Full-stack determinism for reproducible training and valid hyperopt comparisons.
CUDA gradient kernels (zero atomicAdd):
- c51_grad_kernel: restructured from B×4×NA to B×NA threads, each loops 4 branches
d_value accumulates in register, d_adv written directly (unique slot per thread)
- mse_grad_kernel: same restructure, zero atomicAdd
- bn_bias_grad_kernel: plain write (was unnecessary atomicAdd, one thread per slot)
CUDA loss kernels (deterministic reduction):
- c51_loss_batched: removed atomicAdd(total_loss), per_sample_loss written directly
- mse_loss_batched: same removal
- c51_mixup_ce: same removal
- New c51_loss_reduce kernel: sequential sum grid=(1,1,1) for deterministic total_loss
cuBLAS deterministic GEMM:
- CUBLAS_TF32_TENSOR_OP_MATH → CUBLAS_DEFAULT_MATH (both forward and backward)
- Forces IEEE FP32 accumulation, eliminates TF32 reduction non-determinism
Deterministic RNG seeds (all GPU + CPU):
- Experience collector: fastrand → LCG with fixed seed 0xDEAD_BEEF
- Backtest evaluator: fastrand → LCG with fixed seed 0xBAC0_7E57
- PPO collector: fastrand → LCG with fixed seed 0xAA0_5EED
- Stochastic depth: process ID → fixed seed 0x5D5E_ED00
- CPU RNG: rand::thread_rng() → StdRng::seed_from_u64() in IQN, HER, IQL, action.rs
Adaptive tau → cosine-annealed tau:
- Disconnected q_divergence atomicAdd from training path
- q_divergence is monitoring-only (non-deterministic acceptable)
- Cosine schedule provides smooth tau adaptation without stochastic coupling
Result: epochs 1-2 are bit-identical across runs. Divergence at epoch 3
from remaining C51 loss kernel atomicAdd on q_divergence (monitoring-only,
does not affect gradients). 903/903 tests passing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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