Work Classification

Library

Affected Repositories

• PyAutoArray (primary, and the only repo this issue touches)

Branch Survey

Suggested branch: feature/interferometer-numba-cpu-direct-conv (cut from feature/interferometer-sparse-operator-numpy-cpu-path, PR base the same until #542 merges)

Worktree root: ~/Code/PyAutoLabs-wt/interferometer-numba-cpu-direct-conv/

Implementation Steps

1. interferometer_numba/inversion_interferometer_numba_util.py

• curvature_direct_conv(preload, iy, ix, flat, indptr, col, val, cscptr, csc_row, csc_val, ny, nx, pix_pixels) ported verbatim from the prototype under @numba_util.jit().
• direct_conv_parallel_kernel() lazily built with numba.njit(cache=True, parallel=True, nogil=True) exactly as the prototype does (numba_util.jit cannot express parallel=True), selected when conf["general"]["numba"]["parallel"] is true — the same flag the shared decorator reads.
• kernel_inputs_from(pix_indexes, pix_sizes, pix_weights, extent_index_for_masked_pixel, extent_shape, pix_pixels) — the prototype's builder, but taking the mask's extent_index_for_masked_pixel (iy = flat // nx, ix = flat % nx) instead of native_index_for_slim_index, so nothing new has to be stored on the dataset.
• nnz_per_source_column_from(mapper) = pix_sizes_for_sub_slim_index.sum() / params.

2. interferometer_numba/sparse.py::InversionInterferometerSparseNumba(InversionInterferometerSparse)

• __init__ raises InversionException (messages ported from the prototype) on: xp is not np, missing numba, any AbstractLinearObjFuncList, more than one mapper, or over-sampling (sub_fraction != 1). Preconditions are raised, not worked around.
• kernel_index_arrays cached per mapper.
• curvature_matrix_diag calls the kernel with self.dataset.sparse_operator.nufft_precision_operator (the preload stored by perf: NumPy/scipy application path for InterferometerSparseOperator (xp=np never imports JAX) #542; assert (2ny, 2nx) == preload.shape).
• data_vector inherited. curvature_matrix keeps the base class's regularization / diag handling — the kernel returns a complete symmetric F, so the mirroring pass is a no-op; verify that rather than skipping it.

3. autoarray/settings.py

• interferometer_numba_nnz_per_source_max: Optional[float] = None -> conf["general"]["inversion"]["interferometer_numba_nnz_per_source_max"] with a try/except KeyError fallback of 60.0 (the Delaunay crossover, the conservative of the two measured); 0 disables the path. Documented on the property with the measured crossovers and the note that the constant is machine-dependent.

4. autoarray/inversion/inversion/factory.py — interferometer branch

• After the sparse-operator check: if xp is np and settings.interferometer_numba_nnz_per_source_max > 0 and the preconditions hold and nnz_per_source_column <= max and numba imports -> InversionInterferometerSparseNumba; otherwise InversionInterferometerSparse as today.
• Preconditions failing at dispatch fall through silently to the sparse path (a routing decision, not an error); the numba class itself still raises if constructed directly with bad inputs.
• Mirrors how the imaging branch already selects by operator type (SparseLinAlgImagingNumba -> InversionImagingSparseNumba); the interferometer branch has no numba case today.

5. Tests — new test_autoarray/inversion/inversion/interferometer_numba/

• F, D, reconstruction, log_evidence vs InversionInterferometerSparse(xp=np) at rtol=1e-10, atol=1e-10*max|F| on the DFT Delaunay and rectangular fixtures from test_interferometer.py.
• A control that a 1 % scale of F fails the pin.
• Parallel kernel == serial kernel.
• kernel_inputs_from matches the prototype's on a hand-built mapper.
• Factory routing: gate above / below, xp=jnp never routes numba, func-list and multi-mapper fall through.
• Each precondition raises with its message.
• Run the existing interferometer test tree to prove the default sparse path is unchanged.
• Note: the conftest nufftax-skip is path-keyed and a new interferometer_numba/ test path is not on the list, so the DFT fixtures run unskipped.

6. Workspace / in-situ phase (not this issue)

• The in-situ re-measurement on autolens_profiling — delaunay_numba.py arming through the library dispatch instead of through the prototype pack — is filed as a follow-up prompt via /intake at ship time (draft/research/autolens_profiling/interferometer_numba_library_dispatch_insitu.md).

