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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(
x: &Tensor,
k: &Tensor,
padding: usize,
output_padding: usize,
stride: usize,
dilation: usize,
) {
x.conv_transpose2d(k, padding, output_padding, stride, dilation)
.unwrap();
}
fn run_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
let t = Tensor::arange(0.0f32, 10000.0, device)
.unwrap()
.reshape((1, 4, 50, 50))
.unwrap()
.to_dtype(dtype)
.unwrap();
let kernel = Tensor::arange(0.0f32, 100.0, device)
.unwrap()
.reshape((4, 1, 5, 5))
.unwrap()
.to_dtype(dtype)
.unwrap();
let flops = t.dims().iter().product::<usize>() * dtype.size_in_bytes();
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(&t), black_box(&kernel), 1, 0, 1, 2);
}
device.sync().unwrap();
start.elapsed()
})
});
group.finish();
}
fn criterion_benchmark(c: &mut Criterion) {
let handler = BenchDeviceHandler::new().unwrap();
for device in handler.devices {
run_benchmark(c, &device, DType::F32, "conv_transpose2d_f32");
run_benchmark(c, &device, DType::F16, "conv_transpose2d_f16");
run_benchmark(c, &device, DType::BF16, "conv_transpose2d_bf16");
}
}
criterion_group!(benches, criterion_benchmark);
|
