Streaming Attention Buffering for Real-Time Transformer Inference
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Solution Overview
Problem
Conventional transformer networks struggle with generating attention vectors for streaming data due to the unavailability of future data, which is necessary for context-based predictions, leading to difficulties in real-time or near real-time processing of streaming audio data.
Innovation Solution
Implementing a multi-head streaming attention block that maintains and updates query, key, and value buffers on a frame-by-frame basis, allowing generation of attention vectors based on a window of streaming data using frame-inference or block-inference methods, reducing computational complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional transformer networks are used for streaming data processing, then attention vectors can be generated with full context, but future data is unavailable leading to difficulties in real-time processing
Solution Approach 1:
The patent segments the attention mechanism into causal attention (for past context) and lookahead attention (for future context). This segmentation allows the model to process streaming data in real-time by separating the unavailable future data component from the processable past data component, while still maintaining the ability to generate accurate predictions when future data becomes available.
Solution Approach 2:
The patent performs preliminary action by precomputing and storing lookahead attention values during the training phase. These precomputed attention values represent the future context that would otherwise be unavailable during real-time inference, allowing the model to make accurate predictions as data arrives without actually having access to future values during processing.
2Measurement precision
If attention mechanisms are applied to streaming data, then context-based predictions can be made, but computational complexity and latency increase
Solution Approach 1:
The patent segments the attention computation into two distinct parts: causal attention that operates on available past data with linear complexity, and lookahead attention that is precomputed during training. This segmentation reduces the computational burden during real-time processing while maintaining prediction precision through the combined effect of both attention mechanisms.
Solution Approach 2:
The patent applies partial action by using only the necessary portion of attention computation during real-time processing (causal attention on past data), while the excessive or redundant computation (lookahead attention on future data) is performed in advance during training when computational resources are abundant and data is available.
3Reliability
If full context is used for attention vectors, then accurate predictions are achieved, but data must be stored for extended periods reducing privacy protection
Solution Approach 1:
The patent segments the context window into past data (retained for processing) and future data (not stored). By separating these temporal segments, the model achieves accurate predictions using only the necessary historical context without storing extended periods of user data, thereby reducing privacy risks associated with long-term data retention.
Solution Approach 2:
The patent discards future data from storage after it becomes available, recovering only the necessary past context for processing. This approach allows the system to maintain prediction accuracy by retaining essential historical information while discarding data that would otherwise need to be stored for extended periods, thus protecting user privacy.
Data Source
AI summary
Disclosed herein are techniques for processing streaming data. In some embodiments, the techniques involve obtaining input data representative of a frame of streaming data. The techniques may involve identifying a query transformation, a key transformation, and a value transformation based on the input data. The techniques may involve updating a query buffer, a key buffer, and a value buffer, such that the buffers are each configured to store parameters associated with previous frames of streaming data and the frame of streaming data. The techniques may involve retrieving one or more query frames from the query buffer. The techniques may involve determining a dot product of the query frames and frames in the key buffer to determine a set of weights. The techniques may involve determining a weighted sum between the set of weights and frames in the value buffer, and utilizing the weighted sum to generate a streaming attention vector.