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Add DiskANN graph-based index: builds a Vamana graph with configurable R (max degree) and L (search list size, separate for insert/query), supports int8 quantization with rescore, lazy reverse-edge replacement, pre-quantized query optimization, and insert buffer reuse. Includes shadow table management, delete support, KNN integration, compile flag (SQLITE_VEC_ENABLE_DISKANN), release-demo workflow, fuzz targets, and tests. Fixes rescore int8 quantization bug.
131 lines
4.2 KiB
C
131 lines
4.2 KiB
C
/**
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* Fuzz target for DiskANN RobustPrune algorithm (diskann_prune_select).
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*
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* diskann_prune_select is exposed for testing and takes:
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* - inter_distances: flattened NxN matrix of inter-candidate distances
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* - p_distances: N distances from node p to each candidate
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* - num_candidates, alpha, max_neighbors
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*
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* This is a pure function that doesn't need a database, so we can
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* call it directly with fuzz-controlled inputs. This gives the fuzzer
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* maximum speed (no SQLite overhead) to explore:
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*
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* - alpha boundary: alpha=0 (prunes nothing), alpha=very large (prunes all)
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* - max_neighbors = 0, 1, num_candidates, > num_candidates
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* - num_candidates = 0, 1, large
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* - Distance matrices with: all zeros, all same, negative values, NaN, Inf
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* - Non-symmetric distance matrices (should still work)
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* - Memory: large num_candidates to stress malloc
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*
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* Key code paths:
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* - diskann_prune_select alpha-pruning loop
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* - Boundary: selectedCount reaches max_neighbors exactly
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* - All candidates pruned before max_neighbors reached
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*/
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#include <stdint.h>
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#include <stddef.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <math.h>
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#include "sqlite-vec.h"
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#include "sqlite3.h"
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#include <assert.h>
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/* Declare the test-exposed function.
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* diskann_prune_select is not static -- it's a public symbol. */
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extern int diskann_prune_select(
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const float *inter_distances, const float *p_distances,
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int num_candidates, float alpha, int max_neighbors,
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int *outSelected, int *outCount);
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static uint8_t fuzz_byte(const uint8_t **data, size_t *size, uint8_t def) {
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if (*size == 0) return def;
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uint8_t b = **data;
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(*data)++;
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(*size)--;
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return b;
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}
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int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
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if (size < 8) return 0;
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/* Consume parameters from fuzz data */
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int num_candidates = fuzz_byte(&data, &size, 0) % 33; /* 0..32 */
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int max_neighbors = fuzz_byte(&data, &size, 0) % 17; /* 0..16 */
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/* Alpha: pick from interesting values */
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uint8_t alpha_idx = fuzz_byte(&data, &size, 0) % 8;
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float alpha_values[] = {0.0f, 0.5f, 1.0f, 1.2f, 1.5f, 2.0f, 10.0f, 100.0f};
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float alpha = alpha_values[alpha_idx];
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if (num_candidates == 0) {
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/* Test empty case */
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int outCount = -1;
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int rc = diskann_prune_select(NULL, NULL, 0, alpha, max_neighbors,
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NULL, &outCount);
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assert(rc == 0 /* SQLITE_OK */);
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assert(outCount == 0);
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return 0;
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}
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/* Allocate arrays */
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int n = num_candidates;
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float *inter_distances = malloc(n * n * sizeof(float));
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float *p_distances = malloc(n * sizeof(float));
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int *outSelected = malloc(n * sizeof(int));
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if (!inter_distances || !p_distances || !outSelected) {
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free(inter_distances);
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free(p_distances);
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free(outSelected);
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return 0;
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}
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/* Fill p_distances from fuzz data (sorted ascending for correct input) */
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for (int i = 0; i < n; i++) {
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uint8_t raw = fuzz_byte(&data, &size, (uint8_t)(i * 10));
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p_distances[i] = (float)raw / 10.0f;
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}
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/* Sort p_distances ascending (prune_select expects sorted input) */
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for (int i = 1; i < n; i++) {
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float tmp = p_distances[i];
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int j = i - 1;
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while (j >= 0 && p_distances[j] > tmp) {
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p_distances[j + 1] = p_distances[j];
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j--;
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}
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p_distances[j + 1] = tmp;
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}
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/* Fill inter-distance matrix from fuzz data */
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for (int i = 0; i < n * n; i++) {
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uint8_t raw = fuzz_byte(&data, &size, (uint8_t)(i % 256));
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inter_distances[i] = (float)raw / 10.0f;
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}
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/* Make diagonal zero */
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for (int i = 0; i < n; i++) {
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inter_distances[i * n + i] = 0.0f;
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}
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int outCount = -1;
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int rc = diskann_prune_select(inter_distances, p_distances,
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n, alpha, max_neighbors,
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outSelected, &outCount);
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/* Basic sanity: should not crash, count should be valid */
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assert(rc == 0);
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assert(outCount >= 0);
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assert(outCount <= max_neighbors || max_neighbors == 0);
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assert(outCount <= n);
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/* Verify outSelected flags are consistent with outCount */
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int flagCount = 0;
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for (int i = 0; i < n; i++) {
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if (outSelected[i]) flagCount++;
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}
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assert(flagCount == outCount);
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free(inter_distances);
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free(p_distances);
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free(outSelected);
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return 0;
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}
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