Numba sparse dot: use final precision in intermediate computations#2159
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ricardoV94 wants to merge 1 commit into
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Numba sparse dot: use final precision in intermediate computations#2159ricardoV94 wants to merge 1 commit into
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Numba underpromotes relative to numpy/scipy mixed scalar * array dtypes
ricardoV94
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May 20, 2026
| col_idx = x_ind[idx] | ||
| value = x_data[idx] | ||
| value = out_type(x_data[idx]) | ||
| z[row_idx] += value * y[col_idx] |
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these are the branches where we do scalar * array, that numba may underpromote relative to numpy/scipy
ricardoV94
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Fixes a flaky test (see https://github.com/pymc-devs/pytensor/actions/runs/26150694899/job/76918627812#step:6:5422)
It doesn't cause meaningful slowdown in some benchmarks I did and it matches scipy in precision for mixed dtypes
Numba does scalar int32|64 * array float32 in float32 precision, whereas numpy does it in float64 precision. Similar for discrete * complex64