Sparse Attention Algorithm Dynamic Pruning Reduces Transformer Compute
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Solution Overview
Problem
Transformer models require significant computational resources and energy consumption, especially when handling extensive data or conducting real-time calculations, necessitating algorithms that reduce computational costs without compromising model performance.
Innovation Solution
A dynamic pruning strategy for sparse attention operations that adjusts the pruning ratio based on input text characteristics, involving the calculation of an attention probability matrix, its pruning, and multiplication with a pruned value matrix to reduce computational load while maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformer models process extensive data or conduct real-time calculations, then model performance and accuracy are maintained, but computational resources and energy consumption increase significantly
Solution Approach 1:
The patent extracts and removes redundant or less important tokens from the input sequence based on attention probability thresholds. By identifying and eliminating tokens that contribute minimally to the final output, the system reduces computational load while preserving the essential information needed for accurate model performance.
Solution Approach 2:
The patent applies partial action by processing only a subset of tokens rather than the entire sequence. Through dynamic token pruning, the system performs computations on a reduced set of significant tokens, achieving sufficient model performance with less computational effort than processing all tokens would require.
2Productivity
If static token pruning algorithms are used to reduce computational load, then processing speed increases, but adaptability to different input characteristics decreases
Solution Approach 1:
The patent implements dynamic token pruning where the pruning ratio and threshold are adjusted based on the specific characteristics of each input sequence. The system calculates attention probabilities and adapts the pruning strategy accordingly, allowing the processing speed and accuracy to be optimized for each individual input rather than using a fixed static approach.
Solution Approach 2:
The patent changes the pruning parameters (such as pruning ratio and threshold) dynamically based on input characteristics. By modifying these parameters according to the specific needs of each processing task, the system achieves both high processing speed and adaptability across different scenarios.
3Quantity of substance
If aggressive pruning is applied to reduce data volume, then computational costs decrease, but semantic information preservation may be compromised
Solution Approach 1:
The patent uses attention probabilities as feedback to guide the pruning process. By calculating attention scores for each token and using these scores to determine which tokens to retain or remove, the system ensures that tokens with high semantic importance are preserved while removing only those with minimal contribution, thus reducing data volume without significant information loss.
Solution Approach 2:
The patent adjusts the pruning threshold parameter dynamically to balance between data reduction and information preservation. By carefully controlling the threshold at which tokens are pruned, the system achieves effective compression while maintaining the semantic integrity of the remaining tokens.
Data Source
AI summary
An algorithm for a sparse attention operation is provided, and the algorithm includes following steps. An attention probability matrix is calculated based on a query matrix and a key matrix. A pruning ratio is calculated based on the attention probability matrix. The attention probability matrix is pruned based on the pruning ratio to obtain a pruned attention probability matrix. A value matrix is pruned based on the pruning ratio to obtain a pruned value matrix. The pruned attention probability matrix and the pruned value matrix are multiplied to obtain an attention matrix.