Add some very simple sum benchmark. (#108)

* Add some very simple sum benchmark.

* Rename the file.

candle-core/examples/cuda_sum_benchmark.rs

@@ -0,0 +1,51 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use std::str::FromStr;
+
+use anyhow::Result;
+use candle::{Device, Tensor};
+
+fn cos_sin(n: usize, device: &Device) -> Result<Tensor> {
+    let thetas: Vec<_> = (0..n).map(|i| (i as f32 / n as f32)).collect();
+    let xs: Vec<_> = thetas.iter().map(|t| t.cos().abs()).collect();
+    let ys: Vec<_> = thetas.iter().map(|t| t.sin().abs()).collect();
+    let xs = Tensor::from_vec(xs, (n, 1), device)?;
+    let ys = Tensor::from_vec(ys, (1, n), device)?;
+    let ys = Tensor::cat(&[&ys, &ys, &ys, &ys, &ys, &ys], 1)?;
+    Ok(xs.matmul(&ys)?)
+}
+
+fn main() -> Result<()> {
+    let device = Device::new_cuda(0)?;
+    let args = std::env::args().collect::<Vec<String>>();
+    let n = if args.len() < 2 {
+        2000usize
+    } else {
+        usize::from_str(&args[1])?
+    };
+    let xys_cpu = cos_sin(n, &Device::Cpu)?;
+    let xys = cos_sin(n, &device)?;
+    println!("{xys_cpu:?} {xys:?}");
+    let sum_cpu = xys_cpu.sum(&[1])?;
+    println!("{sum_cpu}");
+    let sum = xys.sum(&[1])?;
+    println!("{sum}");
+    let start = std::time::Instant::now();
+    let n_iters = 100;
+    let mut v = 0f32;
+    for _i in 0..n_iters {
+        let sum = xys.sum(&[1])?;
+        let sum = sum.sum(&[0])?;
+        let sum: f32 = sum.reshape(&[])?.to_scalar()?;
+        v += sum;
+    }
+    let elapsed = start.elapsed();
+    if v > 0. {
+        println!(
+            "ran {n_iters} iterations, time per iter: {:?} ({v})",
+            elapsed.div_f64(n_iters as f64)
+        );
+    }
+    Ok(())
+}