autoattn 0.1.0

Automatic routing between attention backends (dense, flash, sparse)

autoattn

Automatic routing between attention backends for LLMs/VLMs.

Install

pip install -e .

Usage

import torch
from autoattn import AutoAttention

attn = AutoAttention(d_model=256, num_heads=8, causal=True)

q = torch.randn(2, 128, 256)
k = torch.randn(2, 128, 256)
v = torch.randn(2, 128, 256)

out = attn(q, k, v)  # Automatically picks best backend

Backends

Backend	When Used	Memory	Exact?
dense	CPU, fallback	O(N²)	✅
flash	GPU, seq ≤ 2048	O(N)	✅
local	GPU, seq > 4096, memory mode	O(N·W)	❌

Modes

# Auto (default) - picks based on device/seq length
AutoAttention(d_model=256, num_heads=8, mode="auto")

# Performance - prefer flash on GPU
AutoAttention(d_model=256, num_heads=8, mode="performance")

# Memory - prefer local/sparse
AutoAttention(d_model=256, num_heads=8, mode="memory")

Requirements

• Python ≥ 3.9
• PyTorch ≥ 2.0