This is an unofficial implementation of: BitNet: Scaling 1-bit Transformers for Large Language Models and The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

BitLinear and BitLinear1_58 are candle modules for training and inferencing binarized linear layers, which is a key component of the BitNet and BitNet1.58 architecture. It can effectively reduce the memory footprint and energy consumption of large language models while maintaining competitive performance compared to full-precision models.

• Quantization of weights and activations to a specified bit-width
• Support for bias addition and optional use of bias
• Option to apply quantization before or after the non-linear activation
• Configurable parameters for bit-width, epsilon value, and quantization method

1. Import the necessary modules:

use candle_core::{Result, Tensor};
use candle_nn::{VarBuilder, init};
use bitnet_rs::bitlinear::{bitlinear, BitLinearConfig};

1. Create a BitLinearConfig with your desired settings:

let config = BitLinearConfig {
    bit_width: 8,
    eps: 1e-5,
    bias: true,
    use_before_nonlinear: false,
};

1. Create a BitLinear layer using the bitlinear function:

let bitlinear = bitlinear(input_dim, output_dim, config, var_builder)?;

1. Forward pass through the BitLinear layer:

let output = bitlinear.forward(&input_tensor)?;

The BitLinearConfig struct allows you to customize the behavior of the BitLinear layer:

• bit_width: The number of bits to use for quantization (default: 8).
• eps: A small value used to clamp the quantized values (default: 1e-5).
• bias: Whether to include a bias term (default: true).
• use_before_nonlinear: Whether to apply quantization before or after the non-linear activation (default: false).

You can find examples of using BitLinear and BitLinear1_58 in the examples.

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

If you use BitLinear in your research, please cite the following paper:

BitNet: Scaling 1-bit Transformers for Large Language Models

@article{wang2023bitnet,
  title={BitNet: Scaling 1-bit Transformers for Large Language Models},
  author={Wang, Hongyu and Ma, Shuming and Dong, Li and Huang, Shaohan and Wang, Huaijie and Ma, Lingxiao and Yang, Fan and Wang, Ruiping and Wu, Yi and Wei, Furu},
  journal={arXiv preprint arXiv:2310.11453},
  year={2023}
}

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

@article{ma2024era,
title={The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits},
author={Ma, Shuming and Wang, Hongyu and Ma, Lingxiao and Wang, Lei and Wang, Wenhui and Huang, Shaohan and Dong, Li and Wang, Ruiping and Xue, Jilong and Wei, Furu},
journal={arXiv preprint arXiv:2402.17764},
year={2024}
}