Files
foxhunt/crates/ml-alpha/build.rs
jgrusewski 55aeddaebd feat(ml-alpha): anchor_l2 kernel + Wiener-α controller (v2 B) [V8]
L2 anchor regularization toward initialization (axis B). Anchors
horizon_tokens + Q + MoE experts toward their init values to prevent
the calibration drift observed in v1 (where val_loss climbed as α
opened past epoch 1 in 2 of 3 folds).

KERNEL (`anchor_l2_fwd_bwd`):
  loss_out = λ · Σ_i (p[i] − p_init[i])²
  grad_p[i] += 2λ · (p[i] − p_init[i])

  - Warp-shuffle reduce; one block per parameter group; strided thread
    loop over n. Cross-warp reduce uses one __syncthreads.
  - Coalesced grad write via stride loop.
  - λ passed as device-side [1]-buffer (host writes scalar before launch
    — capture-safe).

CONTROLLER (`trainer::anchor_controller::AnchorController`):
  - Signal-driven λ floor: λ_floor = ‖p_init‖ / (100 · √numel).
    Cross-fold-persistent per pearl_kelly_cap_signal_driven_floors.
  - Wiener-α smoother (α = diff_var / (diff_var + sample_var + ε))
    on val_loss change; α floored at 0.4 per
    pearl_wiener_alpha_floor_for_nonstationary.
  - λ blends toward target = |ema_change|·scale with α; floored at
    λ_floor per pearl_blend_formulas_must_have_permanent_floor.
  - First-observation bootstrap (sentinel state replaced directly on
    first step) per pearl_first_observation_bootstrap.
  - 4/4 unit tests PASS: signal-floor init, bootstrap returns floor,
    floor protection across 1000 steps, λ_max cap.

NUMGRAD VERIFICATION (RTX 3050 sm_86):
  anchor_l2_numgrad PASSES with closed-form parity (machine precision)
  and central-difference parity (4 random positions) within 5e-2 rel.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 14:26:31 +02:00

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//! Pre-compile all ml-alpha CUDA kernels into arch-specific cubins.
//!
//! Per `feedback_no_nvrtc.md`: no runtime kernel compilation.
//! Per `pearl_build_rs_rerun_if_env_changed.md`: every `std::env::var`
//! is paired with `cargo:rerun-if-env-changed`.
use std::path::{Path, PathBuf};
use std::process::Command;
const KERNELS: &[&str] = &[
"mamba2_alpha_kernel", // Mamba2 SSM scan kernel (used by PerceptionTrainer's encoder prefix)
"snap_feature_assemble",
"cfc_step",
"multi_horizon_heads",
"projection",
"bce_loss_multi_horizon",
"bce_loss_multi_horizon_sigma", // v2-A: Kendall σ-weighted BCE
"adamw_step",
"grad_norm",
"horizon_lambda", // ISV-driven per-horizon gradient scaler (EMA + lambda)
"layer_norm", // Phase 1: trunk pre-CfC normalisation
"variable_selection", // Phase 2D: TFT-style per-feature gating
"attention_pool", // Phase 3: learned context summary at CfC k=0
"horizon_token_attention_pool", // v2-C: horizon-token K-prepend single-Q attention
"inverted_attention_pool", // v2-E: iTransformer-style cross-variate attention
"regime_moe_gate", // v2-D: top-1 MoE gate + expert dispatch + aux loss
"anchor_l2", // v2-B: L2 anchor regularization toward init
"reduce_axis0", // Phase B: cross-batch param-grad reducer
];
// Cache bust v11 (2026-05-17): K-loop parallelization Phase B —
// new reduce_axis0.cu kernel + block-per-batch refactor of
// cfc_step_batched (fwd+bwd). Old cubins don't have the new symbols.
// Force fresh nvcc compile.
fn main() {
println!("cargo:rerun-if-changed=build.rs");
println!("cargo:rerun-if-env-changed=CARGO_FEATURE_CUDA");
if std::env::var("CARGO_FEATURE_CUDA").is_err() {
eprintln!(" ml-alpha: cuda feature disabled, skipping kernel build");
return;
}
println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
println!("cargo:rerun-if-env-changed=CUDA_HOME");
let cap = std::env::var("CUDA_COMPUTE_CAP").unwrap_or_else(|_| "80".to_string());
let arch = format!("sm_{cap}");
let nvcc = match find_nvcc() {
Some(p) => p,
None => {
eprintln!(" ml-alpha: nvcc not found, skipping kernel build (set CUDA_HOME or install CUDA toolkit)");
return;
}
};
let out = PathBuf::from(std::env::var("OUT_DIR").expect("OUT_DIR not set by cargo"));
for k in KERNELS {
let src = PathBuf::from(format!("cuda/{k}.cu"));
if !src.exists() {
eprintln!(" ml-alpha: skipping {k} — source not yet present");
continue;
}
println!("cargo:rerun-if-changed={}", src.display());
let cubin = out.join(format!("{k}.cubin"));
compile(&nvcc, &src, &cubin, &arch);
}
}
fn compile(nvcc: &Path, src: &Path, cubin: &Path, arch: &str) {
let status = Command::new(nvcc)
.args([
"-cubin",
&format!("-arch={arch}"),
"-O3",
"--use_fast_math",
"--ftz=true",
"--fmad=true",
"-o",
cubin.to_str().unwrap(),
src.to_str().unwrap(),
])
.status()
.unwrap_or_else(|e| panic!("nvcc spawn failed for {}: {e}", src.display()));
if !status.success() {
panic!(
"nvcc failed for {} (exit {})",
src.display(),
status.code().unwrap_or(-1)
);
}
eprintln!(
" ml-alpha: compiled {} -> {} ({arch})",
src.display(),
cubin.display()
);
}
fn find_nvcc() -> Option<PathBuf> {
if let Ok(home) = std::env::var("CUDA_HOME") {
let p = PathBuf::from(home).join("bin/nvcc");
if p.exists() {
return Some(p);
}
}
for cand in ["/usr/local/cuda/bin/nvcc", "/usr/bin/nvcc"] {
let p = PathBuf::from(cand);
if p.exists() {
return Some(p);
}
}
Command::new("nvcc")
.arg("--version")
.output()
.ok()
.filter(|o| o.status.success())
.map(|_| PathBuf::from("nvcc"))
}