aule-attention 0.3.0

FlashAttention that just works. No compilation. Any GPU. AMD ROCm, NVIDIA CUDA, Intel, Apple via Vulkan.

aule-attention

Hardware-agnostic FlashAttention implementation. No compilation required. Works on any GPU.

Version: 0.2.0

Installation

pip install aule-attention

Quick Start

from aule import flash_attention
import torch

q = torch.randn(1, 8, 512, 64, device='cuda')
k = torch.randn(1, 8, 512, 64, device='cuda')
v = torch.randn(1, 8, 512, 64, device='cuda')

output = flash_attention(q, k, v, causal=True)

Features

• No compilation at install time
• Works on AMD, NVIDIA, Intel, and Apple GPUs
• Training support with backward pass (Triton backend)
• Grouped Query Attention (GQA) and Multi-Query Attention (MQA) support
• O(N) memory complexity

Backends

Backend	Hardware	Features
Triton	AMD ROCm, NVIDIA CUDA	Training and Inference
Vulkan	Any Vulkan 1.2+ GPU	Inference
CPU	NumPy	Fallback

API

from aule import flash_attention, get_available_backends, print_backend_info

# Compute attention
output = flash_attention(query, key, value, causal=True, scale=None)

# Check available backends
backends = get_available_backends()

# Display backend information
print_backend_info()

Supported Hardware

Triton Backend (Training + Inference)

• AMD Instinct: MI300X, MI300A, MI250X, MI250, MI210, MI100
• NVIDIA Datacenter: H100, A100, A10, L40S
• NVIDIA Consumer: RTX 4090, 4080, 3090, 3080

Vulkan Backend (Inference)

• AMD RDNA3: RX 7900 XTX, 7900 XT, 7800 XT
• AMD RDNA2: RX 6900 XT, 6800 XT, 6700 XT
• Intel Arc: A770, A750, A580
• Intel Integrated: 12th/13th/14th Gen
• Apple Silicon: M1, M2, M3 (via MoltenVK)

License

MIT License - Aule Technologies

Links

• GitHub Repository
• Documentation