Transformer MoE Inference Using Perplexity-Based Expert Prefetch
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Solution Overview
Problem
Mixture of experts (MoE) models suffer from low resource utilization during inference due to load imbalance and redundancy in the gating process, leading to inefficient hardware utilization and high compute costs.
Innovation Solution
Implementing a mixture of balanced, fine-grained and coarse-grained sparsity in MoE models, where input data shards are batched by modality and perplexity to selectively load relevant experts, using perplexity to determine which experts to pre-fetch and sparsify weight matrices for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mixture of experts models use gating process to route inputs to experts, then task specialization and model accuracy are improved, but resource utilization deteriorates due to load imbalance and gating redundancy
Solution Approach 1:
The patent applies preliminary action by pre-fetching expert weight matrices before they are actually needed for processing. The system predicts which experts will be needed based on current input characteristics and pre-loads their weights into memory, eliminating the gating computation overhead and ensuring experts are ready when needed. This resolves the contradiction by maintaining high accuracy (through proper expert selection) while improving resource utilization (by eliminating gating redundancy and optimizing memory access patterns).
Solution Approach 2:
The patent extracts and removes the gating process entirely from the inference pipeline. Instead of using gating to route inputs to experts, the system directly selects and activates relevant experts based on input characteristics. This extraction eliminates the computationally redundant gating operations while maintaining expert specialization, thereby improving resource utilization without sacrificing model accuracy.
2Ease of operation
If MoE models load all expert weight matrices into memory, then expert accessibility is improved, but memory usage increases significantly
Solution Approach 1:
The system performs preliminary action by pre-fetching only the specific expert weight matrices that are predicted to be needed soon, rather than loading all expert weights at once. This selective pre-loading approach ensures that required experts are readily accessible while keeping memory usage proportional to the actual number of active experts rather than the total number of experts in the model.
Solution Approach 2:
The patent applies dynamics by making the expert weight matrix loading process adaptive and dynamic. The system continuously monitors input characteristics and dynamically adjusts which expert weights are loaded into memory based on current processing needs. This dynamic approach optimizes the balance between expert accessibility and memory usage, loading weights on-demand rather than statically pre-loading all possible experts.
3Productivity
If MoE models use sparse activation of experts, then compute efficiency is improved, but routing complexity increases
Solution Approach 1:
The patent extracts and removes the complex gating-based routing mechanism from the system. Instead of using sophisticated gating networks to determine expert activation, the system employs simpler, direct methods for selecting and activating relevant experts based on input characteristics. This extraction maintains sparse activation benefits for compute efficiency while significantly reducing routing complexity.
Data Source
AI summary
A method for operating a machine learning model is presented. The machine learning model includes a plurality of sequential transformer blocks. The method comprises receiving input data at a transformer block and processing the input data via a mixture of experts layer. At an auxiliary classifier, a measure of perplexity of the processed input data is determined. Based on the determined measure of perplexity, one or more experts in a downstream transformer block that will subsequently process the input data are indicated. Weight matrices are then fetched for the indicated one or more experts.