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🚀 Efficient implementations of state-of-the-art linear attention models in Torch and Triton
Efficient implementations of state-of-the-art linear attention models in Torch and Triton. This project provides high-performance, memory-efficient alternatives to traditional quadratic attention mechanisms, specifically optimized for long sequences and large-scale deep learning models.
Python
Added on 2025年6月11日2,569
Stars189
ForksPython
LanguageProject Introduction
Summary
This project implements various linear attention mechanisms with a focus on computational and memory efficiency using PyTorch and Triton. It aims to provide a drop-in replacement or a highly optimized alternative to standard attention layers in deep learning models, enabling processing of much longer sequences.
Problem Solved
Traditional attention mechanisms have a quadratic complexity concerning sequence length, leading to high computational cost and excessive memory consumption for long sequences, limiting the applicability of attention models in many tasks.
Core Features
High Performance & Efficiency
Offers significant speedups and memory savings compared to standard quadratic attention, especially for long input sequences.
PyTorch & Triton Integration
Built with PyTorch (Torch) and leverages Triton for performance-critical kernels, ensuring compatibility and speed.
Multiple Model Variants
Includes implementations of various state-of-the-art linear attention variants.
Tech Stack
PyTorch
Triton
CUDA (optional, for performance)
使用场景
The high efficiency of linear attention makes it suitable for various applications where processing long sequences or optimizing computational resources is critical.
场景一：长文本序列处理 (LLMs)
Details
Applying linear attention in Large Language Models to handle significantly longer context windows during training and inference, improving coherence and context understanding.
User Value
Enables processing documents, books, or very long conversations that are infeasible with standard attention due to memory constraints.
场景二：高效序列建模
Details
Using linear attention in sequence-to-sequence models for tasks like machine translation or speech recognition where input/output sequences can be quite long.
User Value
Speeds up training and inference, allowing for the use of larger batch sizes or longer sequences within available hardware resources.
场景三：计算机视觉应用
Details
Implementing efficient attention mechanisms in Vision Transformers (ViT) or other attention-based computer vision models to process higher resolution images or videos more efficiently.
User Value
Reduces computational overhead for high-resolution inputs, making attention-based vision models more practical.
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