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

* add benchmarks for the candle-nn package

* uncomment test

* format

candle-nn/benches/benchmarks/conv.rs

@@ -0,0 +1,54 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle::{DType, Device, Module, Tensor};
+use candle_nn::{Conv2d, Conv2dConfig};
+use criterion::{black_box, criterion_group, Criterion};
+use std::time::Instant;
+
+const B: usize = 1;
+const C: usize = 1;
+const M: usize = 128;
+const K: usize = 128;
+const K_SIZE: usize = 3;
+
+fn run(input: Tensor, weight: Tensor, bias: Tensor, config: Conv2dConfig) {
+    Conv2d::new(weight, Some(bias), config)
+        .forward(&input)
+        .unwrap();
+}
+
+fn run_conv2d_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
+    let weight = Tensor::ones((1, 1, K_SIZE, K_SIZE), dtype, device)
+        .unwrap()
+        .to_dtype(dtype)
+        .unwrap();
+    let bias = Tensor::zeros(K, dtype, device).unwrap();
+    let input = Tensor::ones((B, C, M, K), dtype, device).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.clone()),
+                    black_box(weight.clone()),
+                    black_box(bias.clone()),
+                    Default::default(),
+                );
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let device = BenchDeviceHandler::new().unwrap();
+    for d in device.devices {
+        run_conv2d_benchmark(c, &d, DType::F32, "conv2d_f32");
+        run_conv2d_benchmark(c, &d, DType::F16, "conv2d_f16");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);