README.md

PositionalEmbeddings.jl

A Julia package providing various positional embedding implementations for enriching sequence data with position information.

Features

• Absolute Positional Embeddings (AbsolutePE): Implementation of the sinusoidal position embeddings from Attention Is All You Need
• Rotary Position Embeddings (RoPE): Implementation of the rotary position embeddings from RoFormer: Enhanced Transformer with Rotary Position Embedding

Installation

using Pkg
Pkg.add("PositionalEmbeddings")

Quick Start

Different positional embeddings expect different input tensor layouts:

• AbsolutePE works with inputs of shape (seq_len, channels, batch), adding position-specific values uniformly across channels and batches for each sequence position.

• RoPE operates on inputs of shape (head_dim, seq_len, nhead*batch), applying rotations between pairs of dimensions in head_dim independently for each sequence position, across heads and batches.

Example:

using PositionalEmbeddings
# Absolute Positional Embeddings
pe = AbsolutePE(512, 1024)  # embedding_size=512, max_length=1024
x = randn(Float32, 100, 512, 32)  # (seq_len, channels, batch)
x_with_pos = pe(x)

# Rotary Position Embeddings
rope = RoPE(512, 1024)  # head_dim=512, max_length=1024
x = randn(Float32, 512, 100, 2*32)  # (head_dim, seq_len, (nhead*batch_size))
x_with_pos = rope(x)

⚠️ For RoPE users must ensure that all precomputed matrices are not treated as trainable parameters. For example with Optimisers.trainable(::RoPE) = (;)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.