This is an open source implementation for the paper LongNet: Scaling Transformers to 1,000,000,000 Tokens by Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Furu Wei. The LongNet is a Transformer variant designed to scale sequence length up to more than 1 billion tokens without sacrificing performance on shorter sequences.
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You can install LongNet using one of the following methods:
- Clone the LongNet repository from GitHub:
git clone https://github.com/kyegomez/LongNet.git- Navigate to the cloned directory:
cd LongNet- Install the required dependencies:
pip install -r requirements.txt- Note that pip install does not work as the
flash-attnlibrary cannot be compiled since it has custom CUDA Kernels and they need to be built manually.
- Install LongNet directly from PyPI using pip:
pip install LongNetPlease note that LongNet requires a compatible Python version (tested with Python 3.7).
Once you have installed LongNet, you can use the DilatedAttention class as follows:
import timeit
import torch
from LongNet.attention import DilatedAttention
#model config
d_model = 512
num_heads = 8
dilation_rate = 2
segment_size = 64
device = "cuda:0"
dtype=torch.float16
#input data
batch_size = 32
seq_len = 10000000
#create model and data
model = DilatedAttention(d_model, num_heads, dilation_rate, segment_size).to(device)
x = torch.randn((batch_size, seq_len, d_model), device=device, dtype=dtype)
#test forward pass
with torch.no_grad():
output = model(x)
print(f"Output shape: {output.shape}") # expected (batch_size, seq_Len)
#benchmark model
num_runs = 1000
start_time = timeit.default_timer()
for _ in range(num_runs):
model(x)
elapsed_time = timeit.default_timer() - start_time
print(f"Average forward pass time: {elapsed_time / num_runs:.6f} seconds")Scaling sequence length has become a critical bottleneck in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted. In this paper, they introduce LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacrificing the performance on shorter sequences. Specifically, they propose dilated attention, which expands the attentive field exponentially as the distance grows.
LongNet has significant advantages:
- It has a linear computation complexity and a logarithm dependency between tokens.
- It can be served as a distributed trainer for extremely long sequences.
- Its dilated attention is a drop-in replacement for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization.
Experiment results demonstrate that LongNet yields strong performance on both long-sequence modeling and general language tasks. Their work opens up new possibilities for modeling very long sequences, e.g., treating a whole corpus or even the entire Internet as a sequence.
Here's the updated usage and installation section with two methods: git clone or pip install LongNet:
- We're still working on the model configuation as closely in the paper as possible. There are 2 methods, one is
accelerateand the otherfrom LongNet import Train
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Git clone installation
-
Init your parameters
accelerate config -
Then
accelerate launch LongNet/training.py
- Pip install method
from LongNet import Train
Train()Share LongNet with your friends and colleagues who might find it useful. Simply click on the links below to share on various platforms:
Thank you for sharing!
-
Recreate the sparsification mechanism
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Recreate the gathering mechanism
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Implement FlashAttention2.0
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Implement Distributed Setup
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create the all-gather operation in the backward that becomes a reduce-scatter operation
@inproceedings{ding2023longnet,
title={LongNet: Scaling Transformers to 1,000,000,000 Tokens},
author={Ding, Jiayu and Ma, Shuming and Dong, Li and Zhang, Xingxing and Huang, Shaohan and Wang, Wenhui and Wei, Furu},
booktitle={Proceedings of the 10th International Conference on Learning Representations},
year={2023}
}