lucidrains / autoregressive-linear-attention-cuda
CUDA implementation of autoregressive linear attention, with all the latest research findings
☆44Updated last year
Alternatives and similar repositories for autoregressive-linear-attention-cuda:
Users that are interested in autoregressive-linear-attention-cuda are comparing it to the libraries listed below
- ☆29Updated 2 years ago
- Implementation of an Attention layer where each head can attend to more than just one token, using coordinate descent to pick topk☆46Updated last year
- Experiment of using Tangent to autodiff triton☆78Updated last year
- Implementation of GateLoop Transformer in Pytorch and Jax☆87Updated 10 months ago
- Implementation of the Kalman Filtering Attention proposed in "Kalman Filtering Attention for User Behavior Modeling in CTR Prediction"☆57Updated last year
- Explorations into the recently proposed Taylor Series Linear Attention☆99Updated 8 months ago
- Demo of the unit_scaling library, showing how a model can be easily adapted to train in FP8.☆45Updated 9 months ago
- ☆33Updated 7 months ago
- Exploring an idea where one forgets about efficiency and carries out attention across each edge of the nodes (tokens)☆50Updated last month
- Exploration into the proposed "Self Reasoning Tokens" by Felipe Bonetto☆55Updated 11 months ago
- Some personal experiments around routing tokens to different autoregressive attention, akin to mixture-of-experts☆118Updated 6 months ago
- ☆51Updated 10 months ago
- ☆52Updated 7 months ago
- Reference implementation of "Softmax Attention with Constant Cost per Token" (Heinsen, 2024)☆24Updated 10 months ago
- Triton Implementation of HyperAttention Algorithm☆47Updated last year
- Implementation of Infini-Transformer in Pytorch☆110Updated 4 months ago
- Implementation of Gradient Agreement Filtering, from Chaubard et al. of Stanford, but for single machine microbatches, in Pytorch☆24Updated 3 months ago
- ☆37Updated last year
- 32 times longer context window than vanilla Transformers and up to 4 times longer than memory efficient Transformers.☆48Updated last year
- Implementation of some personal helper functions for Einops, my most favorite tensor manipulation library ❤️☆54Updated 2 years ago
- Here we will test various linear attention designs.☆60Updated last year
- Using FlexAttention to compute attention with different masking patterns☆43Updated 7 months ago
- ☆78Updated 10 months ago
- Another attempt at a long-context / efficient transformer by me☆37Updated 3 years ago
- Why Do We Need Weight Decay in Modern Deep Learning? [NeurIPS 2024]☆66Updated 7 months ago
- Blog post☆17Updated last year
- Implementation of Token Shift GPT - An autoregressive model that solely relies on shifting the sequence space for mixing☆48Updated 3 years ago
- Hacks for PyTorch☆19Updated 2 years ago
- Utilities for PyTorch distributed☆24Updated 2 months ago
- [ICLR 2025] Official PyTorch implementation of "Forgetting Transformer: Softmax Attention with a Forget Gate"☆97Updated 3 weeks ago