Conditional Feed-Forward Attention Networks With Expert Routing
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Solution Overview
Problem
Existing neural networks face challenges in increasing computational capacity without a corresponding increase in resource consumption, particularly in self-attention networks, and require efficient training methods for large parameter sets.
Innovation Solution
Implementing conditional computation in feed-forward sub-layers of self-attention neural networks, combined with parallelization across multiple devices during training, to selectively use a subset of experts for each input, thereby enhancing computational efficiency and quality of outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the computational capacity of self-attention neural networks is increased by adding more parameters and layers, then the quality of outputs for sequence processing tasks is improved, but the amount of computational resources consumed during inference increases significantly
Solution Approach 1:
The feed-forward sub-layers are segmented into multiple expert networks instead of using a single monolithic network. Each expert network processes a subset of inputs, allowing the system to achieve high computational capacity through specialization rather than brute-force scaling of all parameters. This segmentation enables selective activation of only necessary computational paths for each input token.
Solution Approach 2:
The system dynamically selects which expert networks to activate based on the specific input token being processed. This dynamic routing mechanism allows the network to adapt its computational capacity utilization in real-time, activating only the experts needed for each particular input rather than always using all parameters, thereby reducing inference computational resources while maintaining output quality.
2Adaptability or versatility
If the number of parameters in self-attention networks is increased to enhance model capacity, then the ability to process complex sequences is improved, but the difficulty and resource requirements for training increase
Solution Approach 1:
By segmenting the large parameter space into multiple smaller expert networks, the training process can be distributed and parallelized more effectively. Each expert network has fewer parameters to learn, reducing the computational burden per device during training while collectively providing high model capacity across the ensemble of experts.
Solution Approach 2:
During training, not all expert networks need to be fully activated or trained to maximum capacity simultaneously. The system can use partial action by training and activating subsets of experts based on current needs, reducing the immediate training resource requirements while maintaining the potential for high model capacity when all experts are utilized during inference.
3Productivity
If conditional computation is implemented with multiple expert feed-forward neural networks, then the computational capacity is increased without proportional increase in inference resources, but the device complexity and selection mechanism become more complex
Solution Approach 1:
A gating mechanism serves as an intermediary that automatically routes each input token to the appropriate expert networks based on learned criteria. This intermediary component manages the complexity of selecting among multiple experts, transforming the complex decision-making process into a differentiable routing operation that can be efficiently computed during both training and inference without requiring complex external selection logic.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes an attention neural network configured to perform the machine learning task, the attention neural network including one or more attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer. Some or all of the attention layers have a feed-forward sub-layer that applies conditional computation to the inputs to the sub-layer.