What Matters in Transformers? Not All Attention is Needed
While scaling Transformer-based large language models (LLMs) has demonstrated
promising performance across various tasks, it also introduces redundant
architectures, posing efficiency challenges for real-world deployment. Despite
some recognition of redundancy in LLMs, the variability of redundancy across
different architectures in transformers, such as MLP and Attention layers, is
under-explored. In this work, we investigate redundancy across different
modules within Transformers, including Blocks, MLP, and Attention layers, using
a similarity-based metric. Surprisingly, despite the critical role of attention
layers in distinguishing transformers from other architectures, we found that a
large portion of these layers exhibit excessively high similarity and can be
pruned without degrading performance. For instance, Llama-2-70B achieved a
48.4\% speedup with only a 2.4\% performance drop by pruning half of the
attention layers. Furthermore, by tracing model checkpoints throughout the
training process, we observed that attention layer redundancy is inherent and
consistent across training stages. Additionally, we further propose a method
that jointly drops Attention and MLP layers, allowing us to more aggressively
drop additional layers. For instance, when dropping 31 layers (Attention +
MLP), Llama-2-13B still retains 90\% of the performance on the MMLU task. Our
work provides valuable insights for future network architecture design. The
code is released at: https://github.com/Shwai-He/LLM-Drop.
670f43065651052491a76639
Shwai He
670f43065651052491a7663a
Guoheng Sun
670f43065651052491a7663b
Zheyu Shen
670f43065651052491a7663c
Ang Li
claimed_verified
2024-10-16T09:43:27.779Z
64a842b1221e6292c9fa7a45
/avatars/7f67f357c830ed6dade7ed7548ac1f05.svg
Ang Li
user
charleslipku
What Matters in Transformers? Not All Attention is Needed
While scaling Transformer-based large language models (LLMs) has demonstrated
promising performance across various tasks, it also introduces redundant
architectures, posing efficiency challenges for real-world deployment. Despite
some recognition of redundancy in LLMs, the variability of redundancy across
different architectures in transformers, such as MLP and Attention layers, is
under-explored. In this work, we investigate redundancy across different
modules within Transformers, including Blocks, MLP, and Attention layers, using
a similarity-based metric. Surprisingly, despite the critical role of attention
layers in distinguishing transformers from other architectures, we found that a
large portion of these layers exhibit excessively high similarity and can be
pruned without degrading performance. For instance, Llama-2-70B achieved a
48.4\% speedup with only a 2.4\% performance drop by pruning half of the
attention layers. Furthermore, by tracing model checkpoints throughout the
training process, we observed that attention layer redundancy is inherent and
consistent across training stages. Additionally, we further propose a method
that jointly drops Attention and MLP layers, allowing us to more aggressively
drop additional layers. For instance, when dropping 31 layers (Attention +
MLP), Llama-2-13B still retains 90\% of the performance on the MMLU task. Our
work provides valuable insights for future network architecture design. The
code is released at: https://github.com/Shwai-He/LLM-Drop.
Investigation reveals significant redundancy in Transformer layers, including Attention and MLP, which can be pruned to enhance speed without substantial performance loss.
Transformer-based large language models LLMs redundancy efficiency challenges similarity-based metric Blocks Attention layers MLP pruning model checkpoints MMLU task