Submitted vector_search/invector

tig-algorithms/src/vector_search/invector/README.md

@@ -0,0 +1,23 @@
+# TIG Code Submission
+
+## Submission Details
+
+* **Challenge Name:** vector_search
+* **Algorithm Name:** invector
+* **Copyright:** 2024 syebastian
+* **Identity of Submitter:** syebastian
+* **Identity of Creator of Algorithmic Method:** null
+* **Unique Algorithm Identifier (UAI):** null
+
+## License
+
+The files in this folder are under the following licenses:
+* TIG Benchmarker Outbound License
+* TIG Commercial License
+* TIG Inbound Game License
+* TIG Innovator Outbound Game License
+* TIG Open Data License
+* TIG THV Game License
+
+Copies of the licenses can be obtained at:
+https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses

tig-algorithms/src/vector_search/invector/kernels.cu

@@ -0,0 +1,19 @@
+/*!
+Copyright 2024 syebastian
+
+Licensed under the TIG Inbound Game License v1.0 or (at your option) any later
+version (the "License"); you may not use this file except in compliance with the
+License. You may obtain a copy of the License at
+
+https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses
+
+Unless required by applicable law or agreed to in writing, software distributed
+under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+CONDITIONS OF ANY KIND, either express or implied. See the License for the specific
+language governing permissions and limitations under the License.
+*/
+
+extern "C" __global__ void do_nothing()
+{
+    // This kernel does nothing
+}             

