21e7dfd63c
Wires the aux supervision path parallel to BCE: - AuxTrunk (64-hidden single-bucket CfC) consumes the same encoder output as the main BCE trunk - AuxHeads (linear regression long/short) maps aux_trunk output to per-(direction, horizon) predicted outcomes - AuxHuberLoss supervises against D-style labels from MultiHorizonLoader Backward path with asymmetric stop-grad at encoder boundary: - aux_trunk gets gradient signal into its OWN params at all times - aux_trunk's encoder-boundary gradient is INITIALLY blocked (stop_grad_aux_to_encoder = true) - Conditional lift per E3 design: if aux_huber_ema < 0.4 AND aux_dir_acc_ema > 0.85 within 200 steps, lift the stop-grad - When lifted, aux_vec_add kernel folds aux's grad_x into the main grad_h_enriched_seq slot (element-wise += per feedback_no_atomicadd) ISV signals added: aux_huber_per_h, aux_dir_acc_per_h (per pearl). Per-trunk scratch + reduced grad buffers (no Adam state sharing per pearl_adam_normalizes_loss_weights — opt_aux is its own Adam group). New helper kernel cuda/aux_vec_add.cu: position-local dst += src for the asymmetric stop-grad lift accumulation. New synthetic test stacked_trainer_aux_supervision_converges_on_constant_signal validates end-to-end: aux_huber_ema_per_h = [0.087, 0.087, 0.087] (converged) aux_dir_acc_ema_per_h = [1.0, 1.0, 1.0] (perfect on constant) stop_grad_aux_to_encoder = false (lift fired) All 5 stacked_trainer tests pass on RTX 3050 (lib still converges, no regression from parallel aux wiring). Not yet consumed by decision policy (B7) — aux output flows through training only. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
739 lines
33 KiB
Rust
739 lines
33 KiB
Rust
//! Stacked Mamba2 -> CfC -> heads trainer CLI.
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//!
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//! Reads predecoded MBP-10 sidecars via `MultiHorizonLoader`, drives
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//! `PerceptionTrainer` end-to-end. Each training step consumes ONE
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//! seq_len-snapshot window and emits one BCE loss against the labels
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//! at the window's last position (per-horizon).
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//!
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//! Emits `alpha_train_summary.json` on exit for downstream gate
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//! consumption.
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//!
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//! Usage:
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//! alpha_train \
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//! --mbp10-data-dir <path> \
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//! --predecoded-dir <path> \
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//! --epochs 5 \
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//! --seq-len 32 \
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//! --mamba2-state-dim 16 \
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//! --lr-cfc 3e-3 \
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//! --lr-mamba2 1e-3 \
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//! --n-train-seqs 8000 \
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//! --n-val-seqs 1000 \
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//! --out artifacts/alpha-train/
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use anyhow::{Context, Result};
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use clap::Parser;
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use ml_alpha::aux_heads::N_AUX_HORIZONS;
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use ml_alpha::cfc::snap_features::Mbp10RawInput;
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use ml_alpha::data::loader::{
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MultiHorizonLoader, MultiHorizonLoaderConfig, DEFAULT_OUTCOME_LABEL_COST_ES,
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};
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use ml_alpha::eval::auc::{compute_auc, AucInput};
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use ml_alpha::heads::{HORIZONS, N_HORIZONS};
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use ml_alpha::trainer::perception::{auto_horizon_weights, PerceptionTrainer, PerceptionTrainerConfig};
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use ml_core::device::MlDevice;
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use serde::Serialize;
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use std::path::PathBuf;
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#[derive(Parser)]
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#[command(name = "alpha_train")]
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struct Cli {
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/// Directory containing MBP-10 .dbn.zst files.
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#[arg(long)]
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mbp10_data_dir: PathBuf,
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/// Directory holding predecoded sidecar caches (created on miss).
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#[arg(long)]
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predecoded_dir: PathBuf,
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/// Output directory for weights + summary JSON.
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#[arg(long)]
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out: PathBuf,
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#[arg(long, default_value_t = 5)]
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epochs: usize,
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/// Snapshots per training sequence (Mamba2 + CfC in-window context).
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/// Default 64 — empirically the 3-fold ISV CV ran K=32 and observed
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/// the longest-horizon AUC saturating around 0.70-0.74. K=64 doubles
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/// in-window context relative to K=32 and gives Mamba2's SSM state
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/// more material to build long-horizon predictions from. Kernel
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/// maximum is 96 (MAMBA2_KERNEL_SEQ_MAX). Per-epoch wall scales ~K
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/// (more K-loop launches in the captured graph).
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#[arg(long, default_value_t = 64)]
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seq_len: usize,
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#[arg(long, default_value_t = 16)]
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mamba2_state_dim: usize,
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#[arg(long, default_value_t = 3e-3)]
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lr_cfc: f32,
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#[arg(long, default_value_t = 1e-3)]
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lr_mamba2: f32,
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#[arg(long, default_value_t = 8000)]
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n_train_seqs: usize,
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#[arg(long, default_value_t = 1000)]
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n_val_seqs: usize,
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#[arg(long, default_value_t = 0x4242)]
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seed: u64,
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/// Linear LR warmup over this many training steps from 0 → configured
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/// lr_cfc / lr_mamba2. 0 disables warmup.
