Add native self-hosted instance connection to fluxer_desktop
Trimmed monorepo checkout (fluxer_desktop + packages/voice_engine_v2 + tools/ci) with a "Connect to a Different Server" menu item and popout that lets the desktop app switch to any self-hosted Fluxer instance, plus fixes for well-known discovery on single-domain self-hosted deployments and a false-positive ERR_ABORTED on same-origin client redirects during the switch. Defaults to chat.fluxr.chat and uses an isolated userData directory from the official build.
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#![allow(dead_code)]
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use std::time::Instant;
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use tract_data::prelude::*;
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use tract_linalg::frame::mmm::*;
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fn ruin_cache() {
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// return;
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let _a = (0..1000000).collect::<Vec<i32>>();
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}
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pub fn reference<T, K>(mr: usize, k: usize, nr: usize) -> Vec<f32>
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where
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T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum,
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K: MatMatMulKer<T>,
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{
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let mut vi = vec![0.0; k * nr];
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for m in 0..mr {
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for n in 0..nr {
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for _ in 0..k {
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let a: f32 = 1.0;
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let b = 1.0;
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let offset = { n + m * nr };
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vi[offset] += a * b;
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}
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}
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}
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vi
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}
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fn bench_to_nanos<
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T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum,
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K: MatMatMulKer<T>,
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>(
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loops: usize,
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m: usize,
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n: usize,
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k: usize,
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) -> f64 {
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let kernel = K::mmm();
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let mut a = Tensor::zero_aligned::<T>(
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&[(k + K::end_padding_packed_a()) * m],
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K::alignment_bytes_packed_a(),
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)
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.unwrap();
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let mut a_plain = a.try_as_plain_mut().unwrap();
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let mut v = a_plain.to_array_view_mut::<f32>().unwrap();
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v += 1.0;
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drop(v);
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drop(a_plain);
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let mut b = Tensor::zero_aligned::<T>(
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&[(k + K::end_padding_packed_b()) * n],
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K::alignment_bytes_packed_b(),
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)
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.unwrap();
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let mut b_plain = b.try_as_plain_mut().unwrap();
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let mut v = b_plain.to_array_view_mut::<f32>().unwrap();
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v += 1.0;
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drop(v);
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drop(b_plain);
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let mut c = Tensor::zero::<T>(&[n, m]).unwrap();
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let ops = unsafe {
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[
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FusedSpec::AddMatMul {
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k,
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a: kernel.a_packed(4, k).wrap(&a.view()),
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b: kernel.b_packed(4, k).wrap(&b.view()),
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},
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// FusedSpec::AddUnicast(kernel.c_view(1, 0).wrap(&c.view_mut())),
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FusedSpec::Store(kernel.c_view(1, 0).wrap(&c.view_mut())),
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]
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};
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let mut values = Vec::with_capacity(loops);
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for _ in 0..loops {
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ruin_cache();
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let start = Instant::now();
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unsafe { kernel.run(m, n, &ops).unwrap() };
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values.push(start.elapsed());
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}
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eprintln!(
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"{:?} -> {:?}",
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values.first().unwrap(),
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values.last().unwrap()
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);
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values.sort();
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values[loops / 2].as_nanos() as f64
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}
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fn model<T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum, K: MatMatMulKer<T>>()
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-> (f64, f64) {
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let x = 1000;
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let zp = bench_to_nanos::<T, K>(1000, K::mr() * 4, K::nr() * 4, 0);
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let y = bench_to_nanos::<T, K>(1000, K::mr() * 4, K::nr() * 4, x);
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let slope = (y - zp) / x as f64;
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(slope, zp)
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}
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fn as_match_line<T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum, K: MatMatMulKer<T>>() {
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let coeffs = model::<T, K>();
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println!(
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"({:?}, {}, {}) => {} * k + {}",
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K::name(),
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K::mr(),
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K::nr(),
