Sequence Parallelism
Sequence parallelism is a technique to manage long sequences by parallelizing the attention operation for a given query. The process involves dividing the Key (K) and Value (V) matrices row-wise into corresponding segments or sub-matrices. Each pair of these sub-matrices is then assigned to a distinct computing node, such as a GPU. This allows all nodes to process their assigned sequence segments in parallel, efficiently calculating the attention for the query across the entire long sequence.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Sequence Parallelism
A team is tasked with using a transformer-based model to summarize an entire book. The standard model architecture cannot process the entire book's text at once due to its length. The team implements a strategy where the book is broken into smaller, manageable chunks, each chunk is processed by the model, and the outputs are then combined. What is the fundamental computational bottleneck in the standard architecture that this segmentation strategy is designed to circumvent?
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