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>
Root cause: shmem_max_in_dim only included trunk dims (state_dim,
shared_h1, shared_h2) but not head dims (value_h, adv_h). When
hidden_dim_base=32 made the trunk narrow while heads stayed at 128,
the BF16 weight tile for branch output (255×128=32640 BF16 elements)
overflowed the shared memory region (12288 BF16 elements). On H100
the overflow landed in unused-but-mapped hardware shmem (silent
corruption). On RTX 3050 (48KB physical shmem) it hit unmapped
memory → CUDA_ERROR_ILLEGAL_ADDRESS.
Changes:
- gpu_dqn_trainer.rs: shmem_max_in_dim includes value_h/adv_h
- Remove all #[ignore] from smoke tests (feature_coverage,
training_stability, gpu_residency)
- Smoke tests use real .dbn data from test_data/ (hard error if missing)
- Remove synthetic_data() fallback — no fake data in tests
- GPU-direct DtoD training path (train_step_gpu, FusedTrainScalars)
- GPU-native PER priority update kernel (zero CPU readback)
- IQN dual-head integration (gpu_iqn_head.rs)
- BF16 dtype fixes across 6 model adapters
- Hyperopt 30D→31D (iqn_lambda)
- portfolio_transformer: unconditional BF16 (remove dead CPU branches)
- liquid/adapter: all tests use Cuda(0) directly
- Fix pre-existing gpu_kernel_parity_test.rs (stale args)
- Fix pre-existing evaluate_baseline.rs (removed fields)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Move TFT, Mamba-2, Liquid, TGGN, TLOB, KAN, xLSTM, and Diffusion model
implementations to ml-supervised. Bridge files (UnifiedTrainable adapters,
Checkpointable impls) stay in ml. Delete AsyncDataLoader (replaced by
StreamingDbnLoader + simple .chunks() batching). Remove empty ml-infra
scaffold — the remaining ml modules are too tightly coupled for clean
extraction, so ml stays as the orchestration facade.
- ml-supervised: 234 tests, 0 failures
- ml: 1687 tests, 0 failures
- Workspace: 0 compilation errors
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>
After converting all VarBuilder sites from F32 to training_dtype (BF16 on
Ampere+ GPUs), input tensors from callers remain F32, causing dtype
mismatches in matmul/add/mul operations. This commit adds systematic
boundary casts across all 10 model architectures:
- Input boundary: ensure_training_dtype() at each model forward() entry
- Output boundary: to_dtype(F32) at each model forward() exit
- Internal intermediates: hidden state init, gradient extraction,
positional encodings, causal masks, SSM state matrices, B-spline
basis values all cast to match computation dtype
- Ensemble adapters: ensure_training_dtype after Tensor::from_vec
36 files, +293/-72 lines. 2640 tests pass, 0 failures, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace DType::F32 with training_dtype(&device) in VarBuilder::from_varmap
calls across TFT, Mamba2, Liquid/CfC, KAN, xLSTM, Diffusion, TGGN, TLOB.
61 sites changed across 25 files (production constructors + test helpers).
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>
- Add dimension validation in DQN, PPO, Mamba2, TGGN, TLOB, Liquid,
KAN, xLSTM, Diffusion constructors (fail-fast on zero-dim inputs
that would cause CUDA_ERROR_INVALID_VALUE at runtime)
- Add num_unknown_features > 0 guard to TFT (temporal input required)
- Fix 12 dead-code/unused warnings in test compilation
- Remove opt-level=3 and codegen-units=1 from target rustflags
(was forcing O3 + single-thread codegen on dev/test builds)
- Remove hardcoded jobs=16 cap (cargo now auto-detects CPU count)
- Switch linker to clang+lld (2-5x faster linking)
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>
