Whats better: Neural nets wider with less layers or thinner with more layers
Experiments compared Transformer models with varying layer depths and widths. Optimal performance was achieved with a model featuring four layers and an embedding dimension of 1024. Balancing layer depth and width is crucial for efficiency and performance improvement.
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The experiments conducted aimed to determine whether Transformers with more thin layers or fewer wide layers perform better. The study concluded that finding a balance between layer depth and width is crucial for optimal performance. Testing five configurations with 50 million parameters each revealed that a model with four layers and an embedding dimension of 1024 had the lowest final validation loss. While deeper models can provide more detailed feature representations, adding too many layers, as observed in some configurations, leads to diminishing returns and increased computational costs without significant improvements. The results emphasize the importance of striking a good balance between layer depth and width to achieve better efficiency and performance in Transformer models. The study also highlighted the impact of layer configurations on training time and model performance metrics.
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3 commentsThe way I grew to understand the relationship, and I am happy to discuss this / receive feedback, is that a layer's width determines how much that layer can memorize while network depth determines the complexity of abstraction possible for the network to learn.
So a wide enough layer can simply remember everything while a deep enough network will be able to, through abstraction, recreate memories of everything using a simplification of the input.
Ideally, you want a balance of the two, since you don't want to rely on memory alone, as this doesn't tend to generalize well, nor do you want to deal with the fantasy outputs from something relying too heavily on abstraction, as this is not likely to be reliable.
Is this taking into account the parameters in the embedding and the output ffn? Because normally when models are really small and the vocab is large they can account for an extremely large number of parameters and would explain why the optimal number of layers here is unusually small.
I suspect it isn’t being taken into account because doubling the embedding and cutting the number of layers in half only holds the parameters constant if you forget the embedding and output, but id need to see more details on the config (mainly the vocab size he used) to confirm.
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