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#[arg(long, default_value_t = 200)]
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lr_warmup_steps: usize,
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/// Floor for the cosine-decay schedule: final LR = lr_max * this factor.
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/// 1.0 disables decay (constant LR after warmup).
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#[arg(long, default_value_t = 0.1)]
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lr_min_factor: f32,
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/// Early-stop training after this many consecutive epochs without
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/// improvement on `early_stop_metric`. 0 disables early stopping.
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#[arg(long, default_value_t = 5)]
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early_stop_patience: usize,
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/// Which metric drives early stopping. Options:
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/// - "val_loss" : stable, tracks probability calibration.
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/// - "mean_auc" : noisier, average ranking across all N_HORIZONS horizons.
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/// - "auc_h1000" : ranking on the long horizon ONLY — recommended
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/// for deployment-oriented runs where the saved checkpoint is
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/// used to trade at multi-minute horizon. The 3-fold ISV CV
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/// showed mean_auc-best-epoch and long-horizon-best-epoch can differ
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/// by 2-6pt within a single run. Using auc_h1000 directly saves
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/// the deployment-relevant checkpoint.
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#[arg(long, default_value = "mean_auc")]
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early_stop_metric: String,
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/// Custom per-horizon BCE weights (N_HORIZONS floats, comma-separated).
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/// When set, overrides the auto/uniform defaults. Example: "1.0,1.0,0.5".
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#[arg(long)]
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horizon_weights: Option<String>,
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/// Use the canonical auto-derived per-horizon BCE weight schedule.
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/// Currently identical to uniform weights: the prior `min(1, K/h)`
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/// formula down-weighted the longest horizon to a tiny fraction of
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/// the loss which suppressed long-horizon training. With uniform
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/// weights, ISV's lambda controller (per-horizon trunk-gradient
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/// scaler driven by BCE EMA) is the single place where per-horizon
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/// rebalancing happens — cleaner than splitting between BCE
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/// coefficients and the trunk lambda. Flag preserved for backward
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/// compat; passing `--horizon-weights` still overrides.
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#[arg(long, default_value_t = false)]
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auto_horizon_weights: bool,
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/// Mini-batch size for training. Each step processes B sequences
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/// in parallel via batched CUDA kernels. Default 1 matches the
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/// previous unbatched behavior. Validation always runs at B=1.
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#[arg(long, default_value_t = 1)]
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batch_size: usize,
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/// Sliding-window cross-validation: index of THIS fold (0-indexed).
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/// Combined with `--cv-n-folds` and `--cv-train-window` to split
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/// the discovered MBP-10 files chronologically. Default 0 means
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/// "no CV — use the first fold of a 1-fold split" (legacy behavior:
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/// train = all-but-last file, val = last file).
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#[arg(long, default_value_t = 0)]
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cv_fold: usize,
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/// Total number of sliding-window CV folds. 1 disables CV (single
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/// train/val split). When >1, fold k trains on the W files starting
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/// at `cv_fold_offset(k)` and validates on the file immediately
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/// after that window. Folds are submitted as independent runs.
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#[arg(long, default_value_t = 1)]
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cv_n_folds: usize,
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/// Size of each fold's TRAIN window in files. 0 means "use all
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/// files except the val file" (expanding-window mode at 1 fold).
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/// >0 enables sliding-window mode where the training data has a
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/// fixed temporal extent.
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#[arg(long, default_value_t = 0)]
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cv_train_window: usize,
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/// CRT.train intervention A — output-smoothness regularizer.
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///
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/// λ[h] = base × (HORIZONS[h] / HORIZONS[0]). Default 0.0 disables
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/// the regularizer (kernel still launches, produces zero gradient).
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/// Recommended sweep: 0.001 / 0.01 / 0.1. See
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/// `docs/superpowers/specs/2026-05-20-crt-train-output-smoothness-design.md`.
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#[arg(long, default_value_t = 0.0)]
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smoothness_base_lambda: f32,
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/// Enable per-step kernel-state JSONL trace to the given file.
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///
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/// When set, the trainer allocates the GPU log ring + spawns a drain
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/// task that writes one JSONL record per kernel emission to this path.
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/// Requires the `kernel-step-trace` Cargo feature at compile time.
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/// When unset (default), no ring allocated, zero overhead.
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///
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/// Example: `--kernel-step-trace /feature-cache/.../step_trace.jsonl`
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#[arg(long)]
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kernel_step_trace: Option<std::path::PathBuf>,
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/// Diagnostic override for the per-horizon CfC bucket warmup hard cap.
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///
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/// `None` (default) lets Controller A derive the cap from the
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/// observed `tau_change` dispersion in the first 100 training steps
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/// (per spec §3.1). `Some(steps)` forces Phase 1→2 transition at
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/// exactly that step count — used by diagnostic Argo runs to test
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/// the routing path without waiting on natural CfC.tau convergence.
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/// Production runs should leave this unset.
