Key Points

1. Fuyou proposes a framework for efficient 100B huge model fine-tuning on a low-end server with a low-end GPU and limited CPU memory capacity, by adding the SSD-CPU communication as an optimization dimension.

2. Fuyou consists of three innovations: a synchronous out-of-core CPU optimizer, a GPU-CPU-SSD fully-pipelined activation swapping mechanism, and an automatic activation swapping management to minimize the epoch time.

3. Experimental results show that Fuyou achieves high GPU utilization and fine-tunes large models on consumer GPUs, outperforming state-of-the-art works like ZeRO-Infinity and Colossal-AI in terms of throughput and model size.

4. The proposed framework enables efficient fine-tuning of extremely large-scale models using only one desktop GPU card, making it cost-effective and accessible to most AI researchers.

5. Fuyou successfully addresses issues related to low GPU utilization and limited trainable model size due to CPU memory capacity, making it a promising solution for training extremely large language models.

6. Fuyou's design enables efficient training on a single GPU, leveraging SSD-CPU communication, out-of-core optimizers, and activation swapping to maximize GPU utilization and model size that can be fine-tuned.

Summary

The paper proposes a training framework called Fuyou, designed to enable efficient fine-tuning of large language models on a single, low-end GPU in a commodity server with limited CPU memory capacity. Fuyou addresses the shortcomings of the state-of-the-art work ZeRO-Infinity and aims to overcome the inefficiencies in GPU utilization and the limitations in trainable model size due to CPU memory capacity.

Main Innovations of Fuyou
Fuyou incorporates three main innovations:
Synchronous Out-of-Core CPU Optimizer
1. Synchronous out-of-core CPU optimizer: This approach maximizes GPU utilization by overlapping the optimizer with backward propagation, ensuring that the GPU is not idle during the optimizer stage.

GPU-CPU-SSD Fully Pipelined Activation Swapping Mechanism
2. GPU-CPU-SSD fully pipelined activation swapping mechanism: Fuyou allows for the swapping of activations between GPU memory, CPU memory, and NVMe SSDs, enabling efficient data swapping and larger model fine-tuning.

Automatic Activation Swapping Management
3. Automatic activation swapping management: Fuyou automatically determines the optimal amount of swapping activations to minimize the epoch time for training on a single GPU in a commodity server.

Experimental Results
Experimental results show that Fuyou is able to achieve a high level of throughput when fine-tuning large language models, such as GPT-3 175B on RTX 4090 and A100-80GB GPUs, while surpassing the performance of other state-of-the-art methods in terms of GPU utilization and training efficiency.

Positioning of Fuyou
The paper positions Fuyou as the first framework designed specifically for fine-tuning large language models on single, low-end GPUs in commodity servers, highlighting its potential to enable efficient and cost-effective model training for AI researchers.

Addressing Memory Capacity Limitations
The paper addresses the challenges of implementing large language models on GPUs with limited memory capacities. It specifically focuses on the difficulties faced by researchers and proposes an approach for fine-tuning huge models on a single, low-end GPU in a commodity server. The authors highlight the shortcomings of the state-of-the-art work ZeRO-Infinity in this context and aim to address the inefficiency in GPU utilization and the limitation in trainable model size due to CPU memory capacity. The approach outlined in the paper introduces a memory-efficient training method that allows researchers to train very large language models on a single, low-end GPU, thus eliminating the need for expensive high-memory GPUs or specialized hardware.

Democratizing Access to Large Models
The paper discusses how the proposed approach allows for the training of increasingly large language models by utilizing smaller, off-the-shelf GPUs. The authors highlight the potential of their approach in democratizing access to high-quality language models, as it does not require expensive and specialized hardware. Additionally, the paper critiques the limitations of existing approaches and emphasizes the need for more efficient utilization of GPU resources, considering the increasing demand for training large language models.

Improving GPU Resource Utilization
Furthermore, the proposed approach aims to improve the utilization of GPU resources and increase the size of trainable language models by addressing the limitations imposed by CPU memory capacity. The authors argue that their approach can bring about significant advancements in the field of language model training by enabling the use of cost-effective, low-end GPUs. The paper provides a detailed analysis of the challenges faced by researchers in implementing large language models and offers a promising solution that could potentially revolutionize the field by allowing broader accessibility to advanced language model training techniques.

Reference: https://arxiv.org/abs/2403.06504