EL-Attention: Memory Efficient Lossless Attention for Generation

• URL: http://arxiv.org/abs/2105.04779v1
• Date: Tue, 11 May 2021 04:37:52 GMT
• Title: EL-Attention: Memory Efficient Lossless Attention for Generation
• Authors: Yu Yan, Jiusheng Chen, Weizhen Qi, Nikhil Bhendawade, Yeyun Gong, Nan Duan and Ruofei Zhang
• Abstract summary: We propose memory-efficient lossless attention (called EL-attention) to address this issue. It avoids heavy operations for building multi-head keys and values, with no requirements of using cache. We conduct extensive experiments on Transformer, BART, and GPT-2 for summarization and question generation tasks.
• Score: 27.59275177303199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks. However, cache brings new memory-related costs and prevents leveraging larger batch size for faster speed. We propose memory-efficient lossless attention (called EL-attention) to address this issue. It avoids heavy operations for building multi-head keys and values, with no requirements of using cache. EL-attention constructs an ensemble of attention results by expanding query while keeping key and value shared. It produces the same result as multi-head attention with less GPU memory and faster inference speed. We conduct extensive experiments on Transformer, BART, and GPT-2 for summarization and question generation tasks. The results show EL-attention speeds up existing models by 1.6x to 5.3x without accuracy loss.

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