Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale
(2503.01868v1)
Published 25 Feb 2025 in cs.LG, cs.AI, cs.CL, and cs.DC
Abstract: We introduce convolutional multi-hybrid architectures, with a design grounded on two simple observations. First, operators in hybrid models can be tailored to token manipulation tasks such as in-context recall, multi-token recall, and compression, with input-dependent convolutions and attention offering complementary performance. Second, co-designing convolution operators and hardware-aware algorithms enables efficiency gains in regimes where previous alternative architectures struggle to surpass Transformers. At the 40 billion parameter scale, we train end-to-end 1.2 to 2.9 times faster than optimized Transformers, and 1.1 to 1.4 times faster than previous generation hybrids. On H100 GPUs and model width 4096, individual operators in the proposed multi-hybrid StripedHyena 2 architecture achieve two-fold throughput improvement over linear attention and state-space models. Multi-hybrids excel at sequence modeling over byte-tokenized data, as demonstrated by the Evo 2 line of models. We discuss the foundations that enable these results, including architecture design, overlap-add blocked kernels for tensor cores, and dedicated all-to-all and point-to-point context parallelism strategies.
The paper "Systems and Algorithms for Convolutional Multi-Hybrid LLMs at Scale" (Ku et al., 25 Feb 2025) presents a methodical framework that integrates convolutional operations with multi-hybrid architectures for large-scale sequence modeling. The approach emphasizes hardware-aware design, operator specialization, and efficient parallelism strategies to address the computational challenges of modeling sequences over byte-tokenized data at scales upwards of 40 billion parameters.
Architecture and System Design
The proposed architecture, exemplified by the StripedHyena 2 model, interleaves multiple specialized operators tailored to distinct token manipulation subtasks. The primary architectural components include:
Hyena-SE (Short Explicit): This operator focuses on local multi-token recall. By explicitly parameterizing short-range dependencies, Hyena-SE maximizes hardware utilization while handling local context effectively.
Hyena-MR (Medium Regularized): Designed for sequences spanning hundreds of tokens, a regularized formulation incorporating exponential decay enforces stability and efficient modeling over medium-range contexts.
Hyena-LI (Long Implicit): Operates over the entire sequence by calculating a linear combination of real exponentials, thereby supporting long-term dependencies with constant memory requirements during autoregressive generation.
The overall multi-hybrid block layout is composed of alternating convolution-based operators and multi-head attention (MHA) units, integrating their complementary strengths. A notable design element is the grouped filter sharing across channel groups; this facilitates the representation of discrete convolutions as generalized matrix-matrix multiplications (GEMM), which is vital for scalability on modern tensor cores.
Algorithmic Innovations
Several algorithmic techniques underpin the efficiency gains achieved by the multi-hybrid design:
Two-Stage Blocked Kernel: This technique extends overlap-add methods to tensor core operations, partitioning the input and output signals into chunks and processing them in a two-stage fashion. By converting GEMV operations into GEMM operations, the mechanism improves throughput considerably. This approach is critical for efficient utilization of tensor hardware, resulting in marked latency improvements over conventional implementations.
Context Parallelism Strategies: The paper delineates strategies for both all-to-all and point-to-point context parallelism. All-to-all parallelism synchronizes data across devices to reconstruct the full sequence, whereas point-to-point communication minimizes overhead by establishing direct exchanges between computing peers. The introduction of channel-pipelined variants for all-to-all strategies further mitigates communication latency, thereby optimizing performance in distributed environments.
Efficient Materialization of Toeplitz Factors: Utilizing frameworks such as Triton for the rapid construction of Toeplitz matrices contributes to overall efficiency. This technique is particularly beneficial when implementing the specialized convolutional operations that underpin the hybrid architecture.
Experimental Results and Performance Improvements
The paper presents comprehensive empirical evaluations that underscore the practical efficacy of the proposed approach:
Training Speed: At the 40 billion parameter scale, StripedHyena 2 exhibits training time reductions of 1.2 to 2.9× faster compared to optimized Transformer models. In comparison to earlier-generation hybrids, the speed improvement is in the range of 1.1 to 1.4× faster.
Throughput Enhancements: Individual convolutional operators within the StripedHyena 2 architecture demonstrate a two-fold throughput advantage relative to linear attention and state-space models. This is achieved without sacrificing the capability of handling long-range dependencies or compromising the model's effectiveness on tokenized data.
Kernel Efficiency: The adaptation of the two-stage blocked kernel, which reformulates small GEMV operations as GEMM operations, contributes significantly to the overall performance. The efficiency gains observed at both the operator and system levels highlight the benefits of intimately coupling hardware-aware algorithms with convolutional operator design.
Benefits for Sequence Modeling
The convolutional multi-hybrid framework offers several tangible benefits for sequence modeling:
Specialized Token Manipulation: By allocating distinct operators for in-context recall, multi-token recall, and compression, the model is capable of handling a wide range of token manipulation tasks with high precision. This specialization is particularly relevant when modeling byte-tokenized inputs where context and recurrence are critical.
Hardware Utilization and Scalability: The co-design of convolution operators and hardware-aware algorithms leads to a significant enhancement in resource efficiency. This is instrumental in scaling models to tens of billions of parameters while maintaining competitive training times and throughput.
Constant Memory During Autoregressive Generation: The design of FIR-like filters intrinsic to the Hyena operations allows for constant memory usage during autoregressive tasks, an important consideration for real-time inference and deployment in resource-constrained environments.
Complementary Role with Attention: By blending convolutional operators with multi-head attention, the approach harnesses the strengths of both paradigms, optimizing both local and global dependency modeling. This complementary design ensures that the models are robust across various sequence lengths and complexities.
Conclusion
Overall, "Systems and Algorithms for Convolutional Multi-Hybrid LLMs at Scale" offers a robust and efficient framework that combines convolutional methods with hybrid architectural design, yielding substantial performance and scalability benefits. The integration of domain-specific convolutional operators, coupled with advanced parallelism strategies and kernel optimizations, results in faster training times and improved throughput relative to traditional Transformers and previous hybrid models. This work exemplifies the successful translation of theoretical innovations into practical, scalable systems for large-scale sequence modeling.