Three root causes fixed:
1. GPU PER + experience collector (cudarc) created dual CUDA allocator
fragmentation on GPUs ≤8 GB, making training impossible even at
batch_size=1. Added `use_gpu_replay_buffer` config flag; both GPU PER
and experience collector now disabled when VRAM ≤8192 MB.
2. Search space bounds were WIDENED instead of capped — max_batch_size
returning 4096 replaced the original 512 upper bound, and
max_hidden_dim_base_full returning 3072 replaced the original 1024.
Fixed with min() to only narrow, never widen.
3. VRAM estimator assumed GPU features always active, overcharging when
they're disabled on small GPUs. Now conditional: when
replay_buffer_capacity=0 (proxy for GPU PER disabled), collector/cudarc
costs are zero and fragmentation multiplier drops from 3× to 1.5×.
Additional small-GPU guard: GPUs ≤8 GB get clamped search space
(batch≤128, hidden≤512, atoms≤51, buffer≤50K) to fit 3 regime heads
+ C51 + noisy nets + dueling in limited VRAM.
Validated: 7/7 trials complete on RTX 3050 Ti 4 GB, zero OOM, best trial
Sharpe 9.8 with 50.6% win rate. Previous runs had 100% OOM failure rate.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU experience kernel had hardcoded STATE_DIM=54, MARKET_DIM=51,
NUM_ATOMS_MAX=51 but the host-side feature buffer uploads 40 features
per bar and networks use state_dim=56 with up to 100 atoms. Past ~702K
bars the stride mismatch caused out-of-bounds GPU reads → ILLEGAL_ADDRESS.
All dimension constants now use #ifndef guards so the NVRTC JIT compiler
receives the actual values via #define injection — zero runtime overhead.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Hyperopt varies hidden_dim_base (256–1024) but the CUDA experience
kernel hardcoded SHARED_H1=256, SHARED_H2=256. When hyperopt picked
larger dims, the kernel did matvec with wrong strides → illegal memory
access → training crash on first epoch.
Fix: wrap CUDA #defines in #ifndef guards and inject actual dims from
the dueling network config at NVRTC compile time. The kernel is now
specialized per-trial with the exact weight matrix dimensions — zero
runtime overhead, no dimension mismatch possible.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tests require a CUDA GPU and are #[ignore]d by default. Run with:
cargo test -p ml --test gpu_kernel_parity_test -- --ignored
Covers:
- Standard dueling forward: finite Q-values, valid action range [0,4]
- C51 distributional forward: Q-values bounded by atom support [-25,25]
- Candle vs kernel Q-value parity: argmax action consistency
- NoisyNet exploration: noise injection produces action diversity
- Weight extraction roundtrip: VarMap → CudaSlice → sync
- Distributional weight shapes: value_out [51,128], advantage_out [255,128]
- RMSNorm gamma extraction: initialized to 1.0
- Repeated kernel launches: 5 consecutive runs all produce finite output
All 8 tests pass on RTX 3050 Ti (4.49s total).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
