fix(numba): non-contiguous shared variable

Shared variables (ie pm.Data) that were non-contiguous
could lead to incorrect results in the pymc numba
backend.
We now ensure that they are always c-contiguous by
copying if they are not.

Author: aseyboldt

python/nutpie/compile_pymc.py

@@ -131,7 +131,7 @@ def with_data(self, **updates):
             if name not in shared_data:
                 raise KeyError(f"Unknown shared variable: {name}")
             old_val = shared_data[name]
-            new_val = np.asarray(new_val, dtype=old_val.dtype).copy()
+            new_val = np.array(new_val, dtype=old_val.dtype, order="C", copy=True)
             new_val.flags.writeable = False
             if old_val.ndim != new_val.ndim:
                 raise ValueError(
@@ -256,7 +256,7 @@ def _compile_pymc_model_numba(
     for val in [*logp_fn_pt.get_shared(), *expand_fn_pt.get_shared()]:
         if val.name in shared_data and val not in seen:
             raise ValueError(f"Shared variables must have unique names: {val.name}")
-        shared_data[val.name] = val.get_value()
+        shared_data[val.name] = np.array(val.get_value(), order="C", copy=True)
         shared_vars[val.name] = val
         seen.add(val)
 

tests/test_pymc.py

@@ -31,6 +31,38 @@ def test_pymc_model(backend, gradient_backend):
     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]])
+    with pm.Model() as model:
+        a = pm.Data("a", np.copy(a_val, order="C"))
+        b = pm.Normal("b", shape=(2, 5))
+        pm.Deterministic("c", (a[:, None, :] * b[:, :, None]).sum(-1))
+
+    compiled = nutpie.compile_pymc_model(model, backend="numba")
+    trace = nutpie.sample(compiled)
+    np.testing.assert_allclose(
+        (
+            trace.posterior.b.values[:, :, :, :, None] * a_val[None, None, :, None, :]
+        ).sum(-1),
+        trace.posterior.c.values,
+    )
+
+    with pm.Model() as model:
+        a = pm.Data("a", np.copy(a_val, order="F"))
+        b = pm.Normal("b", shape=(2, 5))
+        pm.Deterministic("c", (a[:, None, :] * b[:, :, None]).sum(-1))
+
+    compiled = nutpie.compile_pymc_model(model, backend="numba")
+    trace = nutpie.sample(compiled)
+    np.testing.assert_allclose(
+        (
+            trace.posterior.b.values[:, :, :, :, None] * a_val[None, None, :, None, :]
+        ).sum(-1),
+        trace.posterior.c.values,
+    )
+
+
 @pytest.mark.pymc
 @parameterize_backends
 def test_low_rank(backend, gradient_backend):