mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 10:38:54 +00:00
Seperate benchmarks by enabled features (#1538)
* Use cfg to seperate benchmark results based on features * Remove allow pragma * Avoid some unnecessary returns. * Improve benchmarks layout * Derive bench_name from actual device * Run CPU benchmarks even when GPU feature is enabled --------- Co-authored-by: Laurent <laurent.mazare@gmail.com>
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@ -46,6 +46,6 @@ accelerate = ["dep:libc", "dep:accelerate-src"]
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metal = ["dep:metal", "dep:candle-metal-kernels"]
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[[bench]]
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name = "matmul"
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name = "bench_main"
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harness = false
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4
candle-core/benches/bench_main.rs
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4
candle-core/benches/bench_main.rs
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@ -0,0 +1,4 @@
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mod benchmarks;
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use criterion::criterion_main;
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criterion_main!(benchmarks::matmul::benches);
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@ -1,25 +1,25 @@
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use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
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use candle_core::{DType, Device, Tensor};
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use criterion::{black_box, criterion_group, criterion_main, Criterion, Throughput};
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use criterion::{black_box, criterion_group, Criterion, Throughput};
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use std::time::Instant;
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fn run(a: &Tensor, b: &Tensor) {
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a.matmul(&b.t().unwrap()).unwrap();
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}
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fn criterion_benchmark(c: &mut Criterion) {
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fn run_bench(c: &mut Criterion, device: &Device) {
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let b = 1;
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let m = 1;
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let n = 2048;
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let k = 2048;
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let device = Device::new_metal(0).unwrap();
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let dtype = DType::F32;
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let lhs = Tensor::zeros((b, m, k), dtype, &device).unwrap();
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let rhs = Tensor::zeros((b, n, k), dtype, &device).unwrap();
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let lhs = Tensor::zeros((b, m, k), dtype, device).unwrap();
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let rhs = Tensor::zeros((b, n, k), dtype, device).unwrap();
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let flops = b * m * n * k;
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let mut group = c.benchmark_group("matmul_metal");
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let mut group = c.benchmark_group(device.bench_name("matmul"));
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group.throughput(Throughput::Bytes(flops as u64));
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group.bench_function("iter", move |b| {
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b.iter_custom(|iters| {
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@ -27,16 +27,18 @@ fn criterion_benchmark(c: &mut Criterion) {
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for _i in 0..iters {
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run(black_box(&lhs), black_box(&rhs));
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}
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if let Device::Metal(device) = &device {
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device.wait_until_completed().unwrap();
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} else {
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panic!("Expected metal device");
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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 handler = BenchDeviceHandler::new().unwrap();
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for device in handler.devices {
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run_bench(c, &device);
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}
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}
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criterion_group!(benches, criterion_benchmark);
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criterion_main!(benches);
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63
candle-core/benches/benchmarks/mod.rs
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63
candle-core/benches/benchmarks/mod.rs
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@ -0,0 +1,63 @@
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pub(crate) mod matmul;
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use candle_core::{Device, Result};
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pub(crate) trait BenchDevice {
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fn sync(&self) -> Result<()>;
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fn bench_name<S: Into<String>>(&self, name: S) -> String;
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}
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impl BenchDevice for Device {
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fn sync(&self) -> Result<()> {
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match self {
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Device::Cpu => Ok(()),
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Device::Cuda(device) => {
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#[cfg(feature = "cuda")]
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return Ok(device.synchronize()?);
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#[cfg(not(feature = "cuda"))]
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panic!("Cuda device without cuda feature enabled: {:?}", device)
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}
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Device::Metal(device) => {
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#[cfg(feature = "metal")]
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return Ok(device.wait_until_completed()?);
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#[cfg(not(feature = "metal"))]
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panic!("Metal device without metal feature enabled: {:?}", device)
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}
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}
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}
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fn bench_name<S: Into<String>>(&self, name: S) -> String {
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match self {
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Device::Cpu => {
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let cpu_type = if cfg!(feature = "accelerate") {
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"accelerate"
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} else if cfg!(feature = "mkl") {
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"mkl"
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} else {
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"cpu"
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};
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format!("{}_{}", cpu_type, name.into())
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}
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Device::Cuda(_) => format!("cuda_{}", name.into()),
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Device::Metal(_) => format!("metal_{}", name.into()),
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}
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}
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}
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struct BenchDeviceHandler {
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devices: Vec<Device>,
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}
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impl BenchDeviceHandler {
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pub fn new() -> Result<Self> {
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let mut devices = Vec::new();
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if cfg!(feature = "metal") {
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devices.push(Device::new_metal(0)?);
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} else if cfg!(feature = "cuda") {
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devices.push(Device::new_cuda(0)?);
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}
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devices.push(Device::Cpu);
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Ok(Self { devices })
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}
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}
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