fxcache: OFI_DIM=20 (pub const), RECORD_F64_COUNT=66, 272 bytes/bar.
constructor: state_dim 74→86 (aligned 88) with OFI.
experience collector: ofi_dim detection 8→20.
All [f64; 8] → [f64; 20]. Existing 8 features preserved at 0-7.
New 12 features zero-padded until precompute is extended.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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>
Complete bf16 elimination across all crates (ml, ml-core, ml-dqn, ml-ppo,
ml-supervised). Zero half::bf16, __nv_bfloat16, or CudaSlice<half::bf16>
references remain (verified by grep).
CUDA: All 60+ .cu kernels and .cuh headers converted to native float.
- Half-precision intrinsics (__hmul, __hadd, __hdiv) → float operators
- atomicAddBF16 → native atomicAdd(float)
- bf16 wrapper functions → f32 identity passthroughs
Rust: All CudaSlice<half::bf16> → CudaSlice<f32> across 90+ files.
- htod_f32_to_bf16/dtoh_bf16_to_f32 → htod_f32/dtoh_f32 (direct, no conversion)
- Deleted bf16 mirror infrastructure (DuelingWeightSetBf16, alloc_bf16_mirror, etc.)
- Renamed params_bf16→params_flat, d_value_logits_bf16→d_value_logits, etc.
- Fixed .to_f32() sed damage on Decimal::to_f32() and rng.f32()
FxCache: Single f32 disk format (was bf16/f64 dual-version).
- Deleted --bf16 CLI flag from precompute_features
- PVC cache files need regeneration via precompute_features
TF32 tensor cores activated via cublasLtMatmul CUBLAS_COMPUTE_32F — no
explicit TF32 types needed. Storage is pure f32 everywhere.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
CUDA kernels: 36 of 37 compiled with __nv_bfloat16* (dt_kernels.cu remaining)
Rust types: ALL CudaSlice<f32> → CudaSlice<half::bf16> across ml + ml-core
Build: all kernels now compiled with common_device_functions.cuh (BF16 helpers)
BF16 math wrappers: bf16_sqrt, bf16_log, bf16_exp, bf16_pow, bf16_fabs, bf16_fmax,
bf16_fmin, bf16_cos, bf16_zero, bf16_one, bf16(), atomicAddBF16
NOT YET COMPILING — ml-core boundary errors (Vec<f32> → Vec<half::bf16>)
and dt_kernels.cu float*__nv_bfloat16 ambiguity remain.
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>
PpoExperienceBatch: all 6 fields Vec<T>→CudaSlice<T>. Data stays on GPU
from kernel collection through training. Zero DtoH for batch data.
- collect_experiences(): returns CudaSlice via D2D copy, no memcpy_dtoh
- gpu_batch_to_trajectory_batch(): DELETED (was CPU conversion)
- PPO::update_gpu(): reads states via D2D mini-batch, forward on GPU
- compute_metrics_from_gpu(): downloads only scalars (returns/advantages/
actions), NOT the 123 MB state tensor
- download_*() methods for debug/checkpoint only
States (123 MB/epoch) never leave GPU. Only 2 scalar losses come to CPU.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace deprecated cudarc memcpy_stod with clone_htod across all GPU
upload paths (14 call sites in trainers, cuda_pipeline, hyperopt).
Replace deprecated memcpy_dtov with clone_dtoh in ml-supervised
gpu_tensor.rs.
Bridge GpuTensor-migrated submodules (xLSTM, Liquid CfC, TFT GRN)
with Candle Tensor callers via from_candle_tensor/to_candle_tensor
conversion utilities at API boundaries. Fix CudaDevice->CudaContext
in ml-dqn distributional_dueling.rs.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eliminate all 26 Candle Tensor references from signal_adapter.rs.
Three functions (ppo_to_exposure_scores, signal_to_action_scores,
tft_quantile_to_signal) now take CudaSlice<f32> + CudaStream and
return CudaSlice<f32>, backed by three fused CUDA kernels in
signal_adapter_kernel.cu. Dead code evaluate_supervised_gpu_backtest
removed (zero callers). Added cuda_f32_to_tensor helper to
gpu_action_selector for DtoD copy back to Candle Tensor at API
boundaries (PPO hyperopt adapter, evaluate_baseline example).