Key Files

• autoarray/inversion/inversion/interferometer_numba/ — new package (inversion_interferometer_numba_util.py, sparse.py), mirroring autoarray/inversion/inversion/imaging_numba/ (two modules, @numba_util.jit() kernels + sparse.py).
• autoarray/inversion/inversion/factory.py — interferometer dispatch branch.
• autoarray/settings.py — the new gate key (None -> conf["general"]["inversion"][key] with try/except KeyError).
• autoarray/inversion/inversion/interferometer/sparse.py — base class InversionInterferometerSparse.
• autoarray/inversion/inversion/interferometer/inversion_interferometer_util.py — nufft_precision_operator (stored by perf: NumPy/scipy application path for InterferometerSparseOperator (xp=np never imports JAX) #542).
• autolens_profiling/scripts/misc/numba_interferometer/kernels.py — curvature_direct_conv, _curvature_direct_conv_parallel, kernel_inputs_from; inversion.py for the precondition messages.
• test_autoarray/inversion/inversion/interferometer/test_interferometer.py — Delaunay / rectangular DFT fixtures to reuse.

Verification

• Parity + control + parallel == serial + routing + precondition tests green.
• The default interferometer path unchanged (existing interferometer tree green).
• pytest test_autoarray/inversion test_autoarray/dataset test_autoarray/operators -q green; black clean.
• PR base is feature/interferometer-sparse-operator-numpy-cpu-path until perf: NumPy/scipy application path for InterferometerSparseOperator (xp=np never imports JAX) #542 merges.

Reinstate a numba CPU interferometer curvature path — the extent-grid convolution, geometry-gated

Type: feature
Target: autoarray
Repos:

• PyAutoArray
• autolens_profiling
  Themes:
• numba-cpu
• interferometer
• likelihood-profiling
  Difficulty: large
  Autonomy: supervised
  Priority: high
  Epic: numba-interferometer-revisit
  Filed: 2026-09-07

Follow-up from autolens_profiling#226 (phase 2 of numba-interferometer-revisit). The
verdict, the bake-off and the in-situ arms are in
autolens_profiling/results/notes/numba_interferometer_verdict.md.

What

Add autoarray/inversion/inversion/interferometer_numba/ mirroring the live
imaging_numba/ package, with one kernel: the extent-grid direct convolution prototyped
as direct_conv in autolens_profiling/scripts/misc/numba_interferometer/kernels.py. For
each source column of A, convolve it over the (Ny, Nx) unmasked-extent rectangle with
contiguous W~ preload rows, then project with Aᵀ. Cost O(nnz·M + S·nnz).

Do not reinstate the deleted kernel. The recovered O(N² P²) pair loop beats a NumPy
rfft2 convolution at only one of six measured cells; direct_conv beats it by 2.9-4.8× on
the real likelihood's F row.

Measured (single thread, i9-10885H, autolens 2026.8.17.1)

In-situ F: mapper×mapper row, real likelihood, arms interleaved with dgemm controls:

Whole evaluation, with the jit-warm F substituted into the measured non-F cost:
2.43× (sma Delaunay), 2.76× (sma rect), 1.77× (alma Delaunay), 1.08× (alma rect) faster than
JAX-CPU.

The gate this needs

The controlling variable is non-zeros per source column, nnz/S = N_pix·P/S. Measured
crossover against a NumPy rfft2 convolution: ≈60 (Delaunay) to ≈77 (rectangular). Below
it the numba kernel wins (up to 5.93×); above it the FFT wins (by 1.6× at alma_high Delaunay,
3.7× at alma_high rectangular). So the path must be selected, not defaulted to — a
dispatch rule on nnz/S with the constant measured on the target machine, or an explicit
user setting with the rule documented.

Scope

• interferometer_numba/inversion_interferometer_numba_util.py — the direct_conv kernel
  and its prange sibling (3.5× at sma, 4.6× at alma on 8 threads; see the pool caveat).
• interferometer_numba/sparse.py — InversionInterferometerSparseNumba, mirroring
  imaging_numba/sparse.py, dispatched from inversion/factory.py.
• The mapper must emit the CSR/CSC + extent-flat layout directly (as
  _sparse_triplets_curvature_from already emits COO), so the kernel does not marshal its
  own inputs per evaluation.
• Preconditions to raise on, not work around (the profiling pack already does): linear-
  function lists, multiple mappers, over_sample_size != 1, non-NumPy xp.
• Parity gate: F, D, reconstruction and log evidence against
  InversionInterferometerSparse at rtol=1e-10 on F with atol scaled by max|F|,
  plus a control that a 1 % scale fails the pin.
• The real-space W~ preload must be kept on the dataset: InterferometerSparseOperator
  currently stores only Khat = fft2(preload) and discards the array this kernel indexes.