FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
(2205.14135)Abstract
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware -- accounting for reads and writes between levels of GPU memory. We propose FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of FlashAttention, showing that it requires fewer HBM accesses than standard attention, and is optimal for a range of SRAM sizes. We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method. FlashAttention trains Transformers faster than existing baselines: 15% end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the MLPerf 1.1 training speed record, 3$\times$ speedup on GPT-2 (seq. length 1K), and 2.4$\times$ speedup on long-range arena (seq. length 1K-4K). FlashAttention and block-sparse FlashAttention enable longer context in Transformers, yielding higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on long-document classification) and entirely new capabilities: the first Transformers to achieve better-than-chance performance on the Path-X challenge (seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1% accuracy).
Overview
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FlashAttention introduces a method to improve the efficiency and speed of attention mechanisms in transformers.
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The approach splits attention calculations into smaller stages, leveraging modern GPUs' high memory bandwidth.
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Traditional large matrix multiplication bottlenecks are bypassed by strategic segmenting of computations.
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FlashAttention offers faster processing and reduced memory requirements, especially beneficial for longer sequences.
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This method provides a foundation for tackling more complex AI tasks, making advanced AI more accessible.
Introduction to FlashAttention
The recent advancement in natural language processing has been largely propelled by the use of models endowed with attention mechanisms, utilizing significant computational resources. A pivotal work in this domain has introduced FlashAttention, an innovative method designed to optimize the performance of the attention mechanism in transformers, notably enhancing their efficiency and speed.
The Mechanics of FlashAttention
FlashAttention redefines the attention calculation by segmenting the process into smaller, more manageable stages that capitalize on the high memory bandwidth of modern GPUs. By cleverly rearranging the computation into these phases, it avoids the traditional trade-off between computation speed and memory usage. Traditional attention mechanisms perform large matrix multiplications which can become a computational bottleneck, especially as the sequence length and model size grow. In contrast, FlashAttention intelligently divides this task and distributes it, thereby exploiting the strength of SRAM (Static Random-Access Memory) within GPUs.
Performance Advantages
The experimental results exhibit significant enhancements courtesy of FlashAttention. The technique allows for a faster processing time while simultaneously reducing memory demands, which is particularly noticeable when dealing with longer sequences. This means that models can be trained more effectively and can handle larger datasets, which are typically representative of real-world tasks. Moreover, FlashAttention has shown favorable outcomes in terms of scaling, suggesting that it becomes even more effective as models and datasets increase in size.
Future Implications
The implications of FlashAttention are vast, as it opens the door to more accessible and efficient use of attention-based models for both researchers and practitioners. Given its ability to capably cope with the increasing complexity of models and the expanding volume of data, it offers a path forward to tackle more sophisticated tasks. This development is likely to accelerate progress in the realm of AI, particularly in NLP, where demand for processing power previously served as a limitation. FlashAttention thus represents a significant leap toward making advanced AI more attainable and versatile for a broader range of applications.