Mastering Positional Embeddings in Transformer Papers: A Comprehensive Guide

Positional embeddings in transformer models are a crucial component that often gets overlooked. When reading transformer papers, it is easy to assume that positional embeddings are straightforward. However, when you try to implement them, it can get quite confusing. In this blog post, we will delve into the importance of positional embeddings and break down their implementation.

Positional embeddings, also known as PE, play a significant role in adding positional information to transformer models. While sinusoidal positional encodings are commonly used in NLP tasks, for computer vision problems, images require a more structured form of positional information.

Incorporating positional embeddings inside the multi-head self-attention (MHSA) block is essential for enforcing the sense of order in transformer models. Without this positional information, the attention mechanism lacks the ability to capture the spatial structure of images effectively.

There are two main types of positional embeddings: absolute and relative. Absolute positional embeddings add learned trainable vectors to each position of the input sequence, enhancing the representation with position-specific information. On the other hand, relative positional embeddings represent the distance between tokens, providing translation equivariance similar to convolutions.

Implementing absolute positional embeddings is relatively straightforward, involving initializing trainable components and multiplying them with the query at each forward pass. On the other hand, relative positional embeddings require converting relative distances to absolute distances, which can be tricky. By understanding the underlying concepts and leveraging the right tools like einsum operations, you can successfully implement both types of positional embeddings in your transformer models.

Furthermore, extending positional embeddings to a 2D grid for image data involves considering the row and column offsets between pixels. By factoring tokens across dimensions and providing each pixel with two independent distances, you can effectively incorporate 2D relative positional embeddings in transformer models for computer vision tasks.

In conclusion, mastering positional embeddings is essential for fully leveraging the power of transformer models in computer vision applications. By understanding the theory behind absolute and relative positional embeddings and implementing them correctly, you can enhance the spatial awareness and performance of your transformer models. With the right tools and a solid grasp of positional embeddings, you can take your transformer implementations to the next level.