Apple Silicon

On Apple Silicon (MPS backend, PyTorch <2.4), `addcmul_` and `addcdiv_` GPU kernel operations silently fail when writing to non-contiguous output tensors. This caused optimizer state to not update encoder weights, producing a loss plateau that was indistinguishable from a hyperparameter issue and took days to diagnose.

Multiple hardware-specific issues affect PyTorch across different backends: LayerNorm/BatchNorm fail to compile on Apple M4 MPS, Conv2d is slower on macOS without MKLDNN, CUDA CI tests exhibit memory corruption (SIGIOT), and ONNX exports with dynamic inputs regressed between versions. These issues require constant per-platform debugging.