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arXiv · 2101.05615

FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Daya Khudia·Jianyu Huang·Protonu Basu·Summer Deng·Haixin Liu·Jongsoo Park·Mikhail Smelyanskiy
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Abstract

Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that computations with reduced-precision data types (FP16, 16-bit integers, 8-bit integers or even 4- or 2-bit integers) are enough to achieve same accuracy as FP32 and are much more efficient. Therefore, we designed fbgemm, a high-performance kernel library, from ground up to perform high-performance quantized inference on current generation CPUs. fbgemm achieves efficiency by fusing common quantization operations with a high-performance gemm implementation and by shape- and size-specific kernel code generation at runtime. The library has been deployed at Facebook, where it delivers greater than 2x performance gains with respect to our current production baseline.

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Daya Khudia, Jianyu Huang, Protonu Basu, Summer Deng, Haixin Liu, Jongsoo Park, Mikhail Smelyanskiy. 2021-01-13. FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference. https://arxiv.org/abs/2101.05615

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