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arXiv Machine Learning By Junichiro Niimi

Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend

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Apple Silicon machines with large unified memory allow large tensors on a desktop GPU, but PyTorch’s Metal Performance Shaders (MPS) backend silently returns incorrect results for batched matrix multiplication when the output exceeds $2^{32}$ elements. The authors swept over dtypes, memory layouts, shapes, and batch sizes around $2^{31}$ and $2^{32}$ elements, finding that errors arise when operands are transposed views or when contiguous inputs exceed $2^{32}$ elements, with the backward pass also affected. They reproduced the issue on multiple machines and macOS versions, confirmed correct behavior on an NVIDIA A100, and demonstrated real‑world impact in a sentiment classifier, releasing a harness and guard to prevent such silent failures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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