BASS: Batched Attention-optimized Speculative Sampling

Batched Attention-optimized Speculative Sampling: Innovations in Multi-Sequence Generation for LLMs

Introduction

The evolution of language model inference methods continues to be a critical area of research, particularly as models are scaled to billions of parameters. Batched Attention-optimized Speculative Sampling (BASS) emerges as a significant enhancement over existing methods, addressing the inefficiencies of speculative decoding with single-sequence batches by introducing a method capable of handling multiple sequences simultaneously. Demonstrating significant improvements in latency, GPU utilization, and generation quality under tight time constraints, BASS optimizes speculative decoding across multiple dimensions.

Key Challenges and Methodology

Existing Limitations

Traditional speculative decoding techniques are largely limited to single-sequence processing, curbing the potential to exploit parallelism in modern GPU hardware. This limitation is particularly impactful in scenario where multiple sequence outputs are required simultaneously, as is common in practical AI applications, where latency and throughput are critical.

BASS Overview

BASS successfully extends speculative decoding beyond these limitations, leveraging batch processes and a novel approach to calculating attention across variable-length sequences within batches. The core technique involves speculative token drafts performed in parallel, with dynamic adjustments based on the acceptability of these drafts, which significantly enhances throughput.

Technical Innovations

• CUDA Kernels and Ragged Tensors: Handling of ragged tensors via customized CUDA kernels, facilitating efficient memory management and parallel computation.
• Dynamic Draft Length: Algorithmic innovation to dynamically adjust draft token length, enhancing flexibility and adaptability during inference.

Experimental Setup and Results

Models and Metrics

The system was evaluated using models like OPT 13B and CodeGen-Mono 16B, with metrics including HumanEval pass@k for coding, and ROUGE for summarization tasks.

Findings

With BASS, notable improvements were observed:

• Latency and Throughput: Achieved up to 2.15 times speed-up over optimized regular decoding methods.
• GPU Utilization: Enhanced peak usage markedly, showing more than threefold improvement over regular decoding approaches.

These improvements catalyze better performance in applications like coding assistants, conversational agents, and more, without the expense of extended wait times typical of earlier methods.

Implications and Future Directions

Practical Implications

BASS allows real-world AI systems, especially those requiring real-time interaction and generation of multiple responses, to function more efficiently. This capability can transform user experiences across interfaces where rapid responses are essential.

Theoretical Contributions

This research contributes to the understanding of how speculative decoding can be synergized with batch processing to overcome core challenges in AI inference, such as those posed by memory bandwidth and latency.

Future Research

Further exploration into reducing disparities in GPU utilization across different phases of model inference could yield even faster and more efficient systems. Adapting BASS to a wider range of model architectures and sizes, and further optimizing CUDA implementations could also extend the benefits observed.

In conclusion, Batched Attention-optimized Speculative Sampling sets a new benchmark in the utilization and efficiency of LLMs, offering pathways to both theoretical and practical enhancements in the field of AI model inference.