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#[arg(long)]
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bucket_warmup_cap_steps: Option<u64>,
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/// Round-trip trade cost in price units used when generating the
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/// D-style asymmetric outcome labels (Phase B aux supervision).
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/// Default 0.5 = 2 ticks × $0.25/tick for ES. Setting 0.0 disables
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/// the cost penalty in the aux head's target and is almost never
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/// useful — defeats the cost-aware objective of the aux supervision.
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#[arg(long, default_value_t = DEFAULT_OUTCOME_LABEL_COST_ES)]
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outcome_label_cost: f32,
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}
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#[derive(Serialize, serde::Deserialize, Default)]
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struct AlphaTrainSummary {
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epochs: usize,
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seq_len: usize,
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horizons: [usize; N_HORIZONS],
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final_train_loss: f32,
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final_val_loss: f32,
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final_val_auc: [f32; N_HORIZONS],
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n_train_seqs_consumed: usize,
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n_val_seqs_consumed: usize,
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/// Epoch index that achieved the lowest val_loss.
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best_epoch: usize,
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/// Lowest val_loss observed across all epochs.
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best_val_loss: f32,
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/// Per-horizon AUCs at the best-val-loss epoch.
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best_val_auc: [f32; N_HORIZONS],
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/// Epoch index that achieved the highest mean AUC across the N_HORIZONS horizons.
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/// Tracked independently of best_epoch because val_loss + AUC can
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/// disagree — a calibration-flat / ranking-sharp epoch beats a
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/// calibration-sharp / ranking-flat one for downstream trading.
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best_mean_auc_epoch: usize,
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/// Mean AUC at the best-mean-AUC epoch.
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best_mean_auc: f32,
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/// Per-horizon AUCs at the best-mean-AUC epoch.
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best_mean_auc_per_horizon: [f32; N_HORIZONS],
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/// Epoch with the highest long-horizon AUC (deployment-relevant).
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/// Saved independently of best_mean_auc_epoch because the two
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/// frequently disagree by 1-2 epochs and the long horizon is the
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/// metric we care about for multi-minute trading deployment.
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best_auc_h1000_epoch: usize,
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/// Highest long-horizon AUC observed across all epochs.
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best_auc_h1000: f32,
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/// Per-horizon AUCs at the best-long-horizon epoch.
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best_auc_h1000_per_horizon: [f32; N_HORIZONS],
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/// True if training stopped early (patience exceeded).
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early_stopped: bool,
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/// Relative path (within `out_dir`) to the best-long-horizon trunk
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/// checkpoint file, or None if no long-horizon improvement was ever recorded.
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/// Written by the X14 wiring inside the `auc_h1000_improved` block.
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#[serde(default, skip_serializing_if = "Option::is_none")]
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best_h1000_ckpt_path: Option<String>,
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/// Per-horizon raw mean-squared adjacent-prob diff at the final epoch
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/// (CRT.train intervention A telemetry). All zeros when
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/// `--smoothness-base-lambda` is unset (default).
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#[serde(default)]
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final_smooth_loss_per_horizon: [f32; N_HORIZONS],
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}
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/// Linear warmup then cosine decay to `lr_min`. `step_idx` is the
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/// global training-step counter (0-indexed). `total_steps` is the
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/// budget the cosine targets.
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fn lr_schedule(lr_max: f32, lr_min: f32, step_idx: usize, warmup_steps: usize, total_steps: usize) -> f32 {
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if step_idx < warmup_steps {
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return lr_max * (step_idx as f32 + 1.0) / warmup_steps as f32;
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}
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if total_steps <= warmup_steps {
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return lr_max;
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}
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let progress = (step_idx - warmup_steps) as f32 / (total_steps - warmup_steps) as f32;
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let progress = progress.clamp(0.0, 1.0);
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let cos = 0.5 * (1.0 + (std::f32::consts::PI * progress).cos());
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lr_min + (lr_max - lr_min) * cos
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}
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fn main() -> Result<()> {
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tracing_subscriber::fmt()
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.with_env_filter(tracing_subscriber::EnvFilter::from_default_env())
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.init();
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let cli = Cli::parse();
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std::fs::create_dir_all(&cli.out).with_context(|| format!("mkdir {}", cli.out.display()))?;
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let dev = MlDevice::cuda(0).context("CUDA 0 init")?;
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tracing::info!(?dev, "MlDevice initialized");
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// Resolve per-horizon BCE weights: explicit > auto > uniform.
