Federated LLM Fine-Tuning With Outlier Channel Quantization
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Solution Overview
Problem
Existing quantization systems for fine-tuning large language models (LLMs) face challenges in federated learning scenarios due to significant noise and resource constraints, leading to decreased model accuracy and inefficiency, particularly on resource-constrained devices with strict energy and memory requirements, and lack of support for low-precision operations in frameworks like PyTorch and TensorFlow.
Innovation Solution
A federated quantization-aware system that freezes weight matrices, identifies outlier channels, quantizes them, injects adapters, and performs channel-wise scaling to facilitate low-precision fine-tuning on devices, reducing memory and computing resources while preserving model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization is applied to improve efficiency in training and inference, then processing speed and resource utilization are improved, but model accuracy deteriorates due to significant noise from quantization
Solution Approach 1:
The patent applies different quantization strategies to different parts of the model. Specifically, it identifies outlier channels and applies targeted quantization techniques only to those channels rather than uniformly quantizing all channels. This local differentiation allows the system to maintain high accuracy in critical channels while achieving efficiency gains in less sensitive channels.
Solution Approach 2:
The patent dynamically adjusts quantization parameters based on channel characteristics. It identifies outlier channels by analyzing their statistical properties and applies adaptive quantization schemes that modify precision levels according to each channel's sensitivity. This parameter adaptation allows the system to optimize the balance between speed and accuracy for different parts of the model.
2Manufacturing precision
If full-precision training is performed before quantization to reduce noise, then model accuracy is improved, but energy consumption and memory requirements increase
Solution Approach 1:
The patent performs preliminary identification of outlier channels using public datasets before the actual fine-tuning process. By pre-identifying which channels require higher precision, the system avoids performing full-precision operations on all channels during fine-tuning, thereby reducing energy consumption while maintaining necessary accuracy where needed.
Solution Approach 2:
Instead of applying full-precision training to all channels, the patent applies quantization selectively only to identified outlier channels. This partial application of full-precision processing reduces the overall computational burden and energy consumption while maintaining model accuracy by focusing high-precision operations only where necessary.
3Ease of operation
If quantization schemes are deployed on resource-constrained devices, then accessibility and deployment ease are improved, but computational overhead from data type conversions causes efficiency degradation
Solution Approach 1:
The patent segments the model into different channel groups based on their quantization requirements. By dividing channels into outlier and non-outlier groups with different quantization strategies, the system reduces the frequency of data type conversions compared to uniform quantization approaches, thereby improving computational efficiency on resource-constrained devices.
Solution Approach 2:
The patent introduces channel-wise scaling vectors as intermediary representations that facilitate efficient conversion between different precision levels. These scaling vectors act as mediators that enable smooth transitions between full-precision and quantized operations, reducing the overhead associated with data type conversions while maintaining compatibility with existing deep learning frameworks.
4Adaptability or versatility
If adapters are injected into the pre-trained language model for fine-tuning, then task-specific adaptability is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent nests adapters within the existing model structure in a hierarchical manner. Adapters are inserted at specific layers and integrated with the quantization mechanism, creating a nested architecture where adapters and quantization modules work together. This nesting allows the system to achieve task-specific adaptability while keeping the overall structure organized and manageable.
Data Source
AI summary
The present invention provides a quantization-based method and system for federated fine-tuning a pre-trained large language model (LLM). The system comprises an accelerator configured to identify outlier channels of the LLM and inject a set of adapters into the LLM; and local devices configured to: construct channel-wise scaling vectors on basis of the indices of the identified outlier channels; apply the channel-wise scaling vectors on input matrices of the LLM to obtain scaled activation matrices; quantize the scaled activation matrices to obtain quantized activation matrices; apply the channel-wise scaling vectors on the outlier weight matrices of the LLM to obtain scaled outlier weight matrices; quantize the scaled outlier weight matrices to obtain quantized outlier weight matrices; fine-tune the adapters of the LLM with the quantized activation matrices and the quantized outlier weight matrices. The server is further configured to perform weight aggregation to produce new adapters for the LLM.