Iteratively build graph index #612
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This PR is related to #610 |
cjnolet
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Conflicts with branch-25.02 have been fixed. |
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cjnolet
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Jan 30, 2025
| @@ -104,7 +108,8 @@ struct index_params : cuvs::neighbors::index_params { | |||
| */ | |||
| std::variant<std::monostate, | |||
| graph_build_params::ivf_pq_params, | |||
| graph_build_params::nn_descent_params> | |||
| graph_build_params::nn_descent_params, | |||
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We have explicit docs for each of these arguments. I understand the iterative search params are experimental to start, but can we at least add them to the docs?
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Thanks, I will add comments about iterative_search_params here.
tfeher
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Jan 30, 2025
achirkin
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Jan 31, 2025
achirkin
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Jan 31, 2025
| // Determine initial graph size. | ||
| uint64_t final_graph_size = (uint64_t)dataset.extent(0); | ||
| uint64_t initial_graph_size = (final_graph_size + 1) / 2; | ||
| while (initial_graph_size > graph_degree * 64) { |
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A nitpick, but perhaps we could add RAFT_EXPECTS(graph_degree > 0); assertion at the top of this function to make sure it doesn't cause an infinite loop if invalid (zero) graph_degree is set?
tfeher
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Jan 31, 2025
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This PR is about how CAGRA's search() and optimize() can be used to iteratively create and improve graph index.
Currently, IVFPQ and NND are used to create the initial kNN graph, which is then optimized to create the CAGRA search graph. So, for example, if you want to support a new data type in CAGRA, you need to create an initial kNN graph with that data type, and IVFPQ or NND must also support that new data type. This is a bit of hassle.
This PR is one solution to that problem. With functionality of this PR, once the CAGRA search supports the new data type, it can be used to create a graph index with it.