Adaptive Beamforming by Deep Learning (ABLE) - #357
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Included review availability: Your plan provides up to 2 included reviews per hour; 1 remains after this review. WalkthroughThe PR adds the ABLE adaptive beamforming model and operation, registers it in the pipeline, excludes trained model objects from serialized configuration, adds extensive model and integration tests, and updates notebook execution to cap CPU affinity on Linux. ChangesABLE adaptive beamforming
Notebook test execution
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Suggested reviewers: Merge Risk: ⚪ Minimal · up to The ABLE integration and its validation coverage do not show a concrete unresolved merge risk. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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@benluijten could be worth it to convert this PR to draft. Otherwise the rabbit will spam you. |
tristan-deep
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benluijten
marked this pull request as draft
May 18, 2026 11:40
tristan-deep
changed the title
* dev_able.py script for testing now
Mirrors DelayAndSum: under a mixed-precision policy the weighted data is bfloat16, which loses accuracy over the n_el * n_tx term reduction.
The minimum variance cell killed the notebook-test kernel: at num_patches=10 a patch holds ~10k pixels, and MV keeps a sub-aperture stack, a 64x64 covariance and a 128x128 real block system for each of them. Beamform in 100 patches (the zea default) and give MV a 32-element sub-aperture. Peak RSS of one MV patch at PICMUS dimensions drops from 1.91 GB to 0.60 GB, and the smaller sub-aperture also conditions the covariance better.
test_beamformers walks the beamformer registry, which now also holds "able". That op is not comparable across backends: its weights are randomly initialized per backend, so it only agreed because the test feeds zeros, and under torch its output requires grad, which the harness cannot hand to np.array -- the failure this fixes. Parametrize over the beamformers zea.ops defines itself, the same filter test_ops_infra already uses. ABLEBeamform stays covered by tests/test_able.py.
The ABLE notebook killed its kernel on the minimum variance cell. The runner has no GPU, so the batched solve runs through OpenBLAS, which is compiled for a fixed maximum thread count: with more cores than that it takes an auxiliary thread-metadata path and corrupts the heap, and the log shows the warning followed a second later by "malloc(): invalid size (unsorted)". Cap the pool where the notebook kernels inherit it, next to MPLBACKEND. Also halve the notebook's training steps: that runner is CPU-only, so the cell has to stay inside the per-cell timeout.
Fifty steps was a CI budget leaking into the docs: the notebook runner has no GPU and caps a cell at 600 s. Move the training budget into a papermill parameters cell at 300 steps, the regime the training script this notebook replaced used, and let tests/test_notebooks.py override it to 20 for CI, as the diffusion notebook already does. Histogram match all three images to the delay-and-sum one before the final comparison, with zea.display.histogram_match: adaptive beamformers shift the brightness of the whole image, which otherwise reads as a difference in image quality. Also say more plainly why the f-number mask is switched off.
The kernel still died on the minimum variance cell, at the same place, but without the heap-corruption message this time: the OPENBLAS_NUM_THREADS cap does reach the kernel (verified through papermill) yet OpenBLAS still reports that the machine has more cores than its build allows, so that path is still live and now fails silently. Rather than keep chasing a crash inside the runner's BLAS, put the grid resolution and patch count in the parameters cell and have CI run the notebook at 96x64 with 4 patches, the way custom_models_example and 3d_beamforming_example already trim their cost. The committed notebook keeps full resolution, and CI goes back to checking what it is there to check: that the notebook runs.
The previous OPENBLAS_NUM_THREADS cap did not work, and now that a 256-core machine is available the failure reproduces in seconds: the kernel dies with "malloc(): corrupted top size" in the minimum variance cell. OpenBLAS keeps one metadata slot per thread that calls into it, with the maximum fixed at compile time (64 in these wheels). XLA sizes its CPU worker pool from the process's CPU affinity, so on a big runner the batched solve behind minimum variance calls BLAS from many more threads than there are slots, and the auxiliary array OpenBLAS falls back to corrupts the heap. The slot is per calling thread, not per pool thread, which is why capping the pool changed nothing: OPENBLAS_NUM_THREADS of 1, 4 and 32 all still die. Narrowing the affinity mask is what bounds the callers. Doing it around the papermill call keeps it scoped to the notebook kernels, which inherit the mask at launch, and leaves the other test jobs their full parallelism. Verified both ways on 256 cores with the GPUs hidden: the notebook dies before and passes in 15s after.
