This content originally appeared on HackerNoon and was authored by Auto Encoder: How to Ignore the Signal Noise
:::info Authors:
(1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.);
(2) Thomas Hofmann, Department of Computer Science, ETH Zurich.
:::
Table of Links
Simplifying Transformer Blocks
Discussion, Reproducibility Statement, Acknowledgements and References
A Duality Between Downweighted Residual and Restricting Updates In Linear Layers
6 DISCUSSION
Limitations and future work While we have demonstrated the efficacy of our simplifications across architectures, datasets, and tasks, the models we have considered (100-300M parameters) are small relative to the largest transformers. It would be interesting to investigate the performance of our simplified blocks at larger scales, especially because Chowdhery et al. (2022) report parallel blocks improve relative to Pre-LN blocks with scale. Our depth scaling experiments already show promise in this regard. On the theoretical side, though we were able to match the training speed of Pre-LN blocks with normalisation removed (Fig. 5), there are still unanswered questions regarding the benefits of normalisation for training speed and stability, and we were unable to remove normalisation with good downstream task performance. Moreover, while we tuned key hyperparameters like learning rate, it is possible that many default hyperparameters and choices we inherited, e.g. the AdamW optimiser, or fine-tuning protocol, are overfit to the default Pre-LN block, and an exhaustive hyperparameter search for our simplified blocks would yield further improvements. Finally, on the practical side, we believe that a more hardware-specific implementation of our simplified blocks could give further improvements to training speed and performance.
\ Conclusion In this work, we asked whether it is possible to simplify the standard Transformer block by removing unnecessary components. Combining signal propagation theory and empirical insights, we have shown that it is possible to remove skip connections, sequential sub-blocks, value and projection parameters, without loss of training speed or downstream task performance. As a result, our models have around 15% fewer parameters and 15% increased throughput. We believe our work can lead to simpler architectures being used in practice, thereby helping to bridge the gap between theory and practice in deep learning, and reducing the cost of large transformer models.
REPRODUCIBILITY STATEMENT
Our code for experiments on auto-regressive transformers can be found at https://github. com/bobby-he/simplified_transformers.
ACKNOWLEDGMENTS
We would like to thank Sotiris Anagnostidis, Andrei Ivanov & Lorenzo Noci for helpful discussions in the initial stages of this project, and James Martens, Tiago Pimentel & Imanol Schlag for constructive feedback on an early version of this manuscript.
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This content originally appeared on HackerNoon and was authored by Auto Encoder: How to Ignore the Signal Noise
Auto Encoder: How to Ignore the Signal Noise | Sciencx (2024-06-19T12:00:28+00:00) Simplifying Transformer Models for Faster Training and Better Performance. Retrieved from https://www.scien.cx/2024/06/19/simplifying-transformer-models-for-faster-training-and-better-performance/
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