Commit Graph

2 Commits

Author SHA1 Message Date
jgrusewski
98a81981ac fix(build-infra): cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP across 6 crates
Per pearl_build_rs_rerun_if_env_changed, every std::env::var() must be
paired with cargo:rerun-if-env-changed. Task #321 fixed crates/ml/build.rs
(commit e3d082968) but missed 6 sibling build.rs files. Each reads
CUDA_COMPUTE_CAP without registering the rerun directive, so cargo
sees no env-change between e.g. SM 89 (L40S) → SM 90 (H100) workflow
re-submissions and cache-hits the wrong-arch cubin from the previous
run. At runtime, the H100 fails to load the SM 89 cubin with
CUDA_ERROR_NO_BINARY_FOR_GPU on rmsnorm — exactly what killed
train-multi-seed-vlv8c (commit 0371d6a76, post-Class-B chain).

Files (all add `println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP")`
just before the env::var() read):
- crates/ml-dqn/build.rs (rmsnorm — root cause of vlv8c failure)
- crates/ml-ensemble/build.rs
- crates/ml-explainability/build.rs
- crates/ml-ppo/build.rs
- crates/ml-supervised/build.rs
- crates/ml-core/build.rs

Atomic single commit per feedback_no_partial_refactor.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-08 13:09:21 +02:00
jgrusewski
a6ca9fba4f feat(mamba): wire real GPU Bernoulli dropout in supervised Mamba2
Supervised Mamba2's dropout_rate field was configured and stored but
never applied — comments said "simulated" but the arithmetic was
absent. An operator tuning dropout_rate upward got zero regularisation.

New dropout_kernel.cu with a forward-only Bernoulli dropout (Philox-
seeded, deterministic, in-place). New build.rs matching ml-dqn's
pattern. Applied in forward_with_gradients (training path) only; the
`forward()` path used by validate/predict/SPSA stays deterministic so
SPSA's ±ε finite-difference estimator is not destabilised.

NOT a DQN fix: DQN has its own regime_dropout kernel in
cuda_pipeline/experience_kernels.cu and its own native mamba2_step
in gpu_dqn_trainer.rs; DQN does not call into Mamba2SSM. This commit
affects only the supervised Mamba2 trainer and its hyperopt adapter.

Tests: determinism, eval-mode identity, ctr-increment divergence.
DQN smoke tests (magnitude_distribution, multi_fold_convergence) are
unaffected by this change — ran them for regression assurance only.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-23 10:07:06 +02:00