Fix bug in JAX cloning of RNG shared variables

pytensor/link/jax/linker.py

@@ -44,7 +44,14 @@ class JAXLinker(JITLinker):
                 new_inp_storage = [new_inp.get_value(borrow=True)]
                 storage_map[new_inp] = new_inp_storage
                 old_inp_storage = storage_map.pop(old_inp)
-                input_storage[input_storage.index(old_inp_storage)] = new_inp_storage
+                # Find index of old_inp_storage in input_storage
+                for input_storage_idx, input_storage_item in enumerate(input_storage):
+                    # We have to establish equality based on identity because input_storage may contain numpy arrays
+                    if input_storage_item is old_inp_storage:
+                        break
+                else:  # no break
+                    raise ValueError()
+                input_storage[input_storage_idx] = new_inp_storage
                 fgraph.remove_input(
                     fgraph.inputs.index(old_inp), reason="JAXLinker.fgraph_convert"
                 )

tests/link/jax/test_random.py

@@ -63,6 +63,43 @@ def test_random_updates(rng_ctor):
     )
 
 
+def test_random_updates_input_storage_order():
+    """Test case described in issue #314.
+
+    This happened when we tried to update the input storage after we clone the shared RNG.
+    We used to call `input_storage.index(old_input_storage)` which would fail when the input_storage contained
+    numpy arrays before the RNG value, which would fail the equality check.
+
+    """
+    pt_rng = RandomStream(1)
+
+    batchshape = (3, 1, 4, 4)
+    inp_shared = pytensor.shared(
+        np.zeros(batchshape, dtype="float64"), name="inp_shared"
+    )
+
+    inp = at.tensor4(dtype="float64", name="inp")
+    inp_update = inp + pt_rng.normal(size=inp.shape, loc=5, scale=1e-5)
+
+    # This function replaces inp by input_shared in the update expression
+    # This is what caused the RNG to appear later than inp_shared in the input_storage
+    with pytest.warns(
+        UserWarning,
+        match=r"The RandomType SharedVariables \[.+\] will not be used",
+    ):
+        fn = pytensor.function(
+            inputs=[],
+            outputs=[],
+            updates={inp_shared: inp_update},
+            givens={inp: inp_shared},
+            mode="JAX",
+        )
+    fn()
+    np.testing.assert_allclose(inp_shared.get_value(), 5, rtol=1e-3)
+    fn()
+    np.testing.assert_allclose(inp_shared.get_value(), 10, rtol=1e-3)
+
+
 @pytest.mark.parametrize(
     "rv_op, dist_params, base_size, cdf_name, params_conv",
     [