MOE Layer Distribution Across Heterogeneous Accelerators
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Solution Overview
Problem
Conventional MOE machine learning models face inefficiencies due to idle components, limited scalability, and high computational costs when distributed across homogeneous hardware configurations, particularly when combining dense and sparse layers on a single accelerator.
Innovation Solution
Distribute MOE models across heterogeneous accelerators, segregating dense and sparse layers onto customized hardware with varying memory and processing capabilities, allowing for optimized performance and efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If MOE models are distributed across homogeneous accelerators with mixed dense and sparse layers, then device complexity is reduced, but computational efficiency deteriorates due to idle components and resource mismatches
Solution Approach 1:
The patent segments the accelerator fleet into heterogeneous groups based on their capabilities. Some accelerators are designated for dense layers only, while others handle sparse layers only. This segmentation allows each accelerator to be optimized for its specific function, eliminating the inefficiency of homogeneous accelerators that must handle both dense and sparse operations suboptimally.
Solution Approach 2:
The patent applies local quality by assigning different functional roles to different accelerators within the system. Rather than requiring all accelerators to be identical and capable of handling all operations, the system allows each accelerator to have specialized capabilities matched to its strengths, improving overall computational efficiency while maintaining manageable system complexity.
2Adaptability or versatility
If MOE models are distributed to scale across more accelerators, then model capacity increases, but training time increases due to computational heaviness
Solution Approach 1:
The patent segments the training workload across heterogeneous accelerators, assigning dense layer training to accelerators optimized for dense computations and sparse layer training to accelerators optimized for sparse operations. This parallel segmentation enables simultaneous training of different model components, scaling model capacity without proportionally increasing training time.
Solution Approach 2:
The patent changes the distribution parameters of the model across the accelerator fleet, dynamically assigning different layers and experts to different accelerators based on their capabilities. This parameter optimization allows the system to scale to larger models while maintaining efficient training speeds by matching computational parameters to hardware capabilities.
3Speed
If all layers including sparse layers are placed on accelerators with high processing capability, then processing speed improves, but memory consumption increases
Solution Approach 1:
The patent applies local quality by matching accelerator types to layer types based on their resource profiles. Accelerators with high processing capability are assigned dense layers that require intensive computation, while accelerators with adequate memory are assigned sparse layers that require storing many expert parameters. This local optimization achieves high processing speed for dense operations without unnecessarily consuming memory on all accelerators.
Solution Approach 2:
The patent optimizes the distribution parameters by considering both processing capability and memory capacity when assigning layers to accelerators. By changing the allocation strategy from capability-only to capability-memory matched allocation, the system achieves high processing speeds where needed while controlling overall memory consumption across the fleet.
4Productivity
If accelerators are specialized for specific layer types, then computational efficiency improves, but device complexity increases due to heterogeneous configuration
Solution Approach 1:
The patent segments the system into specialized accelerator groups but manages the complexity through automated assignment algorithms. The segmentation itself is simple - categorizing accelerators by their primary strength - while the complexity of optimizing their utilization is handled by system software that automatically assigns layers and experts to appropriate accelerators based on current workload and capabilities.
Solution Approach 2:
The patent creates a universal management layer that can handle diverse accelerator types through a common interface and assignment framework. This multi-functional management system can allocate workloads across heterogeneous accelerators without requiring complex manual configuration, thereby achieving high computational efficiency while keeping system configuration complexity manageable through automation.
Data Source
AI summary
Some disclosed embodiments are directed to computing systems having different accelerators such that a first set of accelerators has a greater memory capability than a second set accelerators, while the second set of accelerators has a greater processing capability than the first set of accelerators. A machine learning model having different dense layers and sparse layers is distributed on the different accelerators such that the dense layers are distributed on one or more accelerators selected from the first set of accelerators and the sparse layers are distributed on one or more accelerators in the second set of accelerators.