Add benchmarks for the candle-nn package (#1995)

* add benchmarks for the candle-nn package

* uncomment test

* format

candle-nn/benches/benchmarks/layer_norm.rs

@@ -0,0 +1,48 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle::{DType, Device, Module, Tensor};
+use candle_nn::LayerNorm;
+use criterion::{black_box, criterion_group, Criterion};
+use std::time::Instant;
+
+fn run(input: &Tensor, weight: &Tensor, bias: &Tensor) {
+    let _ = LayerNorm::new(weight.clone(), bias.clone(), 1e-5).forward(&input);
+}
+
+const B: usize = 1;
+const M: usize = 1024;
+const K: usize = 1024;
+
+fn run_layer_norm_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
+    let elements = B * M * K;
+
+    let weight = Tensor::arange(0.0, elements as f32, device)
+        .unwrap()
+        .to_dtype(dtype)
+        .unwrap();
+    let bias = weight.ones_like().unwrap();
+    let input = weight.ones_like().unwrap();
+
+    let mut group = c.benchmark_group(device.bench_name(name));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                run(black_box(&input), black_box(&weight), black_box(&bias));
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let device = BenchDeviceHandler::new().unwrap();
+    for d in device.devices {
+        run_layer_norm_benchmark(c, &d, DType::F32, "layer_norm_f32");
+        run_layer_norm_benchmark(c, &d, DType::BF16, "layer_norm_bf16");
+        run_layer_norm_benchmark(c, &d, DType::F16, "layer_norm_f16");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);