I wanted to switch gears into something more fun and realized I could get started on Advent of Code.
The Day 1 problems didn't feel as lightweight as what I would imagine Day 1 problems to be. I was tempted to have a scanner that worked both ways (since you want the first and last number in a string, number defined as '1' or 'one'), but realized after taking a break that it's more of a premature optimization.
The plan was to code in Python and then port the slow parts to Mojo. Jen Hsin shared a very well-written Advent of Mojo series of posts, which on a quick review shared how "68,000x speed up is a bit ambitious here since you're not parallelizing lots and lots of matrix multiplies". What's particularly helpful is one of the posts in the series talks about usability, which would be a good post to start as a newbie.
I realized there was an open question on Mojo from the day before, so got around to take a closer look.
Prompt: I was listening to a talk on Mojo and came across the notion that GPUs are optimized for matrix multiplies but not necessarily for operations relating to the transformer architecture. Is this really the case?Response: The statement you heard about GPUs being optimized for matrix multiplications but not necessarily for operations specific to the transformer architecture is partially true, and it relates to how GPUs are designed and how transformer models work.GPU Optimization for Matrix Multiplications: GPUs are highly efficient at performing matrix multiplications, which are a core operation in many deep learning tasks. This efficiency is due to their parallel processing capabilities and architecture, which is well-suited for operations that can be broken down into smaller, simultaneous tasks – like matrix multiplications.
Transformers and Matrix Multiplications: Transformer models, popularized by architectures like GPT (Generative Pretrained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), rely heavily on matrix multiplications. These are part of the attention mechanisms and feed-forward networks within the transformer layers. GPUs are well-suited for these aspects of transformers.
Other Operations in Transformers: However, transformers also involve other types of operations that are not purely matrix multiplications. For example, operations in the attention mechanism like the calculation of attention scores (involving softmax functions) or certain element-wise operations might not be as optimally performed on GPUs as matrix multiplications. While GPUs can certainly handle these tasks, they are not as inherently optimized for them as they are for matrix multiplications.
In summary, while GPUs are indeed optimized for matrix multiplications and perform well for many operations in transformer architectures, they are not necessarily optimized for every specific operation within those architectures. This has led to an interest in developing hardware that can better cater to the unique demands of transformer models. Emerging Hardware for Transformers: Recognizing the growing importance of transformer models, there's ongoing research and development in creating hardware that's more specifically optimized for the needs of these models. This includes both modifications to GPU architectures and the development of specialized processors (like TPUs - Tensor Processing Units by Google) that can handle the mix of operations in transformers more efficiently.