jgrusewski 68804a1a51 fix(cuda): remove --use_fast_math, eliminate cross-stream NaN race, f32 rewards/dones ...

Three root causes of sporadic NaN during training:

1. --use_fast_math (nvcc) breaks IEEE 754 NaN semantics: fmaxf(NaN,x)
   returns NaN instead of x, isnan()/isinf() compile to false.
   Replaced with --ftz=true --fmad=true --prec-div=true --prec-sqrt=true
   across all 4 build.rs (ml, ml-dqn, ml-ppo, ml-core).

2. Cross-stream race: replay buffer wrote batch data on the device's
   original stream while the trainer read it on a forked stream.
   Fixed by passing the forked stream to the DQN agent via agent_device,
   so all GPU components share a single CUDA stream (zero sync overhead).

3. Rewards/dones stored as bf16 in replay buffer caused done=0xFFFF NaN.
   Converted entire rewards/dones pipeline to f32: experience collector,
   replay buffer storage, nstep kernel, loss/grad kernels.

Also:
- Removed fast_isnan/fast_isinf/fast_isfinite wrappers — standard
  isnan/isinf/isfinite work correctly without --use_fast_math
- Updated dqn-smoketest.toml: lr=1e-4, epsilon=1e-8 (f32 Adam values)
- Removed debug printfs from gather kernels
- Added curiosity_weight to training profile system
- Cleaned up smoke_params() inline overrides

11/11 smoke tests pass, 5/5 stress runs of 50-epoch test pass,
359/359 ml-dqn + 895/895 ml unit tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-29 16:18:37 +02:00

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