Eliminate candle-core/candle-nn from the KAN and Diffusion model forward
paths in ml-supervised. All dense layers now use cuBLAS sgemm via the new
gpu_tensor module. Element-wise ops (SiLU, sigmoid, tanh, exp) use host
roundtrips for now; fused CUDA kernels are a follow-up.
Changes:
- Add gpu_tensor.rs: GpuTensor (CudaSlice<f32> + shape), GpuLinear
(cuBLAS sgemm), and ~30 element-wise GPU ops
- Rewrite kan/{spline,layer,network}.rs to use GpuTensor instead of
candle_core::Tensor and candle_nn::{VarBuilder,Linear}
- Rewrite diffusion/{denoiser,noise,sampler}.rs to use GpuTensor and
GpuLinear instead of candle_nn::Linear
- Update ml crate trainable adapters (kan/trainable.rs,
diffusion/trainable.rs) to bridge Candle<->GpuTensor at the
UnifiedTrainable interface boundary
- Update ensemble inference adapters for both models
- Add cudarc 0.17 with cublas feature to ml-supervised Cargo.toml
- Candle deps retained in ml-supervised for unconverted models (TFT,
Liquid, Mamba, xLSTM) -- will be removed once all 8 models are
converted
Net: -424 lines, 509 -> ~453 Candle refs remaining (KAN: 0, Diffusion: 0)
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>
The ml crate fix alone wasn't enough — ml-core, ml-dqn, ml-ppo,
ml-supervised, ml-ensemble, ml-explainability, ml-hyperopt, and
ml-labeling all had `default = ["cuda"]`, each independently pulling
in cudarc via candle-core/cuda.
Now `default = []` on all sub-crates. CUDA activates only when the
compile-and-train template passes `--features ml/cuda`, which
propagates through ml's cuda feature gate to all sub-crates.
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>
ml-supervised, ml-ensemble, ml-labeling, ml-explainability, ml-hyperopt
all had default = ["cuda"] which pulled cudarc into the CPU services
build, causing compile-services to fail with "nvcc not found".
Changed all to default = [] and wired ml/Cargo.toml cuda feature to
propagate to all 8 sub-crates (was only 3: ml-core, ml-dqn, ml-ppo).
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>
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>
