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PartIR: Composing SPMD Partitioning Strategies for Machine Learning
March 5, 2024, 2:45 p.m. | Sami Alabed, Daniel Belov, Bart Chrzaszcz, Juliana Franco, Dominik Grewe, Dougal Maclaurin, James Molloy, Tom Natan, Tamara Norman, Xiaoyue Pan, Adam
cs.LG updates on arXiv.org arxiv.org
Abstract: Training of modern large neural networks (NN) requires a combination of parallelization strategies encompassing data, model, or optimizer sharding. When strategies increase in complexity, it becomes necessary for partitioning tools to be 1) expressive, allowing the composition of simpler strategies, and 2) predictable to estimate performance analytically. We present PartIR, our design for a NN partitioning system. PartIR is focused on an incremental approach to rewriting and is hardware-and-runtime agnostic. We present a simple but …
abstract arxiv combination complexity cs.dc cs.lg cs.pl data machine machine learning modern networks neural networks parallelization partitioning performance sharding strategies tools training type
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