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let horizon_weights: [f32; N_HORIZONS] = if let Some(spec) = &cli.horizon_weights {
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let parts: Vec<f32> = spec.split(',')
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.map(|s| s.trim().parse().context("horizon weight parse"))
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.collect::<Result<Vec<_>>>()?;
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anyhow::ensure!(
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parts.len() == N_HORIZONS,
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"--horizon-weights expected {} floats, got {}",
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N_HORIZONS, parts.len()
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);
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let mut w = [0.0; N_HORIZONS];
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w.copy_from_slice(&parts);
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w
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} else if cli.auto_horizon_weights {
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auto_horizon_weights(cli.seq_len, &HORIZONS)
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} else {
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[1.0; N_HORIZONS]
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};
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tracing::info!(
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w_h10 = horizon_weights[0],
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w_h100 = horizon_weights[1],
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w_h1000 = horizon_weights[2],
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"per-horizon BCE weights"
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);
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anyhow::ensure!(cli.batch_size >= 1, "batch_size must be >= 1");
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let trainer_cfg = PerceptionTrainerConfig {
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seq_len: cli.seq_len,
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mamba2_state_dim: cli.mamba2_state_dim,
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lr_cfc: cli.lr_cfc,
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lr_mamba2: cli.lr_mamba2,
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seed: cli.seed,
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horizon_weights,
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n_batch: cli.batch_size,
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smoothness_base_lambda: cli.smoothness_base_lambda,
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kernel_step_trace_path: cli.kernel_step_trace.clone(),
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bucket_warmup_cap_override: cli.bucket_warmup_cap_steps,
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// SDD-3 Layer B5: aux supervision uses lr_cfc as default sentinel
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// for now; per `pearl_adam_normalizes_loss_weights` aux has its own
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// Adam group so this is a real lever — expose a CLI flag in a
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// follow-up once we have a smoke-validated value.
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lr_aux: cli.lr_cfc,
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};
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let mut trainer = PerceptionTrainer::new(&dev, &trainer_cfg).context("trainer init")?;
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let mut train_loss_running = 0.0_f32;
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let mut train_steps = 0usize;
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let mut final_val_loss = 0.0_f32;
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let mut final_val_auc = [0.5_f32; N_HORIZONS];
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let mut n_val_seqs_consumed = 0usize;
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// Validate early-stop metric choice upfront.
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let early_stop_metric = cli.early_stop_metric.as_str();
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anyhow::ensure!(
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matches!(early_stop_metric, "val_loss" | "mean_auc" | "auc_h1000" | "none"),
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"--early-stop-metric must be one of: val_loss, mean_auc, auc_h1000, none (got `{}`)",
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early_stop_metric
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);
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// Best-checkpoint tracking (by val_loss).
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let mut best_val_loss = f32::INFINITY;
|
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let mut best_epoch = 0usize;
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let mut best_val_auc = [0.5_f32; N_HORIZONS];
|
||
let mut val_loss_no_improvement = 0usize;
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let mut mean_auc_no_improvement = 0usize;
|
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let mut early_stopped = false;
|
||
let mut epochs_completed = 0usize;
|
||
|
||
// Independent tracker for best mean-AUC across horizons. val_loss and
|
||
// ranking quality (AUC) can disagree — e.g. when the model improves
|
||
// probability calibration on common cases (lower BCE) but loses
|
||
// ranking sharpness on the regime tails. For downstream trading the
|
||
// AUC profile usually matters more, so we publish both bests.
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let mut best_mean_auc = f32::NEG_INFINITY;
|
||
let mut best_mean_auc_epoch = 0usize;
|
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let mut best_mean_auc_per_horizon = [0.5_f32; N_HORIZONS];
|
||
|
||
// Independent tracker for best long-horizon AUC — the deployment-relevant
|
||
// metric. The 3-fold ISV CV showed that mean_auc-best and long-horizon-best
|
||
// epochs frequently differ. For multi-minute trading deployment we want
|
||
// the long-horizon-best checkpoint directly, not the mean_auc proxy.
|
||
let mut best_auc_h1000 = f32::NEG_INFINITY;
|
||
let mut best_auc_h1000_epoch = 0usize;
|
||
let mut best_auc_h1000_per_horizon = [0.5_f32; N_HORIZONS];
|
||
let mut best_h1000_ckpt_path: Option<String> = None;
|
||
let mut auc_h1000_no_improvement = 0usize;
|
||
|
||
let lr_min_cfc = cli.lr_cfc * cli.lr_min_factor;
|
||
let lr_min_m2 = cli.lr_mamba2 * cli.lr_min_factor;
|
||
// Approximate the total step budget: epochs × n_train_seqs (each
|
||
// sequence yields one optimizer step). Used by cosine decay.
|
||
let total_steps_budget = cli.epochs * cli.n_train_seqs;
|
||
|
||
// ── Walk-forward CV file split ────────────────────────────────────
|
||
// Files are discovered in chronological order (filenames follow
|
||
// ES.FUT_<YEAR>-Q<n>.dbn.zst so sort = chronological). Then we
|
||
// slice train_files / val_files based on the CV flags. Train and
|
||
// val NEVER share a file — every val sample is strictly OOS in
|
||
// time vs every train sample.
|
||
let all_files = ml_alpha::data::loader::discover_mbp10_files_sorted(&cli.mbp10_data_dir)
|
||
.context("discover MBP-10 files")?;
|
||
anyhow::ensure!(
|
||
cli.cv_n_folds >= 1,
|
||
"--cv-n-folds must be >= 1 (got {})", cli.cv_n_folds
|
||
);
|
||
anyhow::ensure!(
|
||
cli.cv_fold < cli.cv_n_folds,
|
||
"--cv-fold {} must be < --cv-n-folds {}", cli.cv_fold, cli.cv_n_folds
|
||
);
|
||
let n_files = all_files.len();
|
||
let (train_files, val_files): (Vec<PathBuf>, Vec<PathBuf>) = if cli.cv_n_folds == 1 {
|
||
// Single fold: train on all files except the LAST, val on the LAST file.
