ML Applications
Training Deep Learning Recommendation Model with Quantized Collective Communications
August 24, 2020
Abstract
Deep Learning Recommendation Model (DLRM) captures our representative model architectures developed for click-through-rate (CTR) prediction based on high-dimensional sparse categorical data. Collective communications can account for a significant fraction of time in synchronous training of DLRM at scale. In this work, we explore using fine-grain integer quantization to reduce the communication volume of alltoall and allreduce collectives. We emulate quantized alltoall and allreduce, the latter using ring or recursive-doubling and each with optional carried-forward error compensation. We benchmark accuracy loss of quantized alltoall and allreduce with a representative DLRM model and Kaggle 7D dataset. We show that alltoall forward and backward passes, and dense allreduce can be quantized to 4 bits without accuracy loss compared to full-precision training.
AUTHORS
Written by
Jie (Amy) Yang
Jongsoo Park
Srinivas Sridharan
Ping Tak Peter Tang
Publisher
Knowledge Discovery and Data Mining (KDD) 2020
Research Topics
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