Beyond Attention: Recent Advances in Efficient Transformer Architectures
This article explores several key advancements in Transformer architectures that go beyond the original attention mechanism. These techniques primarily focus on reducing computational complexity and memory requirements. Examples include Group Query Attention (GQA) which reduces memory usage by sharing key/value projections; Multi-head Latent Attention (MHA) which uses latent vectors to decrease computational complexity; Flash Attention which optimizes speed through clever memory management; and Ring Attention which utilizes multi-GPU parallelism for extremely long sequences. Additionally, the article covers pre-normalization, RMSNorm, SwiGLU activation functions and normalization methods, as well as learning rate warmup, cosine scheduling, Mixture of Experts (MoE), multi-token prediction, and speculative decoding. These techniques collectively push the boundaries of Transformers, enabling them to handle longer sequences and higher-dimensional data more efficiently, ultimately improving both speed and performance.
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