|
||
// Legacy callers that didn't think about CV at all get a temporal split
|
||
// by default instead of the old "same files for train and val" bug.
|
||
anyhow::ensure!(n_files >= 2,
|
||
"need >= 2 files for single-fold temporal split, found {} under {}",
|
||
n_files, cli.mbp10_data_dir.display());
|
||
let val_idx = n_files - 1;
|
||
(
|
||
all_files[..val_idx].to_vec(),
|
||
vec![all_files[val_idx].clone()],
|
||
)
|
||
} else {
|
||
// Sliding-window CV. Train window has fixed extent `W`; val is the
|
||
// single file immediately after. Fold k uses files [k .. k+W] for
|
||
// train and file [k+W] for val.
|
||
let w = if cli.cv_train_window > 0 {
|
||
cli.cv_train_window
|
||
} else {
|
||
// Default: leave room for n_folds val files at the tail.
|
||
n_files.saturating_sub(cli.cv_n_folds)
|
||
};
|
||
anyhow::ensure!(w >= 1, "cv_train_window resolves to 0 (n_files={n_files}, n_folds={})", cli.cv_n_folds);
|
||
anyhow::ensure!(
|
||
cli.cv_fold + w + 1 <= n_files,
|
||
"CV layout overflows: cv_fold={} + window={} + 1 val file > {} files available",
|
||
cli.cv_fold, w, n_files
|
||
);
|
||
let train_start = cli.cv_fold;
|
||
let train_end = train_start + w; // exclusive
|
||
let val_idx = train_end;
|
||
(
|
||
all_files[train_start..train_end].to_vec(),
|
||
vec![all_files[val_idx].clone()],
|
||
)
|
||
};
|
||
tracing::info!(
|
||
cv_fold = cli.cv_fold,
|
||
cv_n_folds = cli.cv_n_folds,
|
||
cv_train_window = cli.cv_train_window,
|
||
n_train_files = train_files.len(),
|
||
n_val_files = val_files.len(),
|
||
train_files = ?train_files.iter().map(|p| p.file_name().unwrap().to_string_lossy().to_string()).collect::<Vec<_>>(),
|
||
val_files = ?val_files.iter().map(|p| p.file_name().unwrap().to_string_lossy().to_string()).collect::<Vec<_>>(),
|
||
"walk-forward CV file split",
|
||
);
|
||
|
||
// PRELOAD: construct loaders ONCE (each preloads its files into RAM).
|
||
// Per-epoch we call reset(seed) to re-shuffle anchor sampling — no
|
||
// disk IO. Cuts wall time ~5× on a 6-epoch run, ~7× on 15 epochs.
|
||
tracing::info!("preloading train + val MBP-10 files into RAM (slow startup, fast per-epoch)…");
|
||
let preload_start = std::time::Instant::now();
|
||
let mut train_loader = MultiHorizonLoader::new(&MultiHorizonLoaderConfig {
|
||
files: train_files,
|
||
predecoded_dir: cli.predecoded_dir.clone(),
|
||
seq_len: cli.seq_len,
|
||
horizons: HORIZONS,
|
||
n_max_sequences: cli.n_train_seqs,
|
||
seed: cli.seed,
|
||
inference_only: false,
|
||
outcome_label_cost: cli.outcome_label_cost,
|
||
})
|
||
.context("train loader")?;
|
||
let mut val_loader = MultiHorizonLoader::new(&MultiHorizonLoaderConfig {
|
||
files: val_files,
|
||
predecoded_dir: cli.predecoded_dir.clone(),
|
||
seq_len: cli.seq_len,
|
||
horizons: HORIZONS,
|
||
n_max_sequences: cli.n_val_seqs,
|
||
seed: cli.seed.wrapping_add(0xC0FFEE),
|
||
inference_only: false,
|
||
outcome_label_cost: cli.outcome_label_cost,
|
||
})
|
||
.context("val loader")?;
|
||
tracing::info!(
|
||
elapsed_s = preload_start.elapsed().as_secs(),
|
||
"preload complete",
|
||
);
|
||
|
||
for epoch in 0..cli.epochs {
|
||
// Re-seed loaders for this epoch (anchor sampling differs per epoch).
|
||
train_loader.reset(cli.seed.wrapping_add(epoch as u64));
|
||
val_loader.reset(cli.seed.wrapping_add(0xC0FFEE + epoch as u64));
|
||
|
||
let mut epoch_train_loss = 0.0_f32;
|
||
let mut epoch_train_steps = 0usize;
|
||
// Accumulate B sequences per optimizer step. Sequences with no
|
||
// finite labels at any position are skipped (continue) so they
|
||
// don't pollute the batch.
|
||
let mut snap_batch: Vec<Vec<Mbp10RawInput>> = Vec::with_capacity(cli.batch_size);
|
||
let mut label_batch: Vec<Vec<[f32; N_HORIZONS]>> = Vec::with_capacity(cli.batch_size);
|
||
// SDD-3 Layer B5: aux outcome labels from the loader's D-style
|
||
// generator. NaN slots are natively masked by the Huber loss kernel.
