Transformer Attention Compression Using State Space Models
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Solution Overview
Problem
Transformer neural networks face significant computational and memory bottlenecks due to large key-value caches, particularly in edge devices, leading to reduced inferencing speed and accuracy, especially in tasks involving long-context generation.
Innovation Solution
Partition input tokens into two sets: a first set compressed using a state space model and a second set processed as is, with the compressed tokens representing the first set, allowing efficient inferencing while maintaining contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer neural networks process all input tokens directly, then inferencing accuracy is maintained, but computational expense and memory usage increase significantly
Solution Approach 1:
The patent segments the input token sequence into two distinct sets: a first set of tokens that are compressed using a state space model into a reduced number of compressed tokens, and a second set of tokens that are processed directly by the transformer neural network. This segmentation allows the system to reduce computational expense by processing fewer tokens through the computationally intensive transformer while maintaining inferencing accuracy by preserving the second set of tokens for direct processing.
2Loss of information
If transformer neural networks maintain large key-value caches for all tokens, then contextual information is preserved, but memory usage increases
Solution Approach 1:
The patent extracts and compresses the first set of tokens using a state space model into a condensed representation (compressed tokens) before processing them through the transformer neural network. This extraction and compression approach preserves the essential contextual information from the first set of tokens while significantly reducing the memory required to store and process them, as the state space model creates a more compact representation that captures the key contextual elements.
3Loss of information
If transformer neural networks process long-context sequences, then contextual understanding improves, but inferencing speed decreases
Solution Approach 1:
The patent segments long-context sequences into a first set of tokens compressed by a state space model and a second set of tokens processed directly, enabling the system to handle long-context sequences efficiently. The compression of the first set reduces the overall processing load and increases inferencing speed, while the preservation of the second set maintains contextual understanding by ensuring that recent or important tokens are processed with full attention mechanisms.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for efficient inferencing using a machine learning model. An example method generally includes receiving an input including a set of tokens for processing by a transformer neural network. The set of tokens for processing by the transformer neural network is partitioned into a first set of tokens and a second set of tokens. Using at least one state space model, at least one compressed token representing the first set of tokens is generated. An output token is generated, using the transformer neural network, based on the compressed token and the second set of tokens. A response to the input is generated based on the output token.