MoE Expert Buffering for Low-Memory LLM Inference
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Solution Overview
Problem
The Mixture of Experts (MoE) architecture for Large Language Models (LLMs) faces challenges in memory constraints and inefficient weight management, particularly on resource-constrained devices like AI PCs and consumer GPUs, due to high memory demands and dynamic expert selection, leading to suboptimal performance and computational inefficiencies.
Innovation Solution
Implementing a dual-buffer system with a full hot expert buffer for frequently used experts and a partial hot expert buffer for moderately used experts, using global expert usage tracking to manage weights efficiently, allowing asynchronous prefetching and overlapping computation with memory transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all expert weights are stored in high-speed memory, then expert access speed is improved, but memory capacity requirements increase significantly
Solution Approach 1:
The patent segments expert weights into two categories: hot expert weights (frequently accessed) and cold expert weights (infrequently accessed). Hot expert weights are stored in high-speed memory buffers, while cold expert weights are stored in slower memory. This segmentation allows the system to prioritize speed for critical operations while managing overall memory capacity efficiently.
Solution Approach 2:
The patent applies local quality by providing different storage qualities for different expert weights based on their access frequency. Hot expert weights receive high-speed memory allocation for immediate access, while cold expert weights use standard memory. This differentiated approach optimizes the overall system performance without requiring all weights to be in high-speed memory simultaneously.
2Loss of time
If expert weights are cached in high-speed memory, then inference latency is reduced, but memory bandwidth consumption increases
Solution Approach 1:
The patent implements preliminary action by proactively loading hot expert weights into high-speed memory buffers before they are needed for inference. The system monitors expert access patterns and pre-loads weights for experts that are likely to be accessed soon, reducing inference latency without requiring continuous high-speed memory bandwidth for all operations.
Solution Approach 2:
The patent applies partial action by caching only a subset of expert weights (the hot experts) in high-speed memory rather than all expert weights. This selective caching reduces memory bandwidth consumption compared to caching everything, while still achieving significant latency reduction for the most frequently accessed experts.
3Reliability
If the entire neural network is activated, then model performance is maximized, but computational cost increases
Solution Approach 1:
The patent implements dynamics by making the activation of expert sub-networks adaptive and dynamic rather than static. The system dynamically selects and activates only the relevant expert sub-networks needed for each specific inference task based on the input data characteristics, rather than activating the entire neural network. This dynamic selection maintains model performance while significantly reducing computational cost.
Solution Approach 2:
The patent extracts and isolates specific expert sub-networks from the larger neural network architecture. By separating the neural network into independent expert sub-networks, the system can selectively activate only the necessary experts for each task, removing the need to compute through the entire network and thereby reducing computational cost while preserving performance.
Data Source
AI summary
An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to initialize a full hot expert buffer to store entire weights of an expert used with a first frequency, initialize a partial hot expert buffer to store partial weights of an expert used with a second frequency, wherein the first frequency is higher than the second frequency, identify a selected expert associated with a Mixture of Experts (MoE) layer of a Large Language Model (LLM), and perform a direct computation or a partially direct computation, the direct computation performed when the selected expert is stored in the full hot expert buffer, the partially direct computation performed when the selected expert is stored in the partial hot expert buffer.