FxHashSet

Author: Human9000-bit

Cargo.lock

@@ -8,7 +8,7 @@ use rustc_hash::FxHashMap;
 /// Usage:
 ///   cargo run --release --no-default-features -p yscv-llm-bench --bin nchwc_coverage \
 ///     -- private/private/model.onnx
-use std::collections::HashSet;
+use rustc_hash::FxHashSet;
 use yscv_onnx::{OnnxAttribute, OnnxModel, OnnxNode, load_onnx_model_from_file};
 
 fn main() {
@@ -421,8 +421,8 @@ fn run_coverage_probe(model: &OnnxModel) {
     // And number of exit converters (nchwc tensors consumed by non-nchwc non-Conv nodes)
 
     // Use effective_weights keys as the "static" set (includes Identity-aliased weights)
-    let static_names: HashSet<&str> = effective_weights.keys().map(|s| s.as_str()).collect();
-    let initializer_names: HashSet<&str> = static_names.clone();
+    let static_names: FxHashSet<&str> = effective_weights.keys().map(|s| s.as_str()).collect();
+    let initializer_names: FxHashSet<&str> = static_names.clone();
     let dynamic_inputs: Vec<&str> = model
         .inputs
         .iter()
@@ -437,7 +437,7 @@ fn run_coverage_probe(model: &OnnxModel) {
     }
 
     // Mark which tensors are in nchwc domain (after potential converter)
-    let mut nchwc_tensors: HashSet<String> = HashSet::new();
+    let mut nchwc_tensors: FxHashSet<String> = FxHashSet::default();
     // Graph inputs start as NHWC; we will count converters needed for them
     // if any eligible node consumes them directly.
 
@@ -446,7 +446,7 @@ fn run_coverage_probe(model: &OnnxModel) {
     let mut internal_boundaries = 0usize;
 
     // Track which dynamic inputs get a converter
-    let mut converted_inputs: HashSet<String> = HashSet::new();
+    let mut converted_inputs: FxHashSet<String> = FxHashSet::default();
 
     for (i, node) in model.nodes.iter().enumerate() {
         if !node_eligible[i] && !matches!(node.op_type.as_str(), "Conv" | "Conv_Relu" | "Conv_SiLU")

apps/llm-bench/src/bin/nchwc_coverage.rs

@@ -43,9 +43,9 @@ pub(crate) fn parse_image_id_manifest(
     text: &str,
     format: &'static str,
 ) -> Result<Vec<String>, EvalError> {
-    use std::collections::HashSet;
+    use rustc_hash::FxHashSet;
     let mut image_ids = Vec::new();
-    let mut seen = HashSet::new();
+    let mut seen = FxHashSet::default();
     for (line_idx, raw_line) in text.lines().enumerate() {
         let line_no = line_idx + 1;
         let line = raw_line.trim();

crates/yscv-eval/src/dataset/helpers.rs

@@ -1,5 +1,5 @@
+use rustc_hash::FxHashSet;
 use rustc_hash::{FxBuildHasher, FxHashMap};
-use std::collections::HashSet;
 
 use yscv_detect::{BoundingBox, CLASS_ID_FACE, Detection};
 
@@ -63,7 +63,7 @@ fn parse_widerface_ground_truth(text: &str) -> Result<Vec<WiderFaceGroundTruthFr
     let lines = widerface_significant_lines(text);
     let mut cursor = 0usize;
     let mut frames = Vec::new();
-    let mut seen_ids = HashSet::new();
+    let mut seen_ids = FxHashSet::default();
 
     while cursor < lines.len() {
         let (image_line_no, image_id_raw) = lines[cursor];
@@ -160,7 +160,7 @@ fn parse_widerface_predictions(text: &str) -> Result<Vec<WiderFacePredictionFram
     let lines = widerface_significant_lines(text);
     let mut cursor = 0usize;
     let mut frames = Vec::new();
-    let mut seen_ids = HashSet::new();
+    let mut seen_ids = FxHashSet::default();
 
