391f6d8d58
TLOB attention GEMMs (M=TLOB_OUT=16, K=TLOB_IN=32, N=batch) are too
narrow for cuBLAS-Lt's heuristic search; cublasLtMatmulAlgoGetHeuristic
returns "no algo found" → GpuTlob::new fails → graceful-degrade hides
the bug per feedback_no_hiding.md. The previous symbol-rename fix
(7208836d1) merely surfaced this deeper API-choice problem.
Per NVIDIA best practice, cuBLAS-Lt is for tensor-core-optimised GEMMs
at scale (M,K,N ≥ 64-128); classic cublasSgemm_v2 is for general-
purpose any-shape GEMMs. TLOB's tiny attention dims belong in the
second category. Migrating all 8 TLOB GEMM call sites:
- 3× fwd Q/K/V proj (M=16, N=batch, K=32)
- 1× fwd O proj (M=16, N=batch, K=16)
- 3× bwd dW_QKV (M=16, N=32, K=batch)
- 1× bwd dW_O (M=16, N=16, K=batch)
- 1× bwd dX_O (M=16, N=batch, K=16)
Single API, single code path, no try/catch. Removed graceful-degrade
wrap in fused_training.rs and trainer/metrics.rs — TLOB is no longer
optional. Field type changed from Option<GpuTlob> to GpuTlob; all five
consumer sites (forward/backward in fused_training, mean_max in
training_loop, val-side init+sync in metrics) migrated together per
feedback_no_partial_refactor.md.
PerStreamCublasHandles registry gained a classic_handle field +
classic_for(stream) accessor mirroring the existing lt_for(stream) API.
Both handles share the same 32 MB workspace, both bound to their
stream + workspace at creation time. Default-stream classic handle is
created in PerStreamCublasHandles::new; side-stream handles are
provisioned lazily alongside their cuBLAS-Lt sibling inside
classic_for/lt_for/pre_register_stream. Classic handles default to
CUBLAS_TF32_TENSOR_OP_MATH (matching gpu_curiosity_trainer's existing
convention; same TF32 path as the cuBLAS-Lt CUBLAS_COMPUTE_32F_FAST_TF32
compute type).
Verification:
- SQLX_OFFLINE=true cargo check --workspace — clean
- cargo test -p ml --lib --release gpu_tlob — new
tlob_sgemm_parity_with_cpu_reference test passes; verifies all 8
GEMMs (forward Q/K/V/O, backward dX_O, dW_O, dW_Q/K/V) match a CPU
sgemm reference within 2e-3 absolute on batch_size=64 synthetic
OFI input
- dqn-wire-up-audit.md updated; new pearl
pearl_cublas_lt_vs_classic_sgemm.md captures the rule
The cuBLAS-Lt path remains in use for the larger gpu_attention module
(trunk attention, dims 128+) where tensor-core throughput pays off.
This is the standard split: Lt for big, classic for small.
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.