49 lines
1.5 KiB
Rust
49 lines
1.5 KiB
Rust
use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
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use candle::{DType, Device, Module, Tensor};
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use candle_nn::LayerNorm;
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use criterion::{black_box, criterion_group, Criterion};
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use std::time::Instant;
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fn run(input: &Tensor, weight: &Tensor, bias: &Tensor) {
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let _ = LayerNorm::new(weight.clone(), bias.clone(), 1e-5).forward(&input);
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}
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const B: usize = 1;
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const M: usize = 1024;
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const K: usize = 1024;
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fn run_layer_norm_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
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let elements = B * M * K;
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let weight = Tensor::arange(0.0, elements as f32, device)
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.unwrap()
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.to_dtype(dtype)
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.unwrap();
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let bias = weight.ones_like().unwrap();
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let input = weight.ones_like().unwrap();
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let mut group = c.benchmark_group(device.bench_name(name));
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group.bench_function("iter", move |b| {
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b.iter_custom(|iters| {
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let start = Instant::now();
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for _i in 0..iters {
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run(black_box(&input), black_box(&weight), black_box(&bias));
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}
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device.sync().unwrap();
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start.elapsed()
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})
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});
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group.finish();
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}
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fn criterion_benchmark(c: &mut Criterion) {
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let device = BenchDeviceHandler::new().unwrap();
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for d in device.devices {
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run_layer_norm_benchmark(c, &d, DType::F32, "layer_norm_f32");
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run_layer_norm_benchmark(c, &d, DType::BF16, "layer_norm_bf16");
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run_layer_norm_benchmark(c, &d, DType::F16, "layer_norm_f16");
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}
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}
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criterion_group!(benches, criterion_benchmark);
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