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>
- 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>
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>