|
||
let mut out_long_batch: Vec<Vec<[f32; N_AUX_HORIZONS]>> =
|
||
Vec::with_capacity(cli.batch_size);
|
||
let mut out_short_batch: Vec<Vec<[f32; N_AUX_HORIZONS]>> =
|
||
Vec::with_capacity(cli.batch_size);
|
||
while let Some(seq) = train_loader.next_sequence().context("train next_seq")? {
|
||
let mut labels_per_pos: Vec<[f32; N_HORIZONS]> = Vec::with_capacity(seq.snapshots.len());
|
||
let mut any_finite = false;
|
||
for k in 0..seq.snapshots.len() {
|
||
let row: [f32; N_HORIZONS] = std::array::from_fn(|h| seq.labels[h][k]);
|
||
if row.iter().any(|v| v.is_finite()) { any_finite = true; }
|
||
labels_per_pos.push(row);
|
||
}
|
||
if !any_finite { continue; }
|
||
// SDD-3 Layer B5: pull D-style aux outcomes for both directions.
|
||
// `seq.outcome_long[h]` / `seq.outcome_short[h]` are `Vec<f32>`
|
||
// sliced per-anchor; convert to per-position rows mirroring the
|
||
// BCE label layout (so the trainer's `step_batched` sees one
|
||
// `[N_AUX_HORIZONS]` row per K position).
|
||
let mut out_long_pos: Vec<[f32; N_AUX_HORIZONS]> =
|
||
Vec::with_capacity(seq.snapshots.len());
|
||
let mut out_short_pos: Vec<[f32; N_AUX_HORIZONS]> =
|
||
Vec::with_capacity(seq.snapshots.len());
|
||
for k in 0..seq.snapshots.len() {
|
||
let row_long: [f32; N_AUX_HORIZONS] =
|
||
std::array::from_fn(|h| seq.outcome_long[h][k]);
|
||
let row_short: [f32; N_AUX_HORIZONS] =
|
||
std::array::from_fn(|h| seq.outcome_short[h][k]);
|
||
out_long_pos.push(row_long);
|
||
out_short_pos.push(row_short);
|
||
}
|
||
snap_batch.push(seq.snapshots);
|
||
label_batch.push(labels_per_pos);
|
||
out_long_batch.push(out_long_pos);
|
||
out_short_batch.push(out_short_pos);
|
||
if snap_batch.len() < cli.batch_size { continue; }
|
||
|
||
// Full batch ready — apply LR schedule, fire step.
|
||
let lr_cfc_now = lr_schedule(cli.lr_cfc, lr_min_cfc, train_steps, cli.lr_warmup_steps, total_steps_budget);
|
||
let lr_m2_now = lr_schedule(cli.lr_mamba2, lr_min_m2, train_steps, cli.lr_warmup_steps, total_steps_budget);
|
||
trainer.set_lr_cfc(lr_cfc_now);
|
||
trainer.set_lr_mamba2(lr_m2_now);
|
||
// SDD-3 Layer B5: aux LR follows main `lr_cfc` schedule for
|
||
// now. A dedicated aux schedule could land later once the
|
||
// initial smoke validates the dynamics.
|
||
trainer.set_lr_aux(lr_cfc_now);
|
||
|
||
let snap_refs: Vec<&[Mbp10RawInput]> = snap_batch.iter().map(|v| v.as_slice()).collect();
|
||
let label_refs: Vec<&[[f32; N_HORIZONS]]> = label_batch.iter().map(|v| v.as_slice()).collect();
|
||
let out_long_refs: Vec<&[[f32; N_AUX_HORIZONS]]> =
|
||
out_long_batch.iter().map(|v| v.as_slice()).collect();
|
||
let out_short_refs: Vec<&[[f32; N_AUX_HORIZONS]]> =
|
||
out_short_batch.iter().map(|v| v.as_slice()).collect();
|
||
let loss = trainer
|
||
.step_batched(&snap_refs, &label_refs, &out_long_refs, &out_short_refs)
|
||
.context("train step_batched")?;
|
||
epoch_train_loss += loss;
|
||
epoch_train_steps += 1;
|
||
train_loss_running += loss;
|
||
train_steps += 1;
|
||
// Periodic per-step log (every 500 steps) for liveness +
|
||
// intra-epoch trajectory monitoring. Cheap (~1 line/min on
|
||
// L40S at 8000 steps/epoch / 36s = 220 steps/s ⇒ ~16 lines
|
||
// per 500-step interval per epoch). Consumed by
|
||
// /tmp/alpha_monitor.py.
|
||
if epoch_train_steps % 500 == 0 {
|
||
tracing::info!(
|
||
epoch,
|
||
step = epoch_train_steps,
|
||
loss = loss,
|
||
"step"
|
||
);
|
||
}
|
||
snap_batch.clear();
|
||
label_batch.clear();
|
||
out_long_batch.clear();
|
||
out_short_batch.clear();
|
||
}
|
||
let epoch_avg = if epoch_train_steps > 0 {
|
||
epoch_train_loss / epoch_train_steps as f32
|
||
} else { 0.0 };
|
||
tracing::info!(epoch, train_loss = epoch_avg, train_steps = epoch_train_steps, "epoch complete");
|
||
|
||
// Validation: accumulate (probs, labels) per horizon, compute AUC.
