def test_guide_enumerated_elbo(model, guide, data, history):
pyro.clear_param_store()
with pyro_backend("contrib.funsor"), \
pytest.raises(
NotImplementedError,
match="TraceMarkovEnum_ELBO does not yet support guide side Markov enumeration"):
if history > 1:
pytest.xfail(
reason="TraceMarkovEnum_ELBO does not yet support history > 1")
elbo = infer.TraceEnum_ELBO(max_plate_nesting=4)
expected_loss = elbo.loss_and_grads(model, guide, data, history, False)
expected_grads = (
value.grad
for name, value in pyro.get_param_store().named_parameters())
vectorized_elbo = infer.TraceMarkovEnum_ELBO(max_plate_nesting=4)
actual_loss = vectorized_elbo.loss_and_grads(model, guide, data,
history, True)
actual_grads = (
value.grad
for name, value in pyro.get_param_store().named_parameters())
assert_close(actual_loss, expected_loss)
for actual_grad, expected_grad in zip(actual_grads, expected_grads):
assert_close(actual_grad, expected_grad)
def test_model_enumerated_elbo(model, guide, data, history):
pyro.clear_param_store()
with pyro_backend("contrib.funsor"):
if history > 1:
pytest.xfail(
reason="TraceMarkovEnum_ELBO does not yet support history > 1")
model = infer.config_enumerate(model, default="parallel")
elbo = infer.TraceEnum_ELBO(max_plate_nesting=4)
expected_loss = elbo.loss_and_grads(model, guide, data, history, False)
expected_grads = (
value.grad
for name, value in pyro.get_param_store().named_parameters())
vectorized_elbo = infer.TraceMarkovEnum_ELBO(max_plate_nesting=4)
actual_loss = vectorized_elbo.loss_and_grads(model, guide, data,
history, True)
actual_grads = (
value.grad
for name, value in pyro.get_param_store().named_parameters())
assert_close(actual_loss, expected_loss)
for actual_grad, expected_grad in zip(actual_grads, expected_grads):
assert_close(actual_grad, expected_grad)
def test_elbo_plate_plate(backend, outer_dim, inner_dim):
with pyro_backend(backend):
pyro.get_param_store().clear()
num_particles = 1
q = pyro.param("q", torch.tensor([0.75, 0.25], requires_grad=True))
p = 0.2693204236205713 # for which kl(Categorical(q), Categorical(p)) = 0.5
p = torch.tensor([p, 1 - p])
def model():
d = dist.Categorical(p)
context1 = pyro.plate("outer", outer_dim, dim=-1)
context2 = pyro.plate("inner", inner_dim, dim=-2)
pyro.sample("w", d)
with context1:
pyro.sample("x", d)
with context2:
pyro.sample("y", d)
with context1, context2:
pyro.sample("z", d)
def guide():
d = dist.Categorical(pyro.param("q"))
context1 = pyro.plate("outer", outer_dim, dim=-1)
context2 = pyro.plate("inner", inner_dim, dim=-2)
pyro.sample("w", d, infer={"enumerate": "parallel"})
with context1:
pyro.sample("x", d, infer={"enumerate": "parallel"})
with context2:
pyro.sample("y", d, infer={"enumerate": "parallel"})
with context1, context2:
pyro.sample("z", d, infer={"enumerate": "parallel"})
kl_node = kl_divergence(
torch.distributions.Categorical(funsor.to_data(q)),
torch.distributions.Categorical(funsor.to_data(p)))
kl = (1 + outer_dim + inner_dim + outer_dim * inner_dim) * kl_node
expected_loss = kl
