jgrusewski b6fb720acd perf(ml-alpha): eliminate all dtoh from training hot path
GPU-resident grad-norm + clip-scale; mapped-pinned loss readback.
Replaces 9× memcpy_dtoh per Mamba2 AdamW step (grad-norm host roundtrip)
+ 1× per-step download() (loss). Saves ~10 stream-sync barriers/step.

New kernels (cuda/grad_norm.cu):
  - grad_norm_sq_phase1: per-block tree-reduce of x[i]^2 (no atomicAdd)
  - grad_norm_sq_phase2: cross-tensor accumulator (sequential stream-ordered)
  - grad_clip_scale: writes min(1, max_norm/sqrt(norm_sq)) to device ptr
  - mamba2_alpha_adamw_step_devscale: reads grad_scale from device pointer
    instead of host scalar, allowing AdamW kernels to launch async without
    waiting for a CPU-side norm computation.

Trainer changes (perception.rs):
  - loss_d kept device-side; mapped-pinned MappedF32Buffer shadow.
  - Single stream.synchronize() at end of step (was 2: post-bwd + download).
  - DtoD copy loss_d → loss_host_d queued, then sync flushes both kernels
    + copy in one barrier. Loss read via host_ptr (no dtoh).

Mamba2 AdamW (mamba2_block.rs):
  - step_from_buffers_gpu_clip(): all grad-norm tensors processed via
    phase1+phase2 chain, scale computed on-device, AdamW launches with
    devscale variant. Zero host roundtrips.
  - Pre-allocated block_partials_d, grad_norm_sq_d, grad_scale_d.

Optimizer (optim.rs): removed redundant stream.synchronize() per AdamW step.
Each per-tensor AdamW kernel is stream-ordered; sync only needed before
host reads, which the trainer handles centrally.

Synthetic overfit smoke: initial=0.30 → final=0.0006 (matches pre-refactor
trajectory). Full ml-alpha test suite passes (45 tests across lib +
integration).

Honors:
  - feedback_no_htod_htoh_only_mapped_pinned.md (MappedF32Buffer only)
  - feedback_no_atomicadd.md (block tree-reduce only)
  - feedback_no_legacy_aliases.md (step_from_buffers replaced, not aliased)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-17 13:57:02 +02:00

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.

Description
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