Reapply IndexHNSWFlatPanorama with backward compatible serialization
#4692
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Summary: This PR introduces **Panorama** into `HNSWFlat`, following our [paper](https://arxiv.org/pdf/2510.00566). Panorama achieves up to **4× lower latency** on higher-dimensional data, making it a great option for medium-sized datasets that don't benefit much from quantization. Below are some benchmarks on **SIFT-128**, **GIST-960**, and **synthetic 2048-dimensional** data. I recommend checking out the paper for more results. As expected, Panorama is not a silver bullet when combined with HNSW—it’s only worthwhile for high-dimensional data. It might be worth considering, in the future, adding a function that dynamically sets the number of levels. However, this would require reorganizing the cumulative sums. ## SIFT-128 **Note:** SIFT-128 performs slightly worse here than in our paper because we use 8 levels, whereas the paper explored several level configurations. Eight levels introduce quite a bit of overhead for 128-dimensional data, but I kept it consistent across all benchmarks for comparison. <img width="788" height="435" alt="bench_hnsw_flat_panorama_SIFT1M" src="https://github.com/user-attachments/assets/5004dee4-de0a-44e5-9031-582f7738a348" /> ## GIST-960 <img width="787" height="435" alt="bench_hnsw_flat_panorama_GIST1M" src="https://github.com/user-attachments/assets/ba73d062-be14-44b0-9c46-ac1dafbfaed2" /> ## Synthetic-2048 <img width="794" height="435" alt="bench_hnsw_flat_panorama_Synthetic2048D" src="https://github.com/user-attachments/assets/acf6233c-185b-4295-9c63-7a4b5b037619" /> Pull Request resolved: facebookresearch#4621 Reviewed By: mdouze Differential Revision: D85902427 Pulled By: mnorris11 fbshipit-source-id: 4db9e950ce0c532494fa99ae93d39ccf06779b5d
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Also, @mnorris11 take a look at: #4691 |
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@mnorris11 is out of office for a week. Assigning him to take a look when he's back. |
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@mnorris11 has imported this pull request. If you are a Meta employee, you can view this in D88390767. |
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As title, this PR reverts the prior revert PR.
@mnorris11 is this fix OK? Please feel free to push your test in here.