passing test

Author: eb8680

pyro/nn/module.py

@@ -233,29 +233,6 @@ def __get__(
         return value
 
 
-class PyroSamplePlateScope(pyro.poutine.messenger.Messenger):
-    """
-    Handler for executing PyroSample statements in a more intuitive plate context.
-    """
-    def __init__(self, allowed_plates: Iterable[str] = ()):
-        self._inner_allowed_plates = frozenset(allowed_plates)
-
-    def __enter__(self):
-        self._plates: frozenset[str] = frozenset(p.name for p in pyro.poutine.runtime.get_plates()) | self._inner_allowed_plates
-        return super().__enter__()
-
-    def _is_local_plate(self, m: pyro.poutine.messenger.Messenger) -> bool:
-        return isinstance(m, pyro.poutine.plate_messenger.PlateMessenger) and m.name not in self._plates
-
-    def _pyro_sample(self, msg):
-        if not msg["infer"].get("_is_global_sample", False):
-            return
-        msg["stop"] = True
-        msg["done"] = True
-        with pyro.poutine.messenger.block_messenger(lambda m: m is self or self._is_local_plate(m)):
-            msg["value"] = pyro.sample(msg["name"], msg["fn"], obs=msg["value"] if msg["is_observed"] else None, infer=msg["infer"])
-
-
 def _make_name(prefix: str, name: str) -> str:
     return "{}.{}".format(prefix, name) if prefix else name
 
@@ -639,7 +616,7 @@ def __getattr__(self, name: str) -> Any:
                         value = (
                             pyro.deterministic(fullname, prior)
                             if isinstance(prior, torch.Tensor)
-                            else pyro.sample(fullname, prior, infer={"_is_global_sample": True})
+                            else pyro.sample(fullname, prior, infer={"_original_pyrosample_dist": prior})
                         )
                         context.set(fullname, value)
                     return value

pyro/poutine/plate_messenger.py

@@ -2,8 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Iterator, Optional
+from typing import TYPE_CHECKING, Iterable, Iterator, Optional
 
+import pyro
 from pyro.poutine.broadcast_messenger import BroadcastMessenger
 from pyro.poutine.messenger import Messenger, block_messengers
 from pyro.poutine.subsample_messenger import SubsampleMessenger
@@ -88,3 +89,27 @@ def predicate(messenger: Messenger) -> bool:
                 "setting strict=False."
             )
         yield
+
+
+class PyroSamplePlateScope(Messenger):
+    """
+    Handler for executing PyroSample statements in a more intuitive plate context.
+    """
+    def __init__(self, allowed_plates: Iterable[str] = ()):
+        self._inner_allowed_plates = frozenset(allowed_plates)
+
+    def __enter__(self):
+        self._plates: frozenset[str] = frozenset(p.name for p in pyro.poutine.runtime.get_plates()) | self._inner_allowed_plates
+        return super().__enter__()
+
+    def _is_local_plate(self, m: Messenger) -> bool:
+        return isinstance(m, PlateMessenger) and m.name not in self._plates
+
+    def _pyro_sample(self, msg):
+        if not msg["infer"].get("_original_pyrosample_dist", None):
+            return
+        msg["stop"] = True
+        msg["done"] = True
+        with block_messengers(lambda m: m is self or self._is_local_plate(m)):
+            d = msg["infer"].pop("_original_pyrosample_dist")
+            msg["value"] = pyro.sample(msg["name"], d, obs=msg["value"] if msg["is_observed"] else None, infer=msg["infer"])

tests/nn/test_module.py

@@ -1084,3 +1084,33 @@ def forward(self):
     with pyro.settings.context(module_local_params=use_module_local_params):
         model = Model()
         pyro.render_model(model)
+
+
+def test_pyrosample_platescope():
+
+    class Model(pyro.nn.PyroModule):
+        def __init__(self, num_inputs, num_outputs):
+            super().__init__()
+            self.num_inputs = num_inputs
+            self.num_outputs = num_outputs
+            self.linear = pyro.nn.PyroModule[torch.nn.Linear](num_inputs, num_outputs)
+            self.linear.weight = pyro.nn.PyroSample(dist.Normal(0, 1).expand([num_outputs, num_inputs]).to_event(2))
+            self.linear.bias = pyro.nn.PyroSample(dist.Normal(0, 1).expand([num_outputs]).to_event(1))
+
+        @pyro.nn.PyroSample
+        def scale(self):
+            return pyro.distributions.LogNormal(0, 1).expand([self.num_outputs]).to_event(1)
+
+        @pyro.poutine.plate_messenger.PyroSamplePlateScope()
+        def forward(self, x):
+            with pyro.plate("data", x.shape[-2], dim=-1):
+                assert len(self.linear.weight.shape) == 2 or self.linear.weight.shape[-3] != 1  # sampled outside data plate
+                loc = self.linear(x)
+                assert len(self.scale.shape) == 1 or self.scale.shape[-2] == 1  # sampled outside data plate
+                y = pyro.sample("y", dist.Normal(loc, self.scale).to_event(1))
+                assert y.shape[-2] == x.shape[-2]  # ordinary pyro.sample statement
+                return y
+
+    model = Model(3, 2)
+    x = torch.randn(4, 3)
+    assert model(x).shape == (4, 2)