Update with feature separated benchmarks

candle-core/benches/benchmarks/fill.rs

@@ -0,0 +1,43 @@
+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 run(shape: (usize, usize, usize), dtype: DType, device: &Device) {
+    Tensor::ones(shape, dtype, device).unwrap();
+}
+
+fn run_fill_benchmark(c: &mut Criterion, name: &str, dtype: DType) {
+    let b = 1;
+    let rows = 4096;
+    let columns = 4096;
+
+    let flops = b * rows * columns * dtype.size_in_bytes();
+
+    let device = device().unwrap();
+
+    let mut group = c.benchmark_group(bench_name(name));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |bencher| {
+        bencher.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                run(
+                    black_box((b, rows, columns)),
+                    black_box(dtype),
+                    black_box(&device),
+                );
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    run_fill_benchmark(c, "fill_u8", DType::U8);
+    run_fill_benchmark(c, "fill_f32", DType::F32);
+}
+
+criterion_group!(benches, criterion_benchmark);