Remove remaining uses of default_updates in codebase

Author: ricardoV94

pymc/aesaraf.py

@@ -30,6 +30,7 @@
 import numpy as np
 import scipy.sparse as sps
 
+from aeppl.abstract import MeasurableVariable
 from aeppl.logprob import CheckParameterValue
 from aesara import config, scalar
 from aesara.compile.mode import Mode, get_mode
@@ -978,14 +979,21 @@ def compile_pymc(
     # TODO: This won't work for variables with InnerGraphs (Scan and OpFromGraph)
     rng_updates = {}
     output_to_list = outputs if isinstance(outputs, (list, tuple)) else [outputs]
-    for rv in (
-        node
-        for node in vars_between(inputs, output_to_list)
-        if node.owner and isinstance(node.owner.op, RandomVariable) and node not in inputs
+    for random_var in (
+        var
+        for var in vars_between(inputs, output_to_list)
+        if var.owner
+        and isinstance(var.owner.op, (RandomVariable, MeasurableVariable))
+        and var not in inputs
     ):
-        rng = rv.owner.inputs[0]
-        if not hasattr(rng, "default_update"):
-            rng_updates[rng] = rv.owner.outputs[0]
+        if isinstance(random_var.owner.op, RandomVariable):
+            rng = random_var.owner.inputs[0]
+            if not hasattr(rng, "default_update"):
+                rng_updates[rng] = random_var.owner.outputs[0]
+        else:
+            update_fn = getattr(random_var.owner.op, "update", None)
+            if update_fn is not None:
+                rng_updates.update(update_fn(random_var.owner))
 
     # If called inside a model context, see if check_bounds flag is set to False
     try:

pymc/distributions/mixture.py

@@ -21,7 +21,7 @@
 from aeppl.logprob import _logcdf, _logprob
 from aeppl.transforms import IntervalTransform
 from aesara.compile.builders import OpFromGraph
-from aesara.graph.basic import equal_computations
+from aesara.graph.basic import Node, equal_computations
 from aesara.tensor import TensorVariable
 from aesara.tensor.random.op import RandomVariable
 
@@ -44,6 +44,10 @@ class MarginalMixtureRV(OpFromGraph):
 
     default_output = 1
 
+    def update(self, node: Node):
+        # Update for the internal mix_indexes RV
+        return {node.inputs[0]: node.outputs[0]}
+
 
 MeasurableVariable.register(MarginalMixtureRV)
 
@@ -294,10 +298,6 @@ def rv_op(cls, weights, *components, size=None, rngs=None):
         # Create the actual MarginalMixture variable
         mix_out = mix_op(mix_indexes_rng, weights, *components)
 
-        # We need to set_default_updates ourselves, because the choices RV is hidden
-        # inside OpFromGraph and PyMC will never find it otherwise
-        mix_indexes_rng.default_update = mix_out.owner.outputs[0]
-
         # Reference nodes to facilitate identification in other classmethods
         mix_out.tag.weights = weights
         mix_out.tag.components = components

pymc/model.py

@@ -1363,13 +1363,9 @@ def make_obs_var(
             # size of the masked and unmasked array happened to coincide
             _, size, _, *inps = observed_rv_var.owner.inputs
             rng = self.model.next_rng()
-            observed_rv_var = observed_rv_var.owner.op(*inps, size=size, rng=rng)
-            # Add default_update to new rng
-            new_rng = observed_rv_var.owner.outputs[0]
-            observed_rv_var.update = (rng, new_rng)
-            rng.default_update = new_rng
-            observed_rv_var.name = f"{name}_observed"
-
+            observed_rv_var = observed_rv_var.owner.op(
+                *inps, size=size, rng=rng, name=f"{name}_observed"
+            )
             observed_rv_var.tag.observations = nonmissing_data
 
             self.create_value_var(observed_rv_var, transform=None, value_var=nonmissing_data)

pymc/tests/test_aesaraf.py

@@ -22,7 +22,9 @@
 import pytest
 import scipy.sparse as sps
 
+from aeppl.abstract import MeasurableVariable
 from aeppl.logprob import ParameterValueError
+from aesara.compile.builders import OpFromGraph
 from aesara.graph.basic import Constant, Variable, ancestors, equal_computations
 from aesara.tensor.random.basic import normal, uniform
 from aesara.tensor.random.op import RandomVariable
@@ -681,3 +683,24 @@ def test_compile_pymc_updates_inputs(self):
             assert len(fn_fgraph.apply_nodes) == max(rvs_in_graph, 1)
             # Each RV adds a shared output for its rng
             assert len(fn_fgraph.outputs) == 1 + rvs_in_graph
+
+    def test_compile_pymc_custom_update_op(self):
+        """Test that custom MeasurableVariable Op updates are used by compile_pymc"""
+
+        class UnmeasurableOp(OpFromGraph):
+            def update(self, node):
+                return {node.inputs[0]: node.inputs[0] + 1}
+
+        dummy_inputs = [at.scalar(), at.scalar()]
+        dummy_outputs = [at.add(*dummy_inputs)]
+        dummy_x = UnmeasurableOp(dummy_inputs, dummy_outputs)(aesara.shared(1.0), 1.0)
+
+        # Check that there are no updates at first
+        fn = compile_pymc(inputs=[], outputs=dummy_x)
+        assert fn() == fn() == 2.0
+
+        # And they are enabled once the Op is registered as Measurable
+        MeasurableVariable.register(UnmeasurableOp)
+        fn = compile_pymc(inputs=[], outputs=dummy_x)
+        assert fn() == 2.0
+        assert fn() == 3.0