ce1e13519b
Two concerns in one commit since they're entangled:
## 1. feedback_no_htod_htoh_only_mapped_pinned violations
R7d (PER push/sample) + R8 (CLI binary) + the new per-step diag dump
shipped with 8 raw `stream.memcpy_htod` / `stream.memcpy_dtoh` calls.
The rule is explicit: "mapped-pinned only for CPU↔GPU; tests not
exempt." A raw `stream.memcpy_*` on a regular `&[T]` / `&mut [T]` is
NOT mapped-pinned — the source/dest slice isn't page-locked, so the
CUDA driver does an internal blocking HtoD/DtoH that stalls the
stream.
Refactored all 8 violations to use the mapped-pinned + DtoD pattern
(cuMemHostAlloc DEVICEMAP — host writes via `host_ptr`, kernel reads
`dev_ptr`, DtoD between them via `cudarc::driver::result::memcpy_dtod_async`).
New shared helpers in `trainer/integrated.rs`:
* `read_slice_i32_d` — DtoH for `i32` device buffers via
`MappedI32Buffer` staging. Counterpart to the existing
`read_slice_d` (f32 version).
* `write_slice_f32_d` — CPU→GPU upload for `f32` via
`MappedF32Buffer.write_from_slice` + DtoD into destination.
* `write_slice_i32_d` — CPU→GPU upload for `i32` via
`MappedI32Buffer.host_slice_mut().copy_from_slice` + DtoD.
`pub fn` wrappers (`read_slice_*_d_pub`) expose the f32/i32 helpers
to the CLI binary so the per-step diag DtoH uses the same canonical
pattern.
Call-site refactors:
* `push_to_replay`: 4× `stream.memcpy_dtoh` → `read_slice_*_d`.
* `sample_and_gather`: 3× `stream.memcpy_htod` → `write_slice_*_d`.
* `step_with_lobsim` pre-PER ISV refresh: raw `memcpy_dtoh` →
`read_slice_d` (424 floats per step).
* `step_with_lobsim` post-Q PER priority TD readback: raw
`memcpy_dtoh` → `read_slice_d` (b_size floats per step).
* `step_synthetic` ISV mirror refresh: raw `memcpy_dtoh` →
`read_slice_d` (pre-existing pre-R9 violation; fixed in the
same commit since it's the same pattern in the same file).
* Init-time (one-shot) `prng_state` upload: raw `memcpy_htod` →
inline mapped-pinned DtoD (custom because cast through i32 for
the u32 buffer).
* Init-time (one-shot) `atom_supports` upload: raw `memcpy_htod`
→ `write_slice_f32_d`.
* `examples/alpha_rl_train.rs` per-step diag DtoH (3 calls) →
`read_slice_*_d_pub`.
## 2. Pre-commit guard gap — diff-aware HtoD/DtoH check
The existing GPU hot-path guard (`scripts/gpu-hotpath-guard.sh`)
EXPLICITLY skips memcpy_htod/dtoh on the assumption that such calls
only appear in `cuda_pipeline/` (where mapped-pinned is the
convention). That assumption was falsified by R7d/R8 — the guard
shipped 8 violations green.
Added `check_no_raw_htod_dtoh` to `scripts/pre-commit-hook.sh` (the
real file behind the `.git/hooks/pre-commit` symlink). The check is
DIFF-AWARE: it greps only the `+` lines of `git diff --cached -U0`,
so pre-existing violations elsewhere (143 sites across the codebase)
don't block commits touching unrelated files. NEW additions of
`\.memcpy_(htod|dtoh)\(` are flagged with a clear error pointing at
the mapped-pinned alternative. Suppress per-line with `// gpu-ok:
<reason>` (same convention as the existing guards).
Pre-existing violations in `ml-alpha/src/aux_heads.rs`,
`mamba2_block.rs`, `cfc/`, `data/`, etc. are a separate cleanup —
not blocked by this commit's check because the diff-aware filter
ignores anything that was already on `HEAD~1`.
## Verified gates (post-fix, local sm_86)
G1 isv_bootstrap ✅ unchanged
G3 controllers_emit ✅ unchanged
G4 target_soft_update ✅ unchanged
G6 r7d_per_wiring ✅ unchanged (PER round-trips all
mapped-pinned now)
R3 ema/advantage (3 tests) ✅ unchanged
R4 action kernels (3 tests) ✅ unchanged
end integrated_trainer_smoke ✅ unchanged
Mapped-pinned is semantically equivalent to raw memcpy_htod/dtoh —
just routed through page-locked staging so the driver doesn't have
to do its own internal pinning. Behaviour identical; the cost shifts
from "driver hidden HtoD per call" to "mapped-pinned alloc + DtoD
per call." For the smoke (b_size=1, 1000 steps), the cost difference
is in the microseconds.
## Per-step diag JSONL (separate concern, same commit)
Added `--diag-jsonl <PATH>` flag to `alpha_rl_train.rs` (default:
`<out>/diag.jsonl`). After each `step_with_lobsim`, writes one JSON
record capturing:
* step number, elapsed wall time
* all 5 head losses + λs
* all 7 RL controller outputs (γ τ ε coef n_roll per_α scale)
* all 5 per-head learning rates (lr_bce/q/pi/v/aux)
* all 7 EMA inputs the controllers consume
* replay buffer length
* per-step reward stats (sum, max, min, abs_max)
* per-step done count
* per-step action histogram (9 action classes)
Critical for cluster smoke debugging — the prior CLI only flushed
an `eprintln` progress line every N steps (default 100), making
in-flight controller drift / replay stagnation / reward explosion
invisible until they produced a NaN abort. The JSONL is line-
buffered + flushed every `log_every` steps so `tail -f` shows
progress live.
The stderr progress line is also beefed up to include γ / ε / per_α /
reward_scale / dones / rew_sum at each tick so a casual `argo logs`
inspection sees the controller behaviour without parsing JSONL.
## Why R9 cluster submission needs this
Without the diag dump, an R9 1000-step smoke is "blind" — only the
final summary tells us what happened. With the dump, post-hoc
analysis can answer:
* Did the controllers adapt or stay at bootstrap?
* Did the reward scale stabilise or saturate?
* Did the PER buffer fill?
* Was the action histogram dominated by any one action?
* Where did the per-head losses converge to?
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.