use nalgebra::Matrix3;
use slang_hal::{test_shader_compilation, Shader};
#[cfg(test)]
use {
naga_oil::compose::NagaModuleDescriptor,
wgpu::{ComputePipeline, Device},
};
#[derive(Copy, Clone, Debug, encase::ShaderType)]
#[repr(C)]
pub struct GpuQR3 {
pub q: Matrix3<f32>,
pub r: Matrix3<f32>,
}
#[derive(Shader)]
#[shader(src = "qr3.wgsl")]
pub struct WgQR3;
test_shader_compilation!(WgQR3);
impl WgQR3 {
#[doc(hidden)]
#[cfg(test)]
pub fn tests(device: &Device) -> ComputePipeline {
let test_kernel = r#"
@group(0) @binding(0)
var<storage, read_write> in: array<mat3x3<f32>>;
@group(0) @binding(1)
var<storage, read_write> out: array<QR>;
@compute @workgroup_size(1, 1, 1)
fn test(@builtin(global_invocation_id) invocation_id: vec3<u32>) {
let i = invocation_id.x;
out[i] = qr(in[i]);
}
"#;
let src = format!("{}\n{}", Self::src(), test_kernel);
let module = WgQR3::composer()
.unwrap()
.make_naga_module(NagaModuleDescriptor {
source: &src,
file_path: Self::FILE_PATH,
..Default::default()
})
.unwrap();
slang_hal::utils::load_module(device, "test", module)
}
}
#[cfg(test)]
mod test {
use super::GpuQR3;
use approx::{assert_relative_eq, relative_eq};
use nalgebra::{DVector, Matrix3};
use slang_hal::gpu::GpuInstance;
use slang_hal::kernel::{CommandEncoderExt, KernelDispatch};
use crate::tensor::GpuTensor;
use wgpu::BufferUsages;
#[futures_test::test]
#[serial_test::serial]
async fn gpu_qr3() {
let gpu = GpuInstance::new().await.unwrap();
let svd = super::WgQR3::tests(gpu.device());
let mut encoder = gpu.device().create_command_encoder(&Default::default());
const LEN: usize = 345;
let matrices: DVector<Matrix3<f32>> = DVector::new_random(LEN);
let inputs = GpuTensor::encase(gpu.device(), &matrices, BufferUsages::STORAGE);
let result: GpuTensor<GpuQR3> = GpuTensor::vector_uninit_encased(
gpu.device(),
matrices.len() as u32,
BufferUsages::STORAGE | BufferUsages::COPY_SRC,
);
let staging: GpuTensor<GpuQR3> = GpuTensor::vector_uninit_encased(
gpu.device(),
matrices.len() as u32,
BufferUsages::MAP_READ | BufferUsages::COPY_DST,
);
let mut pass = encoder.compute_pass("test", None);
KernelDispatch::new(gpu.device(), &mut pass, &svd)
.bind0([inputs.buffer(), result.buffer()])
.dispatch(matrices.len() as u32);
drop(pass);
staging.copy_from_encased(&mut encoder, &result);
gpu.queue().submit(Some(encoder.finish()));
let gpu_result = staging.read_encased(gpu.device()).await.unwrap();
let mut allowed_fails = 0;
for (m, qr) in matrices.iter().zip(gpu_result.iter()) {
let qr_na = m.qr();
if allowed_fails == matrices.len() * 2 / 100 {
assert_relative_eq!(qr_na.q(), qr.q, epsilon = 1.0e-4);
assert_relative_eq!(qr_na.r(), qr.r, epsilon = 1.0e-4);
} else if !relative_eq!(qr_na.q(), qr.q, epsilon = 1.0e-4)
|| !relative_eq!(qr_na.r(), qr.r, epsilon = 1.0e-4)
{
allowed_fails += 1;
}
}
println!("Num fails: {}/{}", allowed_fails, matrices.len());
}
}