GPU experience collector:
- Add GpuHardwareInfo with SM count detection from device name lookup
(H100=132, A100=108, L40S=142, RTX 4090=128, etc.)
- optimal_n_episodes() scales to GPU: sm_count × 2 warps × 32 threads,
capped by 15% free VRAM budget, 256-aligned for block scheduling
- Remove hardcoded .min(256) cap in trainer — auto-scales from 128 to 8192
- MAX_EPISODES_LIMIT raised from 256 to 4096
Numerical stability:
- BF16 cross-entropy epsilon: 1e-8 → 1e-4 (below BF16 precision floor
1e-8 rounds to zero, making log-stabilization a no-op → -inf → NaN)
- Add log_probs.clamp(-20, 0) guard against -inf × 0 = NaN in loss
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
clip_grad_norm now returns a GPU-resident Tensor instead of (f64, f64),
keeping backward → clip → optimizer.step fully pipelined on GPU with
zero cuStreamSynchronize stalls. The unconditional multiply trick
(scale = min(max_norm/(norm+eps), 1.0)) avoids the conditional branch
that previously required reading the norm to CPU.
Key changes:
- gradient_utils::clip_grad_norm: return Tensor, unconditional GPU multiply
- Handle BF16 mixed-precision via per-gradient to_dtype cast
- adam.rs: backward_step_with_monitoring returns Tensor (zero sync)
- gradient_accumulation: delegate to gradient_utils (DRY, same GPU path)
- dqn.rs: single sync boundary after ALL GPU work queued
1746 tests passing (ml-core 286, ml-dqn 388, ml-ppo 198, ml 874).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
clip_grad_norm: accumulate squared norms on GPU-resident scalar, single
to_scalar() at end (was 16-20 per-param syncs per step × 2917 steps/epoch).
check_gradients_finite: same GPU-accumulation pattern, single sync.
gpu_replay_buffer sample_proportional/rank_based: generate random targets
via rand(0,1)*total_sum on GPU, normalize weights via broadcast_div
(eliminates 2 to_vec0 syncs per sample call).
gpu_replay_buffer update_priorities_gpu: replace CPU loop of 50 individual
slice_scatter calls with single batched index_add delta trick.
dqn NaN detection (every 500 steps): accumulate 3 NaN counts on GPU,
single to_scalar for total; detailed breakdown only if NaN found.
dqn dead neuron detection (every 1000 steps): accumulate dead count on
GPU-resident scalar (was to_vec0 per parameter tensor, ~16-20 syncs).
Net: ~22 GPU→CPU syncs + 50 micro-kernels per training step → 2 syncs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bring Branching Dueling Q-Network (Tavakoli 2018) to full Rainbow parity
with the existing GPU hotpath. 3 independent advantage heads (exposure=5,
order=3, urgency=3) decompose the 45-action space into learnable branches.
H1 - CUDA fallback: gate GpuExperienceCollector when use_branching=true
(fused kernel hardcodes NUM_ACTIONS=5, incompatible with 45 factored)
H2 - Per-branch C51 distributional: each branch outputs [batch, n_d, atoms]
log-softmax, loss = avg of D cross-entropies vs projected Bellman target
M1 - NoisyNet: MaybeNoisyLinear enum in branch heads, reset_noise/disable_noise
wired through select_action, compute_loss, and set_eval_mode
M2 - Regime-conditional IS weights: Trending=1.2, Ranging=0.8, Volatile=0.6
applied to branching loss via ADX/CUSUM features at state[40:41]
M3 - State dim alignment: align_dim_for_tensor_cores() in from_dqn_params()
for H100 HMMA dispatch (8-byte alignment)
L1 - Fill simulator: splitmix64 replaces golden ratio hash (chi-squared tested)
L2 - Hyperopt 29D: branch_hidden_dim [64,256] added to PSO search space
Config plumbing: branch_hidden_dim, v_min/v_max/num_atoms, use_distributional,
use_noisy, noisy_sigma_init all flow from DQNConfig → BranchingConfig.
