Updated feature separated benchmarks

candle-core/benches/benchmarks/random.rs

@@ -0,0 +1,58 @@
+use crate::benchmarks::{bench_name, device, BenchDevice};
+use candle_core::{DType, Device, Tensor};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn rand_uniform(a: &Tensor) {
+    a.rand_like(-1.0, 123.0).unwrap();
+}
+
+fn rand_normal(a: &Tensor) {
+    a.randn_like(100.0, 15.0).unwrap();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let b = 1;
+
+    let rows = 2048;
+    let cols = 2048;
+
+    let d = device().unwrap();
+    let dtype = DType::F32;
+    let tensor = Tensor::zeros((b, rows, cols), dtype, &d).unwrap();
+
+    let flops = b * rows * cols * dtype.size_in_bytes();
+
+    let mut group = c.benchmark_group(bench_name("random_uniform"));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |benches| {
+        benches.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                rand_uniform(black_box(&tensor));
+            }
+            d.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+
+    let d = device().unwrap();
+    let tensor = Tensor::zeros((b, rows, cols), dtype, &d).unwrap();
+
+    let mut group = c.benchmark_group(bench_name("random_normal"));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |benches| {
+        benches.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                rand_normal(black_box(&tensor));
+            }
+            d.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+criterion_group!(benches, criterion_benchmark);