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>
Delete every CPU fallback block across 14 files (-286 lines):
- dqn.rs: CPU replay buffer, tensor construction, PER fallback, gradient paths
- hyperopt/adapters/ppo.rs: CPU curiosity modules, trajectory generation
- hyperopt/adapters/dqn.rs: CPU backtest fallback, sync no-op
- trainers/ppo.rs: CPU training error stub
- l2_cache.rs: CPU stub functions (gate callers behind cuda too)
- replay_buffer_type.rs: CPU is_gpu_prioritized fallback
- validation/harness.rs: CPU regime breakdown fallback
- build.rs: cpu_only_build cfg (never referenced)
- evaluate_baseline.rs: CPU gpu_handled fallback
- testing/integration/gpu: CPU test stubs
Zero #[cfg(not(feature = "cuda"))] remains in the codebase.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace closure-based evaluate() with evaluate_dqn_graphed() for non-OFI
walk-forward backtest path. Extracts DuelingWeightSet from VarMap (branching
or standard dueling) and runs hand-written warp-cooperative CUDA forward
kernel with CUDA Graph capture — zero Candle dispatch overhead per step.
Key changes:
- GpuBacktestEvaluator::stream() getter for weight extraction on eval stream
- DQNAgentType::is_using_branching() / network_dims() for CUDA kernel config
- Hyperopt evaluate_gpu() non-OFI path: extract_dueling_weights_branching()
→ evaluate_dqn_graphed() (CUDA Graph accelerated)
- OFI path: retains Candle closure for state permutation (gather kernel
layout mismatch — future CUDA permutation kernel)
- 66+ GPU hot-path violations hardened to hard errors across DQN/PPO/supervised
- Stripped all gpu-ok suppression comments
- Proper #[cfg(feature = "cuda")] gating for CUDA-only code paths
77 files, 0 errors, 0 warnings across workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eliminate all CPU fallback paths in the PPO trainer, PPO hyperopt
adapter, and DQN hyperopt adapter. On H100, every GPU operation must
succeed or abort — silent CPU fallback runs at 1/10th throughput and
produces stale/inconsistent results.
PPO trainer (5 sites):
- GPU unavailable → hard error (was warn + CPU fallback)
- Collector init, episode reset, collection, weight sync → hard errors
- Test updated: GPU batch limit test expects error without CUDA
PPO hyperopt adapter (8 sites):
- ensure_gpu_data() now returns Result<(), MLError>
- Features/targets upload, collector init, weight sync → hard errors
- gpu_collect_trajectories() now returns Result<TrajectoryBatch>
- Experience collection caller uses ? instead of Option fallback
- GPU backtest failure → hard error
DQN hyperopt adapter (3 sites):
- CUDA synchronize between trials → hard error
- GPU backtest None on CUDA → hard error (upstream of extract_objective)
- extract_objective: panic! on CUDA build if backtest_metrics is None
- CPU backtest path guarded by #[cfg(not(feature = "cuda"))]
continuous_ppo.rs (1 site):
- clip_grads passed &Device::Cpu instead of actor's actual device
- Forces CPU roundtrip for gradient norm computation on every mini-batch
- Fixed: passes `device` (from self.actor.device()) — stays GPU-resident
gpu_experience_collector.rs (1 site):
- cuCtxSetLimit(STACK_SIZE) failure: warn → hard error
- 16KB stack is required — kernel segfaults without it
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Restore 45-action factored space via Branching DQN (Tavakoli 2018),
outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5
exposure-only actions during debugging and was never intended as permanent.
- Enable use_branching: true by default in DQNConfig and DQNHyperparameters
- Add branching paths to select_action_with_confidence and select_action_inference
- Update agent.rs select_action_factored for branching-aware selection
- Expand CountBonus to per-branch tracking with bonuses_branched()
- Add order_type + urgency distribution tracking in monitoring
- Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header
- Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs
- Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers)
- Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety)
- Delete unused flash_attention submodules (block_sparse, causal_masking, etc.)
- Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements
Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire GpuBacktestEvaluator into PPO hyperopt trials so the optimizer
ranks candidates by walk-forward Sharpe ratio instead of raw episode
reward. The PPO actor's 45-action softmax probabilities are collapsed
to 5 exposure scores via ppo_to_exposure_scores before the backtest
loop. When CUDA is unavailable or the backtest fails, the adapter
falls back to the original -avg_episode_reward objective.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand FeatureVector from 40 to 42 dimensions by including ADX(14) at
index 40 and CUSUM direction at index 41 from the existing CPU feature
extraction pipeline. This eliminates proxy-based regime classification
and enables GPU-native regime detection via tensor narrow/comparison ops.
Key changes:
- extraction.rs: wire RegimeADXFeatures + RegimeCUSUMFeatures into
extract_current_features_v2(), output 42 features per bar
- regime_conditional.rs: classify_regime_masks_gpu() creates per-regime
mask tensors entirely on GPU (ADX > 0.25 = trending, |CUSUM| > 0.7 =
volatile, else ranging). Zero CPU roundtrip in training hot path.
- trainer.rs/config.rs: state_dim 43→45 (no OFI), 51→53 (with OFI),
aligned dims unchanged (48/56). GPU batch insertion for all 3 heads.
- CUDA header: MARKET_DIM 40→42
- walk_forward.rs: FEATURE_DIM 40→42
- 42 files updated, all [f64;40]→[f64;42] propagated across workspace
Test results: ml=874/0, ml-dqn=354/0, ml-features=282/0, ml-core=274/0
Real data GPU smoke tests: 7/7 passed (OHLCV + OFI + trade enrichment)
Hyperopt baseline RL: 2 trials completed on local RTX 3050 Ti
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move all inline type definitions from ml/src/lib.rs to ml-core:
MLError, MLResult, Trade, MarketRegime, HealthStatus, Features,
MLModel trait, ModelRegistry, ParallelExecutor, LatencyOptimizer,
TrainingMetrics, ValidationMetrics, InferenceResult, ModelMetadata.
Dedup: consolidate ConfigError{reason}/ConfigurationError(msg) into
single ConfigError(String) tuple variant (was 2 variants, 174 refs).
Cleanup: convert create_hft_* free functions to associated methods
(HFTPerformanceProfile::ultra_low_latency(), ParallelExecutor::hft()).
ml facade re-exports via `pub use ml_core::*`.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The OFI calculator's compute_ofi_from_file() never called feed_trade(),
leaving 3/8 features (VPIN, Kyle's Lambda, trade_imbalance) at zero in
production. Added compute_ofi_with_trades() that interleaves trades by
timestamp into the MBP-10 streaming callback. Updated DQN and PPO
hyperopt adapters to derive trades_dir as sibling of dbn_data_dir.
Smoke test validates: without trades VPIN=0/5000, with trades VPIN=5000/5000.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move BF16 tensor core alignment from per-forward-pass allocation to
data pipeline boundaries. On H100, state_dim 43→48 and 51→56 (8-aligned)
so cuBLAS dispatches HMMA instructions instead of falling back to scalar FMA.
Architecture:
- Trainer computes aligned state_dim at source (align_dim_for_tensor_cores)
- GPU path: DqnGpuData.pad_state_tensor() pads once at upload boundary
- CPU path: train_batch() fold zero-pads Experience.state vectors
- Networks receive pre-aligned tensors — zero per-step overhead
All state_dim defaults updated to aligned values (43→48, 51→56).
Removed pad_to_aligned() from all network forward() methods.
2758 tests pass, 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both adapters now call load_ofi_features_parallel from mbp10_loader
instead of duplicating the rayon + streaming logic inline.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes:
- Streaming MBP-10 parser (parse_mbp10_streaming): computes OFI inline
during decode — eliminates Vec<Mbp10Snapshot> allocation (41.9M clones)
- Parallel file processing: rayon par_iter across 9 MBP-10 files
(778s sequential → ~90s expected on H100 24-core)
- Fix hardcoded state_dim=54 in walk-forward backtest tensor creation
that caused panic "range end index 55296 out of range for slice of
length 52224" — now uses dynamic state_dim (43 or 51 with OFI)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add device_pool to DQN/PPO hyperopt trainers for round-robin GPU assignment
per trial. Binary detects all CUDA devices, scales VRAM budget by GPU count.