tig-algorithms/src/vector_search/invector/mod.rs

@@ -0,0 +1,358 @@
+use anyhow::{anyhow, Result};
+use cudarc::{
+    driver::{safe::LaunchConfig, CudaModule, CudaStream, PushKernelArg},
+    runtime::sys::cudaDeviceProp,
+};
+use std::sync::Arc;
+use serde_json::{Map, Value};
+use tig_challenges::vector_search::{Challenge, Solution};
+
+pub fn solve_challenge(
+    challenge: &Challenge,
+    save_solution: &dyn Fn(&Solution) -> anyhow::Result<()>,
+    hyperparameters: &Option<Map<String, Value>>,
+    module: Arc<CudaModule>,
+    stream: Arc<CudaStream>,
+    prop: &cudaDeviceProp,
+) -> anyhow::Result<()> {
+    Err(anyhow!("This algorithm is no longer compatible."))
+}
+
+// Old code that is no longer compatible
+#[cfg(none)]
+mod dead_code {
+
+   use anyhow::Ok;
+   use tig_challenges::vector_search::*;
+   use std::cmp::Ordering;
+   use std::collections::BinaryHeap;
+
+   struct KDNode<'a> {
+       point: &'a [f32],
+       left: Option<Box<KDNode<'a>>>,
+       right: Option<Box<KDNode<'a>>>,
+       index: usize,
+   }
+
+   impl<'a> KDNode<'a> {
+       fn new(point: &'a [f32], index: usize) -> Self {
+           KDNode {
+               point,
+               left: None,
+               right: None,
+               index,
+           }
+       }
+   }
+   fn quickselect_by<F>(arr: &mut [(&[f32], usize)], k: usize, compare: &F)
+   where
+       F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering,
+   {
+       if arr.len() <= 1 {
+           return;
+       }
+
+       let pivot_index = partition(arr, compare);
+       if k < pivot_index {
+           quickselect_by(&mut arr[..pivot_index], k, compare);
+       } else if k > pivot_index {
+           quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare);
+       }
+   }
+
+   fn partition<F>(arr: &mut [(&[f32], usize)], compare: &F) -> usize
+   where
+       F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering,
+   {
+       let pivot_index = arr.len() >> 1;
+       arr.swap(pivot_index, arr.len() - 1);
+
+       let mut store_index = 0;
+       for i in 0..arr.len() - 1 {
+           if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less {
+               arr.swap(i, store_index);
+               store_index += 1;
+           }
+       }
+       arr.swap(store_index, arr.len() - 1);
+       store_index
+   }
+
+   fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option<Box<KDNode<'a>>> {
+       if points.is_empty() {
+           return None;
+       }
+
+       const NUM_DIMENSIONS: usize = 250;
+       let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new();
+       let mut root: Option<Box<KDNode<'a>>> = None;
+
+       stack.push((0, points.len(), 0, None, false));
+
+       while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() {
+           if start >= end {
+               continue;
+           }
+
+           let axis = depth % NUM_DIMENSIONS;
+           let median = (start + end) / 2;
+           quickselect_by(&mut points[start..end], median - start, &|a, b| {
+               a.0[axis].partial_cmp(&b.0[axis]).unwrap()
+           });
+
+           let (median_point, median_index) = points[median];
+           let mut new_node = Box::new(KDNode::new(median_point, median_index));
+           let new_node_ptr: *mut KDNode = &mut *new_node;
+
+           if let Some(parent_ptr) = parent_ptr {
+               unsafe {
+                   if is_left {
+                       (*parent_ptr).left = Some(new_node);
+                   } else {
+                       (*parent_ptr).right = Some(new_node);
+                   }
+               }
+           } else {
+               root = Some(new_node);
+           }
+
+           stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false));
+           stack.push((start, median, depth + 1, Some(new_node_ptr), true));
+       }
+
+       root
+   }
+
+   #[inline(always)]
+   fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 {
+       let mut sum = 0.0;
+       for i in 0..a.len() {
+           unsafe {
+               let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
+               sum += diff * diff;
+           }
+       }
+       sum
+   }
+
+   #[inline(always)]
+   fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 {
+       let mut sum = 0.0;
+       for i in 0..180 {
+           unsafe {
+               let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
+               sum += diff * diff;
+           }
+       }
+       if sum > c {
+           sum;
+       }
+       for i in 180..a.len() {
+           unsafe {
+               let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
+               sum += diff * diff;
+           }
+       }
+       sum
+   }
+   #[inline(always)]
+   fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 {
+       let mut sum = 0.0;
+       let mut i = 0;
+       let len = a.len();
+
+       if a.len() != b.len() || a.len() < 8 {
+           return f32::MAX;
+       }
+
+       while i + 7 < len {
+           unsafe {
+               let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i);
+               let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1);
+               let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2);
+               let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3);
+               let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4);
+               let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5);
+               let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6);
+               let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7);
+
+               sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 +
+                   diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7;
+           }
+
+           if sum > current_min {
+               return f32::MAX;
+           }
+
+           i += 8;
+       }
+
+       while i < len {
+           unsafe {
+               let diff = *a.get_unchecked(i) - *b.get_unchecked(i);
+               sum += diff * diff;
+           }
+           i += 1;
+       }
+       sum
+   }
+
+   fn nearest_neighbor_search<'a>(
+       root: &Option<Box<KDNode<'a>>>,
+       target: &[f32],
+       best: &mut (f32, Option<usize>),
+   ) {
+       let num_dimensions = target.len();
+       let mut stack = Vec::with_capacity(64);
+
+       if let Some(node) = root {
+           stack.push((node.as_ref(), 0));
+       }
+
+       while let Some((node, depth)) = stack.pop() {
+           let axis = depth % num_dimensions;
+           let dist = early_stopping_distance(&node.point, target, best.0);
+
+           if dist < best.0 {
+               best.0 = dist;
+               best.1 = Some(node.index);
+           }
+
+           let diff = target[axis] - node.point[axis];
+           let sqr_diff = diff * diff;
+
+           let (nearer, farther) = if diff < 0.0 {
+               (&node.left, &node.right)
+           } else {
+               (&node.right, &node.left)
+           };
+
+           if let Some(nearer_node) = nearer {
+               stack.push((nearer_node.as_ref(), depth + 1));
+           }
+
+           if sqr_diff < best.0 {
+               if let Some(farther_node) = farther {
+                   stack.push((farther_node.as_ref(), depth + 1));
+               }
+           }
+       }
+   }
+
+   fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec<f32> {
+       let num_vectors = vectors.len();
+       let num_dimensions = 250;
+
+       let mut mean_vector = vec![0.0; num_dimensions];
+
+       for vector in vectors {
+           for i in 0..num_dimensions {
+               mean_vector[i] += vector[i];
+           }
+       }
+
+       for i in 0..num_dimensions {
+           mean_vector[i] /= num_vectors as f32;
+       }
+
+       mean_vector
+   }
+
+   #[derive(Debug)]
+   struct FloatOrd(f32);
+
+   impl PartialEq for FloatOrd {
+       fn eq(&self, other: &Self) -> bool {
+           self.0 == other.0
+       }
+   }
+
+   impl Eq for FloatOrd {}
+
+   impl PartialOrd for FloatOrd {
+       fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
+           self.0.partial_cmp(&other.0)
+       }
+   }
+
+   impl Ord for FloatOrd {
+       fn cmp(&self, other: &Self) -> Ordering {
+
+           self.partial_cmp(other).unwrap_or(Ordering::Equal)
+       }
+   }
+
+   fn filter_relevant_vectors<'a>(
+       database: &'a [Vec<f32>],
+       query_vectors: &[Vec<f32>],
+       k: usize,
+   ) -> Vec<(&'a [f32], usize)> {
+       let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect();
+       let mean_query_vector = calculate_mean_vector(&query_refs);
+
+       let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k);
+
+       for (index, vector) in database.iter().enumerate() {
+           if heap.len() < k 
+           {
+               let dist = squared_euclidean_distance(&mean_query_vector, vector);
+               let ord_dist = FloatOrd(dist);
+
+               heap.push((ord_dist, index));
+           } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() 
+           {
+               let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist);
+               let ord_dist = FloatOrd(dist);
+               if dist < top_dist {
+                   heap.pop();
+                   heap.push((ord_dist, index));
+               }
+           }
+       }
+       let result: Vec<(&'a [f32], usize)> = heap
+           .into_iter()
+           .map(|(_, index)| (&database[index][..], index))
+           .collect();
+
+       result
+   }
+
+   pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result<Option<Solution>> {
+       let query_count = challenge.query_vectors.len();
+
+       let max_fuel = 2000000000.0;
+       let base_fuel = 760000000.0;
+       let alpha = 1700.0 * challenge.num_queries as f64;
+
+       let subset_size = ((max_fuel - base_fuel) / alpha) as usize;
+       let subset = filter_relevant_vectors(
+           &challenge.vector_database,
+           &challenge.query_vectors,
+           subset_size,
+       );
+
+
+       let kd_tree = build_kd_tree(&mut subset.clone());
+
+
+       let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len());
+
+       for query in challenge.query_vectors.iter() {
+           let mut best = (std::f32::MAX, None);
+           nearest_neighbor_search(&kd_tree, query, &mut best);
+
+           if let Some(best_index) = best.1 {
+               best_indexes.push(best_index);
+           }
+       }
+
+
+       Ok(Some(Solution {
+           indexes: best_indexes,
+       }))
+   }
+}
+
+pub fn help() {
+    println!("No help information available.");
+}

tig-algorithms/src/vector_search/mod.rs

@@ -64,7 +64,8 @@
 
 // c004_a033
 
-// c004_a034
+pub mod invector;
+pub use invector as c004_a034;
 
 // c004_a035