87ca1f5f55
Captures the full Mamba2 fwd → K-loop fwd → BCE → K-loop bwd →
Mamba2 bwd → AdamW × 7 → loss DtoD chain into a single CUDA Graph.
First step_batched call runs uncaptured (cuBLAS warmup); second
call captures; third+ replays. Replaces ~155 individual kernel
launches per step with one graph launch.
Four root-cause issues had to be fixed in concert to make the
capture region capture-compatible:
1. cuBLAS lazy workspace allocation
crates/ml-alpha/src/mamba2_block.rs — pre-allocate an 8 MiB
workspace via cublasSetWorkspace_v2 at Mamba2Block::new.
cuBLAS would otherwise allocate on first gemm call with each
new shape, breaking capture.
2. cuBLAS heuristic plan-cache lookup
crates/ml-core/src/cuda_autograd/linear.rs — switch
gemm_ex_f32 from CUBLAS_GEMM_DEFAULT_TENSOR_OP to
CUBLAS_GEMM_DFALT. The heuristic algo path triggers
plan-cache allocs; DFALT is deterministic with negligible
perf delta for our shapes (Mamba2: 128×32, 128×state_dim).
3. Per-call cuModuleLoadData in bias kernels
crates/ml-core/src/cuda_autograd/linear.rs — add a
`BiasKernels` struct (add_bias_2d_kernel + reduce_sum_axis0
handles) cached at construction. `BiasKernels::shared(stream)`
uses a per-context OnceLock cache so the cubin loads exactly
once per CUDA context for the process lifetime. Every
`GpuLinear` and `OwnedGpuLinear` constructor now stores its
`BiasKernels`. Removed the per-call `get_bias_kernels`
helper entirely (no legacy aliases — greenfield).
4. Per-call CudaSlice::clone() in Mamba2 fwd + bwd
crates/ml-alpha/src/mamba2_block.rs — three sites cloned
the input slice to build a fresh `GpuTensor` view. Each
`CudaSlice::clone()` does cuMemAlloc + dtod copy
(vendor/cudarc/src/driver/safe/core.rs:1437); cuMemAlloc
is forbidden during capture.
Refactored `forward_with_slices_into` and
`backward_with_slices_into` to take raw `x_data: &CudaSlice<f32>,
batch: usize` instead of `&GpuTensor` / `&LinearActivations`.
All three Mamba2 fwd call sites + three bwd call sites now
pass the underlying CudaSlice directly.
Trainer changes (trainer/perception.rs):
- Three-state machine in step_batched: warmup → capture → replay.
- Labels staging fill moved BEFORE the captured region (host writes
only; replays read whatever the host last wrote).
- Removed the post-snap_batched sync that was inside dispatch (it
would trip STREAM_CAPTURE_ISOLATION; kernels are stream-ordered
so the sync was unnecessary).
- Dispatch extracted into `dispatch_train_step` method; the
captured region brackets exactly this method.
- Final sync + mapped-pinned loss read happens once per step in
step_batched, outside the captured region.
Validation:
- Synthetic overfit smoke: 0.397 → 0.0007 (matches pre-refactor
trajectory; capture+replay produces equivalent loss).
- 26 ml-alpha lib tests + 23 integration tests pass.
- 306 ml-core lib tests pass.
Also fixed (orthogonal but required for green workspace):
crates/ml-core/src/action_space.rs — tests assumed 7-exposure
layout (63 actions). Production `ExposureLevel` enum has 8
variants (Hold inserted at idx 3, Flat moved to idx 7 → 72
total actions). Updated tests + `get_valid_action_mask` to
match the 8-level layout.
Honors:
- feedback_no_legacy_aliases.md (no `add_bias_2d_with_fn` /
legacy `get_bias_kernels` fallback; one canonical API).
- feedback_no_partial_refactor.md (signature change propagated
to every caller atomically; no half-migrated state).
- feedback_wire_everything_up.md (BiasKernels wired into all
GpuLinear/OwnedGpuLinear constructors in same commit).
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.