leetDecoding 0.0.1

Efficient computation library for linear attention.

An efficient Linear Attention Decoding package

1. installation

conda create -n leetDecoding python==3.9
conda activate leetDecoding
pip install leetDecoding

The code has been test under the following environment:

triton>=2.1.0
torch>=2.1.0
pycuda
pynvml
numpy<2

You can use the following command to install:

pip install triton==2.1.0
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118
pip install pycuda
pip install pynvml
pip install numpy

2. usage

import torch
from leetDecoding.efficient_linear_decoding import causal_linear_decoder

# Create input tensor
Q = torch.randn(2,32,1024,128,device='cuda:0')
K = torch.randn(2,32,1024,128,device='cuda:0')
V = torch.randn(2,32,1024,128,device='cuda:0')

# Inference using causal_linear_decoder
output = causal_linear_decoder(Q,K,V)

# If you want to input a mask with weight that values are exp(-gamma), set the is_mask_weight: True and is_need_exp:True
gamma = torch.full((32,),0.5,device='cuda:0')
output = causal_linear_decoder(Q,K,V,is_mask_weight=True,gamma=gamma,is_need_exp=True)

# If you just want to input a mask with weight, set the is_mask_weight: True and is_need_exp:False
gamma = torch.full((32,),0.5,device='cuda:0')
output = causal_linear_decoder(Q,K,V,is_mask_weight=True,gamma=gamma,is_need_exp=False)

3. acknowledgement

method	Title	Paper	Code
causal_dot_product	Fast Transformers with Clustered Attention	arxiv	code
Lighting Attention-2	Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models	arxiv	code