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* move quantization repo * are you happy fmt * are you happy clippy * remove dumping pq to image * workspace deps * are you happy clippy
124 lines
4.0 KiB
Rust
124 lines
4.0 KiB
Rust
#[allow(unused)]
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mod metrics;
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#[cfg(test)]
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#[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
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mod tests {
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use quantization::encoded_vectors::{DistanceType, EncodedVectors, VectorParameters};
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use quantization::encoded_vectors_u8::EncodedVectorsU8;
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use rand::{Rng, SeedableRng};
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use crate::metrics::{dot_similarity, l1_similarity, l2_similarity};
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#[test]
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fn test_dot_sse() {
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let vectors_count = 129;
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let vector_dim = 65;
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let error = vector_dim as f32 * 0.1;
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//let mut rng = rand::thread_rng();
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let mut rng = rand::rngs::StdRng::seed_from_u64(42);
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let mut vector_data: Vec<Vec<f32>> = Vec::new();
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for _ in 0..vectors_count {
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let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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vector_data.push(vector);
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}
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let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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let encoded = EncodedVectorsU8::encode(
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vector_data.iter(),
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Vec::<u8>::new(),
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&VectorParameters {
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dim: vector_dim,
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count: vectors_count,
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distance_type: DistanceType::Dot,
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invert: false,
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},
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None,
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|| false,
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)
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.unwrap();
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let query_u8 = encoded.encode_query(&query);
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for (index, vector) in vector_data.iter().enumerate() {
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let score = encoded.score_point_sse(&query_u8, index as u32);
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let orginal_score = dot_similarity(&query, vector);
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assert!((score - orginal_score).abs() < error);
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}
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}
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#[test]
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fn test_l2_sse() {
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let vectors_count = 129;
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let vector_dim = 65;
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let error = vector_dim as f32 * 0.1;
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//let mut rng = rand::thread_rng();
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let mut rng = rand::rngs::StdRng::seed_from_u64(42);
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let mut vector_data: Vec<Vec<f32>> = Vec::new();
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for _ in 0..vectors_count {
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let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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vector_data.push(vector);
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}
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let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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let encoded = EncodedVectorsU8::encode(
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vector_data.iter(),
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Vec::<u8>::new(),
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&VectorParameters {
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dim: vector_dim,
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count: vectors_count,
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distance_type: DistanceType::L2,
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invert: false,
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},
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None,
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|| false,
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)
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.unwrap();
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let query_u8 = encoded.encode_query(&query);
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for (index, vector) in vector_data.iter().enumerate() {
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let score = encoded.score_point_sse(&query_u8, index as u32);
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let orginal_score = l2_similarity(&query, vector);
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assert!((score - orginal_score).abs() < error);
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}
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}
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#[test]
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fn test_l1_sse() {
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let vectors_count = 129;
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let vector_dim = 65;
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let error = vector_dim as f32 * 0.1;
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//let mut rng = rand::thread_rng();
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let mut rng = rand::rngs::StdRng::seed_from_u64(42);
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let mut vector_data: Vec<Vec<f32>> = Vec::new();
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for _ in 0..vectors_count {
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let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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vector_data.push(vector);
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}
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let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
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let encoded = EncodedVectorsU8::encode(
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vector_data.iter(),
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Vec::<u8>::new(),
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&VectorParameters {
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dim: vector_dim,
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count: vectors_count,
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distance_type: DistanceType::L1,
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invert: false,
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},
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None,
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|| false,
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)
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.unwrap();
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let query_u8 = encoded.encode_query(&query);
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for (index, vector) in vector_data.iter().enumerate() {
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let score = encoded.score_point_sse(&query_u8, index as u32);
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let orginal_score = l1_similarity(&query, vector);
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assert!((score - orginal_score).abs() < error);
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}
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}
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}
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