Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables

Krebber, Manuel

doi:10.48550/arxiv.1705.00907

Cited by 2 publications

(2 citation statements)

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“…• Associative: By leveraging the associative property, the graph rewriting pass can identify an optimized order of operators execution, and hence replace the expensive combination of operators with a new cheaper one. Figure 2 DNNFusion employs pattern matching [36,37] to recognize rewriting candidates. However, associative and commutative matching is NP-complete [5].…”

Section: Mathematical-property-based Graph Rewritingmentioning

confidence: 99%

DNNFusion: accelerating deep neural networks execution with advanced operator fusion

Niu

Guan

Wang

et al. 2021

Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

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Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with hundreds or even thousands of operator layers, leading to high memory and computational requirements for inference. Operator fusion (or kernel/layer fusion) is key optimization in many state-of-the-art DNN execution frameworks, such as TensorFlow, TVM, and MNN, that aim to improve the efficiency of the DNN inference. However, these frameworks usually adopt fusion approaches based on certain patterns that are too restrictive to cover the diversity of operators and layer connections, especially those seen in many extremely deep models. Polyhedral-based loop fusion techniques, on the other hand, work on a low-level view of the computation without operator-level information, and can also miss potential fusion opportunities. To address this challenge, this paper proposes a novel and extensive loop fusion framework called DNNFusion. The basic idea of this work is to work at an operator view of DNNs, but expand fusion opportunities by developing a classification of both individual operators and their combinations. In addition, DNNFusion includes 1) a novel mathematical-propertybased graph rewriting framework to reduce evaluation costs and facilitate subsequent operator fusion, 2) an integrated fusion plan generation that leverages the high-level analysis and accurate light-weight profiling, and 3) additional optimizations during fusion code generation. DNNFusion is extensively evaluated on 15 DNN models with varied types

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Section: Mathematical-property-based Graph Rewritingmentioning

confidence: 99%

DNNFusion: accelerating deep neural networks execution with advanced operator fusion

Niu

Guan

Wang

et al. 2021

Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

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show abstract

“…In this section, we give a brief overview of the many-to-one matching algorithm used by MatchPy. Full details can be found in the master thesis [Kre17b].…”

Section: Optimizationsmentioning

confidence: 99%

MatchPy: A Pattern Matching Library

Krebber,

Barthels,

Bientinesi

2017

Preprint

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Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python [Pö16], [Hao14], [Sch14], [Jen15]. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.

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Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables

Cited by 2 publications

References 0 publications

DNNFusion: accelerating deep neural networks execution with advanced operator fusion

DNNFusion: accelerating deep neural networks execution with advanced operator fusion

MatchPy: A Pattern Matching Library

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