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# 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
```python
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
```bibtext
@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
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"description": "[](https://discord.gg/qUtxnK2NMf)\n\n# SelfExtendAttn\nImplementation 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\n\n\n# Install\n`pip install selfextend`\n\n\n## Usage\n```python\nimport torch\nfrom se_attn import SelfExtendAttn\n\n# Example usage\ndim = 512 # Dimension of model\ng_size = 2 # Group size\nw_size = 4 # Window size for neighbor tokens\nself_extend = SelfExtendAttn(dim, g_size, w_size, qk_norm=True)\n\n# Example tensors for q, k, v, and pos\nq = torch.randn(1, 10, dim)\nk = torch.randn(1, 10, dim)\nv = torch.randn(1, 10, dim)\npos = torch.arange(0, 10).unsqueeze(0) # Example positional indices\n\noutput = self_extend(q, k, v, pos)\nprint(output)\n```\n\n---\n\n## Technical Architecture\n\n### Key Concepts\n\n- **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.\n \n- **Normal Attention**: Standard self-attention used in transformers, focusing on nearby tokens within a specified window.\n\n### Attention Mechanism\n\nThe `SelfExtendAttn` module integrates these two attention strategies:\n\n1. **Normal Attention** is applied to tokens within a neighborhood window, maintaining precise positional information for closely related tokens.\n \n2. **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.\n\n### Merge Strategy\n\nThe 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.\n\n### Positional Encoding\n\nSine and cosine functions generate positional encodings, ensuring that the model retains an understanding of token order and position.\n\n## Implementation Details\n\n- **Module Class**: `SelfExtendAttn` is implemented as a subclass of `nn.Module` in PyTorch.\n- **Configurability**: Key parameters such as group size and neighbor window size are configurable.\n- **Causal Masking**: Ensures that the attention mechanism respects the autoregressive property of language models.\n\n\n\n# Citation\n```bibtext\n@misc{jin2024llm,\n title={LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning}, \n 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},\n year={2024},\n eprint={2401.01325},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n# License\nMIT",
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