Posts tagged "linear-attention"
5 posts found
The Efficient Transformer Design Space: Comparing All Variants and the Three Futures of Attention
A unified view of every efficient attention mechanism — each is a different way of replacing the dense N×N attention matrix (sparsify it, factorize it, approximate the softmax kernel, recurse it, or pool one of its dimensions), every variant computed on a single 8-token example so the differences are visible side by side, ranked on the impossible triangle of training parallelism, low-cost inference, and strong quality, and ending with the three credible futures: hybrid retention, selective state spaces, and latent attention.
The Kernel Zoo: Performers, Fast Weight Programmers, and the Capacity-Approximation Tradeoff in Linear Attention
How to choose the feature map φ in linear attention — the capacity bound that says a state of dimension d_φ stores at most d_φ orthogonal key-value pairs, the Performer's FAVOR+ that approximates softmax with positive orthogonal random features, the DPFP map that grows capacity deterministically through ReLU-gated quadrant projections, and the capacity-versus-approximation tradeoff that defines the design space, with the linear-attention recurrence reframed as a 1990s fast weight programmer.
Targeted Memory: The Delta Rule, Gated DeltaNet, and Kimi Delta Attention
Building targeted memory for linear attention from scratch — the delta rule (erase only the key being overwritten, derived as online gradient descent on a reconstruction loss), the gated delta rule (combine surgical erasure with a global forget gate), and Kimi Delta Attention (replace the scalar gate with a per-dimension one), why uniform decay alone cannot solve the memory-collision problem, and how Kimi Linear interleaves KDA with full attention to outperform pure full-attention models at 48B parameters.
Hybrid Architectures: RetNet and the Three Computation Paradigms
Building RetNet from scratch — how a single retention formula admits three computation modes (parallel for training, recurrent for inference, chunkwise for long sequences) producing identical outputs, why exponential decay and complex-exponential position encoding together fix the two failure modes of linear attention, and what the impossible triangle of training parallelism, low-cost inference, and strong performance actually requires.
Why Replace Attention? The Softmax Bottleneck and the Path to Linear Time
Why softmax is the bottleneck that every attention variant leaves untouched — and how rewriting attention as a kernel exposes an associativity trick that collapses the O(n²) cost to O(n) and turns the transformer into an RNN.