Overview: A Guide to Understanding Patch Merger in Vision Transformers
If you’ve ever worked with Vision Transformers, you know that tokenization can be a major bottleneck when it comes to optimizing models for efficient compute. Luckily, there’s a clever solution to this problem: PatchMerger, a module that reduces the number of tokens passed onto each individual transformer encoder block while maintaining performance, thereby reducing compute load.
Put simply, PatchMerger takes an input block consisting of N patches with D dimensions and linearly transforms it through a learnable weight matrix of shape M output patches with D dimensions. This generates M scores, which are then run through a Softmax function applied to each score. The resulting output has a shape of M × N and is multiplied with the original input to get an output of shape M × D.
Why is Patch Merger Important?
The beauty of PatchMerger is that it enables us to reduce the number of tokens or patches passed onto each transformer encoder block, which in turn reduces the amount of compute needed to run the model. This is a critical advantage when optimizing models for better performance and efficiency, especially when it comes to training with large datasets.
Without a method for managing tokenization, larger datasets can cause models to slow down significantly, increasing the time needed to complete training and increasing overall computing costs. PatchMerger solves this problem by enabling a more efficient use of resources, allowing models to be trained faster and with less overall computation.
How does PatchMerger work?
To understand how PatchMerger works, let’s take a closer look at the math involved. Suppose we have an input block X consisting of N patches with D dimensions, and we want to transform them to output M patches with D dimensions using PatchMerger. We start by applying a learnable weight matrix W of shape D x D, giving us:
$$ {W^T}{X^T} $$
This generates M scores, which can be run through a Softmax function to get a probability distribution of scores for each patch. This is shown mathematically as:
$$ \text{softmax}({W^T}{X^T}) $$
After applying Softmax, we have an output of shape M x N. We then multiply this output with the original input block X to get the final output of shape M x D, as shown mathematically as:
$$ Y = \text{softmax}({W^T}{X^T})X $$
This output can then be passed onto the transformer block for further processing, with the advantage of having fewer tokens and reducing compute overhead.
Benefits of Using Patch Merger
Aside from reducing tokenization overhead and improving model efficiency, there are several key benefits to using PatchMerger in Vision Transformer models.
First and foremost, PatchMerger enables faster training times by reducing the amount of compute needed to process each input block. This can be particularly important for models trained on large datasets or with complex input types, where traditional tokenization methods could grind the model to a halt during training. PatchMerger streamlines the tokenization process, reducing the risk of crashes or long training times.
Another important benefit of PatchMerger is that it allows models to be trained more effectively on smaller datasets. By reducing the number of tokens passed through the transformer encoder block, PatchMerger enables a more efficient use of data, helping models to train more effectively and produce better results with less data.
Finally, PatchMerger can be an important tool for optimizing models for deployment on edge devices, where resources are often more limited. By reducing the amount of compute needed to process each input block, PatchMerger can help ensure that models run more efficiently and more quickly on resource-constrained devices, enabling them to be used more effectively in real-world applications.
All in all, PatchMerger is an elegant solution to the problem of tokenization in Vision Transformer models. By reducing the number of tokens passed onto transformer encoder blocks, PatchMerger can significantly reduce the overall compute load of models, enabling faster training times, better results with less data, and more efficient deployment on edge devices.
If you’re working with Vision Transformers and looking for a way to optimize your models for better performance and efficiency, PatchMerger may be just the tool you need. With its simple mathematical formula and ability to streamline tokenization, PatchMerger is a powerful tool that can make all the difference in the success of your models.
