749fc80edf
Phase 2A of the HEALTH_DIAG GPU port. Lands the simplest member of the
kernel family (single-block single-thread ISV scalar copies) plus the
orchestrator scaffolding (cubin loader, mapped-pinned snapshot owner,
launcher) that subsequent phases will reuse.
Why simplest first: 5 kernels each with different masking semantics
landing in one commit risks silent numerical mismatches that downstream
parsers (aggregate-multi-seed-metrics.py, smoke summarisers) would
mis-parse. Per feedback_no_quickfixes.md, kernel-by-kernel with
parallel-shadow validation. Phase 4 deletes CPU paths in one commit
once all kernels are bit-identical.
What this kernel does: copies 22 ISV signal-bus slots into the matching
HealthDiagSnapshot fields — reward-component EMAs (slots 63-68), VSN
attention focus EMAs (87, 88), target-drift EMAs (92, 93), aux-head
loss EMAs (113, 114, 117), MoE expert utilisation (118-126), gate
entropy (126), and λ_eff (128). 22 of 147 snapshot words populated
(reward_split[6] + noisy_vsn[2] + noisy_drift[2] + aux[3] + aux_moe[10]).
Producer-only — no consumer reads health_diag.snapshot() yet; Phase 3
wires the call site, Phase 4 makes the snapshot the sole emit source.
Architecture rules upheld:
- No HtoD / DtoH (writes through mapped-pinned device pointer).
- No atomicAdd (single-thread kernel; family-wide rule for 2B-2E too).
- No DtoD memcpy on the diag path (kernel pointer-load + store).
- Kernel WORD-index table inline; static_assert pins WORD_TOTAL=147 in
lockstep with snapshot_size_is_stable test.
- ISV indices passed as kernel args (not #define'd) so the kernel
never embeds the numbering — single source of truth lives in
gpu_dqn_trainer.rs constants.
Changes:
- new crates/ml/src/cuda_pipeline/health_diag_kernel.cu (311 lines).
- new crates/ml/src/cuda_pipeline/gpu_health_diag.rs (190 lines)
holds GpuHealthDiag orchestrator (cubin module + isv_mirror_kernel
handle + MappedHealthDiagSnapshot).
- mod.rs: pub mod gpu_health_diag.
- build.rs: registers health_diag_kernel.cu in kernels_with_common.
- gpu_dqn_trainer.rs:
- imports super::gpu_health_diag::GpuHealthDiag;
- adds health_diag: Option<GpuHealthDiag> field next to aux_heads_fwd;
- constructor calls GpuHealthDiag::new() after the cubin loads;
- adds pub fn launch_health_diag_isv_mirror() (uses compile-time
ISV index constants, single source of truth);
- adds pub fn health_diag_snapshot() accessor.
- docs/health_diag_inventory.md: Phase 2A entry + Phase 4 deletion
list pinned per Invariant 7.
- docs/dqn-wire-up-audit.md: new row for health_diag_kernel.cu under
"CUDA Kernels (.cu files)" — classified Pending wire-up (Phase 3).
Verification:
- cargo check -p ml: 0 new warnings (12 lib warnings pre-existing,
none touch health_diag/gpu_health_diag).
- cargo test -p ml --no-run: clean.
- cargo test -p ml --lib cuda_pipeline::health_diag::tests: 3/3 pass
(snapshot_size_is_stable, default_is_zeroed, alignment_is_4_bytes
— the WORD_TOTAL=147 static_assert in the kernel matches the host
size_of test bit-for-bit).
Scope discipline (per prompt's "if kernel #3 takes too long, stop"):
This commit lands the orchestrator scaffolding + simplest kernel only.
Phases 2B-2E (eval-histogram, q-mag-reduce, per-sample-reduce,
finalise) intentionally deferred — masking semantics for q_mag_reduce
in particular need careful per-CPU-getter inspection before kernel-side
implementation. Producer-only by design; no production caller of
launch_health_diag_isv_mirror in this commit (matches AuxHeadsForwardOps
landing pattern from Plan 4 Task 6 Commit A).
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.