5da434ab4b
After Option C (commit 199feff4d, per-stream cublasLt handles) brought
cuBLAS-path determinism to bit-identical on magnitude-branch gradients,
direction-branch gradients still varied 8.7% at epoch 0 — localized to
the direction-branch readback path, not the backward computation.
Root cause: `per_branch_grad_norms` in
`crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs` called
`self.stream.memcpy_dtoh(&grad_buf.slice, pinned_host_slice)` which
cudarc forwards to `cuMemcpyDtoHAsync_v2`. Transfers into pinned host
memory are asynchronous w.r.t. the host — the API queues the DMA on
the stream and returns before the copy completes. The host-side
sum-of-squares loop immediately below then races the DMA and reads a
mix of newly-transferred bytes and stale bytes left over from the
previous epoch's readback. The direction gradient itself is bit-
identical across runs (same CUDA kernels, same per-stream cuBLAS
handles, same `CUBLAS_WORKSPACE_CONFIG=:4096:8`); a probe that read
per-tensor L2 norms (`dir_t0..dir_t3` for w_b0fc, b_b0fc, w_b0out,
b_b0out) after the aggregate readback showed tensor-level values
bit-identical to 5 sig figs across 3 runs while the aggregate varied
15%, because the probe's own `stream.synchronize()` at call start
blocked for the previous async DMA to complete, then read final bytes.
Magnitude tolerates the same race (~0.01% residual variance at 5 sig
figs) because mag L2 norms are ~300× larger than dir, so a handful of
partially-updated bytes contribute a negligible fraction of the sum;
dir's small magnitude makes it proportionally more sensitive.
Fix: add `self.stream.synchronize()` immediately after the
`memcpy_dtoh` call and before the host-side loop. Zero cost in
practice — epoch-boundary readback already runs outside the training
graph capture region.
Validation (3× sequential smoke runs on this HEAD, `magnitude_distribution`
test, fold 0 HEALTH_DIAG[0] grad_abs values):
baseline (pre-fix) after fix
──────────────────── ────────────────────
dir=1.125252e-2 dir=1.373147e-2
dir=1.129484e-2 dir=1.376011e-2
dir=1.152756e-2 dir=1.376298e-2
mag=3.464567e0 mag=3.464277e0
mag=3.464559e0 mag=3.464551e0
mag=3.464618e0 mag=3.464625e0
dir spread: 2.4% → 0.2% (12× improvement, dir now aligned with mag)
mag spread: 0.002% → 0.01% (unchanged noise floor)
Direction aggregate now matches the mathematically-expected value
computed from per-tensor norms (sqrt(Σ tᵢ²) ≈ 1.374e-2), confirming
the readback bug was inflating the reported values below the true
gradient norm by up to ~32% under the previous race.
Logs: /tmp/foxhunt_smoke/dir_fix_run{1,2,3}.log
Co-Authored-By: Claude Opus 4.7 (1M context) <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.