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StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel

Created by
  • Haebom

Author

Dylan Cutler, Arun Kandoor, Nishanth Dikkala, Nikunj Saunshi, Xin Wang, Rina Panigrahy

Outline

In this paper, we propose StagFormer (Staggered Transformer), a novel architecture for parallelizing the decoding process of Transformer-based language models. Unlike the sequential decoding approach of conventional Transformers, StagFormer parallelizes the decoding process along the depth of the model by staggering execution along the sequence axis. This is achieved by disabling the token representation at the $i$-th time step in the $l$ layer from relying on the token representations up to the $i$-th time step in the $l-1$ layer, and instead relying only on the token representations up to the $i-1$-th time step. This allows for parallel execution of different sections of the model, thereby improving decoding speed while maintaining quality. We also explore various extensions, including weight sharing, limited window attention, multi-section extensions, and recurrent model approximation.

Takeaways, Limitations

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Takeaways:
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We present a novel architecture that can improve the decoding speed of Transformer-based language models.
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It demonstrates the potential to improve performance without compromising quality while increasing decoding speed through parallel processing.
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We present a method that achieves memory efficiency and latency reduction by leveraging weight sharing and limited window attention.
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We demonstrate the possibility of extending to multiple sections and suggest that quality improvements can be achieved in short generation by approximating the cyclic model.
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Limitations:
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The practical performance of the proposed architecture needs to be verified through further experiments on various language models and tasks.
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Memory efficiency and latency reduction effects may vary depending on specific hardware environments and applications.
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Further research is needed to address the potential for increased complexity and performance degradation associated with scaling to multiple sections.
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The performance of a cyclic model approximation method may vary depending on the generation length.
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