Dec. 12, 2023, 7:37 a.m. | /u/Emergency_Shoulder27

Machine Learning

**Paper:** [****](

**Code:** [**\_linear\_attention\_layer**](


>Transformers with linear attention allow for efficient parallel training but can simultaneously be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear (with respect to output length) inference complexity. Recent works such as RetNet (Sun et al., 2023) and TransNormerLLM (Qin et al., 2023a) observe that adding a global decay term to the additive RNN update rule greatly improves performance, sometimes outperforming standard Transformers with softmax attention when trained at scale. In …

abstract attention complexity global hidden inference linear machinelearning matrix observe rnn training transformers update

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