ea31ebfe38
Layer A scaffolding for post-clamp feedback-loop self-correction. A14: All 5 Adam kernels (dqn/iqn/iql/attn/curiosity) extended with register-then-tree-reduce engagement counter. Per-thread `local_engage` set on clamp engagement; block tree-reduce (no atomicAdd, mirrors dqn_grad_norm_kernel pattern); single block-leader writes per-block count to clamp_engage_per_block_buf[engage_buf_offset + blockIdx.x]. `engage_buf_offset == -1 (SP4_ENGAGE_OFFSET_DISABLED)` skips writeback. A15: Host-side rate-deficit check in pearl_c_post_adam_engagement_check. Sums per-block counts via mapped-pinned zero-copy reads, computes engagement_rate, rate_deficit = rate - 0.01. Applies Pearls A+D via pearl_c_rate_deficit_ema (host-only [f32;8]) + pearl_c_rate_deficit_state_buf (MappedF32Buffer[24] = 8 groups × 3 Wiener floats). Wired post-graph in FusedTrainingCtx::run_full_step for groups 0/3/4/5/6 and post-curiosity_train in training_loop for group 7. 3 design issues resolved per Layer A scope: 1. Curiosity sub-launches: SP4_ENGAGE_BUF_LEN extended 2048->2816 (= 11 x 256). Curiosity sub-launches use offsets 1792/2048/2304/2560 (W1/b1/W2/b2). Host-side check sums all 4. Allocation + reset-registry updated. 2. TLOB/Attn sharing attn_adam_kernel: TLOB passes SP4_ENGAGE_OFFSET_DISABLED=-1 (silently skips Pearl C). Attn writes to its slot (offset 6x256=1536). Layer B can refactor if needed. 3. DQN main Adam covers groups 0/1/2 in single launch: Layer A accounts only under group 0 (DqnTrunk). Groups 1/2 Pearl C tracking deferred to Layer B's per-group sub-launch decision. Wiring path: GpuDqnTrainer exposes nan_flags_buf_ptr() + clamp_engage_per_block_buf_dev_ptr(). FusedTrainingCtx adds wire_aux_trainer_pearl_c_buffers() for IQN/IQL hi+lo/Attn. GpuExperienceCollector adds set_curiosity_pearl_c_buffers() for the curiosity trainer. Wiring fires once in init_gpu_experience_collector. State reset registry (Task A12 follow-up): - sp4_clamp_engage_counters description updated to reflect 2816 buffer length and 4 distinct curiosity sub-launch offsets - New entries: sp4_pearl_c_rate_deficit_state (24 mapped-pinned floats, Pearls A+D Wiener state) and sp4_pearl_c_rate_deficit_ema (host-only [f32; 8] EMA surrogate). Both reset to zero at fold boundary so Pearl A's first-observation sentinel fires on the new fold's first engagement-rate-deficit observation. Dispatch arms wired in reset_named_state alongside existing sp4_* entries. Layer A: Pearl C is observability scaffolding. Mech 9's clamp still uses hardcoded 100xQ_ABS_REF. Engagement counters fire correctly, rate_deficit EMA tracks. Force-bump branch logs via tracing::debug only; Layer B's atomic flip activates the actual ISV mutation. cargo check --lib --tests clean. state_reset_registry tests pass. sp4_isv_slots::tests::pearl_c_engage_buf_layout pass. 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;