Parallel Attention Computation With Blockwise GPU Pipelining
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Solution Overview
Problem
Attention mechanisms in machine learning models, particularly for large language models (LLMs), are resource-intensive due to sequential execution of matrix multiplication and element-wise operations, leading to inefficient use of GPUs and increased time and memory requirements as models process longer input sequences.
Innovation Solution
An accelerated attention mechanism enables parallel execution of matrix multiplication and element-wise operations through pipelining, allowing certain matrix multiplication operations to be performed concurrently with element-wise operations, thereby overlapping work inside and outside GPU dedicated units for matrix multiplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If matrix multiplication and element-wise operations are executed sequentially in attention mechanisms, then computational accuracy is maintained, but processing speed decreases and GPU efficiency is reduced
Solution Approach 1:
The patent segments the attention mechanism computation into distinct blocks: query blocks, key blocks, and value blocks. This segmentation allows independent parallel processing of different block combinations, enabling matrix multiplication and element-wise operations to execute concurrently without interfering with each other, thus improving processing speed while maintaining computational accuracy.
Solution Approach 2:
The patent performs preliminary segmentation of input sequences into blocks before computation begins. By pre-organizing the data into query blocks, key blocks, and value blocks with predetermined dimensions, the system prepares the computational structure in advance, allowing subsequent matrix multiplication and element-wise operations to proceed in parallel without sequential dependencies.
2Adaptability or versatility
If attention mechanisms process longer input sequences, then model capability improves, but time and memory requirements increase
Solution Approach 1:
The patent divides long input sequences into smaller blocks (query blocks, key blocks, value blocks) with fixed maximum dimensions. This segmentation allows the model to handle arbitrarily long sequences by processing them in parallel blocks, reducing the time and memory overhead associated with processing entire long sequences as single units while maintaining the model's ability to capture long-range dependencies.
3Adaptability or versatility
If attention mechanisms process longer input sequences, then model capability improves, but memory requirements increase
Solution Approach 1:
The patent segments the computation into blocks that can be processed independently, allowing memory to be reused across different block computations. Instead of allocating memory for entire long sequences at once, the system processes blocks in parallel with controlled memory footprint, reducing overall memory requirements while maintaining the ability to process long sequences through repeated use of the same memory resources.
Solution Approach 2:
The patent enables memory recovery by processing blocks in parallel and independent batches. After each batch of block computations is completed, the intermediate results are discarded and memory is recovered for reuse in the next batch, allowing the system to handle longer sequences without proportionally increasing peak memory requirements.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
An accelerated attention mechanism with parallel operations can improve machine learning technology by enabling execution of certain matrix multiplication operations in parallel with element-wise operations, leading to an increase in speed without quality loss. To compute attention values in a machine learning model, the mechanism can receive a query vector, key vector, and value vector and split each of these vectors into blocks. For a given query block, the mechanism can determine attention values by performing element-wise operations to update the attention values for the given query block based at least in part on previously computed attention scores for the given query block and a given key block. Concurrent with performance of at least some of the element-wise operations, the mechanism can perform a matrix multiplication operation using given query block and a next key block to determine attention scores for the given query block and next key block.