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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);
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