Efficient Systems for Foundation Models

Code it, run it, crash it–restart it.

🔥 the gist

what? A workshop to bring together interdisciplinary experts working on the emerging research questions and challenges associated with foundation model training and inference.
when & where?
- Submit your papers by 2023/05/31 (see our 📝 call for papers for details or go to 🤝 OpenReview);
- Attend the workshop, 29th of July 2023, in Honolulu, Hawaii—or join us virtually.
questions? Contact us at esfomo.workshop@gmail.com.

Banner Best paper and poster prizes sponsored by Together–contact us if you are interested in sponsoring as well!

🦾 the pitch

As models increase in size and training budget, they not only systematically improve in upstream quality, but also exhibit novel emergent capabilities. This increase in scale raises proportionate difficulties for practitioners: foundation model training and inference lie at a unique interdisciplinary crossroad, combining open problems in algorithms, system design, and software engineering.

Machine learning practitioners are key stakeholders here: on the one hand, researchers may contribute algorithmic insights and novel methods to improving training and inference of large models; on the other hand, novel research findings may be best demonstrated at scale—which may require training models as efficiently as possible to make the best use of available resources.

The goal of this workshop is to bring together interdisciplinary experts working on the emerging research questions and challenges associated with foundation model training and inference. We welcome submissions around training and inference systems/algorithms for foundation models, focusing on scaling-up or on reducing compute, time, memory, bandwidth, and energy requirements. Notably, we encourage submissions concerning the entire spectrum of foundation models: from BERT-sized Transformers, to large models with 100B+ parameters. Topics include but are not limited to (see our 📝 call for papers for details):

Training and inference systems, either distributed at large scale or in resource-constrained scenarios;
Algorithms for improved training and inference efficiency;
Systems for foundation models, such as novel programming languages or compilers.

📆 the plan

(timings are provided tentatively while we wait for the logistics to be confirmed, all times HST, UTC-10)

	Topic	Speaker
8:45am	Opening remarks
9:00am	Session I: Training Strategies and Libraries
	Scaling Up Models and Data with `t5x` and `seqio`	Adam Roberts (Google Research)
	Using Megatron to Train Large Language Models	Deepak Narayanan (Microsoft Research)
	Training Large Language Models on Cerebras Wafer-Scale Clusters	Natalia Vassilieva (Cerebras)
10:15am	Contributed Talk 1
10:30am	Coffee break
10:35am	Contributed Talk 2
11:00am	Session II: Efficient Inference
	The Case for 4-bit Inference	Tim Dettmers (University of Washington)
	Efficienly Scaling Transformer Inference	Aakanksha Chowdhery (Google Research)
noon	Lunch break
1pm	Session III: Deep Optimization
	PyTorch 2.x: Faster, More Pythonic, and as Dynamic as Ever	Natalia Gimelshein (Meta)
	High-Performance Kernel Programming with Triton	Philippe Tillet (OpenAI)
2pm	Contributed Talk 3
2:15pm	Poster session
3:15pm	Coffee break
3:30pm	Panel: Large Language Models Tooling Across Industry and Academia	Anna Goldie (Anthropic), Rishi Bommasani (Stanford University), Susan Zhang (Meta), Emily Webber (AWS), James Bradbury (Google)
4:30pm	Session IV: Collaborative Approaches
	Distributed Systems for Decentralized AI	Ce Zhang (ETH, Together)
	Open Tooling for Large Language Models	Thomas Wang (HuggingFace)
5:30pm	Awards