d
def model():
plate_i = pyro.plate("i", 2, dim=-1)
plate_j = pyro.plate("j", 3, dim=-2)
plate_k = pyro.plate("k", 3, dim=-2)
with plate_i:
with plate_j:
a = pyro.sample("a", dist.Normal(0, 1))
with plate_k:
b = pyro.sample("b", dist.Normal(0, 1))
c = pyro.sample("c", dist.Normal(a.sum(0) + b.sum([0, 1]), 1))
pyro.sample("d", dist.Normal(c.sum(), 1), obs=torch.zeros(()))
actual = get_dependencies(model)
_ = set()
expected = {
"prior_dependencies": {
"a": {
"a": _
},
"b": {
"b": _
},
"c": {
"c": _,
"a": _,
"b": _
},
"d": {
"d": _,
"c": _
},
},
"posterior_dependencies": {
"a": {
"a": {"j"},
"b": _,
"c": _
},
"b": {
"b": {"k"},
"c": _
},
"c": {
"c": {"i"},
"d": _
},
},
}
assert actual == expected
def test_plate_collider():
# x x y y
# \\ //
# zzzz
#
# This results in posterior dependency structure:
#
# x x y y z z z z
# x ? ? ? ? ?
# x ? ? ? ? ?
# y ? ? ?
# y ? ? ?
def model(data):
i_plate = pyro.plate("i", data.shape[0], dim=-2)
j_plate = pyro.plate("j", data.shape[1], dim=-1)
with i_plate:
x = pyro.sample("x", dist.Normal(0, 1))
with j_plate:
y = pyro.sample("y", dist.Normal(0, 1))
with i_plate, j_plate:
pyro.sample("z", dist.Normal(x, y.exp()), obs=data)
data = torch.randn(3, 2)
actual = get_dependencies(model, (data, ))
_ = set()
expected = {
"prior_dependencies": {
"x": {
"x": _
},
"y": {
"y": _
},
"z": {
"x": _,
"y": _,
"z": _
},
},
"posterior_dependencies": {
"x": {
"x": _,
"y": _,
"z": _
},
"y": {
"y": _,
"z": _
},
},
}
assert actual == expected
def test_plate_coupling_3():
# x x x x
# // \\
# y y z z
#
# This results in posterior dependency structure:
#
# x x y y z
# x ? ? ? ? ?
# x ? ? ? ? ?
# y ? ? ?
# y ? ? ?
def model(data):
i_plate = pyro.plate("i", data.shape[0], dim=-2)
j_plate = pyro.plate("j", data.shape[1], dim=-1)
with i_plate, j_plate:
x = pyro.sample("x", dist.Normal(0, 1))
with i_plate:
pyro.sample("y",
dist.Normal(x.sum(-1, True), 1),
obs=data.sum(-1, True))
with j_plate:
pyro.sample("z",
dist.Normal(x.sum(-2, True), 1),
obs=data.sum(-2, True))
data = torch.randn(3, 2)
actual = get_dependencies(model, (data, ))
expected = {
"prior_dependencies": {
"x": {
"x": set()
},
"y": {
"y": set(),
"x": set()
},
"z": {
"z": set(),
"x": set()
},
},
"posterior_dependencies": {
"x": {
"x": {"i", "j"},
"y": set(),
"z": set()
},
},
}
assert actual == expected
def test_discrete():
def model():
a = pyro.sample("a", dist.Dirichlet(torch.ones(3)))
b = pyro.sample("b", dist.Categorical(a))
c = pyro.sample("c", dist.Normal(torch.zeros(3), 1).to_event(1))
d = pyro.sample("d", dist.Poisson(c[b].exp()))
pyro.sample("e", dist.Normal(d, 1), obs=torch.ones(()))
actual = get_dependencies(model)
expected = {
"prior_dependencies": {
"a": {
"a": set()
},
"b": {
"a": set(),
"b": set()
},
"c": {
"c": set()
},
"d": {
"b": set(),
"c": set(),
"d": set()
},
"e": {
"d": set(),
"e": set()
},
},
"posterior_dependencies": {
"a": {
"a": set(),
"b": set()
},
"b": {
"b": set(),
"c": set(),
"d": set()
},
"c": {
"c": set(),
"d": set()
},
"d": {
"d": set(),
"e": set()
},
},
}
assert actual == expected
def test_plate_coupling_2():
