scalable-softmax 0.1.5

PyTorch implementation of Scalable-Softmax for attention mechanisms

ScalableSoftmax

An unofficial PyTorch implementation of Scalable-Softmax (Ssmax) from the paper "Scalable-Softmax Is Superior for Attention" (Nakanishi, 2025).

Overview

ScalableSoftmax is a drop-in replacement for standard Softmax that helps prevent attention fading in transformers by incorporating input size scaling. This helps maintain focused attention distributions even with large input sizes.

Installation

pip install scalable-softmax

Usage

import torch
from scalable_softmax import ScalableSoftmax

# Initialize with default parameters
ssmax = ScalableSoftmax()

# Or customize parameters
ssmax = ScalableSoftmax(
    s=0.43,  # scaling parameter
    learn_scaling=True,  # make scaling parameter learnable
    bias=False  # whether to use bias term
)

# Apply to input tensor
x = torch.randn(batch_size, sequence_length)
output = ssmax(x)

Features

• Drop-in replacement for standard softmax
• Learnable scaling parameter
• Optional bias term
• Maintains focused attention with large inputs

Citation

@article{nakanishi2025scalable,
  title={Scalable-Softmax Is Superior for Attention},
  author={Nakanishi, Ken M.},
  journal={arXiv preprint arXiv:2501.19399},
  year={2025}
}

License

MIT License