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There's hope in intermediate representations, in OpenXLA: https://opensource.googleblog.com/2023/03/openxla-is-ready-to-accelerate-and-simplify-ml-development.
by BenoitP 4y ago
There's hope in intermediate representations, in OpenXLA:
https://opensource.googleblog.com/2023/03/openxla-is-ready-to-accelerate-and-simplify-ml-development.html?m=1 https://opensource.googleblog.com/2023/03/openxla-is-ready-t...
> OpenXLA is an open source ML compiler ecosystem co-developed by AI/ML industry leaders including Alibaba, Amazon Web Services, AMD, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, and NVIDIA. It enables developers to compile and optimize models from all leading ML frameworks for efficient training and serving on a wide variety of hardware
- junrushao1994 4y agoOne thing I really love about XLA is GSPMD which effectively allows scalable distributed training in practice. However, I was quite curious how it is related to matrix multiplication though, given XLA is more focusing on graph-level optimization and basically offloads matmul to other libraries like Triton and cuBLAS