Implementing multi head attention with tensors | Avoiding loops to enable LLM scale-up

Welcome back to the Transformers for Vision series.

In this detailed lecture, we explore one of the most important efficiency techniques used in implementing multi-head attention - **Weight Splitting**.

In the previous lecture, we learnt how to implement multi-head attention in a naive way by looping through attention heads and concatenating context vectors. In this lecture, we go a step further and see how large language models like GPT-3 handle dozens of attention heads efficiently using a single matrix multiplication instead of multiple for-loop based operations.

We will understand:

Why naive multi-head attention does not scale well as the number of heads increases
The concept of weight splitting and how it avoids redundant matrix multiplications
How to manage dimensionality across batches, tokens, and heads
How queries, keys, and values are computed and reshaped into 4D tensors
How attention scores, masks, softmax, and dropout are applied efficiently
How the final context vectors are constructed using tensor operations without any for-loops

By the end of this lecture, you will clearly understand how modern Transformers achieve scalability through tensor-based operations and why weight-splitting is fundamental in building efficient architectures like GPT, BERT, and ViT.

If you want to strengthen your understanding of Transformers and Vision models, watch the complete playlist on Transformers for Vision on our channel.

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Watch the complete playlist on Transformers for Vision to master the foundations of attention and modern deep learning architectures.