Prepare 0.15.1

CHANGELOG.md

@@ -2,6 +2,22 @@
 
 All notable changes to this project will be documented in this file.
 
+## [0.15.1] - 2025-05-28
+
+### Bug Fixes
+
+- Incorrect results with non-contiguous shared variable (Adrian Seyboldt)
+
+- Allow upper case backend string (Adrian Seyboldt)
+
+- Allow data named x with unfrozen model (Adrian Seyboldt)
+
+
+### Styling
+
+- Fix small typing issue (Adrian Seyboldt)
+
+
 ## [0.15.0] - 2025-05-27
 
 ### Bug Fixes
@@ -39,7 +55,9 @@ All notable changes to this project will be documented in this file.
 
 - Add entries to gitignore (Adrian Seyboldt)
 
-- Bump dependencies (Adrian Seyboldt)
+- Update dependencies (Adrian Seyboldt)
+
+- Update changelog (Adrian Seyboldt)
 
 
 ### Styling

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "nutpie"
-version = "0.15.0"
+version = "0.15.1"
 authors = [
   "Adrian Seyboldt <adrian.seyboldt@gmail.com>",
   "PyMC Developers <pymc.devs@gmail.com>",

python/nutpie/compile_pymc.py

@@ -32,7 +32,7 @@ def intrinsic(f):
 
 
 def _rv_dict_to_flat_array_wrapper(
-    fn: Callable[[SeedType], dict[str, np.ndarray]],
+    fn: Callable[[SeedType | None], dict[str, np.ndarray]],
     names: list[str],
     shapes: list[tuple[int]],
 ) -> Callable[[SeedType], np.ndarray]:
@@ -61,7 +61,7 @@ def _rv_dict_to_flat_array_wrapper(
     """
 
     @wraps(fn)
-    def seeded_array_fn(seed: SeedType = None):
+    def seeded_array_fn(seed: SeedType | None = None):
         initial_value_dict = fn(seed)
         total_size = sum(np.prod(shape).astype(int) for shape in shapes)
         flat_array = np.empty(total_size, dtype="float64", order="C")
@@ -406,8 +406,8 @@ def logp_fn_jax_grad(x, *shared):
         seen.add(val)
 
     def make_logp_func():
-        def logp(x, **shared):
-            logp, grad = logp_fn(x, *[shared[name] for name in logp_shared_names])
+        def logp(_x, **shared):
+            logp, grad = logp_fn(_x, *[shared[name] for name in logp_shared_names])
             return float(logp), np.asarray(grad, dtype="float64", order="C")
 
         return logp
@@ -418,8 +418,8 @@ def logp(x, **shared):
 
     def make_expand_func(seed1, seed2, chain):
         # TODO handle seeds
-        def expand(x, **shared):
-            values = expand_fn(x, *[shared[name] for name in expand_shared_names])
+        def expand(_x, **shared):
+            values = expand_fn(_x, *[shared[name] for name in expand_shared_names])
             return {
                 name: np.asarray(val, order="C", dtype=dtype).ravel()
                 for name, val, dtype in zip(names, values, dtypes, strict=True)
@@ -499,6 +499,11 @@ def compile_pymc_model(
             "and restart your kernel in case you are in an interactive session."
         )
 
+    if gradient_backend is not None:
+        gradient_backend = gradient_backend.lower()  # type: ignore[assignment]
+    if backend is not None:
+        backend = backend.lower()  # type: ignore[assignment]
+
     from pymc.model.transform.optimization import freeze_dims_and_data
     from pymc.initial_point import make_initial_point_fn
 

tests/test_pymc.py

@@ -31,6 +31,21 @@ def test_pymc_model(backend, gradient_backend):
     trace.posterior.a  # noqa: B018
 
 
+@pytest.mark.pymc
+@parameterize_backends
+def test_name_x(backend, gradient_backend):
+    with pm.Model() as model:
+        x = pm.Data("x", 1.0)
+        a = pm.Normal("a", mu=x)
+        pm.Deterministic("z", x * a)
+
+    compiled = nutpie.compile_pymc_model(
+        model, backend=backend, gradient_backend=gradient_backend, freeze_model=False
+    )
+    trace = nutpie.sample(compiled, chains=1)
+    trace.posterior.a  # noqa: B018
+
+
 @pytest.mark.pymc
 def test_order_shared():
     a_val = np.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]])