def assert_ok(model, guide, elbo, *args, **kwargs):
"""
Assert that inference works without warnings or errors.
"""
pyro.get_param_store().clear()
adam = optim.Adam({"lr": 1e-6})
inference = infer.SVI(model, guide, adam, elbo)
for i in range(2):
inference.step(*args, **kwargs)
def assert_error(model, guide, elbo, match=None):
"""
Assert that inference fails with an error.
"""
pyro.get_param_store().clear()
adam = optim.Adam({"lr": 1e-6})
inference = infer.SVI(model, guide, adam, elbo)
with pytest.raises((NotImplementedError, UserWarning, KeyError, ValueError, RuntimeError),
match=match):
inference.step()
def assert_warning(model, guide, elbo):
"""
Assert that inference works but with a warning.
"""
pyro.get_param_store().clear()
adam = optim.Adam({"lr": 1e-6})
inference = infer.SVI(model, guide, adam, elbo)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
inference.step()
assert len(w), 'No warnings were raised'
for warning in w:
print(warning)
def test_gaussian_probit_hmm_smoke(exact, jit):
def model(data):
T, N, D = data.shape # time steps, individuals, features
# Gaussian initial distribution.
init_loc = pyro.param("init_loc", torch.zeros(D))
init_scale = pyro.param("init_scale",
1e-2 * torch.eye(D),
constraint=constraints.lower_cholesky)
# Linear dynamics with Gaussian noise.
trans_const = pyro.param("trans_const", torch.zeros(D))
trans_coeff = pyro.param("trans_coeff", torch.eye(D))
noise = pyro.param("noise",
1e-2 * torch.eye(D),
constraint=constraints.lower_cholesky)
obs_plate = pyro.plate("channel", D, dim=-1)
with pyro.plate("data", N, dim=-2):
state = None
for t in range(T):
# Transition.
if t == 0:
loc = init_loc
scale_tril = init_scale
else:
loc = trans_const + funsor.torch.torch_tensordot(
trans_coeff, state, 1)
scale_tril = noise
state = pyro.sample("state_{}".format(t),
dist.MultivariateNormal(loc, scale_tril),
infer={"exact": exact})
# Factorial probit likelihood model.
with obs_plate:
pyro.sample("obs_{}".format(t),
dist.Bernoulli(logits=state["channel"]),
obs=data[t])
def guide(data):
pass
data = torch.distributions.Bernoulli(0.5).sample((3, 4, 2))
with pyro_backend("funsor"):
Elbo = infer.JitTraceEnum_ELBO if jit else infer.TraceEnum_ELBO
elbo = Elbo()
adam = optim.Adam({"lr": 1e-3})
svi = infer.SVI(model, guide, adam, elbo)
svi.step(data)
def main(args):
# Define a basic model with a single Normal latent random variable `loc`
# and a batch of Normally distributed observations.
def model(data):
loc = pyro.sample("loc", dist.Normal(0., 1.))
with pyro.plate("data", len(data), dim=-1):
pyro.sample("obs", dist.Normal(loc, 1.), obs=data)
# Define a guide (i.e. variational distribution) with a Normal
# distribution over the latent random variable `loc`.
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = pyro.param("guide_scale",
torch.tensor(1.),
constraint=constraints.positive)
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))
# Generate some data.
torch.manual_seed(0)
data = torch.randn(100) + 3.0
# Because the API in minipyro matches that of Pyro proper,
# training code works with generic Pyro implementations.
with pyro_backend(args.backend), interpretation(monte_carlo):
# Construct an SVI object so we can do variational inference on our
# model/guide pair.
Elbo = infer.JitTrace_ELBO if args.jit else infer.Trace_ELBO
elbo = Elbo()
adam = optim.Adam({"lr": args.learning_rate})
svi = infer.SVI(model, guide, adam, elbo)
# Basic training loop
pyro.get_param_store().clear()
for step in range(args.num_steps):
loss = svi.step(data)
if args.verbose and step % 100 == 0:
print("step {} loss = {}".format(step, loss))
# Report the final values of the variational parameters
# in the guide after training.
if args.verbose:
for name in pyro.get_param_store():
value = pyro.param(name).data
print("{} = {}".format(name, value.detach().cpu().numpy()))
# For this simple (conjugate) model we know the exact posterior. In
# particular we know that the variational distribution should be
# centered near 3.0. So let's check this explicitly.
assert (pyro.param("guide_loc") - 3.0).abs() < 0.1
def test_optimizer(backend, optim_name, jit):
def model(data):
p = pyro.param("p", torch.tensor(0.5))
pyro.sample("x", dist.Bernoulli(p), obs=data)
def guide(data):
pass
data = torch.tensor(0.)
with pyro_backend(backend):
pyro.get_param_store().clear()
Elbo = infer.JitTrace_ELBO if jit else infer.Trace_ELBO
elbo = Elbo(ignore_jit_warnings=True)
optimizer = getattr(optim, optim_name)({"lr": 1e-6})
inference = infer.SVI(model, guide, optimizer, elbo)
for i in range(2):
inference.step(data)
def build_svi(model, guide, elbo):
pyro.get_param_store().clear()
adam = optim.Adam({"lr": 1e-6})
return infer.SVI(model, guide, adam, elbo)
