Transformer Layers as Painters
The study "Transformer Layers as Painters" by Qi Sun et al. delves into transformer models, showcasing layer impact variations and potential for model optimization through strategic layer adjustments.
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The paper titled "Transformer Layers as Painters" by Qi Sun and colleagues explores the internal workings of transformers, focusing on the impact of removing or reorganizing information within pretrained models. The study reveals differences between lower, middle, and final layers of transformers, highlighting surprising uniformity in middle layers. The research demonstrates that certain problems exhibit robustness to layer skipping, reordering, or parallel processing, suggesting that pretrained models can trade accuracy for latency effectively. The findings aim to enhance the understanding of transformer behavior, potentially leading to improved model utilization and the development of new architectural variants. The study consists of empirical studies on frozen models, indicating the potential for optimizing model performance through strategic layer manipulation.
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3 commentsThey find:
* Inner layers of transformers share a representation space
* Some middle layers can be dropped without total failure (though it results in reduced performance)
* Middle layers are not interchangeable, they are performing different functions
* Order of layers only matters somewhat
* Layers can somewhat be executed in parallel
Each layer performs a different function but speaks the same language as other layers. A stack of transformers isn’t performing a sequence of fundamental transformations as much as it as performing a sequence of additions, each layer adding new paint to a shared canvas.
Since the layers speak the same language, it makes me wonder how we could modify and extend a transformer. Can you train other models to share the same representational space and have them “plug in” to the transformer? Does this shared representational space make it easier to perform RL and unlock agentic behavior?
Context: I found the same structure: early - middle - end layers serving different purposes, including the permutability of the middle layers, a year or so ago, but never got to testing more models rigerously or publishing it.
We talked about it a bit in a hackernews thread a few months ago. (https://news.ycombinator.com/item?id=39504780#39505523)
> One interesting finding though (now that I'm rambling and just typing a lot) is that in a static model, you can "shuffle" the layers (eg. swap layer 4's weights with layer 7's weights) and the resulting tokens roughly seem similar (likely caused by the ResNet style backbone). Only the first ~3 layers and last ~3 layers seem "important to not permute". It kinda makes me interpret models as using the first few layers to get into some "universal" embedding space, operating in that space "without ordering in layer-order", and then "projecting back" to token space at the end. (rather than staying in token space the whole way through).
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