
YOSO
Overview
The YOSO model was proposed in You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh. YOSO approximates standard softmax self-attention
via a Bernoulli sampling scheme based on Locality Sensitive Hashing (LSH). In principle, all the Bernoulli random variables can be sampled with
a single hash. 
The abstract from the paper is the following:
Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is 
the self-attention mechanism, which captures the interactions of token pairs in the input sequences and depends quadratically 
on the sequence length. Training such models on longer sequences is expensive. In this paper, we show that a Bernoulli sampling 
attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear. 
We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random 
variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). 
This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of 
LSH (to enable deployment on GPU architectures). We evaluate our algorithm on the GLUE benchmark with standard 512 sequence 
length where we see favorable performance relative to a standard pretrained Transformer. On the Long Range Arena (LRA) benchmark, 
for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable 
speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at this https URL
This model was contributed by novice03. The original code can be found here.
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