TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding (2506.09507v3)

Abstract: Transformers exhibit proficiency in capturing long-range dependencies, whereas State Space Models (SSMs) facilitate linear-time sequence modeling. Notwithstanding their synergistic potential, the integration of these architectures presents a significant challenge, primarily attributable to a fundamental incongr inuity their respective positional encoding mechanisms: Transformers rely on explicit Rotary Position Embeddings (RoPE), while SSMs leverage implicit positional representations via convolutions. This divergence often precipitates discontinuities and suboptimal performance.To address this impediment, we propose a unified rotary position embedding (Unified RoPE) methodology, thereby establishing a consistent positional encoding framework for both self-attention and state-space components. Using this Unified RoPE, we introduce TransXSSM, a hybrid architecture that coherently integrates the Transformer and SSM layers under this unified positional encoding scheme. At a 4 sequenceK length, TransXSSM exhibits training and inference speeds that are 42.3% and 29.5% faster, respectively, relative to standard Transformer models. It also delivers higher accuracy: under comparable settings, it surpasses a Transformer baseline by over 4% on LLMing benchmarks.TransXSSM furthermore scales more effectively: TransXSSM-1.3B gains 7.22% in average accuracy over its 320M version (versus about 6% gains for equivalent Transformers or SSMs). Our results show that unified positional encoding resolves positional incompatibility in hybrid models, enabling efficient, high-performance long-context modeling.

• The paper presents TransXSSM, a hybrid model that combines Transformers and SSMs using a unified rotary position embedding to improve long-range sequence modeling.
• The model architecture strategically stacks SSM layers with Transformer attention, achieving 42.3% faster training and 29.5% faster inference on 4K sequences.
• The paper demonstrates that scaling the model from 320M to 1.3B parameters yields a 7.22% accuracy boost, outperforming existing hybrid designs.

An Analysis of TransXSSM: A Hybrid Transformer-State Space Model with Unified Rotary Position Embedding

The paper introduces TransXSSM, a novel hybrid model architecture combining Transformers and State Space Models (SSMs) under a unified rotary position embedding scheme (Unified RoPE). This integration aims to harness the strengths of both model types in sequence modeling—namely, the long-range dependency handling proficiency of Transformers and the linear computational efficiency of SSMs. A key contribution lies in the proposed Unified RoPE, which provides a consistent positional encoding framework across both self-attention (SA) and state-space components, overcoming the traditional incompatibilities associated with disparate encoding mechanisms.

Key Contributions and Findings

The researchers set out to improve upon existing sequence modeling paradigms by addressing the divergence in positional encoding between Transformers and SSMs. In doing so, they propose a unified approach to positional embedding that seamlessly bridges these two architectures, allowing for improved performance and efficiency.

1. Model Architecture and Efficiency:
   • TransXSSM, through its hybrid approach, offers substantial gains in training and inference speed, achieving speeds that are 42.3% and 29.5% faster, respectively, than standard Transformers when processing 4K sequences.
   • The model's structure includes a strategic stacking of SSM layers with Transformer attention layers, exhibiting a 7:1 ratio. This configuration effectively balances the computational demands with the need for comprehensive sequence understanding.
2. Unified Rotary Position Embedding (RoPE):
   • Unified RoPE ensures consistent position encoding across both SA and SSM modules. By introducing a shared encoding scheme, the model facilitates coherent information propagation without the spectrum discontinuities observed in prior hybrids.
   • The rotational position embedding is adapted for compatibility with both Transformers and SSMs, applied in a manner that maintains the linear-time processing capabilities inherent to state-space models.
3. Performance Evaluation:
   • The TransXSSM model consistently outperforms both pure Transformer and SSM models and other hybrid designs across various benchmark tasks, including LLMing and long-context processing challenges.
   • The 1.3B parameter version of TransXSSM demonstrates a notable 7.22% increase in average accuracy compared to its 320M counterpart, highlighting its scalability and efficiency gains with model upscaling.

Implications for Future Research

The integration of a Unified RoPE framework into hybrid models such as TransXSSM represents a significant advancement in addressing the positional encoding challenges inherent in previous architectures. By providing a coherent and unified encoding scheme, this approach allows for enhanced hybrid model performance with reduced computational complexity. As such, future research could explore further optimizations in hybrid model architectures and potentially extend this unified approach to other variations of sequence models.

Conclusion

TransXSSM establishes a new benchmark in hybrid modeling by adeptly combining Transformers and SSMs with a unified positional encoding strategy. By alleviating previous positional encoding mismatches, TransXSSM not only achieves superior computational efficiency but also delivers enhanced performance on a range of benchmarks. This work opens avenues for further exploration into efficient hybrid architectures that can handle increasingly complex and large-scale sequence modeling tasks, pushing the boundaries of what such models can achieve in practice and theory.