072ca45686
nsys profile of multi_fold_convergence on L40S identified backtest_state_gather as the #1 GPU consumer at 37.2% (596 ms / 60,588 calls — kernel launch latency dominated compute) and the per-step TLOB cuBLAS gemvx calls as #2 at 25% (242K calls). Both share the same per-step amplification: chunk_len=512 separate gather launches + 512 separate TLOB.forward calls (4 SGEMMs each) per chunk before any Q-values can be computed. This commit replaces the per-step gather + DtoD pattern with a single batched launch, and reuses the same chunked buffer for a single chunk-wide TLOB forward. Per-chunk launch reduction: from 2*chunk_len + chunk_len*7 to 1 + 7 for the gather+TLOB phase (4608 -> 8 with chunk_len=512, a 576x reduction). New kernel `backtest_state_gather_chunk` (experience_kernels.cu): - Writes [chunk_len, N, padded_sd] directly into chunked_states_buf - chunk_len * N threads, 1 thread per output row - Mathematically identical to per-step gather: portfolio_buf and plan_isv_buf are CONSTANT within a chunk (env_step + plan_state_isv update at chunk boundary only). Each thread reads independent feature offsets, no atomics, no reordering. Chunked val TLOB (gpu_backtest_evaluator.rs + metrics.rs): - Val TLOB instance now sized to DQN_BACKTEST_CHUNK_SIZE * n_windows via new GpuBacktestEvaluator::val_tlob_batch_size() helper. - submit_dqn_step_loop_cublas calls tlob.forward(chunked_states, batch) ONCE per chunk on the chunk-wide buffer instead of chunk_len times on states_buf. - Partial last chunks reuse the same buffers (forward(b) accepts any b <= construction_batch). Borrow restructure: - Removed top-of-function `let ch_states = self.chunked_states_buf.as_ref()?` binding (TLOB needs &mut). Replaced with per-chunk ch_states_base raw u64 device pointer extracted in tight scope, reused by Phase 1 (gather) and Phase 2+3 (compute_q_values_to + last_step_states_ptr). The pointer is stable across the chunk because the Option<CudaSlice<f32>> does not reallocate. Per-step gather kernel `backtest_state_gather` retained unchanged for evaluate() / evaluate_ppo() / evaluate_supervised() paths that still need a single-step writer (closure-based callers with no chunked buffer). Audit doc dqn-gpu-hot-path-audit.md updated with Fix 18 entry per Invariant 7. Build: SQLX_OFFLINE=true cargo check -p ml --lib clean (12 warnings, baseline). Tests: cargo test -p ml --lib --no-run clean. 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;