SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning

• URL: http://arxiv.org/abs/2207.04606v1
• Date: Mon, 11 Jul 2022 03:49:53 GMT
• Title: SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
• Authors: Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen, Luis Ceze
• Abstract summary: Sparse tensor compilers simplify the development of operators, but efficient sparse compilation for deep learning remains challenging. We show that the key to addressing both challenges is two forms of composability. In this paper, we propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations.
• Score: 11.251022748134215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and existing vendor libraries cannot satisfy the escalating demands from new operators. Sparse tensor compilers simplify the development of operators, but efficient sparse compilation for deep learning remains challenging because a single sparse format cannot maximize hardware efficiency, and single-shot compilers cannot keep up with latest hardware and system advances. We show that the key to addressing both challenges is two forms of composability. In this paper, we propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads. SparseTIR constructs a search space over these composable components for performance tuning. With these improvements, SparseTIR obtains consistent performance speedups vs vendor libraries on GPUs for single operators: 1.1-3.3x for GNN operators and 1.1-4.4x for sparse transformer operators. SparseTIR also accelerates end-to-end GNNs by 1.1-2.2x for GraphSAGE training and 0.9-26x for RGCN inference.

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