Image source: https: //ai.googleblog.com/2020/10/rethinking-attention-with-performers.html
The Transformer model, a deep learning framework, achieves cutting-edge results across domains such as natural language, conversation, images, and even music. The core block of the Transformer architecture is an attention module that calculates the similarity score for all pairs of positions in the input sequence. Longer input sequences are less efficient because they require quadratic computation time and quadratic memory size for the storage matrix.
Therefore, for long-distance attention, one of the most common methods is sparse attention. It reduces complexity by calculating selective similarity scores from sequences based on a variety of methods. There are still certain limitations, such as the lack of efficient sparse matrix multiplication operations on all accelerators, no theoretical guarantees, and insufficientness to address the full range of problems.
Introduction to performers
To solve these problems, Google AI has introduced the Performer, a Transformer architecture with a linearly scaling attention mechanism. The framework is implemented by fast attention via a positive orthogonal random feature (FAVOR +) algorithm, and a scalable low variance and non-bias estimation of the attention mechanism represented by random feature map decomposition (especially normal softmax attention). To provide. Mappings help maintain the complexity of linear space and time.
In the original attention mechanism, the query, the row matrix and the key, the column matrix, are multiplied and passed through the softmax operation to form the attention matrix. However, in this case, the query key product matrix cannot be decomposed into the original components after going through the softmax operation.
You can use the matrix factorization factorization to get the linear time attention mechanism. After decomposing the attention matrix, you can reposition the matrix multiplications to approximate the results of the normal attention mechanism without explicitly creating a quadratic-sized attention matrix. This provides FAVOR + (fast attention due to matrix connectivity).
Therefore, using the above algorithm, Performer comes up with the idea of reducing space and time complexity and reducing energy costs by improving inference speed.
Github: https: //github.com/google-research/google-research
Paper: https: //arxiv.org/pdf/2009.14794.pdf
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