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use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{DType, Device, Tensor};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(a: &Tensor, b: &Tensor, c: &Tensor) {
a.where_cond(b, c).unwrap();
}
const fn create_cond_arr<const N: usize>() -> [u8; N] {
let mut arr = [0u8; N];
let mut i = 0;
while i < N {
arr[i] = (i % 2) as u8;
i += 1;
}
arr
}
const B: usize = 1;
const M: usize = 1024;
const K: usize = 1024;
const SIZE: usize = B * M * K;
const DATA: [u8; SIZE] = create_cond_arr::<SIZE>();
fn run_where_cond_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
let tensor = Tensor::from_slice(DATA.as_slice(), (B, M, K), &device).unwrap();
let on_true = Tensor::ones((B, M, K), dtype, &device).unwrap();
let on_false = Tensor::zeros((B, M, K), dtype, &device).unwrap();
let elements = B * M * K;
// E.g. 2 f32 tensors + 1 u8 tensor
let flops = (2 * elements * dtype.size_in_bytes()) + elements;
let mut group = c.benchmark_group(device.bench_name(name));
group.throughput(Throughput::Bytes(flops as u64));
group.bench_function("iter", move |b| {
b.iter_custom(|iters| {
let start = Instant::now();
for _i in 0..iters {
run(
black_box(&tensor),
black_box(&on_true),
black_box(&on_false),
);
}
device.sync().unwrap();
start.elapsed()
})
});
group.finish();
}
fn criterion_benchmark(c: &mut Criterion) {
let device = BenchDeviceHandler::new().unwrap();
for d in device.devices {
run_where_cond_benchmark(c, &d, DType::F32, "where_cond_f32");
run_where_cond_benchmark(c, &d, DType::BF16, "where_cond_bf16");
run_where_cond_benchmark(c, &d, DType::F16, "where_cond_f16");
}
}
criterion_group!(benches, criterion_benchmark);
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