4714c02fcf
compute-sanitizer caught 16 OOB errors in magma_sgemmEx_kernel
<f,f,f,1,0,6,4,6,3,4>+0xe90 surfacing as mamba2_scan_projected_bwd
LAUNCH_FAILED. Pre-existing latent, exposed when q_var_buf_trainer
allocator reshuffle (fa92cb8db) moved subsequent buffer pointers.
Root cause: DuelingWeightSet::from_flat_buffer and
BranchingWeightSet::from_flat_buffer in gpu_weights.rs used hard-coded
layout indices [0..11] / [12..23] from before the GRN trunk expansion
(Plan 4 Task 2c.3a). The expansion inserted 9 GRN tensors at indices
1..12, shifting every value/branch tensor by +9. The 12-index sequential
walk in from_flat_buffer silently slid every dueling-weight pointer
forward into adjacent tensors:
- online_dueling.w_v1 → w_residual_h_s1 (sized SH1*s1_input_dim, not
VALUE_H*SH2)
- online_dueling.b_v1 → gamma_h_s1 (sized SH1=64 floats=256 bytes,
not VALUE_H=128 floats=512 bytes)
- online_dueling.w_v2 → beta_h_s1; b_v2 → w_a_h_s2; w_a1 → b_a_h_s2; …
The pathological alias surfaced via the ensemble multi-head clone path
(forward_value_head_for_ensemble): the cuBLAS RELU_BIAS epilogue tried
to read bias[0..128] from b_v1's 64-float buffer, producing exactly the
observed 16 thread (0..7, 2..3) reads at 1..61 bytes past a 256-byte
allocation.
Production GEMMs never tripped this because they read from
params_buf via f32_weight_ptrs_from_base which has always used the
correct 163-tensor GRN layout. flatten/unflatten paths used
matched-stale self-copies (no-op when online_d.w_s1 == params_buf_ptr).
Only the ensemble clone (`clone_dueling_weights`) made independent
copies of the misaliased pointers and then handed them to the GEMM as
if they were value-head tensors.
Per feedback_no_partial_refactor: every consumer of the weight-set/
flat-buffer contract migrated in lockstep:
- New DUELING_FLAT_INDICES = [0,1,2,3, 13,14,15,16, 17,18,19,20]
and BRANCHING_FLAT_INDICES = [21..32] in gpu_weights.rs encode the
authoritative mapping from DWS/BWS slots to GRN-expanded layout
indices.
- DuelingWeightSet::from_flat_buffer + BranchingWeightSet::from_flat_buffer
rewritten to use these mappings with a full prefix-sum byte-offsets
table (matches f32_weight_ptrs_from_base byte layout).
- flatten_online_weights, unflatten_online_weights,
flatten_target_weights, unflatten_target_weights (gpu_dqn_trainer.rs)
rewritten to keyed [(ptr, layout_idx); 24] pairs and write at
byte_offsets[layout_idx] instead of sequential prefix-sum over
sizes[0..23]. The no-op zero-copy check (online_d.w_s1 == src_base)
is preserved because DUELING_FLAT_INDICES[0] == 0.
Sanitizer (RTX 3050 Ti, magnitude_distribution smoke):
magma_sgemmEx_kernel OOB count: 16 → 0
Non-sanitizer smoke completes all 20 epochs without LAUNCH_FAILED
(was crashing on epoch 1 prior to fix); MAG_DIST/EVAL_DIST results
reflect real model behavior (Q=0.349, H=0.298, F=0.353 train-mode).
The unrelated F_Full eval-cap assertion is the ongoing Kelly cap
issue (project_magnitude_eval_collapse_kelly_capped.md).
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.