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(coeffs.0),
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(coeffs.1),
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);
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}
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fn main() {
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let core_id = core_affinity::get_core_ids().unwrap()[0];
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core_affinity::set_for_current(core_id);
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// as_match_line::<f32, fma_mmm_f32_64x1>();
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// as_match_line::<f32, avx512_mmm_f32_128x1>();
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// as_match_line::<f32, avx512_mmm_f32_16x1>();
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// as_match_line::<f32, fma_mmm_f32_40x2>();
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// as_match_line::<f32, fma_mmm_f32_32x3>();
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// as_match_line::<f32, fma_mmm_f32_24x4>();
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// as_match_line::<f32, fma_mmm_f32_16x5>();
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// as_match_line::<f32, fma_mmm_f32_16x6>();
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// as_match_line::<f32, fma_mmm_f32_8x8>();
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// mmv_perf_m();
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mmm_perf_batch_size();
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}
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// for mmv
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fn mmv_perf_m() {
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use tract_linalg::x86_64_fma::mmm::*;
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let core_id = core_affinity::get_core_ids().unwrap()[0];
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core_affinity::set_for_current(core_id);
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fn bench<T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum, K: MatMatMulKer<T>>(
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m: usize,
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) {
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let val = bench_to_nanos::<T, K>(1000, m, 1, 100) / (m * 100) as f64;
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print!("{val}\t");
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}
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print!("N\t");
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print!("fma_mmm_f32_64x1\t");
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print!("avx512_mmm_f32_128x1\t");
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print!("avx512_mmm_f32_16x1\t");
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println!();
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for n in 1..=128 {
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eprintln!("{n}");
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print!("{n}\t");
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bench::<f32, fma_mmm_f32_64x1>(n);
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bench::<f32, avx512_mmm_f32_128x1>(n);
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bench::<f32, avx512_mmm_f32_16x1>(n);
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println!();
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}
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}
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// output a csv file with the perf of the kernels wrt batch size
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fn mmm_perf_batch_size() {
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use tract_linalg::x86_64_fma::mmm::*;
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let core_id = core_affinity::get_core_ids().unwrap()[0];
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core_affinity::set_for_current(core_id);
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fn bench<T: Datum + Copy + num_traits::Zero + tract_linalg::LADatum, K: MatMatMulKer<T>>(
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n: usize,
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) {
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let val =
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bench_to_nanos::<T, K>(1000, K::mr() * 4, n, 100) / (K::mr() * 4 * 100 * n) as f64;
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print!("{val}\t");
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}
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print!("N\t");
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print!("fma_mmm_f32_8x8\t");
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print!("fma_mmm_f32_16x6\t");
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print!("fma_mmm_f32_16x5\t");
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print!("fma_mmm_f32_24x4\t");
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print!("fma_mmm_f32_32x3\t");
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print!("fma_mmm_f32_40x2\t");
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print!("fma_mmm_f32_64x1\t");
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print!("avx512_mmm_f32_128x1\t");
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print!("avx512_mmm_f32_16x1\t");
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print!("avx512_mmm_f32_16x12\t");
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print!("avx512_mmm_f32_16x8\t");
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print!("avx512_mmm_f32_32x6\t");
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print!("avx512_mmm_f32_32x5\t");
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print!("avx512_mmm_f32_48x4\t");
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print!("avx512_mmm_f32_64x3\t");
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print!("avx512_mmm_f32_80x2\t");
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println!();
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for n in 1..=128 {
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eprintln!("{n}");
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print!("{n}\t");
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bench::<f32, fma_mmm_f32_8x8>(n);
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bench::<f32, fma_mmm_f32_16x6>(n);
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bench::<f32, fma_mmm_f32_16x5>(n);
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bench::<f32, fma_mmm_f32_24x4>(n);
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bench::<f32, fma_mmm_f32_32x3>(n);
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bench::<f32, fma_mmm_f32_40x2>(n);
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bench::<f32, fma_mmm_f32_64x1>(n);
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bench::<f32, avx512_mmm_f32_128x1>(n);
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bench::<f32, avx512_mmm_f32_16x1>(n);
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bench::<f32, avx512_mmm_f32_16x12>(n);
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bench::<f32, avx512_mmm_f32_16x8>(n);
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bench::<f32, avx512_mmm_f32_32x6>(n);
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bench::<f32, avx512_mmm_f32_32x5>(n);
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bench::<f32, avx512_mmm_f32_48x4>(n);
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bench::<f32, avx512_mmm_f32_64x3>(n);
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bench::<f32, avx512_mmm_f32_80x2>(n);
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println!();
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}
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}
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