Commit Graph

5 Commits

Author SHA1 Message Date
jgrusewski
e068224830 refactor(dedup): consolidate GpuTensor/GpuLinear — ml-supervised→ml-core
Moved ml-supervised's duplicate GpuTensor (stream-carrying) + GpuLinear +
50 free functions to ml-core/cuda_autograd/stream_ops.rs as StreamTensor
and StreamLinear. ml-supervised/gpu_tensor.rs → 53 lines of re-exports.

Two tensor flavors now canonical in ml-core:
- GpuTensor: takes &Arc<CudaStream> per-call (autograd integration)
- StreamTensor: carries Arc<CudaStream> internally (self-contained ops)

Zero consumer import changes. Both crates compile clean.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 07:37:49 +01:00
jgrusewski
6d8ba0708c fix(ml-supervised): resolve all 104 compile errors — clean build
- mamba/mod.rs: ~90 errors fixed — _candle suffixed functions replaced,
  operator overloads→free functions, autograd→pseudo-gradients,
  checkpoint→JSON serialization
- gpu_tensor.rs: added gpu_eye, gpu_cat_dim0, gpu_stack_tensors
- TFT/SSD: unused imports cleaned, type mismatches fixed
- ml-core: GpuTensor algebra methods (17 new), cuda_compat.rs deleted,
  GpuVarStore::vars/all_vars/linear_xavier added

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 07:26:56 +01:00
jgrusewski
20319618b6 refactor(ml-supervised): eliminate candle — GpuTensor/GpuLinear throughout
Zero candle_core/candle_nn imports remain. All 13 source files migrated:

- TFT: GatedResidualNetwork, LSTMEncoder, TemporalAttention → GpuLinear
- Mamba2: SSM layers → GpuLinear + local GpuTensor ops, grad norm host-side
- Liquid CfC: Training rewritten with perturbation-based gradient estimation
- SSD layer: Constructor takes Arc<CudaStream> not VarBuilder
- gpu_tensor.rs: from_candle/to_candle bridge methods deleted

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-17 23:32:39 +01:00
jgrusewski
c326b7f654 refactor(cuda): migrate Candle references to native cudarc in ml crate
Replace deprecated cudarc memcpy_stod with clone_htod across all GPU
upload paths (14 call sites in trainers, cuda_pipeline, hyperopt).
Replace deprecated memcpy_dtov with clone_dtoh in ml-supervised
gpu_tensor.rs.

Bridge GpuTensor-migrated submodules (xLSTM, Liquid CfC, TFT GRN)
with Candle Tensor callers via from_candle_tensor/to_candle_tensor
conversion utilities at API boundaries. Fix CudaDevice->CudaContext
in ml-dqn distributional_dueling.rs.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-17 18:20:26 +01:00
jgrusewski
f92c9d5f79 feat(cuda): replace Candle with cudarc CudaSlice + cuBLAS in KAN and Diffusion models
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>
2026-03-17 14:58:44 +01:00