41b0c559c9
Task 0.4's grad_ratio_mag_dir returns 0.0 whenever dir_norm < 1e-9, so the epoch-end reading of 0.0000 doesn't disambiguate "magnitude starved" vs "direction starved". Task 2.0's per-component data showed CQL and C51 each sending 100-400x more gradient to magnitude than direction — implying direction is the starved one, not magnitude. This adds a HEALTH_DIAG field exposing the raw absolute norms: grad_abs [dir=<sci-notation> mag=<sci-notation>] Along the way uncovered + fixed two latent bugs that had been silently zeroing the ratio signal since Task 0.4 landed: 1. `per_branch_grad_norms` read `grad_buf.len()` = total_params + cutlass_tile_pad (~4096 elements of GEMM tile padding) but the pinned readback slot was sized at construction to total_params exactly. The size check `grad_len > grad_readback_pinned_capacity` was always true, so the accessor returned Err on every call — and FusedTrainingCtx's proxy coerced Err to 0.0 via `.unwrap_or(0.0)`. Root cause for the "always 0.0000" grad_ratio_mag_dir. Fix: read only the first `total_params` prefix of grad_buf (the tail is pure GEMM padding, never holds gradient values). 2. Readback timing: process_epoch_boundary calls estimate_avg_q_value_with_early_stopping early, which replays `eval_forward_exec` — the SAME captured graph as forward_child whose first op is `cuMemsetD32Async(grad_buf, 0, total_params)`. Any grad_buf readback AFTER the avg_q call sees all zeros. Fix: snapshot grad_dir_abs / grad_mag_abs / grad_ratio_mag_dir at the TOP of process_epoch_boundary, before avg_q runs, and consume the cached values in the HEALTH_DIAG block. Last 5 epochs of fold 3 on the magnitude_distribution baseline smoke (FOXHUNT_TEST_DATA=test_data/futures-baseline): HEALTH_DIAG[15] ratio=42.85 grad_abs [dir=6.804900e0 mag=3.989081e2] HEALTH_DIAG[16] ratio=232.66 grad_abs [dir=6.500046e0 mag=3.603353e0] HEALTH_DIAG[17] ratio=318.34 grad_abs [dir=2.720317e-2 mag=1.713861e1] HEALTH_DIAG[18] ratio=367.59 grad_abs [dir=5.180866e-2 mag=8.076681e0] HEALTH_DIAG[19] ratio=298.55 grad_abs [dir=1.989553e-2 mag=6.498848e0] Across all 60 epoch-boundary readings (3 folds × 20 epochs) dir ∈ [~4e-3, ~6e0] and mag ∈ [~5e-2, ~5e2], with ratio mag/dir consistently 50-400× (matching Task 2.0's per-component ratios). Neither branch is near float precision — direction is PROPORTIONALLY starved, not numerically zero. Scenario confirmed: direction is starved relative to magnitude, NOT the reverse. Phase 2's Task 2.1 (architectural fix on magnitude branch) should pivot toward increasing direction's gradient flow instead. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;