QPyTorch: A Low-Precision Arithmetic Simulation Framework

Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to empirically evaluate the statistical effects of quantization while avoiding the substantial overhead of building specific...

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Bibliographic Details
Published in:	2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS) pp. 10 - 13
Main Authors:	Zhang, Tianyi, Lin, Zhiqiu, Yang, Guandao, De Sa, Christopher
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2019
Subjects:	Computational modeling Conferences Energy efficiency Hardware low-precision arithmetic low-precision training Machine learning Quantization (signal) Training
Online Access:	Get full text
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Summary:	Low-precision training reduces computational cost and produces efficient models. Recent research in developing new low-precision training algorithms often relies on simulation to empirically evaluate the statistical effects of quantization while avoiding the substantial overhead of building specific hardware. To support this empirical research, we introduce QPyTorch, a low-precision arithmetic simulation framework. Built natively in PyTorch, QPyTorch provides a convenient interface that minimizes the efforts needed to reliably convert existing codes to study low-precision training. QPyTorch is general, and supports a variety of combinations of precisions, number formats, and rounding options. Additionally, it leverages an efficient fused-kernel approach to reduce simulator overhead, which enables simulation of large-scale, realistic problems. QPyTorch is publicly available at https://github.com/Tiiiger/QPyTorch.
DOI:	10.1109/EMC2-NIPS53020.2019.00010