256a5fa5ab
Phase B4b-2 introduced `exp_aux_to_label_per_sample` sized
`[alloc_episodes × alloc_timesteps]` (no cf-mult expansion — the
B4b-1 per-step kernel only writes the on-policy half). The batch
finalisation cloned into the emitted `aux_outcome_labels` with size
`total = base_total × 2` (cf-mult expanded), so
`dtod_clone_i32(src, total, ...)` invoked `src.slice(..total)` on a
half-sized source → `CudaSlice::try_slice` returned `None` → the
internal `unwrap()` panicked at cudarc safe/core.rs:1648.
Repro: workflow train-xzv56 panicked after rollout completed
(timestep=999) on fold 0; rollout itself ran clean, the OOM from
the prior commit is gone.
Fix: replace the single `dtod_clone_i32` with a 3-step build:
1. alloc_zeros::<i32>(total)
2. cuMemsetD32Async(ptr, 0xFFFFFFFFu32, total, stream) — fills
all `total` slots with i32 mask sentinel -1 (byte pattern
0xFFFFFFFF reinterprets as i32(-1))
3. memcpy_dtod the first `base_total` real labels from
exp_aux_to_label_per_sample into the on-policy half
CF half remains at -1. The K=3 sparse-CE loss masks `label == -1`
out of the mean and B_valid count, so CF samples contribute zero
gradient — exactly the semantic we want (CF actions have no
observed trade-close outcome to predict against).
Lib suite: 1016/0 green. Audit doc updated.
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;