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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);
|