|
||
// val_loader was preloaded above and reset at top of loop.
|
||
|
||
let mut val_probs: [Vec<f32>; N_HORIZONS] = Default::default();
|
||
let mut val_labels: [Vec<f32>; N_HORIZONS] = Default::default();
|
||
let mut val_loss_sum = 0.0_f32;
|
||
let mut val_steps = 0usize;
|
||
let mut val_snap_batch: Vec<Vec<Mbp10RawInput>> = Vec::with_capacity(cli.batch_size);
|
||
let mut val_label_batch: Vec<Vec<[f32; N_HORIZONS]>> = Vec::with_capacity(cli.batch_size);
|
||
let mut val_last_label_batch: Vec<[f32; N_HORIZONS]> = Vec::with_capacity(cli.batch_size);
|
||
while let Some(seq) = val_loader.next_sequence().context("val next_seq")? {
|
||
let mut labels_per_pos: Vec<[f32; N_HORIZONS]> = Vec::with_capacity(seq.snapshots.len());
|
||
let mut any_finite = false;
|
||
for k in 0..seq.snapshots.len() {
|
||
let row: [f32; N_HORIZONS] = std::array::from_fn(|h| seq.labels[h][k]);
|
||
if row.iter().any(|v| v.is_finite()) { any_finite = true; }
|
||
labels_per_pos.push(row);
|
||
}
|
||
if !any_finite { continue; }
|
||
let last = seq.snapshots.len().saturating_sub(1);
|
||
let last_labels: [f32; N_HORIZONS] = std::array::from_fn(|h| seq.labels[h][last]);
|
||
val_snap_batch.push(seq.snapshots);
|
||
val_label_batch.push(labels_per_pos);
|
||
val_last_label_batch.push(last_labels);
|
||
if val_snap_batch.len() < cli.batch_size { continue; }
|
||
|
||
// FORWARD-ONLY batched evaluation — no backward, no AdamW.
|
||
let snap_refs: Vec<&[Mbp10RawInput]> = val_snap_batch.iter().map(|v| v.as_slice()).collect();
|
||
let label_refs: Vec<&[[f32; N_HORIZONS]]> = val_label_batch.iter().map(|v| v.as_slice()).collect();
|
||
let (l, probs_all) = trainer.evaluate_batched(&snap_refs, &label_refs).context("val eval_batched")?;
|
||
val_loss_sum += l;
|
||
val_steps += 1;
|
||
// probs_all is [K, B, N_HORIZONS] row-major. Score AUC from
|
||
// the LAST-position predictions for each sample in the batch.
|
||
let last = cli.seq_len - 1;
|
||
for (b_idx, last_lbl) in val_last_label_batch.iter().enumerate() {
|
||
let off = (last * cli.batch_size + b_idx) * N_HORIZONS;
|
||
let last_probs = &probs_all[off..off + N_HORIZONS];
|
||
for h in 0..N_HORIZONS {
|
||
if last_lbl[h].is_finite() {
|
||
val_probs[h].push(last_probs[h]);
|
||
val_labels[h].push(last_lbl[h]);
|
||
}
|
||
}
|
||
}
|
||
val_snap_batch.clear();
|
||
val_label_batch.clear();
|
||
val_last_label_batch.clear();
|
||
}
|
||
let mut per_horizon_auc = [0.5_f32; N_HORIZONS];
|
||
for h in 0..N_HORIZONS {
|
||
per_horizon_auc[h] = compute_auc(&AucInput {
|
||
probs: val_probs[h].clone(),
|
||
labels: val_labels[h].clone(),
|
||
}).context("AUC")?;
|
||
}
|
||
let val_avg = if val_steps > 0 { val_loss_sum / val_steps as f32 } else { 0.0 };
|
||
tracing::info!(
|
||
epoch,
|
||
val_loss = val_avg,
|
||
auc_h10 = per_horizon_auc[0],
|
||
auc_h100 = per_horizon_auc[1],
|
||
auc_h1000 = per_horizon_auc[2],
|
||
"validation"
|
||
);
|
||
|
||
// Snapshot the ISV controller's per-horizon BCE EMA + lambda
|
||
// multipliers — diagnostic on what the horizon-aware gradient
|
||
// scaler is doing each epoch. Reads happen via mapped-pinned
|
||
// (not in the training hot path; we're already mid-validation
|
||
// sync window). Surfaces fold-specific lambda trajectories so
|
||
// we can diagnose why some folds gain and others regress.
