Automatic ML Model Partitioning Across Multiple Devices
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Solution Overview
Problem
State-of-the-art machine learning models with billions of parameters exceed the memory capacity of individual processing devices like GPUs, limiting their size and accuracy during training.
Innovation Solution
Automatic partitioning of machine learning models across multiple devices, allowing for efficient splitting and parallel execution management, which integrates with existing frameworks like TensorFlow and PyTorch, enabling training of large models without memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of model parameters is increased to improve accuracy, then model accuracy is improved, but memory usage exceeds the capacity of individual processing devices
Solution Approach 1:
The patent applies segmentation by dividing the machine learning model into multiple partitions that can be distributed across multiple processing devices. Each partition contains a subset of model parameters and operations, allowing the model to be trained on devices with limited memory capacity. This resolves the contradiction by enabling large accurate models to be trained without requiring all parameters to reside in single-device memory.
Solution Approach 2:
The patent transitions from a single-device training paradigm to a multi-device distributed training paradigm, adding the dimension of device distribution. By organizing model parameters across multiple devices with communication between them, the system achieves the memory capacity equivalent of a single large-device while using multiple smaller devices, thus resolving the memory capacity constraint.
2Measurement precision
If the size of the machine learning model is increased to improve accuracy, then model accuracy is improved, but the model cannot be trained with limited memory devices
Solution Approach 1:
The model is segmented into multiple partitions that can be automatically distributed across available devices. This segmentation enables large accurate models to be trained on devices with limited memory while managing complexity through automated partitioning rather than manual configuration.
Solution Approach 2:
The system performs automatic partitioning of the model without requiring manual intervention to configure device assignments. The automatic partitioning mechanism self-manages the distribution of model parameters across devices, reducing the operational complexity burden on users while enabling training of large accurate models.
3Measurement precision
If automatic partitioning is implemented to train large models across multiple devices, then model size and accuracy are improved, but existing training code requires changes
Solution Approach 1:
The automatic partitioning system is designed to work with existing machine learning frameworks and training code through universal interfaces. By providing framework-agnostic partitioning capabilities, the system can train large models across multiple devices without requiring fundamental changes to existing training code, thus resolving the contradiction between enabling large model training and maintaining code compatibility.
Data Source
AI summary
Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.