jgrusewski 7b3309edcc fix(rl): eliminate atomicAdd in PPO+DQN loss reduction (F4.1)
Replaces atomicAdd accumulators in `ppo_clipped_surrogate_fwd` and
`dqn_distributional_q_bwd` with per-batch [B] outputs + a dedicated
single-block tree-reduce kernel. Per feedback_no_atomicadd.

Root cause: `ss_pi_loss_dev_ptr` was zero-init at trainer construction
but never reset between steps; the PPO atomicAdd accumulated across
every step since startup. Result: `loss.pi` = step_count × mean_per_step,
bit-exact step-count fingerprints:
  - local 1k × ~9 ≈ 9080 ✓
  - cluster 20k × ~1200 ≈ 24M ✓

The DQN distributional Q kernel had the same atomicAdd pattern. Its
trainer caller happened to memset between launches in dqn_replay_step
so the symptom was masked, but the anti-pattern was identical. Fixed
in the same commit per the user's "atomicAdd should not be used at all"
reminder.

Local smoke (RTX 3050, b=16, 1k steps, seed=16962):

  step | l_pi (pre → post)  | l_q (pre → post)
   100 |   27.5 → 0.56      |  18.1 → 0.19
   500 |  743.1 → 4.59      |  93.3 → 0.19
   999 | 9080.5 → 65.1      | 186.1 → 0.19

l_q now bit-flat at per-step mean (~0.19) — no step-count fingerprint.
l_pi grows organically with policy excursion (PPO surrogate magnitude
when ratio→clamp_max under Q-distillation-driven policy updates),
which is the legitimate diagnostic the prior staleness was burying.

Changes:
  - ppo_clipped_surrogate_fwd: scalar [1] outputs → per-batch [B]
  - dqn_distributional_q_bwd: remove atomicAdd; per_batch[B] only
  - ppo_loss_reduce_b.cu (NEW): two block tree-reduce kernels
      • ppo_loss_reduce_b (dual: PPO loss + entropy loss)
      • mean_reduce_b_f32 (single, reused by DQN head)
  - PolicyHead + DqnHead: load reducer cubin, add reduce_loss_to_scalar
  - Trainer: allocate ss_pi_loss_per_b_d + ss_pi_loss_entropy_per_b_d,
    invoke reducer after each backward (PPO + DQN replay paths)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-31 14:40:58 +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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