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>
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>
Cast all Tensor::full() / Tensor::from_vec() call sites to training_dtype
instead of defaulting to F32. Fixes dtype mismatch errors (BF16 vs F32)
in PPO training on CUDA:
- tensor_ops: scalar_mul, clamp, normalize match operand dtype
- trajectories: TrajectoryBatch/MiniBatch to_tensors cast to training dtype
- continuous_ppo: ContinuousTrajectoryBatch/MiniBatch to_tensors cast
- adaptive_entropy: cast entropy to F32 for alpha multiplication boundary
- continuous_policy: forward() input cast, Tensor::full scalars match dtype
- flow_policy: sample_base_noise cast to training dtype
- hidden_state_manager: reset tensors use training_dtype
- ensemble/ppo adapter: predict input cast to training dtype
- trainable_adapter: test uses training_dtype instead of hardcoded F32
Verified: 198/198 ml-ppo tests pass, 63/63 ml PPO tests pass.
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>
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>
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>
