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Scaled Dot-Product Attention is a hard coding interview question covering deep learning, math, nlp — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Implement the scaled dot-product attention mechanism from the Transformer architecture (Vaswani et al., 2017).
Attention(Q, K, V) = softmax(Q * K^T / sqrt(d_k)) * V
Where Q (queries), K (keys), V (values) are matrices and d_k is the key/query dimension.
The sqrt(d_k) scaling prevents the dot products from growing too large, which causes vanishing gradients through softmax.
Return both the attention output and the attention weights matrix.
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