expected_grad = grad(kl, [funsor.to_data(q)])[0]
elbo = infer.TraceEnum_ELBO(num_particles=num_particles,
vectorize_particles=True,
strict_enumeration_warning=True)
elbo = elbo.differentiable_loss if backend == "pyro" else elbo
actual_loss = funsor.to_data(elbo(model, guide))
actual_loss.backward()
actual_grad = funsor.to_data(pyro.param('q')).grad
assert ops.allclose(actual_loss, expected_loss, atol=1e-5)
assert ops.allclose(actual_grad, expected_grad, atol=1e-5)
def train(model, guide, lr=1e-3, n_steps=1000, jit=True, verbose=False, **kwargs):
pyro.clear_param_store()
optimizer = optim.Adam({"lr": lr})
elbo = (
infer.JitTraceEnum_ELBO(max_plate_nesting=2)
if jit
else infer.TraceEnum_ELBO(max_plate_nesting=2)
)
svi = infer.SVI(model, guide, optimizer, elbo)
for step in range(n_steps):
svi.step(**kwargs)
if step % 100 == 99 and verbose:
values = tuple(f"{k}: {v}" for k, v in pyro.get_param_store().items())
print(values)
def test_elbo_enumerate_plate_7(backend):
# Guide Model
# a -----> b
# | |
# +-|--------|----------------+
# | V V |
# | c -----> d -----> e N=2 |
# +---------------------------+
# This tests a mixture of model and guide enumeration.
with pyro_backend(backend):
pyro.param("model_probs_a",
torch.tensor([0.45, 0.55]),
constraint=constraints.simplex)
pyro.param("model_probs_b",
torch.tensor([[0.6, 0.4], [0.4, 0.6]]),
constraint=constraints.simplex)
pyro.param("model_probs_c",
torch.tensor([[0.75, 0.25], [0.55, 0.45]]),
constraint=constraints.simplex)
pyro.param("model_probs_d",
torch.tensor([[[0.4, 0.6], [0.3, 0.7]],
[[0.3, 0.7], [0.2, 0.8]]]),
constraint=constraints.simplex)
pyro.param("model_probs_e",
torch.tensor([[0.75, 0.25], [0.55, 0.45]]),
constraint=constraints.simplex)
pyro.param("guide_probs_a",
torch.tensor([0.35, 0.64]),
constraint=constraints.simplex)
pyro.param(
"guide_probs_c",
torch.tensor([[0., 1.], [1., 0.]]), # deterministic
constraint=constraints.simplex)
def auto_model(data):
probs_a = pyro.param("model_probs_a")
probs_b = pyro.param("model_probs_b")
probs_c = pyro.param("model_probs_c")
probs_d = pyro.param("model_probs_d")
probs_e = pyro.param("model_probs_e")
a = pyro.sample("a", dist.Categorical(probs_a))
b = pyro.sample("b",
dist.Categorical(probs_b[a]),
infer={"enumerate": "parallel"})
with pyro.plate("data", 2, dim=-1):
c = pyro.sample("c", dist.Categorical(probs_c[a]))
d = pyro.sample("d",
dist.Categorical(Vindex(probs_d)[b, c]),
infer={"enumerate": "parallel"})
pyro.sample("obs", dist.Categorical(probs_e[d]), obs=data)
def auto_guide(data):
probs_a = pyro.param("guide_probs_a")
probs_c = pyro.param("guide_probs_c")
a = pyro.sample("a",
dist.Categorical(probs_a),
infer={"enumerate": "parallel"})
with pyro.plate("data", 2, dim=-1):
pyro.sample("c", dist.Categorical(probs_c[a]))
def hand_model(data):
probs_a = pyro.param("model_probs_a")
probs_b = pyro.param("model_probs_b")
probs_c = pyro.param("model_probs_c")
probs_d = pyro.param("model_probs_d")
probs_e = pyro.param("model_probs_e")
a = pyro.sample("a", dist.Categorical(probs_a))
b = pyro.sample("b",
