mirror of https://github.com/tracel-ai/burn.git
65 lines
1.4 KiB
Rust
65 lines
1.4 KiB
Rust
use backend_comparison::persistence::save;
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use burn::tensor::{backend::Backend, Distribution, Shape, Tensor};
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use burn_common::{
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benchmark::{run_benchmark, Benchmark},
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sync_type::SyncType,
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};
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use derive_new::new;
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#[derive(new)]
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struct UnaryBenchmark<B: Backend, const D: usize> {
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shape: Shape<D>,
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device: B::Device,
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}
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impl<B: Backend, const D: usize> Benchmark for UnaryBenchmark<B, D> {
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type Args = Tensor<B, D>;
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fn name(&self) -> String {
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"unary".into()
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}
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fn shapes(&self) -> Vec<Vec<usize>> {
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vec![self.shape.dims.into()]
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}
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fn execute(&self, args: Self::Args) {
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// Choice of tanh is arbitrary
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B::float_tanh(args.clone().into_primitive().tensor());
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}
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fn prepare(&self) -> Self::Args {
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Tensor::random(self.shape.clone(), Distribution::Default, &self.device)
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}
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fn sync(&self) {
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B::sync(&self.device, SyncType::Wait)
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}
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}
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#[allow(dead_code)]
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fn bench<B: Backend>(
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device: &B::Device,
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feature_name: &str,
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url: Option<&str>,
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token: Option<&str>,
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) {
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const D: usize = 3;
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let shape: Shape<D> = [32, 512, 1024].into();
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let benchmark = UnaryBenchmark::<B, D>::new(shape, device.clone());
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save::<B>(
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vec![run_benchmark(benchmark)],
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device,
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feature_name,
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url,
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token,
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)
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.unwrap();
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}
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fn main() {
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backend_comparison::bench_on_backend!();
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}
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