Facebook Develops a New Method for Doubling the Performance of AI Transformers

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Facebook has developed a new method for doubling the performance of AI transformers based on the transformer architecture.

The new method finds the most similar patches in the gaps between processing different blocks and combines them to reduce computational complexity.

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Facebook has developed a new method for doubling the performance of AI transformers. The method is based on the transformer architecture and is specifically designed for long-form text such as books, articles, and blogs. The goal of the new AI transformer is to improve the performance of transformer-based models on long-form text by making them more efficient and effective at handling long sequences. The results of the AI transformer are very promising, and this new method has a chance to help improve the performance of transformer-based models on a variety of tasks.

This new method is expected to have a significant impact on natural language processing tasks, such as language translation, summarization, and question-answering systems. It is also expected to lead to the development of more sophisticated AI models that can handle longer and more complex texts.

Facebook developed a new method for doubling the performance of AI transformers
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To process the image, modern transformers cut it into patches (usually squares: see the gif below) and then operate on representations of these particles, each of which is represented by a “token.” Transformers, as we know, work slower the more of these token pieces there are (this applies to both texts and images), and the most common transformer has a quadratic relationship. That is, as more tokens are added, the slower the processing becomes. To address this issue, researchers have proposed various techniques to reduce the number of tokens required for image processing, such as hierarchical and adaptive pooling. These methods aim to maintain the quality of the output while minimizing the computational cost.

The new method finds the most similar patches in the gaps between processing different blocks and combine them to reduce computational complexity. The share of merged tokens is a hyperparameter; the higher it is, the lower the quality but also the higher the acceleration. Experiments show that it is possible to merge approximately 40% of tokens with a quality loss of 0.1-0.4% and get double acceleration (thus consiming less memory). This new method is a promising solution for reducing the computational complexity of image processing and could allow for faster and more efficient processing without compromising the quality of the final output.

When we visualize which patches we merge, we can see that they are 1) near each other and 2) describe the same object (see areas of the same color on the GIF). That is, no significant information is lost; the object remains “in the field of view” of the model. The later this is applied in the transformer, the more tokens are merged (since these are higher-level representations that themselves describe the content of the image well).

Such engineering approaches based on ingenuity and understanding how something works look very appealing. Also, Meta’s developers promise to bring more to StableDiffusion in order to speed things up there as well. It’s awesome that, because transformers are everywhere, such tricks can be quickly implemented in a wide range of models. This shows the potential for engineering solutions to have a broad impact across various industries. It will be interesting to see how these advancements in transformer models will continue to evolve and improve over time.

  • Meta AI and Paperswithcode have released the first 120B model Galactica trained on scientific texts, allowing for more accurate and faster predictions. The goal of Galactica is to help researchers separate the important from the irrelevant.

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Damir Yalalov

Damir is the Editor/SEO/Product Lead at mpost.io. He is most interested in SecureTech, Blockchain, and FinTech startups. Damir earned a bachelor's degree in physics.

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