# Differential Transformer
An open source community implementation of the model from "DIFFERENTIAL TRANSFORMER" paper by Microsoft. [Paper Link](https://arxiv.org/abs/2410.05258). "Differential attention takes the difference between two softmax attention functions to eliminate attention noise. The idea is analogous to differential amplifiers [19] proposed in electrical engineering,where the difference between two signals is used as output, so that we can null out the common-mode noise of the input. In addition, the design of noise-canceling headphones is based on a similar idea. We can directly reuse FlashAttention [8] as described in Appendix A, which significantly improves model efficiency."
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
## Install
```bash
$ pip3 install differential-transformers
```
## Usage Transformer
```python
import torch
from differential_transformer.main import DifferentialTransformer
from loguru import logger
# Example usage:
# Example dimensions
batch_size = 32
seq_len = 128
embedding_dim = 64
h = 8
λ = 0.1
λinit = 0.05
# Create random input tensor
x = torch.randint(0, 256, (1, 1024))
# Instantiate and run the multi-head attention
multi_head = DifferentialTransformer(heads=h, dim=embedding_dim, λinit=λinit)
output = multi_head(x, λ=λ)
logger.info(f"Output shape: {output.shape}")
```
# License
MIT
## Citation
```bibtex
@misc{ye2024differentialtransformer,
title={Differential Transformer},
author={Tianzhu Ye and Li Dong and Yuqing Xia and Yutao Sun and Yi Zhu and Gao Huang and Furu Wei},
year={2024},
eprint={2410.05258},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.05258},
}
```
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"description": "\n# Differential Transformer \n\nAn open source community implementation of the model from \"DIFFERENTIAL TRANSFORMER\" paper by Microsoft. [Paper Link](https://arxiv.org/abs/2410.05258). \"Differential attention takes the difference between two softmax attention functions to eliminate attention noise. The idea is analogous to differential amplifiers [19] proposed in electrical engineering,where the difference between two signals is used as output, so that we can null out the common-mode noise of the input. In addition, the design of noise-canceling headphones is based on a similar idea. We can directly reuse FlashAttention [8] as described in Appendix A, which significantly improves model efficiency.\"\n\n\n\n[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)\n\n\n## Install\n\n```bash\n$ pip3 install differential-transformers\n```\n\n## Usage Transformer\n\n```python\n\nimport torch\nfrom differential_transformer.main import DifferentialTransformer\nfrom loguru import logger\n\n# Example usage:\n# Example dimensions\nbatch_size = 32\nseq_len = 128\nembedding_dim = 64\nh = 8\n\u03bb = 0.1\n\u03bbinit = 0.05\n\n# Create random input tensor\nx = torch.randint(0, 256, (1, 1024))\n\n# Instantiate and run the multi-head attention\nmulti_head = DifferentialTransformer(heads=h, dim=embedding_dim, \u03bbinit=\u03bbinit)\noutput = multi_head(x, \u03bb=\u03bb)\n\nlogger.info(f\"Output shape: {output.shape}\")\n\n\n```\n\n# License\nMIT\n\n\n## Citation\n\n\n```bibtex\n@misc{ye2024differentialtransformer,\n title={Differential Transformer}, \n author={Tianzhu Ye and Li Dong and Yuqing Xia and Yutao Sun and Yi Zhu and Gao Huang and Furu Wei},\n year={2024},\n eprint={2410.05258},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2410.05258}, \n}\n\n```",
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