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Junru Shao

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2 papers

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NeurIPS Conference 2022 Conference Paper

Tensor Program Optimization with Probabilistic Programs

Junru Shao
Xiyou Zhou
Siyuan Feng
Bohan Hou
Ruihang Lai
Hongyi Jin
Wuwei Lin
Masahiro Masuda

Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain experts to grow the search space. This paper introduces MetaSchedule, a domain-specific probabilistic programming language abstraction to construct a rich search space of tensor programs. Our abstraction allows domain experts to analyze the program, and easily propose stochastic choices in a modular way to compose program transformation accordingly. We also build an end-to-end learning-driven framework to find an optimized program for a given search space. Experimental results show that MetaSchedule can cover the search space used in the state-of-the-art tensor program optimization frameworks in a modular way. Additionally, it empowers domain experts to conveniently grow the search space and modularly enhance the system, which brings 48% speedup on end-to-end deep learning workloads.

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NeurIPS Conference 2021 Conference Paper

TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers

Lianmin Zheng
Ruochen Liu
Junru Shao
Tianqi Chen
Joseph Gonzalez
Ion Stoica
Ameer Haj-Ali

Search-based tensor compilers can greatly accelerate the execution of machine learning models by generating high-performance tensor programs, such as matrix multiplications and convolutions. These compilers take a high-level mathematical expression as input and search for the fastest low-level implementations. At the core of the search procedure is a cost model which estimates the performance of different candidates to reduce the frequency of time-consuming on-device measurements. There has been a growing interest in using machine learning techniques to learn a cost model to ease the effort of building an analytical model. However, a standard dataset for pre-training and benchmarking learned cost models is lacking. We introduce TenSet, a large-scale tensor program performance dataset. TenSet contains 52 million program performance records collected from 6 hardware platforms. We provide comprehensive studies on how to learn and evaluate the cost models, including data collection, model architectures, loss functions, transfer learning, and evaluation metrics. We also show that a cost model pre-trained on TenSet can accelerate the search time in the state-of-the-art tensor compiler by up to 10$\times$. The dataset is available at https: //github. com/tlc-pack/tenset.

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