Giant Model Lightweighting and Dynamic Partitioning
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Solution Overview
Problem
Existing technologies face challenges in managing giant AI models that are too large to be loaded into memory, leading to inefficiencies in training and inference processes, particularly in distributed environments where resource management is dynamic.
Innovation Solution
The proposed solution involves lightweighting a giant model into a smaller version that can be loaded into memory, generating partitioning information based on hardware resources, and dynamically managing model partitions across multiple servers to ensure efficient training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a giant model is partitioned and distributed across multiple GPUs/servers, then the model can be trained and inferred, but memory resources are inefficiently managed and data accumulates in memory over time causing shortages
Solution Approach 1:
The patent implements dynamic model partitioning that adapts to changing memory resources during training. The system continuously monitors memory usage and dynamically adjusts the partitioning strategy, moving model parts between memory and storage, and reassigning partitions across GPUs/servers based on available resources. This dynamic approach prevents memory shortages while maintaining training productivity.
Solution Approach 2:
The giant model is divided into multiple partitions that can be independently managed and distributed across different GPUs/servers. Each partition can be loaded into memory only when needed, and the system manages which partitions are active in memory versus stored on disk. This segmentation allows efficient memory utilization while maintaining the ability to train the complete model.
2Productivity
If model partitioning is determined initially, then training can start, but GPU/server failures require new partitioning and redeployment which is complex and time-consuming
Solution Approach 1:
The system employs dynamic repartitioning capabilities that automatically respond to GPU/server failures. When a failure occurs, the system dynamically recalculates the optimal partitioning based on remaining resources and automatically redistributes model partitions without requiring manual intervention. This maintains training continuity while managing the complexity of partitioning changes automatically.
Solution Approach 2:
The patent changes the partitioning parameters dynamically based on system conditions. Instead of fixed partitioning, the system adjusts partition boundaries, sizes, and assignments as parameters based on available memory, active GPUs/servers, and training progress. This allows automatic adaptation to failures and resource changes without complex manual repartitioning.
3Measurement precision
If the entire giant model instance is loaded into memory for analysis, then partitioning information can be extracted, but memory requirements are not met
Solution Approach 1:
Instead of loading the entire model into memory for analysis, the system segments the model analysis process itself. It loads only the necessary portions of the model definition and metadata required for partitioning analysis, performs the analysis on these segments, and uses the results to create the partitioning plan. This avoids the need to load the complete giant model instance into memory while still achieving accurate partitioning.
Solution Approach 2:
The system extracts only the essential partitioning information from the model definition without requiring the full model instance in memory. It takes out and analyzes the model architecture description, parameter specifications, and computational graph structure to determine optimal partitioning, then uses this extracted information to guide the actual model distribution across GPUs/servers.
4Duration of action of stationary object
If memory resources are allocated for continuous training, then long-term training is possible, but data accumulates in memory over time causing memory shortages
Solution Approach 1:
The system implements periodic checkpointing and memory management where model partitions are systematically saved to storage at intervals, and unnecessary data is periodically evicted from memory. This periodic action prevents unbounded memory accumulation while enabling long-term training by ensuring memory capacity is continuously reclaimed and reused.
Solution Approach 2:
The patent implements mechanisms to discard data from memory when it is no longer immediately needed for training, while maintaining the ability to recover it from storage when required. Model partitions that have been processed can be discarded from memory and recovered from disk when needed again, allowing continuous training without unbounded memory growth.
Data Source
AI summary
Disclosed herein is an apparatus and method for managing a giant model. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may perform lightweighting a first model into a second model in consideration of hardware resources, generating partitioning information of the first model based on a result of analysis of the second model, and performing training or inference for the first model based on the generated partitioning information.