PyTorch integrates FlyDSL backend on AMD GPUs
The official PyTorch blog introduces that users of AMD MI350 series GPUs can now use FlyDSL through existing operator APIs, covering dense and grouped GEMM, MXFP8/MXFP4 scaled GEMM, RMSNorm, TopK, and attention kernels provided via the AOTriton 0.14b series, without calling custom kernels. On MI355X, BF16 dense GEMM achieves a 1.10x geometric mean speedup over the faster baseline among ATen and Triton, MXFP8 and MXFP4 achieve 1.58x and 1.68x over ATen respectively, and grouped GEMM achieves 1.20x over Triton. In vLLM end-to-end A/B tests, whole-request speedups are BF16 1.13x, MXFP8 2.04x, and MXFP4 3.01x, depending on the model and concurrency.

#Product updates#English text is being prepared.#Open-source ecosystem


