1112abc2a4
Layer C close-out C4 — the most architecturally substantive site of the feedback_no_cpu_compute_strict sweep. GpuDqnTrainer::update_adaptive_clip was running a 6-step host-side compute chain on GPU-produced inputs: winsor (1) + cold-start sentinel + EMA (3) + scalar reduction (4) + ISV upper-bound clamp (5) + mapped-pinned write (6). Per feedback_no_partial_refactor the chain must migrate coherently — splitting EMA-only into a kernel and leaving the surrounding scalar reductions on host would be a partial migration violating the rule. Migration: new update_adaptive_clip_kernel.cu (single-thread, single-block). Takes host-passed observed_grad_norm (already a mapped-pinned readback) + 6 fixed structural constants (legacy values preserved per feedback_no_quickfixes) + 4 mapped-pinned dev_ptrs + ISV[GRAD_CLIP_BOUND_INDEX]. Writes the full output chain (adaptive_clip_pinned, grad_norm_ema_pinned, outlier_diag_pinned). Storage migration: - grad_norm_ema migrated from host-resident f32 to mapped-pinned scalar (matches C1/C2/C3 pattern). - New outlier_diag_pinned mapped-pinned slot for the GRAD_CLIP_OUTLIER warn diagnostic. The kernel writes `delta = observed - clamped` and the host reads it post-launch to format the warn log without running scalar arithmetic on the host. All structural constants preserved: EMA_BETA=0.95, CLIP_MULTIPLIER=2.0, MIN_CLIP=1.0, GRAD_CLIP_OUTLIER_K=100, EPS_CLAMP_FLOOR (SP4). Same cold-start sentinel `prev_ema <= 0.0 ⇒ assign clamped directly`; same Mech 6 (SP3) + Layer B (SP4) bound design. Same outlier-warn log format reconstructed from the mapped-pinned diagnostic slot. Host-side early-return guard preserved (the pre-existing pattern from C1 redesigned). grad_norm_emas_step_count counter unchanged (scalar control-flow metadata, not compute, per the rule's explicit carve-out). Verification: SP4 lib tests + 16 SP4 GPU producer unit tests pass on RTX 3050 Ti. 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;