xformers

Provide optimized attention and sparse tensor operations for Transformer models.

4|Updated May 6, 2026

Search Tags:#optimization #deep learning #cuda #attention #sparse #triton #transformers

Ready to run

npx skills add https://github.com/jstzwj/ai-infra-plugins --skill xformers

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: xformers
Source: https://github.com/jstzwj/ai-infra-plugins/tree/main/plugins/xformers/skills/xformers
Command: npx skills add https://github.com/jstzwj/ai-infra-plugins --skill xformers

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, triton, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides optimized building blocks for Transformer models, reducing development time and improving performance across training and inference tasks.

Core Features & Use Cases

Memory-Efficient Attention: Enables fast, exact attention computations suitable for large-scale models.
Structured Sparse Operations: Implements 2:4 sparsity, supporting faster training and inference with reduced memory footprint.
Research and Deployment: Supplies custom CUDA, Triton kernels, and model parallel layers for cutting-edge Transformer research, including heterogeneous batching and inference acceleration.
Example Scenario: Use this Skill to replace standard attention with a memory-efficient version in a language model, reducing GPU memory usage and speeding up training.

Quick Start

Use the xformers skill to replace the standard attention with the memory-efficient attention function in your transformer code.

Frequently Asked Questions about xformers

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce GPU memory usage for attention computations in transformer models?▼

You can reduce GPU memory usage by replacing standard attention with memory-efficient attention computations. This provides exact attention results while significantly lowering the memory footprint for large-scale models.

What is 2:4 structured sparsity for deep learning inference?▼

2:4 structured sparsity is a sparse tensor operation that reduces memory footprint and accelerates deep learning inference. It enforces structured sparsity patterns to support faster training and deployment of transformer models.

Do I need CUDA and Triton to use custom transformer model parallel layers?▼

Yes, custom transformer model parallel layers depend on CUDA and Triton. The package includes custom CUDA and Triton kernels to deliver heterogeneous batching and inference acceleration for research.

What's the best way to accelerate transformer training with sparse tensor operations?▼

The best way to accelerate transformer training is implementing structured sparse tensor operations like 2:4 sparsity. This approach optimizes memory efficiency and speeds up both training and inference tasks.

Can I use PyTorch with memory-efficient attention for large-scale models?▼

Yes, you can use PyTorch with memory-efficient attention for large-scale models. The implementation depends on PyTorch and provides optimized building blocks to enhance training speed and memory efficiency.

When should I use custom CUDA kernels for transformer deployment?▼

Use custom CUDA kernels for transformer deployment when requiring custom parallelism strategies, heterogeneous batching, or inference acceleration. These kernels optimize research and deployment scenarios for large models.