Interleaved Transformers With Latent Tokens
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Solution Overview
Problem
Conventional machine learning models, particularly deep neural networks, face challenges in efficiently processing high-dimensional data due to complexity and scalability issues with attention mechanisms, which are not easily accelerable and require significant computational resources.
Innovation Solution
The Far-reaching Interleaved Transformers (FIT) architecture partitions input data into groups, introduces adaptive latent tokens for global attention, and interleaves local and global attention layers, enabling efficient information exchange and reducing computational complexity by using parallel processing and compression techniques, making it suitable for deployment on hardware accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full attention mechanism is used to process high-dimensional data, then information exchange completeness is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the input data into multiple groups and processes each group separately through local attention mechanisms. This divides the computationally intensive full attention problem into smaller, manageable segments that can be processed in parallel, reducing overall complexity while maintaining information exchange effectiveness within each segment.
Solution Approach 2:
The patent introduces latent tokens as intermediary representations between input data and output predictions. These latent tokens serve as compressed summaries that capture essential information from input groups, enabling efficient information exchange without requiring direct full attention between all input tokens, thus reducing computational complexity.
2Measurement precision
If conventional attention mechanisms are used, then processing accuracy is maintained, but scalability to large datasets deteriorates
Solution Approach 1:
By segmenting data into groups and processing them through parallel local attention mechanisms, the system maintains processing accuracy within each segment while achieving linear scalability with input size. The parallel processing of multiple segments eliminates the quadratic complexity bottleneck of conventional attention.
Solution Approach 2:
The patent changes the parameter representation by introducing latent tokens with fixed dimensionality regardless of input size. This parameter transformation enables the system to maintain constant memory usage and computational complexity per processing unit while scaling to larger datasets through increased parallelism.
3Adaptability or versatility
If adaptive computation is increased to handle diverse tasks, then task performance is improved, but resource consumption increases
Solution Approach 1:
The patent creates a universal processing framework where the same group-based attention mechanism with latent tokens can handle diverse tasks including image, text, and video understanding. This multi-functional design eliminates the need for task-specific architectural modifications, reducing overall resource consumption while maintaining high adaptability across different domains.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing data using machine learning models. One of the methods includes obtaining a network input for the time step, wherein the network input comprises a plurality of data tokens; generating, from at least the network input for the time step, a plurality of groups of data tokens; initializing a plurality of sets of latent tokens for the time step, each set corresponding to a respective one of the plurality of groups; processing the data tokens in each group and the plurality of sets of latent tokens through each neural network block in a sequence of neural network blocks; and after processing each group of data tokens and the latent tokens through the sequence of neural network blocks, generating a network output for the time step.