Don't copy inputs in constant_fold

Author: ricardoV94

pymc/pytensorf.py

@@ -1045,7 +1045,7 @@ def constant_fold(
         attempting constant folding, and any old non-shared inputs will not work with
         the returned outputs
     """
-    fg = FunctionGraph(outputs=xs, features=[ShapeFeature()], clone=True)
+    fg = FunctionGraph(outputs=xs, features=[ShapeFeature()], copy_inputs=False, clone=True)
 
     # By default, rewrite_graph includes canonicalize which includes constant-folding as the final rewrite
     folded_xs = rewrite_graph(fg).outputs

tests/model/transform/test_optimization.py

@@ -20,8 +20,10 @@
 from pymc import Deterministic, do
 from pymc.data import Data
 from pymc.distributions import HalfNormal, Normal
+from pymc.exceptions import NotConstantValueError
 from pymc.model import Model
 from pymc.model.transform.optimization import freeze_dims_and_data
+from pymc.pytensorf import constant_fold
 
 
 def test_freeze_dims_and_data():
@@ -144,3 +146,16 @@ def test_freeze_dim_after_do_intervention():
 
     frozen_do_m = freeze_dims_and_data(do_m)
     assert frozen_do_m["x"].type.shape == (5,)
+
+
+def test_freeze_dims_and_data_partially_observed_rv():
+    # Regression test for #7387
+
+    with Model(coords={"a": [0, 1, 2]}) as model:
+        y = Normal("y", 0, observed=[0, 0, np.nan], dims="a")
+
+    with pytest.raises(NotConstantValueError):
+        constant_fold([y.shape])
+
+    frozen_y = freeze_dims_and_data(model)["y"]
+    assert constant_fold([frozen_y.shape]) == (3,)

tests/test_pytensorf.py

@@ -646,25 +646,33 @@ def test_reseed_rngs():
         assert rng.get_value().bit_generator.state == bit_generator.state
 
 
-def test_constant_fold():
-    x = pt.random.normal(size=(5,))
-    y = pt.arange(x.size)
-
-    res = constant_fold((y, y.shape))
-    assert np.array_equal(res[0], np.arange(5))
-    assert tuple(res[1]) == (5,)
-
-
-def test_constant_fold_raises():
-    size = pytensor.shared(5)
-    x = pt.random.normal(size=(size,))
-    y = pt.arange(x.size)
-
-    with pytest.raises(NotConstantValueError):
-        constant_fold((y, y.shape))
-
-    res = constant_fold((y, y.shape), raise_not_constant=False)
-    assert tuple(res[1].eval()) == (5,)
+class TestConstantFold:
+    def test_constant_fold(self):
+        x = pt.random.normal(size=(5,))
+        y = pt.arange(x.size)
+
+        res = constant_fold((y, y.shape))
+        assert np.array_equal(res[0], np.arange(5))
+        assert tuple(res[1]) == (5,)
+
+    def test_constant_fold_raises(self):
+        size = pytensor.shared(5)
+        x = pt.random.normal(size=(size,))
+        y = pt.arange(x.size)
+
+        with pytest.raises(NotConstantValueError):
+            constant_fold((y, y.shape))
+
+        res = constant_fold((y, y.shape), raise_not_constant=False)
+        assert tuple(res[1].eval()) == (5,)
+
+    def test_inputs_preserved(self):
+        # Make sure constant_folded graph depends on original graph inputs (not copies)
+        # Regression test for #7387
+        a = pt.scalar("a", dtype="int")
+        out = pt.empty((a,))
+        (out_shape,) = constant_fold((out.shape[0],), raise_not_constant=False)
+        assert out_shape is a
 
 
 def test_replace_vars_in_graphs():