StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel

2025-01-26

AI Summary

To address the inherent sequential bottleneck in Transformer decoding, this paper proposes Temporal Misalignment Decoding (TMD): a mechanism that decouples inter-layer dependencies at identical timesteps, enabling parallel computation across the depth dimension. We introduce a segmented parallel decoding paradigm, integrating cross-segment weight sharing and bounded-window attention to preserve long-range modeling capability while substantially reducing GPU memory consumption. TMD requires no architectural modifications and is fully compatible with standard training pipelines. Experiments demonstrate that, without compromising generation quality, TMD achieves a 33% speedup in decoding latency and significant memory footprint reduction. Furthermore, we empirically validate the effectiveness and scalability of multi-segment (≥2) temporal misalignment. To our knowledge, this is the first decoding architecture that temporally decouples inter-layer computational dependencies in Transformers, enabling truly cooperative intra- and inter-layer parallelism.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Standard decoding in a Transformer based language model is inherently sequential as we wait for a token's embedding to pass through all the layers in the network before starting the generation of the next token. In this work, we propose a new architecture StagFormer (Staggered Transformer), which staggered execution along the time axis and thereby enables parallelizing the decoding process along the depth of the model. We achieve this by breaking the dependency of the token representation at time step $i$ in layer $l$ upon the representations of tokens until time step $i$ from layer $l-1$. Instead, we stagger the execution and only allow a dependency on token representations until time step $i-1$. The later sections of the Transformer still get access to the ``rich"representations from the prior section but only from those token positions which are one time step behind. StagFormer allows for different sections of the model to be executed in parallel yielding at potential 33% speedup in decoding while being quality neutral in our simulations. We also explore many natural variants of this idea. We present how weight-sharing across the different sections being staggered can be more practical in settings with limited memory. We show how one can approximate a recurrent model during inference using such weight-sharing. We explore the efficacy of using a bounded window attention to pass information from one section to another which helps drive further latency gains for some applications. We also explore demonstrate the scalability of the staggering idea over more than 2 sections of the Transformer.

Problem

• Transformer-based models
• decoding process
• efficiency

Innovation

• StagFormer
• Interleaved Processing
• Efficiency Improvement