Zero-valued PyTorch loss can run code in the backward pass
IEEE 754 rounding and autodiff disagree, so a loss logged as 0.0 can still set gradients that rewrite weights.
IEEE 754 rounding and autodiff disagree, so a loss logged as 0.0 can still set gradients that rewrite weights.
TPU support also landed in vLLM, SGLang, TorchTitan, Ray, and Hugging Face libraries, with two pieces still in preview.
Amazon released TorchNeuron a day earlier to port PyTorch apps to Trainium without rewriting them.
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The title names Metal Performance Shaders support in PyTorch 2.15; the post body was not available.
Amazon says one configuration line moves PyTorch apps to Trainium and it will upstream changes to loosen Nvidia ties in PyTorch.
doubleAI says Maxwell proves each rewrite equivalent to the original and claims large speedups on GPUs and CPUs.
A paper shows neural nets can cryptographically hide arbitrary capabilities that elicitation audits cannot reliably surface.
A tutorial shows AugLy augmentations and small robustness tests for image, text, and audio in PyTorch.
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