From 93ddf1fe790ea1d358559992ca9fecffba9e77ba Mon Sep 17 00:00:00 2001
From: Eric Buehler <ericlbuehler@gmail.com>
Date: Tue, 29 Apr 2025 08:00:30 -0400
Subject: [PATCH] Better benchmark

---
 constensor-core/benches/cpu_graph.rs | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/constensor-core/benches/cpu_graph.rs b/constensor-core/benches/cpu_graph.rs
index 4e62613..904e1e3 100644
--- a/constensor-core/benches/cpu_graph.rs
+++ b/constensor-core/benches/cpu_graph.rs
@@ -46,10 +46,10 @@ fn bench_cpu_graph_matmul_256(c: &mut Criterion) {
 
 fn bench_candle_matmul_64(c: &mut Criterion) {
     const N: usize = 64;
-    let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
-    let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
     c.bench_function("candle_matmul_64x64", |bencher| {
         bencher.iter(|| {
+            let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
+            let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
             let _ = a.matmul(&b).unwrap();
         });
     });
@@ -57,10 +57,10 @@ fn bench_candle_matmul_64(c: &mut Criterion) {
 
 fn bench_candle_matmul_128(c: &mut Criterion) {
     const N: usize = 128;
-    let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
-    let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
     c.bench_function("candle_matmul_128x128", |bencher| {
         bencher.iter(|| {
+            let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
+            let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
             let _ = a.matmul(&b).unwrap();
         });
     });
@@ -68,10 +68,10 @@ fn bench_candle_matmul_128(c: &mut Criterion) {
 
 fn bench_candle_matmul_256(c: &mut Criterion) {
     const N: usize = 256;
-    let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
-    let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
     c.bench_function("candle_matmul_256x256", |bencher| {
         bencher.iter(|| {
+            let a = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
+            let b = Tensor::rand(0f32, 1f32, &[1, N, N], &Device::Cpu).unwrap();
             let _ = a.matmul(&b).unwrap();
         });
     });