10 files, +3207/-125 lines, 33 branching tests + 387 ml-dqn + 284 ml-core pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
training_dtype() now returns BF16 on all CUDA devices, enabling full
tensor-core utilization. F32 is used only at boundaries (scalar
extraction, loss computation, softmax). VRAM estimator updated to
account for BF16 byte sizes, C51/QR atoms, dueling streams, NoisyNet
param doubling, and GPU PER buffer pre-allocation.
Changes:
- mixed_precision.rs: training_dtype() returns BF16 on CUDA
- curiosity.rs: F32 cast before scalar extraction
- network.rs: F32 output at NetworkLayers forward boundary
- traits.rs: estimate_trial_vram_mb_full() with BF16-aware sizing
- dqn.rs adapter: uses full estimator with worst-case architecture
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Four monitoring functions (estimate_avg_q_value_with_early_stopping,
collect_qvalue_statistics, compute_q_gap_for_epoch, compute_per_action_q_values)
iterated over batch_sample.experiences to build CPU tensors for forward passes.
When GPU PER (GpuPrioritized) is active, experiences is always vec![] — all data
lives on GPU tensors in gpu_batch. This created zero-element tensors with non-zero
shapes, triggering CUBLAS_STATUS_INVALID_VALUE on the next forward pass.
Fix: all four functions now check for gpu_batch.states and use it directly,
falling back to CPU experiences only for non-GPU buffers. Also removes debug
eprintln probes, wires new_on_device for DQN/RegimeConditionalDQN construction,
and adds GPU-aware smoke tests (33 pass, 874 total, 0 failures).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eliminates the CPU roundtrip in PER binary search: previously the
cumsum tensor (~400KB for 100K buffer) was downloaded to CPU, searched
in a loop, then indices uploaded back. Now a CUDA kernel runs one
thread per target with O(log n) binary search — zero DMA.
- Add searchsorted_kernel.cu (40-line CUDA binary search kernel)
- Implement SearchSorted as Candle CustomOp2 with CPU fallback
- Wire into sample_proportional() and sample_rank_based()
- Fix all clippy warnings in gpu_replay_buffer.rs (const fn, shadow,
doc backticks, to_owned, div_ceil, module_name_repetitions)
- Fix wildcard_enum_match_arm in mixed_precision.rs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace expensive CPU↔GPU data transfers with GPU-native operations:
- check_gradients_finite: sum_all() scalar readback (4B per tensor)
replaces flatten_all()+to_vec1() that copied entire gradients to CPU
(up to 200MB per step across 9 call sites in DQN+PPO)
- Huber loss: affine() replaces 3× Tensor::from_vec(vec![const; N])
CPU Vec allocations in the inner loss computation loop
- update_priorities_gpu: per-index slice_scatter (~1KB DMA) replaces
full-buffer to_vec1()+from_vec() roundtrip (1.2MB DMA per step)
- PER sampling: single from_vec + GPU to_dtype replaces duplicate
from_vec calls and CPU type conversion roundtrips
- RegimeConditional: log warning on silent GPU batch drops
822 tests pass (350 ml-dqn, 274 ml-core, 198 ml-ppo), 0 clippy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes for GPU PER hot path:
1. IQN quantile loss used empty CPU weights Vec instead of GPU-resident
weights_tensor_cached — caused CUDA_ERROR_ILLEGAL_ADDRESS from
uninitialized GPU memory. Now uses cached GPU tensor matching C51
and standard DQN paths.
2. GpuPrioritized add()/add_batch() replaced with StagedGpuBuffer:
add() stages on CPU (Vec::push, zero GPU ops), sample() batch-flushes
staging→GPU in one DMA before sampling. Production path (insert_batch_tensors)
bypasses staging entirely — GPU→GPU with zero CPU.
3. All 9 ML sub-crates default to cuda feature so `cargo test -p ml-dqn`
exercises GPU code paths on CUDA workstations. CI service crates use
default-features=false, unaffected.
