The 4-branch DQN (direction x magnitude) had 3 degenerate variants
(Short25, Flat, Long25) that all mapped to 0.0 target exposure when
direction=Flat, causing 82% Flat collapse. Collapse these into a
single Flat variant, giving 7 levels (ShortSmall/Half/Full, Flat,
LongSmall/Half/Full) and 63 total factored actions (7x3x3).
- ExposureLevel enum: 9 variants -> 7 (add direction/magnitude/from_dir_mag)
- FactoredAction: 81 -> 63 total actions, from_index/to_index updated
- DQN epsilon-greedy: use from_dir_mag() instead of dir*3+mag indexing
- DQN config: num_actions default 9 -> 7
- PPO action space: 45 -> 63 actions, action masking updated
- Signal adapter CUDA kernel: 5-bin -> 7-bin exposure aggregation
- All tests updated for new variant names and index ranges
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Sharpe calculation:
- Use per-trade returns (sum_returns/sum_sq_returns) instead of per-bar
step_returns. Per-bar Sharpe collapsed variance → bogus 19.75 with PF=0.03.
- Annualize by sqrt(trades_per_year) not sqrt(bars_per_year).
Batch sizing:
- Cap auto-computed batch_size at 8192 (VRAM ceiling of 2M is OOM limit, not
optimal RL batch).
- Add VRAM floor: batch_size < ceiling/4096 gets scaled UP (128 → 512 on H100).
- Only let hyperopt override batch_size when explicitly non-zero — preserve
profile's batch_size=0 auto-compute sentinel.
Replay buffer:
- Divide per_max_memory_bytes by 3.0 for regime heads (PER budget was 3x too
large, causing OOM cascade 74M → 37M → 18M → 9M → 4.6M).
- HER buffer uses original_buffer_size (pre-autosizer), not inflated 74M.
Q-value clipping:
- Wire hyperparams.q_clip_min/max to DQNConfig (was hardcoded ±500, production
TOML has ±50). Prevents Q-value overestimation ratio of 94.6x at epoch 2.
Training stability:
- Anti-LR warmup: skip first 5 epochs (early Sharpe unreliable from random
policy). Prevents bogus 3x LR boost at epoch 2.
- min_replay_size from profile (1000), not hyperopt batch_size (128).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 1: Remove 5 DQNConfig boolean flags (use_soft_updates, use_iqn,
enable_q_value_clipping, use_cvar_action_selection, use_count_bonus).
All features now unconditionally active — no dead toggle branches.
Phase 2: Add spectral decoupling (L2 on Q-value logits, Pezeshki 2021)
and manifold mixup (Beta-sampled distribution interpolation with atomic
barrier sync) directly in C51 CUDA loss kernel. Zero CPU involvement.
Phase 3: Tag Experience transitions with market regime (ADX/CUSUM GPU
classifier kernel). Add regime-biased PER sampling via rejection with
IS weight correction. Decay factor controls cross-regime bleeding.
Phase 4: 6 family intensity scalars for hyperopt (adversarial,
regularization, augmentation, loss shaping, ensemble, causal). Scales
34 generalization params through 6 PSO dimensions instead of 34.
Search space: 24D → 30D (families additive, individual params kept).
20 files changed, +563/-69 lines. Full workspace compiles clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove use_double_dqn, use_dueling, use_per, use_branching,
use_distributional, use_noisy_nets, use_huber_loss, and use_cql
from DQNConfig, DQNHyperparameters, and DqnParams structs.
These features are always enabled (Rainbow DQN standard). The boolean
flags were dead code — every constructor set them to true, and the
only code paths that set them to false were in tests that disabled
features for simplicity. With the fields removed, the features are
unconditionally active, eliminating ~490 lines of dead configuration.
Key changes:
- Struct field declarations removed from 3 core config structs
- Conditional branches (if use_X { ... } else { ... }) simplified:
dueling/branching/PER network creation is now unconditional
- Checkpoint metadata hardcodes "true" for backward compatibility
- Hyperopt search space index 11 (use_branching) fixed at 1.0
- TOML/YAML config files cleaned of removed fields
- Tests that toggled these flags updated or rewritten
45 files changed, -487 net lines. Zero new test failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
DQN pipeline tests:
- Split dqn-pipeline into per-test cargo invocations in CI (CUDA Graph
capture corrupts async memory pool between sequential tests)
- Drop impl for GpuDqnTrainer: sync stream + destroy graph before buffers
- check_err drains in constructor and after graph capture
TFT fixes:
- forward_loss: reshape output [batch,horizon,quantiles] → [batch,quantiles]
to match target shape (fixes DimensionMismatch {expected:3, actual:3})
- smoke test: accept step=0 for models without backward support
- benchmark: remove hardcoded batch_size≤4 assertion (H100 can be larger)
Cleanup:
- Remove debug eprintln from elementwise.rs
- GPU-native cat bounds check
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Tests that load full 163K-bar training datasets take 30+ min in CI.
Mark them #[ignore] — run via nightly cron or manual trigger.
Affected: gpu_residency(2), training_stability(2), performance(2),
feature_coverage(1), ppo_benchmark(1), cache(1)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Final 31 ml crate fixes: unsafe_code allows, unused vars prefixed,
boolean simplification, dead code removal, integer suffix, drop cleanup.
cargo fix auto-removed ~30 unused imports from ml crate.
Total clippy cleanup: 278 errors → 0 across all ML crates.
