Ring Attention Explained
The Ring Attention technique is a fascinating approach to extend the context length in Large Language Models (LLMs) using multiple GPUs. This method promises to overcome significant technical challenges by cleverly orchestrating computation and communication across devices. Here's a simplified breakdown of how Ring Attention works.
The Challenge of Long Contexts
Large Language Models like GPT-4, Claude 3.5 Sonnet, and Gemini 1.5 Pro have been pushing the limits of context length, enabling them to incorporate and reason about more information. However, increasing the context length leads to higher memory demands, which is constrained by GPU memory capacity. The solution? Distribute the memory load across multiple GPUs.
Basics of Attention in Transformers
In a Transformer model, attention involves three matrices: Query (Q), Key (K), and Value (V). Each of these matrices has a size dependent on the sequence length (s) and the model dimension (d). The core operation is to compute the attention output using the formula:
This operation generates two large matrices, which makes the memory requirement grow quadratically with the sequence length.
Splitting the computation
To manage the memory requirement, the idea is to split the computation into smaller chunks that can be distributed across multiple GPUs. This involves dividing the Q, K, and V matrices along the sequence dimension.
Step 1: Splitting Q
We split the Query matrix (Q) into smaller chunks. Each GPU gets one chunk of Q to work with. For instance, if we have four GPUs, we divide Q into four parts.
Step 2: Splitting K and V
Splitting the Key (K) and Value (V) matrices is trickier because each chunk of Q still needs to interact with the entire K and V matrices. This means that each GPU needs to access all the parts of K and V, but we need to do it in a way that minimizes communication overhead.
The Ring Attention Technique
The key to Ring Attention is the clever orchestration of computation and communication. Here’s how it works:
Setup: Each GPU starts with a chunk of Q and the corresponding parts of K and V.
Compute: Each GPU computes its part of the attention output using its chunk of Q and the current K, V blocks.
Rotate: After computing, each GPU passes its K and V blocks to the next GPU in a ring-like fashion.
This way, while a GPU is computing with one set of K, V blocks, it is simultaneously receiving the next set from its neighbor. This hides the communication time within the computation time, making the process efficient.
Handling Softmax
The softmax operation in the attention mechanism requires computing exponentials and normalising by the sum. Ring Attention manages this by:
Local Computation: Each GPU computes the exponentials and keeps track of the maximum value to prevent overflow.
Normalisation: After computing with all parts of K and V, the GPUs normalize the results.
Putting it all together
In summary, Ring Attention splits the attention computation across multiple GPUs, with each GPU handling a portion of the Query matrix. The Key and Value matrices are rotated among the GPUs, allowing each GPU to compute its part without waiting for data. This parallelism reduces memory load and hides communication overhead, enabling LLMs to handle longer contexts efficiently.
Visual Summary
Here's a simplified visual explanation of the Ring Attention process:
Initialisation:
Image from Coconut Mode Each GPU starts with a chunk of Q and a part of K and V.
Computation and Rotation:
Image from Coconut Mode Each GPU computes its part and passes the K, V blocks to the next GPU.
Final Output: The GPUs combine their results to form the final attention output.
Conclusion
Ring Attention is a powerful technique that allows Large Language Models to efficiently handle long contexts by distributing computation and memory load across multiple GPUs. By cleverly rotating data and overlapping communication with computation, it minimizes overhead and maximizes efficiency.