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Posted to issues@lucene.apache.org by "Mayya Sharipova (Jira)" <ji...@apache.org> on 2022/05/19 12:48:00 UTC
[jira] [Resolved] (LUCENE-10527) Use bigger maxConn for last layer in HNSW
[ https://issues.apache.org/jira/browse/LUCENE-10527?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Mayya Sharipova resolved LUCENE-10527.
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Fix Version/s: 9.2
Resolution: Fixed
> Use bigger maxConn for last layer in HNSW
> -----------------------------------------
>
> Key: LUCENE-10527
> URL: https://issues.apache.org/jira/browse/LUCENE-10527
> Project: Lucene - Core
> Issue Type: Task
> Reporter: Julie Tibshirani
> Assignee: Mayya Sharipova
> Priority: Minor
> Fix For: 9.2
>
> Attachments: Screen Shot 2022-05-18 at 4.26.14 PM.png, Screen Shot 2022-05-18 at 4.26.24 PM.png, Screen Shot 2022-05-18 at 4.27.37 PM.png, image-2022-04-20-14-53-58-484.png
>
> Time Spent: 4h 40m
> Remaining Estimate: 0h
>
> Recently I was rereading the HNSW paper ([https://arxiv.org/pdf/1603.09320.pdf)] and noticed that they suggest using a different maxConn for the upper layers vs. the bottom one (which contains the full neighborhood graph). Specifically, they suggest using maxConn=M for upper layers and maxConn=2*M for the bottom. This differs from what we do, which is to use maxConn=M for all layers.
> I tried updating our logic using a hacky patch, and noticed an improvement in latency for higher recall values (which is consistent with the paper's observation):
> *Results on glove-100-angular*
> Parameters: M=32, efConstruction=100
> !image-2022-04-20-14-53-58-484.png|width=400,height=367!
> As we'd expect, indexing becomes a bit slower:
> {code:java}
> Baseline: Indexed 1183514 documents in 733s
> Candidate: Indexed 1183514 documents in 948s{code}
> When we benchmarked Lucene HNSW against hnswlib in LUCENE-9937, we noticed a big difference in recall for the same settings of M and efConstruction. (Even adding graph layers in LUCENE-10054 didn't really affect recall.) With this change, the recall is now very similar:
> *Results on glove-100-angular*
> Parameters: M=32, efConstruction=100
> {code:java}
> k Approach Recall QPS
> 10 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.563 4410.499
> 50 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.798 1956.280
> 100 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.862 1209.734
> 500 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.958 341.428
> 800 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.974 230.396
> 1000 luceneknn dim=100 {'M': 32, 'efConstruction': 100} 0.980 188.757
> 10 hnswlib ({'M': 32, 'efConstruction': 100}) 0.552 16745.433
> 50 hnswlib ({'M': 32, 'efConstruction': 100}) 0.794 5738.468
> 100 hnswlib ({'M': 32, 'efConstruction': 100}) 0.860 3336.386
> 500 hnswlib ({'M': 32, 'efConstruction': 100}) 0.956 832.982
> 800 hnswlib ({'M': 32, 'efConstruction': 100}) 0.973 541.097
> 1000 hnswlib ({'M': 32, 'efConstruction': 100}) 0.979 442.163
> {code}
> I think it'd be nice update to maxConn so that we faithfully implement the paper's algorithm. This is probably least surprising for users, and I don't see a strong reason to take a different approach from the paper? Let me know what you think!
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