Apple last week gave us "Sigmoid Self-Attention" - Paper Podcast

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Paper Podcast - Apple last week gave us something more exciting than Iphone 16

📚 "Sigmoid Self-Attention"

Replace the traditional Softmax in Attention with a Sigmoid and a constant (not learned) scalar bias based on the sequence length.

Will give you a 17% inference kernel speed-up over FlashAttention-2 on H100 GPUs.

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*Key Insights from this Paper* 💡:

• SigmoidAttn has improved regularity compared to SoftmaxAttn

• Stabilizing large initial attention norms is crucial for successful training

• FLASHSIGMOID implementation offers significant inference speed-up

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The Podcast is generated with Google's illuminate, the tool trained on AI & science-related Arxiv papers.

Sigmoid Attention

📚 https://arxiv.org/pdf/2409.04431

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