Pipeline-Parallel LoRA Fine-Tuning Across Heterogeneous GPUs
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Solution Overview
Problem
Existing LLM fine-tuning technologies face challenges in multi-job fine-tuning, including inefficient resource allocation, unbalanced computing resource utilization, and inconsistent model convergence across GPUs, particularly in heterogeneous environments, leading to suboptimal performance and increased training costs.
Innovation Solution
A distributed pipeline-parallel LLM fine-tuning method for heterogeneous GPUs, employing efficient gradient descent, job scheduling strategies, and model partitioning to optimize resource utilization and convergence, using a multi-job system with a job configuration module, profiler, and dynamic job scheduler to manage resources and ensure fair allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional full fine-tuning methods are used to update all model parameters, then model adaptation effectiveness is improved, but computing resource consumption and time are significantly increased
Solution Approach 1:
The patent segments the fine-tuning process into multiple independent jobs that can be processed in parallel across different GPUs. Each job represents a separate fine-tuning task that can be scheduled and executed independently, enabling efficient resource utilization while maintaining adaptation effectiveness through selective parameter updates using LoRA technology.
Solution Approach 2:
The patent implements dynamic job scheduling that adapts to real-time GPU resource availability and job priorities. The scheduling system dynamically allocates computing resources, adjusts job execution sequences, and optimizes resource utilization based on current system state, thereby improving fine-tuning efficiency without compromising model adaptation quality.
2Productivity
If multiple LoRA adapters are managed using heterogeneous batch processing, then system throughput is improved, but resource allocation complexity and computation overhead increase
Solution Approach 1:
The patent creates a universal job scheduling system that can handle multiple types of fine-tuning jobs across heterogeneous GPUs through a unified interface. The scheduling framework provides multi-functional capabilities including job submission, resource allocation, parallel execution management, and result aggregation, thereby improving throughput while managing complexity through standardization.
Solution Approach 2:
The patent introduces an intermediary job scheduling system that mediates between multiple LoRA adapters and the underlying GPU resources. This intermediary layer abstracts the complexity of resource allocation and batch processing, providing efficient management of multiple adapters through centralized scheduling while maintaining high system throughput.
3Productivity
If dynamic job scheduling and distributed computation optimization are implemented, then fine-tuning efficiency is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a self-service job scheduling system that automatically manages fine-tuning job execution without requiring manual intervention. The system autonomously performs job submission, resource allocation, parallel execution coordination, and result aggregation, thereby improving fine-tuning efficiency while keeping the implementation manageable through automation of routine tasks.
4Ease of operation
If GPUs are allocated statically without dynamic scheduling, then resource allocation simplicity is maintained, but computing resource utilization becomes unbalanced with some GPUs idle while others are overloaded
Solution Approach 1:
The patent transitions from static to dynamic GPU allocation through an intelligent job scheduling system. The scheduler dynamically assigns jobs to GPUs based on real-time resource availability, job priorities, and load balancing considerations, thereby optimizing computing resource utilization while maintaining operational simplicity through automated decision-making.
Data Source
AI summary
This application relates to the technical field of natural language processing, and provides a distributed pipeline-parallel LLM fine-tuning method for heterogeneous GPUs. A plurality of LoRA models are fine-tuned simultaneously based on a multi-job fine-tuning system; each LoRA model is partitioned into a plurality of parts distributed on a corresponding number of GPUs, and the GPUs are sorted. A job configuration module generates a plurality of jobs according to a user request, and divides each job into a plurality of training batches; a dynamic job scheduler generates a scheduling scheme based on a training batch sequence of each job and a dynamic scheduling strategy; and the scheduling scheme is sent to a multi-job training module on each corresponding GPU according to a positive sequence of the GPUs, to train all the LoRA models.