Single-GPU: no behavior change (pool of 1).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Change PercentileScaler, RewardNormalizer, CompositeReward, and
PPORewardShaper public APIs from f64 to f32 — matching what callers
actually pass. Internal EMA/percentile math stays f64 for precision.
Keep data_loader SMA/EMA/MACD accumulators in f64 throughout (was
f64→f32→f64 per step), cast to f32 only at output boundary.
Remove dead _transition computation in rainbow_agent_impl and unused
bigdecimal deps from broker_gateway_service and trading_service.
2704 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN: disable all exploration for production (warmup=0, epsilon=0,
noisy_nets=false, count_bonus=false), read feature_count from
checkpoint metadata instead of hardcoding 54, add loaded guard
and NaN check on predict output.
PPO: fix confidence formula — use act_with_log_prob() instead of
act() which returns value_estimate not log_prob, add loaded guard
and NaN check, remove WorkingPPO alias.
Liquid: add loaded flag for is_ready()/predict() guards.
Remove EgoboxOptimizer/EgoboxOptimizerBuilder backward-compat
aliases — replaced with canonical ArgminOptimizer everywhere.
323 trading_service tests, 200 hyperopt tests, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Integrate three standalone PPO modules into the hyperopt training loop:
- PPORewardShaper: hold penalty + rolling Sharpe + diversity bonus per step
- CompositeReward: risk-adjusted reward (downside dev + differential return)
- TrajectoryReplayBuffer: ExO-PPO M=4 rollouts with IS-weighted replay
Also fixes GAE hardcoded gamma=0.99/lambda=0.95 — now uses hyperopt params.
2726 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Integrate all PPO improvement modules into the core training paths:
- Symlog value predictions in compute_value_loss (MLP + LSTM)
- Adaptive entropy auto-tuning replaces fixed entropy_coeff
- Percentile P5/P95 advantage scaling for heavy-tailed returns
- DAPO clip_epsilon_high wired in all 7 PPOConfig construction sites
- Shape mismatch fix in adaptive_entropy (unsqueeze scalar)
2726 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split model overhead into two constants: MODEL_OVERHEAD_MB (pure
model weights for batch-size capping) and TRIAL_VRAM_MB (total
per-trial VRAM for concurrent hyperopt planning). DQN trials
empirically consume ~7 GB each on L40S (model + GPU replay buffer +
experience collector + CUDA allocations + fragmentation), not the
200 MB previously estimated. This caused plan_hyperopt to compute
128 concurrent trials instead of the actual 5, inflating PSO
particles from 20→128 and total trials from 20→384 via .max()
instead of .min(), guaranteeing a 4h timeout kill.
Fix auto-scaling to: (1) match particles to GPU concurrency for
maximum hardware utilization on any node, (2) cap particles at
max_trials to never inflate the trial budget, (3) never auto-inflate
the total trial count.
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>
- DQN PER: defer td_errors to_vec1() after loss.to_scalar() — piggyback on
existing pipeline flush instead of forcing premature GPU→CPU stall
- PPO trajectories: capacity-hint Vec allocations, extend_flat_states methods,
states_flat field on TrajectoryBatch for zero-copy GPU upload
- TGGN validate(): batch N per-sample losses on GPU → single to_scalar() sync
(was N GPU→CPU syncs)
- Liquid backward(): batch grad-norm per-param sqr().sum_all() on GPU → single
to_scalar() sync (was N GPU→CPU syncs per optimizer step)
- Liquid validate(): same N→1 GPU sync reduction as TGGN
- DQN trainer: restore EpochPrefetcher/DoubleBufferedLoader API (wrongly deleted)
- train_baseline_rl: wire DoubleBuffer GPU pre-upload — after CPU prefetch
completes, immediately upload next fold to GPU via DqnGpuData::upload() so
next fold starts with data already resident on GPU
2478 tests pass, 0 clippy warnings, 0 compile errors.
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>
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>
