Merge 9488da8 into 91bc2b3

pyro/infer/predictive.py

@@ -60,7 +60,14 @@ def _predictive_sequential(
         )
         collected_trace.append(trace)
         collected_samples.append(
-            {site: trace.nodes[site]["value"] for site in return_site_shapes}
+            {
+                site: (
+                    trace.nodes[site]["value"]
+                    if site in trace.nodes
+                    else samples[i][site]
+                )
+                for site in return_site_shapes
+            }
         )
 
     return _predictiveResults(
@@ -84,6 +91,7 @@ def _predictive(
     model_args=(),
     model_kwargs={},
     mask=True,
+    posterior_deterministic_sites=(),
 ):
     model = torch.no_grad()(poutine.mask(model, mask=False) if mask else model)
     max_plate_nesting = _guess_max_plate_nesting(model, model_args, model_kwargs)
@@ -122,6 +130,9 @@ def _predictive(
         elif site not in posterior_samples:
             return_site_shapes[site] = site_shape
 
+    for site in posterior_deterministic_sites:
+        return_site_shapes[site] = posterior_samples[site].shape
+
     # handle _RETURN site
     if return_sites is not None and "_RETURN" in return_sites:
         value = model_trace.nodes["_RETURN"]["value"]
@@ -143,7 +154,10 @@ def _predictive(
     ).get_trace(*model_args, **model_kwargs)
     predictions = {}
     for site, shape in return_site_shapes.items():
-        value = trace.nodes[site]["value"]
+        if site in trace.nodes:
+            value = trace.nodes[site]["value"]
+        else:
+            value = reshaped_samples[site]
         if site == "_RETURN" and shape is None:
             predictions[site] = value
             continue
@@ -179,6 +193,8 @@ class Predictive(torch.nn.Module):
     :param bool parallel: predict in parallel by wrapping the existing model
         in an outermost `plate` messenger. Note that this requires that the model has
         all batch dims correctly annotated via :class:`~pyro.plate`. Default is `False`.
+    :param return_deterministic_guide_sites: include deterministic sites from the guide
+        in returned samples; this does not affect the returned trace.
     """
 
     def __init__(
@@ -189,6 +205,7 @@ def __init__(
         num_samples=None,
         return_sites=(),
         parallel=False,
+        return_deterministic_guide_sites=False,
     ):
         super().__init__()
         if posterior_samples is None:
@@ -231,6 +248,7 @@ def __init__(
         self.guide = guide
         self.return_sites = return_sites
         self.parallel = parallel
+        self.return_deterministic_guide_sites = return_deterministic_guide_sites
 
     def call(self, *args, **kwargs):
         """
@@ -262,18 +280,37 @@ def forward(self, *args, **kwargs):
         """
         posterior_samples = self.posterior_samples
         return_sites = self.return_sites
+
+        guide_deterministic_sites = ()
+
         if self.guide is not None:
             # return all sites by default if a guide is provided.
             return_sites = None if not return_sites else return_sites
-            posterior_samples = _predictive(
+            guide_pred_res = _predictive(
                 self.guide,
                 posterior_samples,
                 self.num_samples,
                 return_sites=None,
                 parallel=self.parallel,
                 model_args=args,
                 model_kwargs=kwargs,
-            ).samples
+            )
+            posterior_samples = guide_pred_res.samples
+
+            if self.return_deterministic_guide_sites:
+                if isinstance(guide_pred_res, Trace):
+                    guide_tr = guide_pred_res.trace
+                else:
+                    guide_tr = guide_pred_res.trace[0]
+
+                guide_deterministic_sites = tuple(
+                    name
+                    for name, site in guide_tr.nodes.items()
+                    if site["type"] == "sample"
+                    if site["infer"].get("_deterministic")
+                    if (return_sites is None or name in return_sites)
+                )
+
         return _predictive(
             self.model,
             posterior_samples,
@@ -282,6 +319,7 @@ def forward(self, *args, **kwargs):
             parallel=self.parallel,
             model_args=args,
             model_kwargs=kwargs,
+            posterior_deterministic_sites=guide_deterministic_sites,
         ).samples
 
     def get_samples(self, *args, **kwargs):

pyro/util.py

@@ -266,6 +266,12 @@ def check_model_guide_match(model_trace, guide_trace, max_plate_nesting=math.inf
         if site["type"] == "sample"
         if site["infer"].get("is_auxiliary")
     )
+    det_vars = set(
+        name
+        for name, site in guide_trace.nodes.items()
+        if site["type"] == "sample"
+        if site["infer"].get("_deterministic")
+    )
     model_vars = set(
         name
         for name, site in model_trace.nodes.items()
@@ -284,7 +290,7 @@ def check_model_guide_match(model_trace, guide_trace, max_plate_nesting=math.inf
         warnings.warn(
             "Found auxiliary vars in the model: {}".format(aux_vars & model_vars)
         )
-    if not (guide_vars <= model_vars | aux_vars):
+    if not (guide_vars <= model_vars | aux_vars | det_vars):
         warnings.warn(
             "Found non-auxiliary vars in guide but not model, "
             "consider marking these infer={{'is_auxiliary': True}}:\n{}".format(

tests/infer/test_predictive.py

@@ -269,6 +269,44 @@ def model(y=None):
     assert_close(actual["x3"].mean(), y, rtol=0.1)
 
 
+@pytest.mark.parametrize("with_plate", [True, False])
+@pytest.mark.parametrize("event_shape", [(), (2,)])
+@pytest.mark.parametrize("return_deterministic_guide_sites", [True, False])
+def test_deterministic_guide_return(
+    with_plate, event_shape, return_deterministic_guide_sites
+):
+    def model(y=None):
+        with pyro.util.optional(pyro.plate("plate", 3), with_plate):
+            x = pyro.sample("x", dist.Normal(0, 1).expand(event_shape).to_event())
+            x2 = pyro.deterministic("x2", x**2, event_dim=len(event_shape))
+
+        pyro.deterministic("x3", x2)
+        return pyro.sample("obs", dist.Normal(x2, 0.1).to_event(), obs=y)
+
+    def guide(y=None):
+        with pyro.util.optional(pyro.plate("plate", 3), with_plate):
+            x = pyro.sample("x", dist.Normal(0, 1).expand(event_shape).to_event())
+
+        pyro.deterministic("x4", x)
+
+    y = torch.tensor(4.0)
+    svi = SVI(model, guide, optim.Adam(dict(lr=0.1)), Trace_ELBO())
+    for i in range(100):
+        svi.step(y)
+
+    actual = Predictive(
+        model,
+        guide=guide,
+        num_samples=1000,
+        return_deterministic_guide_sites=return_deterministic_guide_sites,
+    )()
+
+    if return_deterministic_guide_sites:
+        assert "x4" in actual
+    else:
+        assert "x4" not in actual
+
+
 def test_get_mask_optimization():
     def model():
         x = pyro.sample("x", dist.Normal(0, 1))