selfextend 0.0.1

SelfExtendAttn - Pytorch

SelfExtendAttn

Implementation of SelfExtendAttn from the paper "LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning" from Pytorch and Zeta. This implementation is based mostly on the pseudocode listed in Algorithm 1 in page 4

Install

pip install selfextend

Usage

import torch
from se_attn import SelfExtendAttn

# Example usage
dim = 512  # Dimension of model
g_size = 2  # Group size
w_size = 4  # Window size for neighbor tokens
self_extend = SelfExtendAttn(dim, g_size, w_size, qk_norm=True)

# Example tensors for q, k, v, and pos
q = torch.randn(1, 10, dim)
k = torch.randn(1, 10, dim)
v = torch.randn(1, 10, dim)
pos = torch.arange(0, 10).unsqueeze(0)  # Example positional indices

output = self_extend(q, k, v, pos)
print(output)

---

Technical Architecture

Key Concepts

• Grouped Attention: This mechanism divides the input sequence into groups and applies the attention operation within each group. It uses a floor operation to adjust the positions within the groups, enabling efficient handling of longer sequences.

• Normal Attention: Standard self-attention used in transformers, focusing on nearby tokens within a specified window.

Attention Mechanism

The SelfExtendAttn module integrates these two attention strategies:

1. Normal Attention is applied to tokens within a neighborhood window, maintaining precise positional information for closely related tokens.

2. Grouped Attention is used for tokens outside this neighborhood window. It reduces the granularity of positional information for distant tokens, which is less critical but still contributes to the overall context understanding.

Merge Strategy

The attention values outside the neighborhood window are replaced by those obtained from the grouped attention. This merging strategy ensures a smooth transition and efficient processing of longer sequences while preserving the essential context captured by the normal attention within the neighborhood window.

Positional Encoding

Sine and cosine functions generate positional encodings, ensuring that the model retains an understanding of token order and position.

Implementation Details

• Module Class: SelfExtendAttn is implemented as a subclass of nn.Module in PyTorch.
• Configurability: Key parameters such as group size and neighbor window size are configurable.
• Causal Masking: Ensures that the attention mechanism respects the autoregressive property of language models.

Citation

@misc{jin2024llm,
    title={LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning}, 
    author={Hongye Jin and Xiaotian Han and Jingfeng Yang and Zhimeng Jiang and Zirui Liu and Chia-Yuan Chang and Huiyuan Chen and Xia Hu},
    year={2024},
    eprint={2401.01325},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

License

MIT