Native Go bindings for MLX-C gradient computation on Apple Silicon.
Foundation for LoRA training without Python.
- VJP (reverse-mode autodiff) for backward pass
- JVP (forward-mode autodiff) for directional derivatives
- ValueAndGrad for combined loss + gradient computation
- Checkpoint for memory-efficient gradient recomputation
- CrossEntropyLoss (numerically stable via LogSumExp)
- MSELoss, Log, SumAll, MeanAll, OnesLike helpers
- TakeAlongAxis and LogSumExp ops
- Fix closure callback null vector bug (affects compile.go too)
- Fix Float() returning 0 for float32 arrays
14 tests passing on Metal GPU.
Co-Authored-By: Virgil <virgil@lethean.io>
e9973aef3c