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Pytorch fsdp: experiences on scaling fully sharded data parallel
It is widely acknowledged that large models have the potential to deliver superior
performance across a broad range of domains. Despite the remarkable progress made in …
performance across a broad range of domains. Despite the remarkable progress made in …
With shared microexponents, a little shifting goes a long way
This paper introduces Block Data Representations (BDR), a framework for exploring and
evaluating a wide spectrum of narrow-precision formats for deep learning. It enables …
evaluating a wide spectrum of narrow-precision formats for deep learning. It enables …
Ads recommendation in a collapsed and entangled world
We present Tencent's ads recommendation system and examine the challenges and
practices of learning appropriate recommendation representations. Our study begins by …
practices of learning appropriate recommendation representations. Our study begins by …
Causality-based CTR prediction using graph neural networks
As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention
from both academia and industry. Recent studies have been reported to establish CTR …
from both academia and industry. Recent studies have been reported to establish CTR …
Grace: A scalable graph-based approach to accelerating recommendation model inference
The high memory bandwidth demand of sparse embedding layers continues to be a critical
challenge in scaling the performance of recommendation models. While prior works have …
challenge in scaling the performance of recommendation models. While prior works have …
InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is
a fundamental task in recommender systems. The emergence of heterogeneous information …
a fundamental task in recommender systems. The emergence of heterogeneous information …
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Effective user representations are pivotal in personalized advertising. However, stringent
constraints on training throughput, serving latency, and memory, often limit the complexity …
constraints on training throughput, serving latency, and memory, often limit the complexity …
AutoML for Large Capacity Modeling of Meta's Ranking Systems
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking
models is essential but engineering heavy. Automated Machine Learning (AutoML) can …
models is essential but engineering heavy. Automated Machine Learning (AutoML) can …
Towards GPU Memory Efficiency for Distributed Training at Scale
The scale of deep learning models has grown tremendously in recent years. State-of-the-art
models have reached billions of parameters and terabyte-scale model sizes. Training of …
models have reached billions of parameters and terabyte-scale model sizes. Training of …
Rankitect: Ranking architecture search battling world-class engineers at meta scale
Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and
potential for ranking systems. However, prior work focused on academic problems, which …
potential for ranking systems. However, prior work focused on academic problems, which …