Full workspace: `cargo clippy --workspace --lib -- -D warnings` = 0 errors.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Removed #[ignore] from tests that have local infrastructure:
- 3 data_loader tests: auto-detect test_data/real/databento/ via workspace
- 3 memory_profiler tests: nvidia-smi at /usr/bin/nvidia-smi
- 4 benchmark tests (TFT, Mamba2, DQN, PPO): GPU + DBN data available
- 1 inference test: model loading (slow but should run)
- 3 DQN performance smoke tests: GPU available
PPO benchmark: fixed data_path to test_data/real/databento/6E.FUT
Sequential: added vars_mut() accessor
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator
Zero warnings, zero errors across entire workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cast input to weight dtype in DQN residual, rmsnorm, noisy_layers
- Set use_gpu=true in QNetworkConfig defaults and all config sites
- Resolve BF16 boundary mismatches in attention, curiosity, branching,
distributional_dueling across ml-dqn
- GPU-resident regime ops with BF16 boundary casts, eliminate .expect() in CUDA paths
- Eliminate all Device::Cpu fallbacks — GPU-only across 10 ML crates
- PPO: cast logits to F32 before softmax, cast batch tensors to training dtype
- Gradient collapse detection for RegimeConditionalDQN
- Wire halt_grad_collapse from CUDA guard kernel to halt training
- Dead neuron detection uses active network VarMap + squeeze factored readback
- Increment gradient_logging_step in GPU PER path
- Gradient collapse warmup guards use original buffer_size
- Cap training steps per epoch + tracing migration
- Replace Tensor::all() with sum_all() for pinned Candle compatibility
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Forward-port from worktree-gpu-hotpath-audit (2 commits):
1. Zero-roundtrip DQN experience collection via cuMemcpyDtoDAsync:
- GpuExperienceCollector uses device-to-device copies for state tensors
- Shared memory weight caching in GPU replay buffer
- NaN priority clamping on GPU (no CPU readback)
2. Eliminate all GPU→CPU roundtrips from training loop:
- GpuTrainResult: loss + grad_norm stay as GPU scalar tensors
- Single to_scalar() readback at epoch boundary (not per step)
- GPU-resident loss accumulation across training steps
- RegimeConditional head selection via GPU tensor ops
- Removed per-step NaN diagnostic checks (now epoch-level)
Also removes dead `states_tensor` field from ComputeLossResult and
fixes redundant field name clippy warning.
Expected: ~15-20% training throughput improvement on H100 by
eliminating synchronous GPU→CPU transfers in the inner loop.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN: wire SAC-style entropy regularizer into TD loss, fix diversity
penalty normalization from ln(3) to ln(45), add epsilon floor (0.05)
for noisy nets, enable sigma scheduling (0.8→0.4 with 0.3 floor),
add UCB count-based exploration bonus, expand hyperopt search space
from 43D to 45D.
PPO: replace fabricated entropy metric (value_loss×0.5) with real
Shannon entropy from action distribution, fix EntropyRegularizer
normalization from ln(3) to ln(45), implement Monte Carlo entropy
estimate for flow policy (was returning zeros), fix hyperopt VRAM
bound from 3 to 45 actions.
Monitoring: add normalized action entropy and diversity Prometheus
gauges, add exploration diagnostics logging per epoch.
2503 ml tests pass, 277 common tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Hyperopt adapter and PPO benchmark still had num_actions: 3, which would
produce misconfigured PPO models when used with the 45-action FactoredAction
sampling path. Found by spec compliance review.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
sample_action(), act(), act_with_log_prob(), greedy_action() now return
FactoredAction instead of TradingAction. Fixes the architectural disconnect
where num_actions=45 output neurons were sampled through a 3-action bottleneck.
TrajectoryStep.action and TrajectoryBatch.actions now use FactoredAction.
Added FactoredAction::from_legacy() for backward compatibility in tests.
Updated all PPO consumers: trainers/ppo.rs, hyperopt/adapters/ppo.rs,
validation/ppo_adapter.rs, benchmark/ppo_benchmark.rs.
2487 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The real_ prefix was misleading — there is no fake data loader.
Mechanical rename across 18 source files, no logic changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN and PPO trainers now resolve hidden_dim_base from GPU VRAM when no
explicit override is given, so L4/L40S/H100 GPUs use proportionally
larger networks instead of being stuck at RTX 3050 Ti defaults (256).
- Add resolve_hidden_dim_base() tiered lookup (256/512/768/1024 by VRAM)
- Add network_param_count() for accurate model size estimation
- DQN: pre-compute hidden dims, use real param count for batch sizing
- PPO: add hidden_dim_base field, VRAM resolution in PpoTrainer::new()
- PPO hyperopt: raise hidden_dim_base ceiling from 2048 to 4096
- TFT hyperopt: expand hidden_sizes from [128,256,512] to 5 tiers
- Update stale estimates (DQN 50K→200K, PPO 100K→400K params)
- Fix pre-existing clippy lints in prefetch.rs and ppo.rs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove stale test reports, quick-start guides, benchmark analyses,
profiling reports, and tool artifacts from across the workspace.
Keeps only root README.md per crate/service.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire BF16/FP16 mixed precision end-to-end for DQN and PPO with auto-detection
from GPU name (Ampere+ → BF16, Volta/Turing → FP16). Add hidden_dim_base to
hyperopt and wire through training/eval binaries. Reduce GPU sync points: make
DQN NaN checks periodic (every 100 steps), replace PPO GAE GPU round-trip with
pure CPU implementation. Cache training data across hyperopt trials for all 10
models via Arc. Batch DQN experience storage (128x fewer lock acquisitions).
Correct VRAM constants and batch bounds for all 9 supervised model adapters.
28 files changed, +1207/-208 lines. 2418 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.
Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
