f2335b3e50
Replace the proposed static `include_str!` regression guard for the fold-boundary IQN target hard-sync (issue #84, root-cause fix in commit `7c19b5903`) with a real GPU runtime test that exercises the contract end-to-end. The static guard only caught literal deletion of the call line — a stub body returning `Ok(())`, a copy against the wrong buffer, the wrong copy direction, or a queue against the wrong stream all pass the textual assertion silently. Test (`cuda_pipeline::gpu_iqn_head::tests::iqn_sync_target_from_online_makes_target_equal_online`): 1. Construct a `GpuIqnHead` with default `GpuIqnConfig` on the default CUDA stream. 2. Fill `online_params` ← 0.42 and `target_params` ← 0.99 via a single mapped-pinned staging buffer + `cuMemcpyDtoDAsync`. No HtoD copy is issued; the host write through `MappedF32Buffer::host_ptr` reaches the GPU through the device-mapped alias and the DtoD copies the staged values into each parameter buffer. The witnesses 0.42 / 0.99 are arbitrary distinct fp32 constants — the contract asserted is buffer equality, independent of magnitude. 3. Sanity: read both buffers back via fresh mapped-pinned destinations + DtoD, assert they differ pointwise. 4. Call `iqn.sync_target_from_online()`. 5. `stream.synchronize()` so the queued DtoD has retired. 6. Read both buffers back and assert bit-for-bit equality across all `total_params` slots using `f32::to_bits` (so any future NaN-bearing implementation also fails loud). Per `feedback_no_htod_htoh_only_mapped_pinned.md`, all CPU↔GPU communication routes through `cuMemHostAlloc(DEVICEMAP|PORTABLE)` mapped pinned memory. Tests are not exempt — fills and read-backs both use `MappedF32Buffer` + `cuMemcpyDtoDAsync`. Buffer access exposed via four new `#[cfg(test)] pub(crate)` accessors on `GpuIqnHead` (`online_params_slice`, `target_params_slice`, `total_params_for_test`, `stream_for_test`) so the public API is not widened. Test carries `#[ignore = "gpu"]` matching the smoke-test convention already used in `regression_detection.rs`. `cargo test -p ml --lib` on a CPU-only host (the worktree environment) skips it cleanly; the L40S smoke validation pool runs it via `--ignored`. Paired with a strengthened doc-block at the call site in `fused_training.rs::reset_for_fold` (boxed `DO NOT DELETE` warning + reference to the new test name and issue #84) so anyone touching the line sees the regression context inline before deleting. Touched: `gpu_iqn_head.rs` (4 cfg(test) accessors + tests mod with helpers + the runtime test, +217 LOC), `fused_training.rs` (boxed comment + test reference, +16 LOC, no behaviour change), `docs/dqn-wire-up-audit.md` (audit entry replacing the static-test entry from the previous proposal, +33 LOC). Verified: * `cargo check -p ml --lib` — clean at 13 warnings (workspace baseline). * `cargo test -p ml --lib --no-run` — clean at 24 warnings (test profile baseline). * `cargo test -p ml --lib state_reset_registry` — 3/3 existing tests pass (no 4th static-source test added). * `cargo test -p ml --lib gpu_iqn_head` — 1 test discovered, correctly reports `ignored, gpu` on this CPU-only worktree. Local run not attempted — worktree environment lacks a GPU. The test runs as part of L40S smoke validation via `--ignored`. No fingerprint change. 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;