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>
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>
- 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>
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 safety/ module (6.5K lines) from ml to ml-core (self-contained)
- Keep security/ in ml (depends on ensemble::EnsembleDecision)
- Move From<ProductionTrainingError> for MLSafetyError to ml (cross-crate)
- Rename Real-prefixed inference types to proper names:
RealInferenceError → InferenceError
RealInferenceConfig → InferenceConfig
RealPredictionResult → InferencePrediction
RealNeuralNetwork → NeuralNetwork
RealMLInferenceEngine → MLInferenceEngine
- Re-export safety from ml facade for backward compatibility
129 ml-core tests + 2629 ml tests = 2758 total, 0 failures
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>
Replace per-param gradient norm extraction loop with batched
Tensor::stack pattern. Single GPU→CPU sync instead of one per
parameter tensor.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-batch loss.to_vec0() in TFT training/validation loops
with GPU tensor accumulation. Single extraction per epoch + NaN guard
every 100 batches.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The validate() method called loss.to_scalar::<f64>() on an F32 tensor,
causing "unexpected dtype, expected: F64, got: F32" at runtime.
Add .to_dtype(F64) before scalar extraction, matching the pattern
used in all other TFT gradient/loss code paths.
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>
CUDA cannot handle linear layers with zero input dimensions.
When num_static_features=0 or num_known_features=0, the
VariableSelectionNetwork constructor creates linear(0, 0, ...)
which triggers CUDA_ERROR_INVALID_VALUE on GPU.
- Make static/future VSN and GRN encoder fields Option<T>
- Skip layer creation when feature count is 0
- Forward pass skips absent feature paths gracefully
- Trainable adapter creates empty placeholder tensors
- Add cilium CNI toleration to training job template and runner
All 103 TFT 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>
