GoKawiil
Tech News
A feature recounts how in 2014, University of Montreal intern Dzmitry Bahdanau, working under Yoshua Bengio and Kyunghyun Cho, developed an 'attention' mechanism for neural networks tackling English-to-French translation. That innovation, which let models weigh which parts of input mattered most, is described as a foundational step leading toward today's large language models like ChatGPT.
A visual explainer tool called Transformer Explainer illustrates how Transformer-based neural networks work, using the 124-million-parameter GPT-2 (small) model as its example. It walks through core components like tokenization, embeddings, attention mechanisms, and Transformer blocks to show how these models predict the next word in a sequence.
A newly published technical handbook explains FlashAttention, the exact attention algorithm that speeds up Transformer models by minimizing memory traffic rather than reducing computation. It walks through the standard attention implementation's repeated reads and writes to GPU high-bandwidth memory, then details how tiling, online softmax, and recomputation eliminate most of that overhead across FlashAttention-1 through FlashAttention-4.