dist.Categorical(probs_b[a]),
infer={"enumerate": "parallel"})
for i in range(2):
c = pyro.sample("c_{}".format(i), dist.Categorical(probs_c[a]))
d = pyro.sample("d_{}".format(i),
dist.Categorical(Vindex(probs_d)[b, c]),
infer={"enumerate": "parallel"})
pyro.sample("obs_{}".format(i),
dist.Categorical(probs_e[d]),
obs=data[i])
def hand_guide(data):
probs_a = pyro.param("guide_probs_a")
probs_c = pyro.param("guide_probs_c")
a = pyro.sample("a",
dist.Categorical(probs_a),
infer={"enumerate": "parallel"})
for i in range(2):
pyro.sample("c_{}".format(i), dist.Categorical(probs_c[a]))
data = torch.tensor([0, 0])
elbo = infer.TraceEnum_ELBO(max_plate_nesting=1)
elbo = elbo.differentiable_loss if backend == "pyro" else elbo
auto_loss = elbo(auto_model, auto_guide, data)
elbo = infer.TraceEnum_ELBO(max_plate_nesting=0)
elbo = elbo.differentiable_loss if backend == "pyro" else elbo
hand_loss = elbo(hand_model, hand_guide, data)
_check_loss_and_grads(hand_loss, auto_loss)
def test_elbo_enumerate_plates_1(backend):
# +-----------------+
# | a ----> b M=2 |
# +-----------------+
# +-----------------+
# | c ----> d N=3 |
# +-----------------+
# This tests two unrelated plates.
# Each should remain uncontracted.
with pyro_backend(backend):
pyro.param("probs_a",
torch.tensor([0.45, 0.55]),
constraint=constraints.simplex)
pyro.param("probs_b",
torch.tensor([[0.6, 0.4], [0.4, 0.6]]),
constraint=constraints.simplex)
pyro.param("probs_c",
torch.tensor([0.75, 0.25]),
constraint=constraints.simplex)
pyro.param("probs_d",
torch.tensor([[0.4, 0.6], [0.3, 0.7]]),
constraint=constraints.simplex)
b_data = torch.tensor([0, 1])
d_data = torch.tensor([0, 0, 1])
def auto_model():
probs_a = pyro.param("probs_a")
probs_b = pyro.param("probs_b")
probs_c = pyro.param("probs_c")
probs_d = pyro.param("probs_d")
with pyro.plate("a_axis", 2, dim=-1):
a = pyro.sample("a",
dist.Categorical(probs_a),
infer={"enumerate": "parallel"})
pyro.sample("b", dist.Categorical(probs_b[a]), obs=b_data)
with pyro.plate("c_axis", 3, dim=-1):
c = pyro.sample("c",
dist.Categorical(probs_c),
infer={"enumerate": "parallel"})
pyro.sample("d", dist.Categorical(probs_d[c]), obs=d_data)
def hand_model():
probs_a = pyro.param("probs_a")
probs_b = pyro.param("probs_b")
probs_c = pyro.param("probs_c")
probs_d = pyro.param("probs_d")
for i in range(2):
a = pyro.sample("a_{}".format(i),
dist.Categorical(probs_a),
infer={"enumerate": "parallel"})
pyro.sample("b_{}".format(i),
dist.Categorical(probs_b[a]),
obs=b_data[i])
for j in range(3):
c = pyro.sample("c_{}".format(j),
dist.Categorical(probs_c),
infer={"enumerate": "parallel"})
pyro.sample("d_{}".format(j),
dist.Categorical(probs_d[c]),
obs=d_data[j])
def guide():
pass
elbo = infer.TraceEnum_ELBO(max_plate_nesting=1)
elbo = elbo.differentiable_loss if backend == "pyro" else elbo
auto_loss = elbo(auto_model, guide)
elbo = infer.TraceEnum_ELBO(max_plate_nesting=0)
elbo = elbo.differentiable_loss if backend == "pyro" else elbo
hand_loss = elbo(hand_model, guide)
_check_loss_and_grads(hand_loss, auto_loss)
