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fix(numba): non-contiguous shared variable #217

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Merged
merged 1 commit into from
May 28, 2025
Merged

fix(numba): non-contiguous shared variable #217

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4 changes: 2 additions & 2 deletions python/nutpie/compile_pymc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down Expand Up @@ -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)
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if you're copying anyway you can do get_value(borrow=True) to avoid doing 2 copies

shared_vars[val.name] = val
seen.add(val)

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32 changes: 32 additions & 0 deletions tests/test_pymc.py
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Expand Up @@ -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):
Expand Down