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Transcript

"Sigmoid Self-Attention"

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

Apple just gave us something more exciting than Iphone 16

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.

📚 https://arxiv.org/pdf/2409.04431

Original Problem 🔍:

Softmax attention in transformers has limitations, including potential focus on few features and computational inefficiency in efficient attention kernels.

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Key Insights from this Paper 💡:

• SigmoidAttn is a universal function approximator for seq-to-seq tasks

• 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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Solution in this Paper 🛠️:

• Replace softmax with sigmoid activation in attention mechanism

• Introduce bias term b = -log(n) to mitigate large initial attention norms

• Implement FLASHSIGMOID, a hardware-aware, memory-efficient version

• Apply LayerScale and QK norm for improved stability

• Use ALiBi or RoPE positional embeddings for language modeling tasks

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Results 📊:

• SigmoidAttn matches SoftmaxAttn performance across various tasks:

- Language modeling: Comparable train/validation NLL at 85M and 1B scale

- Vision: Equivalent performance in supervised and self-supervised learning

- ASR: Similar WER on LibriSpeech and TED-LIUM v3 datasets

• Improved stability and generalization to longer sequences in some cases

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📚 https://arxiv.org/pdf/2409.04431

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