Add tests, update docs

pyro/infer/autoguide/gaussian.py

@@ -4,7 +4,7 @@
 import itertools
 from collections import OrderedDict, defaultdict
 from contextlib import ExitStack
-from typing import Callable, Dict, Set, Tuple, Union
+from typing import Callable, Dict, Optional, Set, Tuple, Union
 
 import torch
 from torch.distributions import biject_to
@@ -54,12 +54,14 @@ class AutoGaussian(AutoGuide, metaclass=AutoGaussianMeta):
     the model [1]. Depending on model structure, this can have asymptotically
     better statistical efficiency than :class:`AutoMultivariateNormal` .
 
-    The default "dense" backend should have similar computational complexity to
-    :class:`AutoMultivariateNormal` . The experimental "funsor" backend can be
-    asymptotically cheaper in terms of time and space (using Gaussian tensor
-    variable elimination [2,3]), but incurs large constant overhead. The
-    "funsor" backend requires `funsor <https://funsor.pyro.ai>`_ which can be
-    installed via ``pip install pyro-ppl[funsor]``.
+    This guide implements multiple backends for computation. All backends use
+    the same statistically optimal parametrization. The default "dense" backend
+    has computational complexity similar to :class:`AutoMultivariateNormal` .
+    The experimental "funsor" backend can be asymptotically cheaper in terms of
+    time and space (using Gaussian tensor variable elimination [2,3]), but
+    incurs large constant overhead. The "funsor" backend requires `funsor
+    <https://funsor.pyro.ai>`_ which can be installed via ``pip install
+    pyro-ppl[funsor]``.
 
     The guide currently does not depend on the model's ``*args, **kwargs``.
 
@@ -105,7 +107,7 @@ def __init__(
         *,
         init_loc_fn: Callable = init_to_feasible,
         init_scale: float = 0.1,
-        backend=None,
+        backend: Optional[str] = None,  # used only by metaclass
     ):
         if not isinstance(init_scale, float) or not (init_scale > 0):
             raise ValueError(f"Expected init_scale > 0. but got {init_scale}")

tests/infer/autoguide/test_gaussian.py

@@ -14,7 +14,7 @@
 from pyro.infer.autoguide.gaussian import _break_plates
 from pyro.infer.reparam import LocScaleReparam
 from pyro.optim import Adam
-from tests.common import assert_equal
+from tests.common import assert_equal, xfail_if_not_implemented
 
 BACKENDS = [
     "dense",
@@ -180,6 +180,40 @@ def model():
     check_structure(model, expected)
 
 
+@pytest.mark.parametrize("backend", BACKENDS)
+def test_broken_plates_smoke(backend):
+    def model():
+        with pyro.plate("i", 2):
+            x = pyro.sample("x", dist.Normal(0, 1))
+        pyro.sample("y", dist.Normal(x.mean(-1), 1), obs=torch.tensor(0.0))
+
+    guide = AutoGaussian(model, backend=backend)
+    svi = SVI(model, guide, Adam({"lr": 1e-8}), Trace_ELBO())
+    for step in range(2):
+        with xfail_if_not_implemented():
+            svi.step()
+    guide()
+    predictive = Predictive(model, guide=guide, num_samples=2)
+    predictive()
+
+
+@pytest.mark.parametrize("backend", BACKENDS)
+def test_intractable_smoke(backend):
+    def model():
+        with pyro.plate("i", 2):
+            x = pyro.sample("x", dist.Normal(0, 1))
+        pyro.sample("y", dist.Normal(x.mean(-1), 1), obs=torch.tensor(0.0))
+
+    guide = AutoGaussian(model, backend=backend)
+    svi = SVI(model, guide, Adam({"lr": 1e-8}), Trace_ELBO())
+    for step in range(2):
+        with xfail_if_not_implemented():
+            svi.step()
+    guide()
+    predictive = Predictive(model, guide=guide, num_samples=2)
+    predictive()
+
+
 # Simplified from https://github.com/pyro-cov/tree/master/pyrocov/mutrans.py
 def pyrocov_model(dataset):
     # Tensor shapes are commented at the end of some lines.
@@ -486,7 +520,7 @@ def test_profile(backend, n=1, num_steps=1):
     """
     Helper function for profiling.
     """
-    model = pyrocov_model_plated
+    model = pyrocov_model_poisson
     T, P, S, F = 2 * n, 3 * n, 4 * n, 5 * n
     dataset = {
         "features": torch.randn(S, F),