MoE Token Routing for Lower-Complexity Transformer Processing
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Solution Overview
Problem
The Transformer-based models face challenges with high computational complexity, resource consumption, and limited capability in processing long sequences due to their complex structure and large number of parameters, which are bottlenecks for large-scale data sets and real-time applications.
Innovation Solution
A mix of experts (MoE) network is employed, where input tokens are selectively distributed to different expert networks for computation, using a gating matrix to ensure uniform allocation and leveraging the optimal transport algorithm to optimize resource usage, thereby improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Transformer-based models use a large number of parameters and complex structure, then model expression capability is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The model parameters are segmented into multiple expert networks (e.g., sparse expert networks, dense expert networks) that process different portions of the input data. Each expert network handles specific computational tasks, allowing the system to maintain high expression capability while distributing computational load across multiple specialized components rather than requiring all parameters to be active simultaneously.
Solution Approach 2:
Different expert networks are assigned different local qualities or specializations based on the input data characteristics. The gating network dynamically routes inputs to appropriate experts based on their specialized capabilities, ensuring that each parameter subset is optimized for specific functions while maintaining overall model versatility.
2Adaptability or versatility
If Transformer-based models use a large number of parameters, then model expression capability is improved, but resource consumption increases
Solution Approach 1:
Instead of activating all model parameters for every input, the system uses partial action by selectively engaging only the necessary expert networks for each input token. The gating mechanism determines which subset of experts should process each input, ensuring that resource consumption is proportional to the actual computational needs rather than the total model capacity.
Solution Approach 2:
The system dynamically changes parameter utilization based on input characteristics. By modifying which parameters are active through the gating network's routing decisions, the model adapts its resource consumption to match the complexity of the input data, maintaining high expression capability when needed while conserving resources for simpler tasks.
3Adaptability or versatility
If Transformer-based models process data with complex structure, then model expression capability is improved, but processing speed decreases
Solution Approach 1:
The processing pipeline is segmented into parallel expert networks that can simultaneously process different input tokens or data portions. This segmentation enables parallel computation, where multiple expert networks work concurrently on different aspects of the input, maintaining high expression capability while improving overall processing throughput and speed.
Solution Approach 2:
The system dynamically adjusts the routing of inputs to expert networks based on real-time gating decisions. This dynamic allocation optimizes processing speed by directing simple inputs to faster-processing experts while reserving more computationally intensive experts for complex inputs, thereby improving average processing speed without sacrificing expression capability.
4Adaptability or versatility
If Transformer-based models are designed for large-scale data sets, then model expression capability is improved, but real-time processing capability deteriorates
Solution Approach 1:
The system changes parameter activation patterns dynamically based on input characteristics and resource availability. For real-time processing requirements, the gating network can prioritize faster-processing expert networks while still maintaining access to the full model capacity when needed, effectively adapting the model's operational parameters to balance expression capability with real-time processing demands.
Data Source
AI summary
A data processing method, and a data processing model and a training method therefor are provided, and relate to the field of artificial intelligence, and specifically, to natural language processing, deep learning technologies, and large model technologies. An implementation solution includes: determining input data, where the input data includes a plurality of tokens; determining a correlation between each of the plurality of tokens and each of a plurality of expert networks based on a gating matrix, where the plurality of expert networks are used to reinforce the plurality of tokens; allocating the plurality of tokens to the plurality of expert networks in a uniform manner based on the correlation and a preset capacity of each expert network, to reinforce the plurality of tokens; and determining a data processing result based on the plurality of reinforced tokens.