     while cursor < lines.len() {
         let (image_line_no, image_id_raw) = lines[cursor];

crates/yscv-eval/src/dataset/widerface.rs

@@ -1,4 +1,4 @@
-use std::collections::HashSet;
+use rustc_hash::FxHashSet;
 use std::path::Path;
 
 use yscv_detect::{BoundingBox, Detection};
@@ -42,7 +42,7 @@ pub(crate) fn load_yolo_label_dirs(
 
 fn parse_yolo_manifest(text: &str) -> Result<Vec<YoloManifestEntry>, EvalError> {
     let mut entries = Vec::new();
-    let mut seen_ids = HashSet::new();
+    let mut seen_ids = FxHashSet::default();
     for (line_idx, raw_line) in text.lines().enumerate() {
         let line_no = line_idx + 1;
         let line = raw_line.trim();

crates/yscv-eval/src/dataset/yolo.rs

@@ -15,6 +15,7 @@ yscv-tensor = { version = "0.1", path = "../yscv-tensor" }
 yscv-cpu = { version = "0.1", path = "../yscv-cpu" }
 image = { version = "0.25", default-features = false, features = ["png", "jpeg", "bmp", "webp"] }
 thiserror.workspace = true
+rustc-hash.workspace = true
 rayon = "1.11"
 
 [dev-dependencies]

crates/yscv-imgproc/Cargo.toml

@@ -1586,7 +1586,7 @@ pub fn hough_circles(
         // Generate circle points using angular discretisation
         let circumference = (2.0 * std::f32::consts::PI * r as f32).ceil() as usize;
         let num_steps = circumference.max(36);
-        let mut visited = std::collections::HashSet::new();
+        let mut visited = rustc_hash::FxHashSet::default();
 
         for step in 0..num_steps {
             let angle = 2.0 * std::f32::consts::PI * step as f32 / num_steps as f32;

crates/yscv-imgproc/src/ops/features.rs

@@ -71,7 +71,7 @@ fn test_data_loader_multi_worker() {
     assert_eq!(batches.len(), 8);
 
     // Verify all samples are covered (each sample has a unique fill value).
-    let mut seen = std::collections::HashSet::new();
+    let mut seen = rustc_hash::FxHashSet::default();
     for batch in &batches {
         let batch_size = batch.inputs.shape()[0];
         let sample_len = 3;

crates/yscv-model/src/tests/data_loader.rs

@@ -106,8 +106,8 @@ fn detect_linux_big_cores() -> Option<Vec<usize>> {
 
 #[cfg(target_os = "linux")]
 fn detect_linux_physical_cores() -> Option<usize> {
-    use std::collections::HashSet;
-    let mut cores: HashSet<(usize, usize)> = HashSet::new();
+    use rustc_hash::FxHashSet;
+    let mut cores: FxHashSet<(usize, usize)> = FxHashSet::default();
     for id in 0..1024usize {
         let cpu_dir = format!("/sys/devices/system/cpu/cpu{id}");
         if !std::path::Path::new(&cpu_dir).exists() {

crates/yscv-onnx/src/cpu_topology.rs

@@ -1,5 +1,5 @@
 use prost::Message;
-use std::collections::HashSet;
+use rustc_hash::FxHashSet;
 use yscv_tensor::Tensor;
 
 use crate::error::OnnxError;
@@ -273,8 +273,8 @@ pub fn onnx_model_to_export_graph(model: &crate::loader::OnnxModel) -> OnnxExpor
     }
 }
 
-fn infer_int64_tensor_names(nodes: &[crate::loader::OnnxNode]) -> HashSet<String> {
-    let mut names = HashSet::new();
+fn infer_int64_tensor_names(nodes: &[crate::loader::OnnxNode]) -> FxHashSet<String> {
+    let mut names = FxHashSet::default();
     for node in nodes {
         match node.op_type.as_str() {
             "Reshape" | "Expand" | "Tile" | "Gather" | "GatherElements" | "GatherND" => {