# x x
# \\ y y
# \\ //
# z
#
# This results in posterior dependency structure:
#
# x x y y z
# x ? ? ? ? ?
# x ? ? ? ? ?
# y ? ? ?
# y ? ? ?
def model(data):
with pyro.plate("p", len(data)):
x = pyro.sample("x", dist.Normal(0, 1))
y = pyro.sample("y", dist.Normal(0, 1))
pyro.sample("z", dist.Normal(x.sum(), y.sum().exp()), obs=data.sum())
data = torch.randn(2)
actual = get_dependencies(model, (data, ))
expected = {
"prior_dependencies": {
"x": {
"x": set()
},
"y": {
"y": set()
},
"z": {
"z": set(),
"x": set(),
"y": set()
},
},
"posterior_dependencies": {
"x": {
"x": {"p"},
"y": {"p"},
"z": set()
},
"y": {
"y": {"p"},
"z": set()
},
},
}
assert actual == expected
def test_docstring_example_2():
def model_2():
a = pyro.sample("a", dist.Normal(0, 1))
b = pyro.sample("b", dist.LogNormal(0, 1))
c = pyro.sample("c", dist.Normal(a, b))
pyro.sample("d", dist.Normal(c, 1), obs=torch.tensor(0.0))
actual = get_dependencies(model_2)
expected = {
"prior_dependencies": {
"a": {
"a": set()
},
"b": {
"b": set()
},
"c": {
"a": set(),
"b": set(),
"c": set()
},
"d": {
"c": set(),
"d": set()
},
},
"posterior_dependencies": {
"a": {
"a": set(),
"b": set(),
"c": set()
},
"b": {
"b": set(),
"c": set()
},
"c": {
"c": set(),
"d": set()
},
},
}
assert actual == expected
def test_plate_dependency():
# w w
# \ x1 x2 unroll x1 / \ x2
# \ || y1 y2 =====> y1 | / \ | y2
# \ || // \|/ \|/
# z1 z2 z1 z2
#
# This allows posterior dependency structure:
#
# w x x y y z z
# w ? ? ? ? ? ? ?
# x ? ? ?
# x ? ? ?
# y ? ?
# y ? ?
def model(data):
w = pyro.sample("w", dist.Normal(0, 1))
with pyro.plate("p", len(data)):
x = pyro.sample("x", dist.Normal(0, 1))
y = pyro.sample("y", dist.Normal(0, 1))
pyro.sample("z", dist.Normal(w + x + y, 1), obs=data)
data = torch.rand(2)
actual = get_dependencies(model, (data, ))
_ = set()
expected = {
"prior_dependencies": {
"w": {
"w": _
},
"x": {
"x": _
},
"y": {
"y": _
},
"z": {
"w": _,
"x": _,
"y": _,
"z": _
},
},
"posterior_dependencies": {
"w": {
"w": _,
"x": _,
"y": _,
"z": _
},
"x": {
"x": _,
"y": _,
"z": _
},
"y": {
"y": _,
"z": _
},
},
}
assert actual == expected
def test_discrete_obs():
def model():
a = pyro.sample("a", dist.Normal(0, 1))
b = pyro.sample("b",
dist.Normal(a[..., None], torch.ones(3)).to_event(1))
c = pyro.sample(
"c",
dist.MultivariateNormal(
torch.zeros(3) + a[..., None], torch.eye(3)))
with pyro.plate("i", 2):
d = pyro.sample("d", dist.Dirichlet((b + c).exp()))
pyro.sample("e",
dist.Categorical(logits=d),
obs=torch.tensor([0, 0]))
return a, b, c, d
actual = get_dependencies(model)
expected = {
"prior_dependencies": {
"a": {
"a": set()
},
"b": {
"a": set(),
"b": set()
},
"c": {
"a": set(),
"c": set()
},
"d": {
"b": set(),
"c": set(),
"d": set()
},
"e": {
"d": set(),
"e": set()
},
},
"posterior_dependencies": {
"a": {
"a": set(),
"b": set(),
"c": set()
},
"b": {
"b": set(),
"c": set(),
"d": set()
},
"c": {
"c": set(),
"d": set()
},
"d": {
"d": set(),
"e": set()
},
},
}
assert actual == expected
def test_get_dependencies(grad_enabled):
def model(data):
a = pyro.sample("a", dist.Normal(0, 1))
b = pyro.sample("b", NonreparameterizedNormal(a, 0))
c = pyro.sample("c", dist.Normal(b, 1))
d = pyro.sample("d", dist.Normal(a, c.exp()))
e = pyro.sample("e", dist.Normal(0, 1))
f = pyro.sample("f", dist.Normal(0, 1))
g = pyro.sample("g",
dist.Bernoulli(logits=e + f),
obs=torch.tensor(0.0))
with pyro.plate("p", len(data)):
d_ = d.detach() # this results in a known failure
h = pyro.sample("h", dist.Normal(c, d_.exp()))
i = pyro.deterministic("i", h + 1)
j = pyro.sample("j", dist.Delta(h + 1), obs=h + 1)
k = pyro.sample("k", dist.Normal(a, j.exp()), obs=data)
return [a, b, c, d, e, f, g, h, i, j, k]
data = torch.randn(3)
with torch.set_grad_enabled(grad_enabled):
actual = get_dependencies(model, (data, ))
_ = set()
expected = {
"prior_dependencies": {
"a": {
"a": _
},
"b": {
"b": _,
"a": _
},
"c": {
"c": _,
"b": _
},
"d": {
"d": _,
"c": _,
"a": _
},
"e": {
"e": _
},
"f": {
"f": _
},
"g": {
"g": _,
"e": _,
"f": _
},
"h": {
"h": _,
"c": _,
"d": _
},
"k": {
"k": _,
"a": _,
"h": _
},
},
"posterior_dependencies": {
"a": {
"a": _,
"b": _,
"c": _,
"d": _,
"h": _,
"k": _
},
"b": {
"b": _,
"c": _
},
"c": {
"c": _,
"d": _,
"h": _
},
"d": {
"d": _,
"h": _
},
"e": {
"e": _,
"g": _,
"f": _
},
"f": {
"f": _,
"g": _
},
"h": {
"h": _,
"k": _
},
},
}
assert actual == expected
