FlashEVA: Accelerating LLM inference via Efficient Attention
This work addresses efficiency challenges in large language model inference for users needing faster and more memory-efficient deployments, representing an incremental improvement with specific gains.
The paper tackles the high memory demands and low throughput of Transformer models during inference by introducing FlashEVA, an efficient attention implementation that achieves up to 6.7x higher throughput and 5x lower peak GPU memory usage compared to standard methods, though it has limitations in retrieval-focused tasks.
Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due to maintaining full context in memory, pose significant challenges for inference. In this paper, we present FlashEVA, an efficient implementation of EVA (Efficient Attention via Control Variates), and demonstrate how to finetune transformers to adapt to FlashEVA attention. Our method enables fine-tuning of Transformer models with as few as 1.5B tokens while preserving effectiveness across various downstream tasks. Notably, FlashEVA achieves up to 6.7x higher throughput and 5x lower peak GPU memory usage during inference compared to standard Transformer implementations. Despite these improvements, we observe limitations in retrieval-focused tasks. Our implementation offers control over the trade-off between throughput and accuracy through adjustable hyperparameters, providing flexibility for diverse use cases. This work represents a significant step towards more efficient and adaptable Transformer-based models for inference.