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test: compare same-device regression round-trips at float32 precision #1341

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ggprior merged 2 commits into from
Oct 2, 2026
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test: compare same-device regression round-trips at float32 precision #1341

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test: compare same-device regression round-trips at float32 precision
ggprior Oct 1, 2026
88a387a
Scale tolerance by error in z, excluding shift.
oscarkey Oct 2, 2026
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16 changes: 13 additions & 3 deletions tests/test_save_load_fitted_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,8 +60,8 @@ def _assert_roundtrip_predictions(
) -> None:
"""Check that a save/load round-trip produced an equivalent model.

Same-device round-trips must reproduce predictions near-exactly, so we
assert numerical equivalence. Cross-device round-trips (e.g.
Same-device round-trips must reproduce predictions to float32 precision, so
we assert numerical equivalence. Cross-device round-trips (e.g.
``cpu``<->``cuda``) cannot: CPU and GPU use different default inference
precisions and different matmul/attention kernels whose summation order
differs, so bit-identity is unattainable. For those we only verify that the
Expand All @@ -85,9 +85,19 @@ def _assert_roundtrip_predictions(
return

# Same device: the round-trip must be numerically faithful.
np.testing.assert_array_almost_equal(original_preds, loaded_preds)
if isinstance(original, TabPFNClassifier):
np.testing.assert_array_almost_equal(original_preds, loaded_preds)
np.testing.assert_array_almost_equal(original_probas, loaded_probas)
else:
# original_preds = (y_train_std * z) + y_train_mean
# z is computed in float32, so its error grows with |z|, and the
# predictions must agree to float32 error scaled by y_train_std * |z|.
y_train_std_abs_z = np.abs(original_preds - original.y_train_mean_)
min_abs_z = 1.0
atol = 1e-6 * max(
float(np.max(y_train_std_abs_z)), original.y_train_std_ * min_abs_z
)
np.testing.assert_allclose(loaded_preds, original_preds, rtol=0, atol=atol)


@pytest.mark.parametrize(
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