INT-FlashAttention: Enabling Flash Attention for INT8 Quantization
The paper presents INT-FlashAttention, a new architecture combining FlashAttention with INT8 quantization, achieving 72% faster inference and 82% less quantization error, while supporting various data formats.
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The paper titled "INT-FlashAttention: Enabling Flash Attention for INT8 Quantization" introduces a novel architecture designed to enhance the performance of self-attention mechanisms in large language models (LLMs). The authors, led by Shimao Chen, propose INT-FlashAttention, which integrates FlashAttention with INT8 quantization, addressing the challenges of quadratic time and memory complexity associated with self-attention. This new architecture significantly accelerates inference speed and reduces memory usage by utilizing the GPU memory hierarchy. The implementation features fully INT8 activations and general matrix-multiplication (GEMM) kernels, marking it as the first attention operator to support fully INT8 input. Additionally, INT-FlashAttention is adaptable to other data formats, such as INT4. Experimental results indicate that this approach achieves a 72% increase in inference speed and an 82% reduction in quantization error compared to standard FlashAttention using FP16 and FP8 formats. This advancement represents a significant step forward in optimizing attention mechanisms for efficient processing in machine learning applications.
- INT-FlashAttention integrates FlashAttention with INT8 quantization for improved performance.
- The architecture significantly enhances inference speed and reduces memory usage.
- It is the first attention operator to support fully INT8 input.
- Experimental results show a 72% increase in inference speed and an 82% reduction in quantization error.
- The framework is compatible with other data formats, including INT4.
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