Summary of Hw-gpt-bench: Hardware-aware Architecture Benchmark For Language Models, by Rhea Sanjay Sukthanker et al.
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
by Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Aaron Klein, Lennart Purucker, Joerg K.H. Franke, Frank Hutter
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces HW-GPT-Bench, a hardware-aware benchmark for assessing language models’ performance on different devices. The increasing size of language models requires evaluating trade-offs between latency, energy consumption, GPU memory usage, and performance across multiple devices. To address this challenge, the authors utilize surrogate predictions to approximate various hardware metrics, leveraging 13 devices from the GPT-2 family with up to 1.55B parameters. The surrogates accurately model heteroscedastic noise in energy and latency measurements, while employing weight-sharing techniques from Neural Architecture Search (NAS) estimates perplexity. The authors demonstrate HW-GPT-Bench’s utility by simulating optimization trajectories of multi-objective optimization algorithms in just a few seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better on different devices, like computers or phones. As language models get bigger and more powerful, it’s hard to figure out which settings are best for each device without doing a lot of testing. The authors created a new tool called HW-GPT-Bench that helps predict how well a language model will work on different devices, taking into account things like speed, energy usage, and memory use. This tool is important because it can help us make better decisions about which settings to use for language models in the future. |
Keywords
» Artificial intelligence » Gpt » Language model » Optimization » Perplexity
