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>
After removing ensure_training_dtype(), forward methods that receive F32
inputs from tests/callers now fail with dtype mismatch against BF16 weights.
Add to_dtype(BF16) at forward entry of xLSTM (slstm, mlstm, block, network),
CfC cell, and diffusion time embedding. Fix quantization to accept BF16
tensors by casting to F32 before INT8 conversion. Update guard to catch
#[cfg(not(feature = "cuda"))] dead code. Bump DQN emergency_safe_defaults
replay_buffer_capacity from 1000 to 2048 (GPU PER floor = 1024).
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>