Test results: 350 passed (was 343+7 failed), 0 failed, 1 ignored.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove cuda from default features in ml-core, ml-dqn, ml-ppo, ml
- Propagate cuda feature from ml → ml-core/ml-dqn/ml-ppo
- CI compile-training already uses --features ml/cuda explicitly
- Fix MaxDD log format: {:.1}% → {:.3}% (was rounding 0.033% to 0.0%)
- Suppress unused_labels/unused_variables warnings for cfg(cuda) code
- Add CALLBACK_ENDPOINT env to ml-training-service deployment
- Fix Grafana active_workers query to use sum() with fallback
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move UnifiedTrainable trait, TrainingMetrics, CheckpointMetadata, and
checkpoint helpers from ml to ml-core (zero new dependencies — ml-core
already had candle-core + serde_json)
- Wrap Mamba2SSM in Mamba2TrainableAdapter to satisfy orphan rule (trait
in ml-core, type in ml-supervised — all other 9 models already used
wrapper pattern)
- Make Mamba2SSM::validate() pub for cross-crate adapter access
- Delete 5 permanently disabled deployment modules (cfg(any()) — never
compiled): registry, hot_swap, validation, monitoring, endpoints
(-5,842 lines)
- Delete 2 undeclared dead files in training/: dqn_trainer.rs,
transformer_trainer.rs (-138 lines)
- Fix pre-existing compute_loss test shape mismatch in mamba adapter
UnifiedTrainable in ml-core unblocks future trainers/ extraction (17.8K
lines) since model-specific trainers can now depend on ml-core for the
trait without pulling in the full ml monolith.
14 files changed, +128 -6,497 (net -6,369 lines)
Tests: 274 ml-core + 948 ml = 1,222 passed, 0 failed
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move Sharpe ratio metrics and SIMD performance module to ml-core.
These only depend on MLError which is already in ml-core.
Add approx = "0.5" to ml-core dev-dependencies for Sharpe tests.
Skip observability/ (needs prometheus dep — stays in ml).
Test counts: 271 ml-core + 2487 ml = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 6 shared modules (~2.3K lines) from ml to ml-core:
- trading_action.rs (TradingAction enum)
- action_space.rs (action masking, re-exports)
- xavier_init.rs (Xavier/Glorot weight initialization)
- mixed_precision.rs (AMP utilities, FP16/BF16)
- order_router.rs (deterministic order routing)
- portfolio_tracker.rs (portfolio state tracking)
With TradingAction now in ml-core alongside FactoredAction, the
FactoredActionExt extension trait is eliminated entirely —
from_trading_action()/to_trading_action() become inherent methods
on FactoredAction. This removes the need for `use FactoredActionExt`
imports in reward.rs, gae.rs, and trajectories.rs.
Test counts: 250 ml-core + 2508 ml = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move memory_optimization/ (4.6K lines, 7 files) from ml to ml-core
- Move batch_size_resolver.rs to ml-core (now both deps in same crate)
- Add tempfile to ml-core dev-dependencies (qat tests)
- Re-export both modules from ml facade
- Keep checkpoint/ in ml (model_implementations.rs has model-specific deps)
185 ml-core tests + 2573 ml tests = 2758 total, 0 failures
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 optimizers, gradient_accumulation, gradient_utils, cuda_compat,
tensor_ops, and gpu (device config, capabilities, memory profiling) to
ml-core. These are shared compute primitives used by all models.
Also commit module files for core types (common, config, error, model,
traits, types) that were moved from ml to ml-core in task 5a but left
staged without being committed.
Notable changes:
- resolve_batch_size() stays in ml (new batch_size_resolver module)
because it depends on memory_optimization::auto_batch_size which
has not yet moved to ml-core
- FactoredAction legacy bridge converted from inherent impl to
extension trait (FactoredActionLegacy) since FactoredAction is now
defined in ml-core, not ml
- candle-optimisers added to ml-core dependencies (needed by Adam)
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>
Add 5 empty sub-crates (ml-core, ml-dqn, ml-ppo, ml-supervised, ml-infra)
to workspace. Modules will be moved from monolithic ml crate in subsequent
tasks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
