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use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
use candle_core::{
quantized::{self, GgmlDType, QMatMul},
Device, Module, Tensor,
};
use criterion::{black_box, criterion_group, Criterion, Throughput};
use std::time::Instant;
fn run(matmul: &QMatMul, x: &Tensor) {
matmul.forward(x).unwrap();
}
fn run_bench(c: &mut Criterion, device: &Device, dtype: GgmlDType) {
let b = 1;
let m = 1;
let n = 1024;
let k = 1024;
let lhs = (0..(m * k))
.map(|v| v as f32 / (m * k) as f32)
.collect::<Vec<_>>();
let rhs = (0..(k * n))
.map(|v| v as f32 / (n * k) as f32)
.collect::<Vec<_>>();
let lhs = Tensor::from_slice(&lhs, (m, k), device).unwrap();
let rhs = Tensor::from_slice(&rhs, (k, n), device).unwrap();
let qtensor = quantized::QTensor::quantize(&rhs.t().unwrap(), dtype).unwrap();
let matmul = quantized::QMatMul::from_qtensor(qtensor).unwrap();
let flops = b * m * n * k;
let mut group = c.benchmark_group(device.bench_name(format!("qmatmul_{:?}", dtype)));
group.sample_size(200);
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(&matmul), black_box(&lhs));
}
device.sync().unwrap();
start.elapsed()
})
});
group.finish();
}
fn criterion_benchmark(c: &mut Criterion) {
let handler = BenchDeviceHandler::new().unwrap();
for device in handler.devices {
for dtype in [
GgmlDType::F32,
GgmlDType::F16,
GgmlDType::Q4_0,
GgmlDType::Q4_1,
GgmlDType::Q5_0,
GgmlDType::Q5_1,
GgmlDType::Q8_0,
GgmlDType::Q2K,
GgmlDType::Q3K,
GgmlDType::Q4K,
GgmlDType::Q5K,
GgmlDType::Q6K,
] {
run_bench(c, &device, dtype);
}
}
}
criterion_group!(benches, criterion_benchmark);
|
