23b89a90e9
Combined Phase 8.3 (visibility + hard-fail) and Phase 9 (CPU path
removal) per pearl_no_deferrals_for_complementary_fixes. Surfaced by
v6 smoke (train-x4m96) where:
[DQN GPU] Fold 0 GPU eval failed: GpuBacktestEvaluator::evaluate failed
for fold 0. Falling back to CPU path.
[DQN] Fold 0 evaluation failed: Failed to create DQN state tensor for
bar 0: Dimension mismatch: expected 54, got 45
Both fold 0 and fold 1 hit this; workflow exited 0, masking eval
failure for every smoke run since STATE_DIM grew beyond legacy 54.
Root cause (silent GPU failure):
evaluate_dqn_fold_gpu calls evaluator.evaluate(closure, portfolio_dim: 3)
but GpuBacktestEvaluator initialises portfolio_dim = PORTFOLIO_BASE_DIM (8)
+ MTF_DIM (16) = 24 per canonical state layout. gather_states asserts
match → returns MLError::ConfigError. Caller wraps with .with_context()
+ warn!("... {}", e) — `{}` strips anyhow chain, hiding root cause.
The CPU fallback runs with a separate stale 45-dim state builder
(42 from extract_ml_features + 3 portfolio) → fails at GpuTensor::
from_host shape validation against the model's 54-feature default.
Fixes (all atomic):
1. portfolio_dim: 3 → 24 at all 4 call sites (DQN x2, PPO, supervised).
The GPU evaluator's gather_kernel handles full 128-dim state
assembly (Market 42 + OFI 32 + TLOB 16 + MTF 16 + Portfolio 8 +
PlanISV 7 + Padding 7); caller just declares correct portfolio_dim.
2. Surface anyhow chain: {} → {:#} in error messages.
3. Hard-fail on GPU eval failure: anyhow::bail! (no CPU fallback).
Per user directive: "hard fail on gpu panic, cpu path strictly
forbidden should be removed entirely!"
4. DELETE CPU DQN eval path entirely:
- fn evaluate_dqn_fold
- fn build_chunk_states (stale 45-dim state builder)
- fn simulate_chunk_trades
- fn compute_metrics + struct ComputedMetrics
- struct PortfolioState
5. DELETE coupled surrogate-noise machinery:
- struct SurrogateSampler + impl
- fn load_surrogate_marginals
- fn compute_pooled_sharpe
- Surrogate init blocks in main
- ACTION_MARGINALS / POOLED_SHARPE emission blocks
6. DELETE coupled CLI flags:
- --gpu-eval / --no-gpu-eval (GPU mandatory)
- --surrogate-mode, --surrogate-seed, --surrogate-marginals
- --emit-action-marginals, --emit-pooled-sharpe
7. Collapse `if args.gpu_eval { ... }` blocks to direct calls; cleaner
control flow, no gpu_handled tracking.
Pearls honoured:
- feedback_no_cpu_test_fallbacks: GPU oracle only
- feedback_no_partial_refactor: stale CPU layout from pre-STATE_DIM=128 era
- feedback_no_hiding: error chain now visible via {:#}
- feedback_no_legacy_aliases: no deprecated --no-gpu-eval wrapper
- pearl_no_deferrals_for_complementary_fixes: 8.3+9 combined
Files changed:
- crates/ml/examples/evaluate_baseline.rs:
−892 net lines (1066 del, 174 ins; 2817 → 1925)
- docs/dqn-wire-up-audit.md: 2026-05-12 audit entry
Verification:
- cargo check -p ml --example evaluate_baseline --features cuda # clean
- cargo check --workspace --features cuda # clean
- cargo test -p ml --lib --features cuda financials # 7/7
Note: OFI/TLOB/MTF feature-set fidelity is a separate concern. The GPU
gather_kernel handles state assembly; caller currently provides zeroed
OFI (LobBar.ofi = 0.0) and no MTF data. Eval will run, but on degraded
features. Faithful feature wiring deferred to a later Phase once
eval-runs-at-all is validated by v7 smoke.
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;