The notebook had never actually been run, and its training budget did not hold
up once it was. Measured on a GPU, against the 17.2 MSE that simply handing
back the delay-and-sum image scores:
- 300 steps at 1e-3 lands at 16.1, i.e. mid-descent and no better than doing
nothing at all;
- 1e-3 is also the least reliable setting -- one seed in three stalls near
its starting loss and never recovers;
- at 1e-2 every seed tried converged, flattening between step 700 and 1000.
So the notebook now trains for 1200 steps at 1e-2 and reaches 1.50, and seeds
the initialisation. The seed does not make the run bit-reproducible, since the
GPU reductions are not, and the comment says so rather than promising more
than it delivers; 1200 is chosen to leave margin for that spread.
The training loop keeps its losses and a new cell plots them, so the claim of
convergence is something the reader can see rather than take on trust: the
curve drops by an order of magnitude and then runs flat for its last third. A
stalled seed is obvious in the same picture, which is what the surrounding
text tells you to look for.
Panels are a little smaller, to stay under the checker's 400 kB per cell.
Speckle is noise-like and compresses badly, and the cap is measured on the
base64 in the file rather than on the decoded image.
codecov/patch is red: 78.28% of the diff against an 84.49% target, all of it in zea/models/able.py. The gap is the argument checking -- the kernel and latent-layer specifications the docstring documents as accepted or rejected were never exercised, nor were get_config, the rank guard, and the single-channel path through stack_channels. Pin all of them, the accepted forms as well as the rejected ones.
The notebook trained a model and then showed it reproducing the very frame it was fitted to, which does not distinguish a learned weighting rule from a memorised image. PICMUS ships in-vivo carotid scans recorded with the same probe and the same single plane wave, and in the IQ format the model already expects, so the trained network drops into the same three pipelines with nothing changed but the file path. The result is worth stating plainly rather than dressing up. Measured as distance from the minimum variance image, ABLE sits about 12x closer to it than delay-and-sum does on the training frame, and two to three times closer on the carotid. So it does generalise, but a model fitted to one phantom frame carries only part of the way across, and the notebook says so and points at training on more frames as the next step. Both ratios are computed and printed in the notebook, so a rerun quotes its own numbers rather than inheriting mine -- which matters, because the carotid one moves noticeably between runs. The grid-size override is now a small function, applied to both scans. Without it the notebook test would beamform the carotid at full resolution with the four patches CI asks for, which is the same heavy minimum variance path that was killing the kernel. Dynamic range goes from 60 dB to 50: at 60 the speckle washes out and the anechoic lesions barely read. Panels shrink a little again to stay under the checker's per-cell ceiling, since the extra contrast costs bytes.
tristan-deep
marked this pull request as ready for review
September 9, 2026 13:36
Collaborator
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Okay this PR is pretty much ready. Only thing is that I think in the notebook training is slightly underfitting still. Running some improvements now. Also probably rabbit will have some comments. But someone can already have a look and review :) |
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September 9, 2026 13:37
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Inline comments:
In `@zea/models/able.py`:
- Around line 61-63: The ABLE docstring’s latent_layers description incorrectly
says it overrides n_latent_layers. Update the documentation near the
latent_layers parameter to state that it overrides only latent_dim and must
contain exactly n_latent_layers entries, including the default behavior when
n_latent_layers is omitted.
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The docstring claimed it overrides n_latent_layers. It does not: _init_layers raises unless the list holds exactly n_latent_layers entries, which is what test_able_rejects_invalid_configuration pins. It overrides latent_dim, and has to agree with n_latent_layers -- including when that argument is left at its default of 2, which is the case a caller is most likely to trip over. Both the class docstring, which CodeRabbit flagged, and _init_layers' own, which repeats the same claim.
benluijten
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Sep 11, 2026
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Some good additions @tristan-deep !
Fixed one test, and added some text in the notebook.
tristan-deep
approved these changes
Sep 11, 2026
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Adding (back) support for ABLE (Adaptive Beamforming by deep Learning)
TODO:
zea.ops.MinimumVariance)zea.ops.Beamformer@tristan-deepWhat is left on this branch after merging main:
zea.models.able.ABLE: the model predicting the per-pixel apodization weights.zea.models.able.ABLEBeamform: a beamformer operation that applies those weights and sums, registered as"able", soBeamform(beamformer="able", model=able)replaces the previousLambda(...) + DelayAndSum()wiring.docs/source/notebooks/models/adaptive_beamforming_by_deep_learning.ipynb: beamforms a PICMUS plane wave with delay-and-sum and minimum variance, then trains ABLE inside the pipeline to mimic minimum variance and compares images and runtimes.Summary by CodeRabbit
New Features
Tests