|
||
match (trainer.loss_ema_snapshot(), trainer.lambda_snapshot()) {
|
||
(Ok(ema), Ok(lam)) => tracing::info!(
|
||
epoch,
|
||
ema_h10 = ema[0], ema_h100 = ema[1], ema_h1000 = ema[2],
|
||
lam_h10 = lam[0], lam_h100 = lam[1], lam_h1000 = lam[2],
|
||
"isv snapshot"
|
||
),
|
||
(Err(e), _) | (_, Err(e)) => {
|
||
tracing::warn!(epoch, error = %e, "isv snapshot failed (continuing)");
|
||
}
|
||
}
|
||
final_val_loss = val_avg;
|
||
final_val_auc = per_horizon_auc;
|
||
n_val_seqs_consumed = val_loader.yielded();
|
||
epochs_completed = epoch + 1;
|
||
|
||
// Best-checkpoint trackers — independent for each metric.
|
||
let val_loss_improved = val_avg < best_val_loss;
|
||
if val_loss_improved {
|
||
best_val_loss = val_avg;
|
||
best_epoch = epoch;
|
||
best_val_auc = per_horizon_auc;
|
||
val_loss_no_improvement = 0;
|
||
tracing::info!(epoch, val_loss = val_avg, "new best val_loss");
|
||
} else {
|
||
val_loss_no_improvement += 1;
|
||
}
|
||
|
||
let mean_auc = per_horizon_auc.iter().sum::<f32>() / per_horizon_auc.len() as f32;
|
||
let mean_auc_improved = mean_auc > best_mean_auc;
|
||
if mean_auc_improved {
|
||
best_mean_auc = mean_auc;
|
||
best_mean_auc_epoch = epoch;
|
||
best_mean_auc_per_horizon = per_horizon_auc;
|
||
mean_auc_no_improvement = 0;
|
||
tracing::info!(epoch, mean_auc, "new best mean_auc");
|
||
} else {
|
||
mean_auc_no_improvement += 1;
|
||
}
|
||
|
||
// h1000 — deployment-relevant long-horizon ranking.
|
||
let auc_h1000 = per_horizon_auc[N_HORIZONS - 1];
|
||
let auc_h1000_improved = auc_h1000 > best_auc_h1000;
|
||
if auc_h1000_improved {
|
||
best_auc_h1000 = auc_h1000;
|
||
best_auc_h1000_epoch = epoch;
|
||
best_auc_h1000_per_horizon = per_horizon_auc;
|
||
auc_h1000_no_improvement = 0;
|
||
// X14: emit a Checkpoint file alongside summary.json so
|
||
// fxt-backtest --checkpoint can load the trained trunk.
|
||
let ckpt_path = cli.out.join("trunk_best_h1000.bin");
|
||
trainer.save_checkpoint(&ckpt_path)
|
||
.context("save_checkpoint(trunk_best_h1000.bin)")?;
|
||
best_h1000_ckpt_path = Some("trunk_best_h1000.bin".to_string());
|
||
tracing::info!(
|
||
epoch, auc_h1000, path = %ckpt_path.display(),
|
||
"new best auc_h1000 (saved checkpoint)"
|
||
);
|
||
} else {
|
||
auc_h1000_no_improvement += 1;
|
||
}
|
||
|
||
// Early-stop decision based on selected metric.
|
||
if cli.early_stop_patience > 0 {
|
||
let (counter, metric_label, best_val) = match early_stop_metric {
|
||
"val_loss" => (val_loss_no_improvement, "val_loss", best_val_loss),
|
||
"mean_auc" => (mean_auc_no_improvement, "mean_auc", best_mean_auc),
|
||
"auc_h1000" => (auc_h1000_no_improvement, "auc_h1000", best_auc_h1000),
|
||
_ => (0, "none", 0.0), // disabled
|
||
};
|
||
if counter >= cli.early_stop_patience && metric_label != "none" {
|
||
tracing::warn!(
|
||
epoch, metric = metric_label, best = best_val,
|
||
patience = cli.early_stop_patience,
|
||
"early stopping",
|
||
);
|
||
early_stopped = true;
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
let summary = AlphaTrainSummary {
|
||
epochs: epochs_completed,
|
||
seq_len: cli.seq_len,
|
||
horizons: HORIZONS,
|
||
final_train_loss: if train_steps > 0 { train_loss_running / train_steps as f32 } else { 0.0 },
|
||
final_val_loss,
|
||
final_val_auc,
|
||
n_train_seqs_consumed: train_steps,
|
||
n_val_seqs_consumed,
|
||
best_epoch,
|
||
best_val_loss,
|
||
best_val_auc,
|
||
best_mean_auc_epoch,
|
||
best_mean_auc,
|
||
best_mean_auc_per_horizon,
|
||
best_auc_h1000_epoch,
|
||
best_auc_h1000,
|
||
best_auc_h1000_per_horizon,
|
||
early_stopped,
|
||
best_h1000_ckpt_path,
|
||
final_smooth_loss_per_horizon: trainer.last_smoothness_loss_per_horizon(),
|
||
};
|
||
let summary_path = cli.out.join("alpha_train_summary.json");
|
||
std::fs::write(&summary_path, serde_json::to_vec_pretty(&summary).context("serialize summary")?)
|
||
.with_context(|| format!("write {}", summary_path.display()))?;
|
||
tracing::info!(summary_path = %summary_path.display(), "wrote summary");
|
||
|
||
Ok(())
|
||
}
|