PyTorch 2.13 shipped July 8 with changes worth acting on before your next training run. FlexAttention now has native Metal support on Apple Silicon — up to 12x faster than SDPA on sparse patterns. A new fused loss function cuts peak GPU memory 4x for large-vocabulary LLM training. And two APIs got renamed, with one feature set hard-removed. Here is what to check before upgrading. FlexAttention on Apple Silicon: Finally a Reason to Train on MPS If you have been running inference on an M-series Mac but sending training jobs to a cloud GPU, PyTorch 2.13 gives you a reason […]
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