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The rapid rise in demand for training large neural network architectures hasbrought into focus the need for partitioning strategies, for example by usingdata, model, or pipeline parallelism. Implementing these methods isincreasingly supported through program primitives, but identifying efficientpartitioning strategies requires expensive experimentation and expertise. Wepresent the prototype of an automated partitioner that seamlessly integratesinto existing compilers and existing user workflows. Our partitioner enablesSPMD-style parallelism that encompasses data parallelism andparameter/activation sharding. Through a combination of inductive tactics andsearch in a platform-independent partitioning IR, automap can recover expertpartitioning strategies such as Megatron sharding for transformer layers.

Automap: Towards Ergonomic Automated Parallelism for ML Models