Iterative CAGRA-Q - #1810
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… search - Configurable growth-phase in-build search params (itopk_size, search_width, max_iterations) and internal/smem dtype; itopk auto-forced on the final full-size iteration. - Decouple compression params used during iterative construction from the target index compression. - Add shuffle_dataset option; fix out-of-bounds access from the in-place raft gather by switching to an out-of-place gather.
…around) The shuffle_dataset path used an out-of-place gather into a temporary buffer to work around an illegal memory access in raft's in-place gather overload when n_rows * row_len exceeded 2^31 (32-bit index overflow). That bug is now fixed upstream in raft (NVIDIA/raft#3059, closes #3055), which the cuvs raft pin now includes. Revert to the in-place gather to drop the extra full-size temporary allocation and copy.
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Build CAGRA on PQ datasets with Iterative CAGRA-Q
Iterative cagra graph construction using CAGRA-Q search.
This PR improves the iterative CAGRA build method by enabling PQ compression: the dataset is compressed before the iterative search starts, and CAGRA-Q is used to iteratively update the KNN graph.
This is the first time we are introducing building CAGRA on (PQ) quantized datasets directly.
This PR also adds support for C and Python APIs for creating a PQ dataset and building a cagra graph on it. We also add support to update_dataset from dense->PQ or PQ->dense. Serializing the index is also supported.
Example Runnable workflow