Post-A/B verdict (see project_ml_alpha_v2_ab_verdict.md): v2 with all
5 axes was marginally tied on h6000 (+0.0013 vs 0.7591 baseline mean,
fails the +0.01 win threshold) and slightly below on mean_auc
(−0.0208 vs 0.7749 baseline mean, within 1σ) at ~5× the wall-time
cost. Per `feedback_v7_gem_methodology` (measure before delete or
wire), the architecture has been measured — it doesn't earn its
compute cost. This commit reverts the axes that didn't lift:
- axis B (L2 anchor + Wiener-α controller) — DROPPED
- axis C (horizon-token attention pool) — DROPPED
- axis D (regime-MoE gate + experts) — DROPPED
- axis E (inverted cross-variate attn) — DROPPED
- axis A (Kendall σ-weighted BCE) — KEPT
Files deleted (kernels, host bindings, numgrad tests, trainer state):
- cuda/{horizon_token_attention_pool, inverted_attention_pool,
inv_pooled_merge, regime_moe_gate, anchor_l2,
horizon_mean_collapse}.cu
- src/{horizon_token_attention_pool, inverted_attention_pool,
inv_pooled_merge, regime_moe_gate, anchor_l2,
horizon_mean_collapse}.rs
- src/trainer/{multi_horizon_attention, anchor_controller}.rs
- tests/{horizon_token_attention_pool_numgrad,
inverted_attention_pool_numgrad,
regime_moe_gate_numgrad,
anchor_l2_numgrad}.rs
Files restored (from V1 commit 41292303d):
- cuda/attention_pool.cu — legacy single-Q attention pool kernel
- src/trainer/perception.rs — pre-MHA trainer state with the
legacy `attn_*` plumbing intact.
Files modified:
- bce_loss_multi_horizon.cu stays σ-aware (kept the V7 work; it
has the kernel function name preserved from V1).
- perception.rs: ADD `log_sigma_h_d [N_HORIZONS]`,
`grad_log_sigma_h_d [N_HORIZONS]`, `opt_log_sigma` AdamW
directly on PerceptionTrainer (no MHA bundle). BCE callsites in
`step_batched` (training) and `evaluate_batched` thread the σ
args. Grad scratch zeroed each step before the BCE launch.
`opt_log_sigma.step` lives in section 9 alongside the other
AdamW updates.
NET DIFF: 23 files, 442 insertions, 3160 deletions (~2700-line
cleanup).
LOCAL VERIFICATION (RTX 3050 sm_86, --test-threads=1):
- ml-alpha builds clean (cuda feature)
- bce_grad_finite_diff 4/4 PASS (BCE still works through σ-kernel)
- perception_overfit 9/9 PASS (full trainer pipeline, loss-shrinks
tests still green)
NEXT: cluster smoke + 3-fold A/B vs task #200 baseline. Expected
wall-time ≈ baseline 17 s/epoch (we're back to baseline architecture
plus 5 scalar Kendall σ params + 1 tiny AdamW). Expected lift on
mean_auc: modest — Kendall σ rebalances per-horizon contributions
based on observed BCE EMA, which may help horizons with intrinsically
higher noise floors.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
116 lines
3.8 KiB
Rust
116 lines
3.8 KiB
Rust
//! Pre-compile all ml-alpha CUDA kernels into arch-specific cubins.
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//!
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//! Per `feedback_no_nvrtc.md`: no runtime kernel compilation.
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//! Per `pearl_build_rs_rerun_if_env_changed.md`: every `std::env::var`
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//! is paired with `cargo:rerun-if-env-changed`.
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use std::path::{Path, PathBuf};
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use std::process::Command;
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const KERNELS: &[&str] = &[
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"mamba2_alpha_kernel", // Mamba2 SSM scan kernel (used by PerceptionTrainer's encoder prefix)
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"snap_feature_assemble",
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"cfc_step",
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"multi_horizon_heads",
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"projection",
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"bce_loss_multi_horizon", // Kendall σ-weighted multi-horizon BCE (axis A)
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"adamw_step",
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"grad_norm",
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"horizon_lambda", // ISV-driven per-horizon gradient scaler (EMA + lambda)
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"layer_norm", // Phase 1: trunk pre-CfC normalisation
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"variable_selection", // Phase 2D: TFT-style per-feature gating
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"attention_pool", // Phase 3: single-Q learned content summary at CfC k=0
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"reduce_axis0", // Phase B: cross-batch param-grad reducer
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];
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// Cache bust v11 (2026-05-17): K-loop parallelization Phase B —
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// new reduce_axis0.cu kernel + block-per-batch refactor of
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// cfc_step_batched (fwd+bwd). Old cubins don't have the new symbols.
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// Force fresh nvcc compile.
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fn main() {
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println!("cargo:rerun-if-changed=build.rs");
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println!("cargo:rerun-if-env-changed=CARGO_FEATURE_CUDA");
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if std::env::var("CARGO_FEATURE_CUDA").is_err() {
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eprintln!(" ml-alpha: cuda feature disabled, skipping kernel build");
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return;
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}
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println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
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println!("cargo:rerun-if-env-changed=CUDA_HOME");
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let cap = std::env::var("CUDA_COMPUTE_CAP").unwrap_or_else(|_| "80".to_string());
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let arch = format!("sm_{cap}");
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let nvcc = match find_nvcc() {
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Some(p) => p,
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None => {
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eprintln!(" ml-alpha: nvcc not found, skipping kernel build (set CUDA_HOME or install CUDA toolkit)");
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return;
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}
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};
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let out = PathBuf::from(std::env::var("OUT_DIR").expect("OUT_DIR not set by cargo"));
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for k in KERNELS {
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let src = PathBuf::from(format!("cuda/{k}.cu"));
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if !src.exists() {
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eprintln!(" ml-alpha: skipping {k} — source not yet present");
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continue;
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}
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println!("cargo:rerun-if-changed={}", src.display());
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let cubin = out.join(format!("{k}.cubin"));
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compile(&nvcc, &src, &cubin, &arch);
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}
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}
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fn compile(nvcc: &Path, src: &Path, cubin: &Path, arch: &str) {
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let status = Command::new(nvcc)
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.args([
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"-cubin",
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&format!("-arch={arch}"),
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"-O3",
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"--use_fast_math",
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"--ftz=true",
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"--fmad=true",
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"-o",
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cubin.to_str().unwrap(),
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src.to_str().unwrap(),
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])
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.status()
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.unwrap_or_else(|e| panic!("nvcc spawn failed for {}: {e}", src.display()));
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if !status.success() {
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panic!(
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"nvcc failed for {} (exit {})",
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src.display(),
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status.code().unwrap_or(-1)
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);
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}
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eprintln!(
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" ml-alpha: compiled {} -> {} ({arch})",
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src.display(),
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cubin.display()
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);
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}
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fn find_nvcc() -> Option<PathBuf> {
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if let Ok(home) = std::env::var("CUDA_HOME") {
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let p = PathBuf::from(home).join("bin/nvcc");
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if p.exists() {
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return Some(p);
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}
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}
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for cand in ["/usr/local/cuda/bin/nvcc", "/usr/bin/nvcc"] {
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let p = PathBuf::from(cand);
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if p.exists() {
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return Some(p);
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}
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}
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Command::new("nvcc")
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.arg("--version")
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.output()
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.ok()
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.filter(|o| o.status.success())
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.map(|_| PathBuf::from("nvcc"))
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}
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