TurboAttention: Efficient Attention Approximation For High Throughputs LLMs (2412.08585v3)

Abstract: LLM inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acceleration algorithms, like FlashAttention, have improved efficiency of the overall inference, they address different aspects of the problem: quantization focuses on weight-activation operations, while FlashAttention improves execution but requires high-precision formats. Recent Key-value (KV) cache quantization reduces memory bandwidth but still needs floating-point dequantization for attention operation. We present TurboAttention, a comprehensive approach to enable quantized execution of attention that simultaneously addresses both memory and computational efficiency. Our solution introduces two key innovations: FlashQ, a headwise attention quantization technique that enables both compression of KV cache and quantized execution of activation-activation multiplication, and Sparsity-based Softmax Approximation (SAS), which eliminates the need for dequantization to FP32 during exponentiation operation in attention. Experimental results demonstrate that TurboAttention achieves 1.2-1.8x speedup in attention, reduces the KV cache size by over 4.4x, and enables up to 2.37x maximum throughput over the FP16 baseline while outperforming state-of-the-art quantization and compression techniques across various datasets and models.

• The paper presents FlashQ, a headwise attention quantization method that reduces memory usage and computational latency.
• The approach includes sparsity-based softmax approximation (SAS) that cuts softmax overhead without dequantization.
• Results show up to 2.37x throughput improvement in LLM attention while maintaining near-lossless accuracy.

Overview of TurboAttention: Efficient Attention Approximation for High Throughput LLMs

The paper entitled "TurboAttention: Efficient attention approximation for High Throughputs LLMs" introduces a novel methodological advancement in optimizing the inference efficiency of LLMs. The primary focus of this research is to resolve the computational and memory efficiency bottlenecks associated with the attention mechanisms within LLMs—a cornerstone of modern AI architectures—by introducing an innovative quantization technique that complements existing accelerative methods like FlashAttention.

Key Contributions

TurboAttention introduces two foundational innovations:

1. FlashQ (Headwise Attention Quantization Technique): This technique facilitates the quantized execution of attention by orchestrating quantization at the granularity of individual attention heads. It compresses the Key-Value (KV) cache and enables quantized execution of activation-activation multiplications. FlashQ combines both symmetric and asymmetric progressive quantization strategies to achieve substantial memory savings and computational efficiency by compressing data to lower bit-widths while maintaining the accuracy performance of LLMs.
2. Sparsity-based Softmax Approximation (SAS): SAS mitigates the computational burden of the softmax operation by introducing a sparsity-aware approximation mechanism, thereby eliminating the need for floating-point dequantization during exponentiation. This strategy considerably reduces the softmax’s computational overhead, further enhancing the efficiency of the attention operation.

Experimental Results and Implications

The paper reports significant improvements with TurboAttention, achieving up to 1.2x-1.8x speedup in attention computation and up to 2.37x maximum throughput improvement over FP16 baselines while maintaining near-lossless accuracy across diverse tasks and datasets. These metrics were obtained without significant accuracy degradation in LLM tasks such as mathematical and symbolic reasoning benchmarks. This efficiency gain establishes TurboAttention as a scalable solution for deploying high-throughput LLMs in real-time applications.

Comparison with Existing Techniques

TurboAttention advances beyond previous attention optimization techniques, such as FlashAttention, by supporting low-precision formats, thereby leveraging faster tensor cores of GPUs. Consequently, it allows for substantial reductions in both memory footprint and computational latency. By bridging attention acceleration and quantization, TurboAttention differentiates itself from prior strategies focused solely on either execution acceleration (e.g., FlashAttention in FP16/FP32) or memory bandwidth reduction via quantization techniques.

Future Directions

The paper paves the way for future research in combining emerging hardware architectures with refined quantization methodologies to further enhance the performance of attention-based neural models. The framework of TurboAttention can be extended with other compression techniques like weight and activation quantization, highlighting the potential for a unified approach that aggressively minimizes both computational overhead and memory usage.

In conclusion, TurboAttention presents a comprehensive advancement in the operational efficiency of attention mechanisms within LLMs, thus offering substantial implications for both theoretical exploration and practical deployment of AI models. The suggested techniques open avenues not only for improving latency and throughput in LLM-based applications but also apply quantitative insights into further model architecture optimizations, addressing the challenges and demands of scalable AI system deployment.