glassroom / heinsen_attentionLinks
Reference implementation of "Softmax Attention with Constant Cost per Token" (Heinsen, 2024)
☆24Updated last year
Alternatives and similar repositories for heinsen_attention
Users that are interested in heinsen_attention are comparing it to the libraries listed below
Sorting:
- Official repository of paper "RNNs Are Not Transformers (Yet): The Key Bottleneck on In-context Retrieval"☆27Updated last year
- ☆51Updated last year
- Here we will test various linear attention designs.☆62Updated last year
- ☆32Updated 2 years ago
- ☆24Updated last year
- ☆20Updated last year
- ☆11Updated 2 years ago
- sigma-MoE layer☆21Updated 2 years ago
- ☆35Updated last year
- HGRN2: Gated Linear RNNs with State Expansion☆56Updated last year
- ☆57Updated last year
- Official code for the paper "Attention as a Hypernetwork"☆46Updated last year
- Combining SOAP and MUON☆17Updated 11 months ago
- Experiments on the impact of depth in transformers and SSMs.☆40Updated 2 months ago
- Xmixers: A collection of SOTA efficient token/channel mixers☆28Updated 4 months ago
- Stick-breaking attention☆62Updated 6 months ago
- Efficient PScan implementation in PyTorch☆17Updated 2 years ago
- Awesome Triton Resources☆39Updated 8 months ago
- [NeurIPS 2023] Sparse Modular Activation for Efficient Sequence Modeling☆40Updated 2 years ago
- ☆19Updated last month
- APPy (Annotated Parallelism for Python) enables users to annotate loops and tensor expressions in Python with compiler directives akin to…☆29Updated last week
- [NeurIPS 2023 spotlight] Official implementation of HGRN in our NeurIPS 2023 paper - Hierarchically Gated Recurrent Neural Network for Se…☆66Updated last year
- Triton Implementation of HyperAttention Algorithm☆48Updated 2 years ago
- Parallel Associative Scan for Language Models☆18Updated 2 years ago
- Official Code Repository for the paper "Key-value memory in the brain"☆31Updated 10 months ago
- Griffin MQA + Hawk Linear RNN Hybrid☆88Updated last year
- ☆106Updated last year
- Official Repository for Efficient Linear-Time Attention Transformers.☆18Updated last year
- 32 times longer context window than vanilla Transformers and up to 4 times longer than memory efficient Transformers.☆49Updated 2 years ago
- Using FlexAttention to compute attention with different masking patterns☆47Updated last year